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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12 November 2025 and 27 February 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Application
Claims 1-20 are pending.
Claims 1, 10, and 19 are independent.
Claims 1, 3, 9, 10, and 19 have been amended.
This FINAL action is in response to “Amendments and Remarks” received on 27 February 2026.
Response to Amendment/Remarks
With respect to Applicant’s remarks filed 27 February 2026, Applicant’s “Amendments and Remarks” have been fully considered and were not wholly persuasive. Applicant’s remarks will be addressed in sequential order as they were presented.
With respect to objection of the claims, Applicant’s “Amendments and Remarks” have been fully considered and are persuasive. Therefore, the objections to the claims have been withdrawn.
With respect to claim rejections under 35 U.S.C. 101, Applicant’s “Amendments and Remarks” have been fully considered and are not wholly persuasive. Therefore, the rejection is withdrawn.
Claim 19 is directed to transitory signals (signal per se). Therefore claim 19 is not within at least one of the four statutory categories.
With respect to claim rejections under 35 U.S.C. 102 and/or 35 U.S.C. 103, Applicant’s “Amendments and Remarks” have been fully considered and are persuasive. Therefore, the rejection is withdrawn. However, upon further consideration, there is a new ground(s) of rejection in view of newly found prior art.
Final Office Action
Claim Interpretation
During examination, claims are given the broadest reasonable interpretation consistent with the specification and limitations in the specification are not read into the claims. See MPEP §2111, MPEP §2111.01 and In re Yamamoto et al., 222 USPQ 934 10 (Fed. Cir. 1984). Under a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. See MPEP 2111.01 (I). It is further noted it is improper to import claim limitations from the specification, i.e., a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment. See 15 MPEP 2111.01 (II).
A first exception to the prohibition of reading limitations from the specification into the claims is when the Applicant for patent has provided a lexicographic definition for the term. See MPEP §2111.01 (IV). Following a review of the claims in view of the specification herein, the Office has found that Applicant has not provided any lexicographic definitions, either expressly or implicitly, for any claim terms or phrases with any reasonable clarity, deliberateness and precision. Accordingly, the Office concludes that Applicant has not acted as his/her own lexicographer.
A second exception to the prohibition of reading limitations from the specification into the claims is when the claimed feature is written as a means-plus-function. See 35 U.S.C. §112(f) and MPEP §2181-2183. As noted in MPEP §2181, a three-prong test is used to determine the scope of a means-plus-function limitation in a claim:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
The Office has found herein that the claims do not contain limitations of means or means type language that must be analyzed under 35 U.S.C. §112 (f).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 19 is directed to transitory signals (signals per se). Therefore, Claim 19 is not within at least one of the four statutory categories and warrants rejections for failure to claim statutory subject matter.
Office Note: In order to overcome this rejection, the Office suggests further defining the limitations of the independent claims, for example claiming non-transitory computer-readable storage media having computer-executable instructions. Limitations such as these suggested above would further bring the claimed subject matter out of the realm of abstract idea and into the realm of a statutory category.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bennet et al. (US 20130322665 A1), hereinafter Bennet, in view of Sharifi et al. (US 20230123323 A1), hereinafter Sharifi.
Regarding claim 1, Bennet discloses:
A system comprising (Fig. 59; [0088], FIG. 59 conceptually illustrates a system architecture that includes mapping and navigation application of some embodiments that generates text instructions for different contexts.):
at least one computer processor ([0860], Many of the above-described features and applications are implemented as software processes that are specified as a set of instructions recorded on a computer readable storage medium (also referred to as computer readable medium). When these instructions are executed by one or more computational or processing unit(s) (e.g., one or more processors, cores of processors, or other processing units), they cause the processing unit(s) to perform the actions indicated in the instructions. Examples of computer readable media include, but are not limited to, CD-ROMs, flash drives, random access memory (RAM) chips, hard drives, erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), etc. The computer readable media does not include carrier waves and electronic signals passing wirelessly or over wired connections.);
and one or more computer storage media storing computer-usable instructions that, when used by the at least one computer processor, cause the at least on computer processor to perform operations comprising ([0860], Many of the above-described features and applications are implemented as software processes that are specified as a set of instructions recorded on a computer readable storage medium (also referred to as computer readable medium). When these instructions are executed by one or more computational or processing unit(s) (e.g., one or more processors, cores of processors, or other processing units), they cause the processing unit(s) to perform the actions indicated in the instructions. Examples of computer readable media include, but are not limited to, CD-ROMs, flash drives, random access memory (RAM) chips, hard drives, erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), etc. The computer readable media does not include carrier waves and electronic signals passing wirelessly or over wired connections.):
and based at least in part on the ranking of the plurality of route candidates, causing presentation, at a map interface of a user device, of an indication of a response to the request to provide navigational directions or a route to the destination location ([0560], The instruction retriever 5960 uses a context analyzer 5965 to determine which of the instruction variants to select for a particular display of a maneuver, depending on the context in which the text instruction will be displayed. These contexts may include different situations for routing directions or different situations for turn-by-tum navigation instructions (e.g., standard mode, lock-screen mode, when a different application is open, when voice navigation is activated, etc.). In some embodiments, the context depends on several factors associated with clearly displaying the route maneuvers required for navigating the route. For example, the context may be based on the amount of space available to display the text instruction (e.g., due to the size of the device on which the route directions are displayed), the conditions under which the indicator will be displayed (e.g., whether the maneuver is a current or future route maneuver, in which particular modality of the navigation application the sign will be displayed, etc.), or other factors. Many such contexts are shown above in subsection A of this Section. The instruction retriever 5960 selects an instruction variant to use for a particular maneuver display and provides this information to the sign generator 5970; [0561], The arrow selector 5975 also uses the context analyzer 5965 to determine which of the directional indicators to use for a particular maneuver, depending on the context in which the indicator will be displayed. The arrow selector chooses one of the graphical indicators described in the previous section (e.g., either a complex or simple representation of a maneuver) and provides this selection to the sign generator 5970. The sign generator 5970 generates a navigation instruction sign for display that includes the selected graphical indicator and instruction text variant. The sign generator 5970 also uses the context analyzer results to generate other aspects of the sign, such as how often to update the distance information and whether to use road sign shields in place of road names.).
However, Bennet does not specifically state:
receiving a natural language question or command issued by a user, the natural language question or command corresponding to a request to provide at least one of navigational directions or a route to a destination location via a user preference to take or avoid a specific road or road type;
providing an indication of the natural language question or command as input into a language model, wherein the language model detects an entity associated with the specific road or road type to take or avoid;
based at least in part on the language model detecting the entity associated with the specific road or road type to take or avoid, instructing a routing component to rank a plurality of route candidates;
Sharifi teaches:
receiving a natural language question or command issued by a user, the natural language question or command corresponding to a request to provide at least one of navigational directions or a route to a destination location via a user preference to take or avoid a specific road or road type ([0192], The one or more user preferences can include a maximum distance of a suggested route, a maximum travel time of a suggested route, a preferred type of road, and/or a preferred mode of travel; [0176], In some embodiments, selection of suggested route 1 or suggested route 2 can be performed via a user input to the audio input component 806 (e.g., a microphone) which can, for example, detect a user's voice. The computing device 800 can then perform one or more voice recognition operations to determine the suggested route that was selected based on what the user said. The user can select suggested route 1 by saying “TAKE THE LONGER ROUTE” or “TAKE THE MORE FAMILIAR ROUTE.” Alternatively, the user can select suggested route 2 by saying “TAKE THE FASTER ROUTE.”);
providing an indication of the natural language question or command as input into a language model, wherein the language model detects an entity associated with the specific road or road type to take or avoid ([0182], Furthermore, a user's feedback to the prompt requesting how familiar the traveled route was to the user can be received via a user input to the audio input component 906 (e.g., a microphone) which can, for example, detect a user's voice. The computing device 900 can then perform one or more voice recognition operations to determine the feedback provided by the user based on what the user said in response to the prompt. The user can reply to an audio output generated by the audio output component 908 by saying how familiar the route was to the user (e.g., the user can say the route was very familiar, somewhat familiar, or not familiar at all).);
based at least in part on the language model detecting the entity associated with the specific road or road type to take or avoid, instructing a routing component to rank a plurality of route candidates ([0057], In some embodiments, a predetermined number of the one or more suggested routes with a threshold ranking (e.g., the suggested route with the highest ranking) can be emphasized. Emphasis of the one or more suggested routes that satisfy a threshold ranking can include emphasizing one or more visual properties of an indication associated with the one or more suggested routes that satisfy the threshold ranking. The one or more visual properties can include a particular color (e.g., unique color or a color that is different from the colors used for indications associated with other suggested routes that are not the highest ranked), a particular or distinctive text size, a particular or distinctive shape, and/or a particular or distinctive pattern. For example, the suggested route with the highest ranking can be indicated in a unique color (e.g., the highest ranked route is highlighted in bright green), associated with a different style of font (e.g., bold font or italics for the highest ranked suggested route), and/or presented in a larger size (e.g., a larger font size for the highest ranked suggested route);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Sharifi into the invention of Bennet to include user preferences to take or avoid a specific road or road type and rank a plurality of route candidates based on user preferences as Sharifi discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that receives a natural language question or command which contains user preferences for routing and presents a ranked set of routes from which a user selects. Additionally, the claimed invention is merely a combination of old, well-known elements of providing a route in response to a voice command from a user as disclosed by Bennet and extracting user preferences from a voice command and providing a ranked list of routes as taught by Sharifi. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 2, Bennet in view of Sharifi:
extracting contextual data, and wherein the contextual data includes at least one of, information from one or more previous turns that are part of a same first conversation as the natural language question or command, one or more previous natural language questions or commands generated prior to the natural language question or command that are a part of a second conversation, a spatial constraint within the natural language question or command, a temporal constraint within the natural language question or command, or context from an output generated by a language model (Bennet: Fig. 59; [0560], The instruction retriever 5960 uses a context analyzer 5965 to determine which of the instruction variants to select for a particular display of a maneuver, depending on the context in which the text instruction will be displayed. These contexts may include different situations for routing directions or different situations for turn-by-tum navigation instructions (e.g., standard mode, lock-screen mode, when a different application is open, when voice navigation is activated, etc.). In some embodiments, the context depends on several factors associated with clearly displaying the route maneuvers required for navigating the route. For example, the context may be based on the amount of space available to display the text instruction (e.g., due to the size of the device on which the route directions are displayed), the conditions under which the indicator will be displayed (e.g., whether the maneuver is a current or future route maneuver, in which particular modality of the navigation application the sign will be displayed, etc.), or other factors. Many such contexts are shown above in subsection A of this Section. The instruction retriever 5960 selects an instruction variant to use for a particular maneuver display and provides this information to the sign generator 5970),
and wherein the ranking of the plurality of route candidates is further based on the contextual data (Bennet: Fig. 59; [0560], The instruction retriever 5960 uses a context analyzer 5965 to determine which of the instruction variants to select for a particular display of a maneuver, depending on the context in which the text instruction will be displayed. These contexts may include different situations for routing directions or different situations for turn-by-tum navigation instructions (e.g., standard mode, lock-screen mode, when a different application is open, when voice navigation is activated, etc.). In some embodiments, the context depends on several factors associated with clearly displaying the route maneuvers required for navigating the route. For example, the context may be based on the amount of space available to display the text instruction (e.g., due to the size of the device on which the route directions are displayed), the conditions under which the indicator will be displayed (e.g., whether the maneuver is a current or future route maneuver, in which particular modality of the navigation application the sign will be displayed, etc.), or other factors. Many such contexts are shown above in subsection A of this Section. The instruction retriever 5960 selects an instruction variant to use for a particular maneuver display and provides this information to the sign generator 5970).
Regarding claim 3, Bennet in view of Sharifi:
generating an augmented query by augmenting the natural language question or command with at least a portion of the contextual data (Bennet: [0669], In some embodiments, the interactive map provides sub-directions as the user navigates from location to location. The sub-directions are provided based on the user's current location, a planned route, a destination, and/or the user's request for information. For example, while driving along a route to a predetermined destination, the user may ask the interactive map "What's the building to the right of me?" "Which way should I go next?" "Where can I get gas?" or "Where can I find an Italian restaurant?" For each of these questions, the interactive map considers the user's current location, the route that the user is currently taking, and/ or the destination, and provides a contextually relevant response, such as "That was the Ferry building," "Tum left at the next corner," "Here is a list of gas stations near the next five exits: …," or "Here is a list of Italian restaurants near your destination: .... "; [0670], In some embodiments, the interactive map processes various natural language utterances from the user and in response to the utterances, retrieves and presents the user's current navigation status while the user is traveling along a route. Example navigation status information includes information regarding the distance between the user's current location and the user's destination, the estimated time of arrival to the user's destination, the distance between the user's current location and the next waypoint (e.g., the next turn, the next exit, or the next landmark) along a current or planned route, the estimated time to reach the next waypoint along a current or planned route, a description of the next waypoint along the route, a description of the destination, and the like.);
and providing the augmented query as input into the language model, and wherein the language model detects, within the augmented query, the user preference (Bennet: [0669], In some embodiments, the interactive map provides sub-directions as the user navigates from location to location. The sub-directions are provided based on the user's current location, a planned route, a destination, and/or the user's request for information. For example, while driving along a route to a predetermined destination, the user may ask the interactive map "What's the building to the right of me?" "Which way should I go next?" "Where can I get gas?" or "Where can I find an Italian restaurant?" For each of these questions, the interactive map considers the user's current location, the route that the user is currently taking, and/ or the destination, and provides a contextually relevant response, such as "That was the Ferry building," "Tum left at the next corner," "Here is a list of gas stations near the next five exits: …," or "Here is a list of Italian restaurants near your destination: .... "; [0670], In some embodiments, the interactive map processes various natural language utterances from the user and in response to the utterances, retrieves and presents the user's current navigation status while the user is traveling along a route. Example navigation status information includes information regarding the distance between the user's current location and the user's destination, the estimated time of arrival to the user's destination, the distance between the user's current location and the next waypoint (e.g., the next turn, the next exit, or the next landmark) along a current or planned route, the estimated time to reach the next waypoint along a current or planned route, a description of the next waypoint along the route, a description of the destination, and the like.).
Regarding claim 4, Bennet in view of Sharifi:
wherein the extracting the user preference is based on at least one of: performing natural language processing of the natural language question or command, performing natural language processing of first text that is part of a current conversation as the natural language question or command, performing natural language processing of second text that is part of a conversation prior to the current conversation, or extracting the user preference from a data store that does not include the natural language question or command (Bennet: [0651], For instance, interactive navigation finds a short route using freeways, a longer route using alternative freeways, and a route that does not use freeways to get from the current location to a particular destination. Some embodiments select one of several routes found (e.g., based on a default set up, user preferences set ups, past user preferences, etc.) during voice-activated navigation and optionally display an overview of the route and wait for the route to be loaded. Anticipating a hands-free interaction, the single route is displayed and the display transitions into full-screen tum-by turn navigation display. As described below, when several destinations (e.g., several gas stations along the route) are found during a search, the voice-activated service in some embodiments uses a list reading mechanism to cycle through the results in a sequential fashion; [0721], When the search request is received while navigation is not going on (not shown in FIGS. 85A-85C), process 8400 retrieves (at 8450) the search results at the vicinity of the current location of the user device (instead of the vicinity of the route as described in operation 8415 above). The process then prepares (at 8420) a sequential list of search results. Different embodiments use different criteria for sorting the list in order to determine which search result is presented to the user first. For instance, some embodiments use the closest location first. Other embodiments utilize different rankings of each item in the search result to sort the list. For instance, a restaurant that has a higher ranking is shown first. Other embodiments utilize user preferences either explicitly set or by using the past preferences of the user. For instance, a restaurant with lower cost may be presented first.).
Regarding claim 5, Bennet in view of Sharifi:
wherein the request to provide at least one of the navigational directions or the route to the destination location includes a request to provide a route navigational directions from a source location to the destination location using or avoiding a first street or location and wherein the operations further comprising (Bennet: [0651], For instance, interactive navigation finds a short route using freeways, a longer route using alternative freeways, and a route that does not use freeways to get from the current location to a particular destination. Some embodiments select one of several routes found (e.g., based on a default set up, user preferences set ups, past user preferences, etc.) during voice-activated navigation and optionally display an overview of the route and wait for the route to be loaded. Anticipating a hands-free interaction, the single route is displayed and the display transitions into full-screen tum-by turn navigation display. As described below, when several destinations (e.g., several gas stations along the route) are found during a search, the voice-activated service in some embodiments uses a list reading mechanism to cycle through the results in a sequential fashion;):
computing a distance from the source location to the first street, and from the source location to the destination location, and wherein the ranking of the plurality of route candidates is further based on the computing of the distance from the source location to the first street and from the source location to the destination location (Bennet: Fig. 79; Fig. 90A; Fig. 90B; Fig. 90C; Fig. 90D; [0645], In addition to presenting information in visual form and receiving inputs and commands through various touch based or motion-based input devices (e.g., keyboard, mouse, joystick, touch-pad, touch-se).
Regarding claim 6, Bennet in view of Sharifi:
tagging, with metadata, each data object representing a road segment of each route candidate, of the plurality of route candidates (Bennet: [0883], A map service in some embodiments provides map services by generating map service data in various formats. In some embodiments, one format of map service data is map image data. Map image data provides image data to a client device so that the client device may process the image data (e.g., rendering and/or displaying the image data as a two-dimensional or three-dimensional map). Map image data, whether in two or three dimensions, may specify one or more map tiles. A map tile may be a portion of a larger map image. Assembling together the map tiles of a map produces the original map. Tiles may be generated from map image data, routing or navigation data, or any other map service data. In some embodiments map tiles are raster-based map tiles, with tile sizes ranging from any size both larger and smaller than a commonly-used 256 pixel by 256 pixel tile. Raster-based map tiles may be encoded in any number of standard digital image representations including, but not limited to, Bitmap (.bmp), Graphics Interchange Format (.gif), Joint Photographic Experts Group (.jpg, .jpeg, etc.), Portable Networks Graphic (.png), or Tagged Image File Format (.tiff). In some embodiments, map tiles are vector-based map tiles, encoded using vector graphics, including, but not limited to, Scalable Vector Graphics (.svg) or a Drawing File (.drw). Some embodiments also include tiles with a combination of vector and raster data. Metadata or other information pertaining to the map tile may also be included within or along with a map tile, providing further map service data to a client device. In various embodiments, a map tile is encoded for transport utilizing various standards and/or protocols, some of which are described in examples below.);
and for each route candidate, of the plurality of route candidates, generating a route document by stitching each data object of a respective road segment together, and wherein the ranking includes ranking each route document based at least in part on a matching of natural language words in each route document to user intent associated with the question or command (Bennet: Fig. 52; Fig. 58; Fig. 59; [0527], Next, the process generates (at 5540) text instruction variants for all the junctures along the route. Text instruction variants are combinations of text strings derived from the decoded juncture and maneuver information. As discussed above in conjunction with FIGS. 52-54, examples of such text strings include "at the second intersection", "turn left", "onto 1st St.", "towards Wolfe Rd.", and "for 0.3 miles". In some embodiments, the process 5500 combines the text strings into text instruction variants. As a first example of such a combination, process 5500 may combine "at the second intersection" and "turn left" to produce a short text instruction variant that reads, "At the second intersection, turn left." As a second example of such a combination, process 5500 may combine all of the previous text strings to produce a long text instruction variant that reads "At the second intersection, turn left onto 1st St., towards Wolfe Rd. for 0.3 Miles." In some embodiments, process 5500 ranks the text instruction variants for each juncture based on the amount of information conveyed in each variant. In some embodiments, the text instruction variants are generated by the device using a process such as the process 5800 described below by reference to FIG. 58; [0559], The instruction generator 5945 generates ranked text instruction variants 5955 for display on a device based on the synthesized instruction elements 5935 received from the juncture decoder 5930. In some embodiments, the instruction generator 5945 executes the process 5800 of FIG. 58, described above. In some embodiments, the ranked text instruction variants 5955 may be stored on the device in random access memory or other volatile storage, for use only during the navigation of the route, or in a more permanent storage such as a hard disk or solid-state memory. FIG. 52, described above, also illustrates an example of ranked instruction variants 5955 generated by the instruction generator 5945. The example table 5950 illustrates the results of synthesizing the elements 5940 into several text instruction variants for use in different contexts of the navigation displays.).
Regarding claim 7, Bennet in view of Sharifi:
wherein each route document includes one or more of: an overall length of a respective route candidate, elevation changes for the respective route candidate, a shape of the route candidate, a smoothness level of the respective route candidate, each type of road segment indicated within the respective route candidate, a type and name of locations along the respective candidate routes (Bennet: [0724], As shown, the voice-activated service shows a map 8555 that identifies the current location of the device 8557, location of the presented search result 8559, and a single route 8558 between the two locations. The screen also shows other useful information 8551 (e.g., the name of the presented search result and ratings when available). The user can also see (or hear) more information about the search result (e.g., by tapping on the banner 8551 or selecting the control 8553 on the banner 8551 that shows the search result name or by verbally asking for more details about the present search result).).
Regarding claim 8, Bennet in view of Sharifi:
providing the natural language question or command and an indication of a map as input into a language model, and wherein the ranking of the plurality of route candidates is further based on the providing of the natural language question or command and the indication of the map as input into the language model (Bennet: Fig. 90A; Fig. 90B; Fig. 90C; Fig. 90D; [0753], Search list generator module prepares a list (e.g., as described in operations 8420 or 8625 described above) of the search result. Search list presenter module 9040 receives the search list, selects a search item, and sends a request to map generator module 9085 of map service 9010 for a map and a route from the current device location to the location of the search result; Fig. 58).
Regarding claim 9, Bennet in view of Sharifi:
wherein the indication of the response to the request to provide navigational directions or a route to the destination location includes at least one of: a graphical element superimposed over the map interface that highlights at least one route candidate, of the plurality of candidates, a language model-generated natural language summary of the user preference or at least one road associated with a respective route candidate, of the plurality of route candidates, or a tooltip user interface element that highlights a property of at least one respective route candidate, of the plurality of route candidates (Bennet: Fig. 80; Fig. 82; Fig. 83; [0021], When the navigation application receives the juncture and maneuver description, the application of some embodiments initially performs a process to simplify the characterization of the juncture and the maneuver, and then uses this simplified characterization to generate the prominent stylized graphical directional indicator for the juncture. To display a maneuver at a juncture, some navigation applications often provide a plain arrow that is not expressed in terms of the juncture and does not convey much information, while other navigation applications provide a very detailed representation of the juncture and a complex directional representation through this detailed representation. Thus, one existing approach provides very little information, while another approach provides so much information that the information is rendered practically useless. By generating the prominent stylized directional indicator based on the simplified description of the juncture, the navigation application of some embodiments displays a detailed representation of the maneuver at the juncture while eliminating some of the unnecessary complexities of the juncture.).
Regarding claim 10, Bennet discloses:
A computer-implemented method comprising (Abstract, A context-aware voice guidance method is provided that interacts with other voice services of a user device. The voice guidance does not provide audible guidance while the user is making a verbal request to any of the voice-activated services. Instead, the voice guidance transcribes its output on the screen while the verbal requests from the user are received. In some embodiments, the voice guidance only provides a short warning sound to get the user's attention while the user is speaking on a phone call or another voice-activated service is providing audible response to the user's inquires. The voice guidance in some embodiments distinguishes between music that can be ducked and spoken words, for example from an audiobook, that the user wants to pause instead of being skipped. The voice guidance ducks music but pauses spoken words of an audio book in order to provide voice guidance to the user.):
and based at least in part on the instructing the routing component to rank the plurality of route candidates, causing presentation, at a user device, of an indication of a response to the request to provide navigational directions or a route to the destination location ([0560], The instruction retriever 5960 uses a context analyzer 5965 to determine which of the instruction variants to select for a particular display of a maneuver, depending on the context in which the text instruction will be displayed. These contexts may include different situations for routing directions or different situations for turn-by-tum navigation instructions (e.g., standard mode, lock-screen mode, when a different application is open, when voice navigation is activated, etc.). In some embodiments, the context depends on several factors associated with clearly displaying the route maneuvers required for navigating the route. For example, the context may be based on the amount of space available to display the text instruction (e.g., due to the size of the device on which the route directions are displayed), the conditions under which the indicator will be displayed (e.g., whether the maneuver is a current or future route maneuver, in which particular modality of the navigation application the sign will be displayed, etc.), or other factors. Many such contexts are shown above in subsection A of this Section. The instruction retriever 5960 selects an instruction variant to use for a particular maneuver display and provides this information to the sign generator 5970; [0561], The arrow selector 5975 also uses the context analyzer 5965 to determine which of the directional indicators to use for a particular maneuver, depending on the context in which the indicator will be displayed. The arrow selector chooses one of the graphical indicators described in the previous section (e.g., either a complex or simple representation of a maneuver) and provides this selection to the sign generator 5970. The sign generator 5970 generates a navigation instruction sign for display that includes the selected graphical indicator and instruction text variant. The sign generator 5970 also uses the context analyzer results to generate other aspects of the sign, such as how often to update the distance information and whether to use road sign shields in place of road names.).
However, Bennet does not specifically state:
receiving a natural language question or command issued by a user, the natural language question or command corresponding to a request to provide at least one of navigational directions or a route to a destination location, and wherein the natural language question or command includes a user preference to take or avoid a specific road type for navigating to the destination location;
providing an indication of the natural language question or command as input into a language model, wherein the language model detects an entity associated with the specific road or road type to take or avoid for navigating to the destination location;
based at least in part on the language model detecting the entity associated with the specific road or road type to take or avoid for navigating to the destination location, instructing a routing component to rank a plurality of route candidates;
Sharifi teaches:
receiving a natural language question or command issued by a user, the natural language question or command corresponding to a request to provide at least one of navigational directions or a route to a destination location, and wherein the natural language question or command includes a user preference to take or avoid a specific road type for navigating to the destination location ([0192], The one or more user preferences can include a maximum distance of a suggested route, a maximum travel time of a suggested route, a preferred type of road, and/or a preferred mode of travel; [0176], In some embodiments, selection of suggested route 1 or suggested route 2 can be performed via a user input to the audio input component 806 (e.g., a microphone) which can, for example, detect a user's voice. The computing device 800 can then perform one or more voice recognition operations to determine the suggested route that was selected based on what the user said. The user can select suggested route 1 by saying “TAKE THE LONGER ROUTE” or “TAKE THE MORE FAMILIAR ROUTE.” Alternatively, the user can select suggested route 2 by saying “TAKE THE FASTER ROUTE.”);
providing an indication of the natural language question or command as input into a language model, wherein the language model detects an entity associated with the specific road or road type to take or avoid for navigating to the destination location ([0182], Furthermore, a user's feedback to the prompt requesting how familiar the traveled route was to the user can be received via a user input to the audio input component 906 (e.g., a microphone) which can, for example, detect a user's voice. The computing device 900 can then perform one or more voice recognition operations to determine the feedback provided by the user based on what the user said in response to the prompt. The user can reply to an audio output generated by the audio output component 908 by saying how familiar the route was to the user (e.g., the user can say the route was very familiar, somewhat familiar, or not familiar at all).);
based at least in part on the language model detecting the entity associated with the specific road or road type to take or avoid for navigating to the destination location, instructing a routing component to rank a plurality of route candidates ([0057], In some embodiments, a predetermined number of the one or more suggested routes with a threshold ranking (e.g., the suggested route with the highest ranking) can be emphasized. Emphasis of the one or more suggested routes that satisfy a threshold ranking can include emphasizing one or more visual properties of an indication associated with the one or more suggested routes that satisfy the threshold ranking. The one or more visual properties can include a particular color (e.g., unique color or a color that is different from the colors used for indications associated with other suggested routes that are not the highest ranked), a particular or distinctive text size, a particular or distinctive shape, and/or a particular or distinctive pattern. For example, the suggested route with the highest ranking can be indicated in a unique color (e.g., the highest ranked route is highlighted in bright green), associated with a different style of font (e.g., bold font or italics for the highest ranked suggested route), and/or presented in a larger size (e.g., a larger font size for the highest ranked suggested route).);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Sharifi into the invention of Bennet to include user preferences to take or avoid a specific road or road type and rank a plurality of route candidates based on user preferences as Sharifi discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that receives a natural language question or command which contains user preferences for routing and presents a ranked set of routes from which a user selects. Additionally, the claimed invention is merely a combination of old, well-known elements of providing a route in response to a voice command from a user as disclosed by Bennet and extracting user preferences from a voice command and providing a ranked list of routes as taught by Sharifi. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 11, Bennet in view of Sharifi teaches:
extracting contextual data, and wherein the contextual data includes at least one of, information from one or more previous turns that are part of a same first conversation as the natural language question or command, one or more previous natural language questions or commands generated prior to the natural language question or command that are a part of a second conversation, a spatial constraint within the natural language question or command, a temporal constraint within the natural language question or command, or context from an output generated by the language model, and wherein the ranking of the plurality of route candidates is further based on the contextual data (Bennet: Fig. 59; [0560], The instruction retriever 5960 uses a context analyzer 5965 to determine which of the instruction variants to select for a particular display of a maneuver, depending on the context in which the text instruction will be displayed. These contexts may include different situations for routing directions or different situations for turn-by-tum navigation instructions (e.g., standard mode, lock-screen mode, when a different application is open, when voice navigation is activated, etc.). In some embodiments, the context depends on several factors associated with clearly displaying the route maneuvers required for navigating the route. For example, the context may be based on the amount of space available to display the text instruction (e.g., due to the size of the device on which the route directions are displayed), the conditions under which the indicator will be displayed (e.g., whether the maneuver is a current or future route maneuver, in which particular modality of the navigation application the sign will be displayed, etc.), or other factors. Many such contexts are shown above in subsection A of this Section. The instruction retriever 5960 selects an instruction variant to use for a particular maneuver display and provides this information to the sign generator 5970).
Regarding claim 12, Bennet in view of Sharifi teaches:
generating an augmented query by augmenting the natural language question or command with at least a portion of the contextual data (Bennet: [0669], In some embodiments, the interactive map provides sub-directions as the user navigates from location to location. The sub-directions are provided based on the user's current location, a planned route, a destination, and/or the user's request for information. For example, while driving along a route to a predetermined destination, the user may ask the interactive map "What's the building to the right of me?" "Which way should I go next?" "Where can I get gas?" or "Where can I find an Italian restaurant?" For each of these questions, the interactive map considers the user's current location, the route that the user is currently taking, and/ or the destination, and provides a contextually relevant response, such as "That was the Ferry building," "Tum left at the next corner," "Here is a list of gas stations near the next five exits: …," or "Here is a list of Italian restaurants near your destination: .... "; [0670], In some embodiments, the interactive map processes various natural language utterances from the user and in response to the utterances, retrieves and presents the user's current navigation status while the user is traveling along a route. Example navigation status information includes information regarding the distance between the user's current location and the user's destination, the estimated time of arrival to the user's destination, the distance between the user's current location and the next waypoint (e.g., the next turn, the next exit, or the next landmark) along a current or planned route, the estimated time to reach the next waypoint along a current or planned route, a description of the next waypoint along the route, a description of the destination, and the like.);
and providing the augmented query as input into the language model, and wherein the language model detects the entity associated with the destination location and the user preference based further on the providing of the augmented query as input into the language model (Bennet: [0669], In some embodiments, the interactive map provides sub-directions as the user navigates from location to location. The sub-directions are provided based on the user's current location, a planned route, a destination, and/or the user's request for information. For example, while driving along a route to a predetermined destination, the user may ask the interactive map "What's the building to the right of me?" "Which way should I go next?" "Where can I get gas?" or "Where can I find an Italian restaurant?" For each of these questions, the interactive map considers the user's current location, the route that the user is currently taking, and/ or the destination, and provides a contextually relevant response, such as "That was the Ferry building," "Tum left at the next corner," "Here is a list of gas stations near the next five exits: …," or "Here is a list of Italian restaurants near your destination: .... "; [0670], In some embodiments, the interactive map processes various natural language utterances from the user and in response to the utterances, retrieves and presents the user's current navigation status while the user is traveling along a route. Example navigation status information includes information regarding the distance between the user's current location and the user's destination, the estimated time of arrival to the user's destination, the distance between the user's current location and the next waypoint (e.g., the next turn, the next exit, or the next landmark) along a current or planned route, the estimated time to reach the next waypoint along a current or planned route, a description of the next waypoint along the route, a description of the destination, and the like.).
Regarding claim 13, Bennet in view of Sharifi teaches:
wherein the user preference includes a preference to navigate to the destination location via a first street or location or avoiding the first street or location (Bennet: [0651], For instance, interactive navigation finds a short route using freeways, a longer route using alternative freeways, and a route that does not use freeways to get from the current location to a particular destination. Some embodiments select one of several routes found (e.g., based on a default set up, user preferences set ups, past user preferences, etc.) during voice-activated navigation and optionally display an overview of the route and wait for the route to be loaded. Anticipating a hands-free interaction, the single route is displayed and the display transitions into full-screen turn-by-turn navigation display. As described below, when several destinations (e.g., several gas stations along the route) are found during a search, the voice-activated service in some embodiments uses a list reading mechanism to cycle through the results in a sequential fashion; [0721], When the search request is received while navigation is not going on (not shown in FIGS. 85A-85C), process 8400 retrieves (at 8450) the search results at the vicinity of the current location of the user device (instead of the vicinity of the route as described in operation 8415 above). The process then prepares (at 8420) a sequential list of search results. Different embodiments use different criteria for sorting the list in order to determine which search result is presented to the user first. For instance, some embodiments use the closest location first. Other embodiments utilize different rankings of each item in the search result to sort the list. For instance, a restaurant that has a higher ranking is shown first. Other embodiments utilize user preferences either explicitly set or by using the past preferences of the user. For instance, a restaurant with lower cost may be presented first.).
Regarding claim 14, Bennet in view of Sharifi teaches:
wherein the request to provide at least one of the navigational directions or the route to the destination location includes a request to provide a route or navigational directions from a source location to the destination location using or avoiding a first street or location, and wherein the operations further comprising (Bennet: [0651], For instance, interactive navigation finds a short route using freeways, a longer route using alternative freeways, and a route that does not use freeways to get from the current location to a particular destination. Some embodiments select one of several routes found (e.g., based on a default set up, user preferences set ups, past user preferences, etc.) during voice-activated navigation and optionally display an overview of the route and wait for the route to be loaded. Anticipating a hands-free interaction, the single route is displayed and the display transitions into full-screen turn-by-turn navigation display. As described below, when several destinations (e.g., several gas stations along the route) are found during a search, the voice-activated service in some embodiments uses a list reading mechanism to cycle through the results in a sequential fashion;):
computing a distance from the source location to the first street, and from the source location to the destination location, and wherein the ranking of the plurality of route candidates is further based on the computing of the distance (Bennet: Fig. 79; Fig. 90A; Fig. 90B; Fig. 90C; Fig. 90D; [0645], In addition to presenting information in visual form and receiving inputs and commands through various touch-based or motion-based input devices (e.g., keyboard, mouse, joystick, touch-pad, touch-se).
Regarding claim 15, Bennet in view of Sharifi teaches:
tagging, with metadata, each data object representing a road segment of each route candidate, of the plurality of route candidates (Bennet: [0883], A map service in some embodiments provides map services by generating map service data in various formats. In some embodiments, one format of map service data is map image data. Map image data provides image data to a client device so that the client device may process the image data (e.g., rendering and/or displaying the image data as a two-dimensional or three-dimensional map). Map image data, whether in two or three dimensions, may specify one or more map tiles. A map tile may be a portion of a larger map image. Assembling together the map tiles of a map produces the original map. Tiles may be generated from map image data, routing or navigation data, or any other map service data. In some embodiments map tiles are raster-based map tiles, with tile sizes ranging from any size both larger and smaller than a commonly-used 256 pixel by 256 pixel tile. Raster-based map tiles may be encoded in any number of standard digital image representations including, but not limited to, Bitmap (.bmp), Graphics Interchange Format (.gif), Joint Photographic Experts Group (.jpg, .jpeg, etc.), Portable Networks Graphic (.png), or Tagged Image File Format (.tiff). In some embodiments, map tiles are vector-based map tiles, encoded using vector graphics, including, but not limited to, Scalable Vector Graphics (.svg) or a Drawing File (.drw). Some embodiments also include tiles with a combination of vector and raster data. Metadata or other information pertaining to the map tile may also be included within or along with a map tile, providing further map service data to a client device. In various embodiments, a map tile is encoded for transport utilizing various standards and/or protocols, some of which are described in examples below.);
and for each route candidate, of the plurality of route candidates, generating a route document by stitching each data object of a respective road segment together, and wherein the ranking includes ranking each route document based at least in part on a matching of natural language words in each route document to user intent associated with the question or command (Bennet: Fig. 52; Fig. 58; Fig. 59; [0527], Next, the process generates (at 5540) text instruction variants for all the junctures along the route. Text instruction variants are combinations of text strings derived from the decoded juncture and maneuver information. As discussed above in conjunction with FIGS. 52-54, examples of such text strings include "at the second intersection", "turn left", "onto 1st St.", "towards Wolfe Rd.", and "for 0.3 miles". In some embodiments, the process 5500 combines the text strings into text instruction variants. As a first example of such a combination, process 5500 may combine "at the second intersection" and "turn left" to produce a short text instruction variant that reads, "At the second intersection, turn left." As a second example of such a combination, process 5500 may combine all of the previous text strings to produce a long text instruction variant that reads "At the second intersection, turn left onto 1st St., towards Wolfe Rd. for 0.3 Miles." In some embodiments, process 5500 ranks the text instruction variants for each juncture based on the amount of information conveyed in each variant. In some embodiments, the text instruction variants are generated by the device using a process such as the process 5800 described below by reference to FIG. 58; [0559], The instruction generator 5945 generates ranked text instruction variants 5955 for display on a device based on the synthesized instruction elements 5935 received from the juncture decoder 5930. In some embodiments, the instruction generator 5945 executes the process 5800 of FIG. 58, described above. In some embodiments, the ranked text instruction variants 5955 may be stored on the device in random access memory or other volatile storage, for use only during the navigation of the route, or in a more permanent storage such as a hard disk or solid-state memory. FIG. 52, described above, also illustrates an example of ranked instruction variants 5955 generated by the instruction generator 5945. The example table 5950 illustrates the results of synthesizing the elements 5940 into several text instruction variants for use in different contexts of the navigation displays.).
Regarding claim 16, Bennet in view of Sharifi teaches:
wherein each route document includes one or more of, an overall length of a respective route candidate, elevation changes for the respective route candidate, a shape of the route candidate, a smoothness level of the respective route candidate, each type of road segment indicated within the respective route candidate, a type and name of locations along the respective candidate routes (Bennet: [0724], As shown, the voice-activated service shows a map 8555 that identifies the current location of the device 8557, location of the presented search result 8559, and a single route 8558 between the two locations. The screen also shows other useful information 8551 (e.g., the name of the presented search result and ratings when available). The user can also see (or hear) more information about the search result (e.g., by tapping on the banner 8551 or selecting the control 8553 on the banner 8551 that shows the search result name or by verbally asking for more details about the present search result).).
Regarding claim 17, Bennet in view of Sharifi teaches:
wherein the ranking of the plurality of route candidates is further based on the providing an indication of a map as input into the language model (Bennet: Fig. 90A; Fig. 90B; Fig. 90C; Fig. 90D; [0753], Search list generator module prepares a list (e.g., as described in operations 8420 or 8625 described above) of the search result. Search list presenter module 9040 receives the search list, selects a search item, and sends a request to map generator module 9085 of map service 9010 for a map and a route from the current device location to the location of the search result; Fig. 58).
Regarding claim 18, Bennet in view of Sharifi teaches:
wherein the indication of the response to the request to provide navigational directions or a route to the destination location includes at least one of: a graphical element superimposed over a map interface that highlights at least one route candidate, of the plurality of candidates, a natural language summary of the user preference or at least one road associated with a respective route candidate, of the plurality of route candidates, or a tooltip user interface element that highlights a property of at least one respective route candidate, of the plurality of route candidates (Bennet: Fig. 80; Fig. 82; Fig. 83; [0021], When the navigation application receives the juncture and maneuver description, the application of some embodiments initially performs a process to simplify the characterization of the juncture and the maneuver, and then uses this simplified characterization to generate the prominent stylized graphical directional indicator for the juncture. To display a maneuver at a juncture, some navigation applications often provide a plain arrow that is not expressed in terms of the juncture and does not convey much information, while other navigation applications provide a very detailed representation of the juncture and a complex directional representation through this detailed representation. Thus, one existing approach provides very little information, while another approach provides so much information that the information is rendered practically useless. By generating the prominent stylized directional indicator based on the simplified description of the juncture, the navigation application of some embodiments displays a detailed representation of the maneuver at the juncture while eliminating some of the unnecessary complexities of the juncture.).
Regarding claim 19, Bennet discloses:
One or more computer storage media having computer-executable instructions embodied thereon that, when executed, by one or more processors, cause the one or more processors to perform operations comprising ([0860], Many of the above-described features and applications are implemented as software processes that are specified as a set of instructions recorded on a computer readable storage medium (also referred to as computer readable medium). When these instructions are executed by one or more computational or processing unit(s) (e.g., one or more processors, cores of processors, or other processing units), they cause the processing unit(s) to perform the actions indicated in the instructions. Examples of computer readable media include, but are not limited to, CD-ROMs, flash drives, random access memory (RAM) chips, hard drives, erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), etc. The computer readable media does not include carrier waves and electronic signals passing wirelessly or over wired connections.):
However, Bennet does not specifically state:
receiving an indication of a natural language query issued by a user, the natural language query corresponding to a request to provide at least one of navigational directions or a route to a destination location via a user preference to take or avoid a specific road or road type;
based at least in part on a language model having detected an entity associated with the user preference to take or avoid the specific road or road type, rank a plurality of route candidates;
and based at least in part on the ranking of the plurality of route candidates, causing presentation, at a user device, of an indication of a response to the request to provide navigational directions or a route to the destination location.
Sharifi teaches:
receiving an indication of a natural language query issued by a user, the natural language query corresponding to a request to provide at least one of navigational directions or a route to a destination location via a user preference to take or avoid a specific road or road type ([0192], The one or more user preferences can include a maximum distance of a suggested route, a maximum travel time of a suggested route, a preferred type of road, and/or a preferred mode of travel; [0176], In some embodiments, selection of suggested route 1 or suggested route 2 can be performed via a user input to the audio input component 806 (e.g., a microphone) which can, for example, detect a user's voice. The computing device 800 can then perform one or more voice recognition operations to determine the suggested route that was selected based on what the user said. The user can select suggested route 1 by saying “TAKE THE LONGER ROUTE” or “TAKE THE MORE FAMILIAR ROUTE.” Alternatively, the user can select suggested route 2 by saying “TAKE THE FASTER ROUTE.”);
based at least in part on a language model having detected an entity associated with the user preference to take or avoid the specific road or road type, rank a plurality of route candidates ([0182], Furthermore, a user's feedback to the prompt requesting how familiar the traveled route was to the user can be received via a user input to the audio input component 906 (e.g., a microphone) which can, for example, detect a user's voice. The computing device 900 can then perform one or more voice recognition operations to determine the feedback provided by the user based on what the user said in response to the prompt. The user can reply to an audio output generated by the audio output component 908 by saying how familiar the route was to the user (e.g., the user can say the route was very familiar, somewhat familiar, or not familiar at all).);
and based at least in part on the ranking of the plurality of route candidates, causing presentation, at a user device, of an indication of a response to the request to provide navigational directions or a route to the destination location ([0057], In some embodiments, a predetermined number of the one or more suggested routes with a threshold ranking (e.g., the suggested route with the highest ranking) can be emphasized. Emphasis of the one or more suggested routes that satisfy a threshold ranking can include emphasizing one or more visual properties of an indication associated with the one or more suggested routes that satisfy the threshold ranking. The one or more visual properties can include a particular color (e.g., unique color or a color that is different from the colors used for indications associated with other suggested routes that are not the highest ranked), a particular or distinctive text size, a particular or distinctive shape, and/or a particular or distinctive pattern. For example, the suggested route with the highest ranking can be indicated in a unique color (e.g., the highest ranked route is highlighted in bright green), associated with a different style of font (e.g., bold font or italics for the highest ranked suggested route), and/or presented in a larger size (e.g., a larger font size for the highest ranked suggested route).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Sharifi into the invention of Bennet to include user preferences to take or avoid a specific road or road type and rank a plurality of route candidates based on user preferences as Sharifi discloses with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art to create a more robust system that receives a natural language question or command which contains user preferences for routing and presents a ranked set of routes from which a user selects. Additionally, the claimed invention is merely a combination of old, well-known elements of providing a route in response to a voice command from a user as disclosed by Bennet and extracting user preferences from a voice command and providing a ranked list of routes as taught by Sharifi. The combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 20, Bennet in view of Sharifi teaches:
tagging, with metadata, each data object representing a road segment of each route candidate, of the plurality of route candidates (Bennet: [0883], A map service in some embodiments provides map services by generating map service data in various formats. In some embodiments, one format of map service data is map image data. Map image data provides image data to a client device so that the client device may process the image data (e.g., rendering and/or displaying the image data as a two-dimensional or three-dimensional map). Map image data, whether in two or three dimensions, may specify one or more map tiles. A map tile may be a portion of a larger map image. Assembling together the map tiles of a map produces the original map. Tiles may be generated from map image data, routing or navigation data, or any other map service data. In some embodiments map tiles are raster-based map tiles, with tile sizes ranging from any size both larger and smaller than a commonly-used 256 pixel by 256 pixel tile. Raster-based map tiles may be encoded in any number of standard digital image representations including, but not limited to, Bitmap (.bmp), Graphics Interchange Format (.gif), Joint Photographic Experts Group (.jpg, .jpeg, etc.), Portable Networks Graphic (.png), or Tagged Image File Format (.tiff). In some embodiments, map tiles are vector-based map tiles, encoded using vector graphics, including, but not limited to, Scalable Vector Graphics (.svg) or a Drawing File (.drw). Some embodiments also include tiles with a combination of vector and raster data. Metadata or other information pertaining to the map tile may also be included within or along with a map tile, providing further map service data to a client device. In various embodiments, a map tile is encoded for transport utilizing various standards and/or protocols, some of which are described in examples below.);
and for each route candidate, of the plurality of route candidates, generating a route document by stitching each data object of a respective road segment together, and wherein the ranking includes ranking each route document based at least in part on a matching of natural language words in each route document to user intent associated with the question or command (Bennet: Fig. 52; Fig. 58; Fig. 59; [0527], Next, the process generates (at 5540) text instruction variants for all the junctures along the route. Text instruction variants are combinations of text strings derived from the decoded juncture and maneuver information. As discussed above in conjunction with FIGS. 52-54, examples of such text strings include "at the second intersection", "turn left", "onto 1st St.", "towards Wolfe Rd.", and "for 0.3 miles". In some embodiments, the process 5500 combines the text strings into text instruction variants. As a first example of such a combination, process 5500 may combine "at the second intersection" and "turn left" to produce a short text instruction variant that reads, "At the second intersection, turn left." As a second example of such a combination, process 5500 may combine all of the previous text strings to produce a long text instruction variant that reads "At the second intersection, turn left onto 1st St., towards Wolfe Rd. for 0.3 Miles." In some embodiments, process 5500 ranks the text instruction variants for each juncture based on the amount of information conveyed in each variant. In some embodiments, the text instruction variants are generated by the device using a process such as the process 5800 described below by reference to FIG. 58; [0559], The instruction generator 5945 generates ranked text instruction variants 5955 for display on a device based on the synthesized instruction elements 5935 received from the juncture decoder 5930. In some embodiments, the instruction generator 5945 executes the process 5800 of FIG. 58, described above. In some embodiments, the ranked text instruction variants 5955 may be stored on the device in random access memory or other volatile storage, for use only during the navigation of the route, or in a more permanent storage such as a hard disk or solid-state memory. FIG. 52, described above, also illustrates an example of ranked instruction variants 5955 generated by the instruction generator 5945. The example table 5950 illustrates the results of synthesizing the elements 5940 into several text instruction variants for use in different contexts of the navigation displays.).
Documents Considered but Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Vellasques et al. (US 20190011278 A1) discloses an approach is provided for mobility-based language model adaptation for navigational speech interfaces. The approach involves receiving a word lattice resulting from a speech recognition process applied on a speech input received from a user via a navigational speech interface. The word lattice includes a respective speech recognition score for each word in the word lattice. The approach further involves adapting a language model comprising a plurality of location entities according to a mobility relevance and a search relevance of the plurality of location entities to the user. In one embodiment, the mobility relevance and the search relevance are determined by computing a probability of the user visiting the location entities associated with said each word after the user queries for said each word. The approach further involves initiating a re-scoring of said each word in the word lattice using the adapted language model to generate a speech recognition output. Sharifi (US 20250237511 A1) discloses a computing device may implement a method for progressively updating a navigation route. The method includes receiving, from a user, an initial input that includes a coarse location as a first destination; determining an initial route including a first set of navigation instructions to the first destination; and initiating a navigation session and providing the initial route to the user to allow the user to follow the first set of navigation instructions to the first destination. The method further includes, during the navigation session, determining a second destination that is a precise location and is different from the first destination; determining an updated route including a second set of navigation instructions from a current location of the user on the initial route to the second destination; updating a portion of the initial route to include the updated route; and providing the updated portion of the initial route to the user. DeLuca et al. (US 20190178671 A1) discloses improving electronic (e.g., GPS) navigation for a user operating a vehicle based on feedback from the user. More specifically, a verbal comment is obtained from the user and is analyzed for a navigation instruction delivery preference. A profile associated with navigation of the vehicle, which can include a user profile, a location profile, or an ambient environment profile, is modified based on the navigation instruction delivery preference. Based on the profile associated with navigation of the vehicle, a navigation instruction is synthesized and provided to the user. A dialogue can be solicited with the user to obtain additional comments from the user, based upon which the profile associated with navigation of the vehicle can be further modified.
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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IZCALLI ANDRE RIOS-AGUIRRE whose telephone number is (571)272-0790. The examiner can normally be reached Monday through Friday 8:30 - 17:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott A. Browne can be reached at (571) 270-0151. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/I.A.R./ Examiner, Art Unit 3666
/SCOTT A BROWNE/ Supervisory Patent Examiner, Art Unit 3666
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