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 . This office action is in response to an amendment filed on 1/21/2026. Claims 1-8,and 11-20 are pending.
Response to Amendment
Amendments filed on 1/21/2026 are under consideration. Claims 1, 11, 19, and 20 are amended. Claims 9 and 10 are cancelled.
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 1-20 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
Claims 1-18 are directed to a system (i.e., a machine). Therefore, claims 1-18 are within at least one of the four statutory categories.
Claim 19 is directed to a method (i.e., a process). Therefore, claim 19 is within at least one of the four statutory categories.
Claim 20 is directed to A non-transitory computer-readable storage medium (i.e., a manufacture). Therefore, claim 20 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed
to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity,
and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (mental process)
and will be used as a representative claim for the remainder of the 101 rejections. Claim 1, 19, and 20 recites:
a transceiver configured to receive historical inputs associated with a vehicle and user preferences associated with a vehicle user; and a processor communicatively coupled to the transceiver, wherein the processor is configured to: determine a routine travel behavior of the vehicle based on the historical inputs, wherein the routine travel behavior comprises a travel pattern, a charging pattern, and a parking pattern; determine an expected set of destinations from a plurality of destinations that the vehicle is expected to visit in a preset time duration based on the routine travel behavior, wherein the plurality of destinations is associated with a plurality of destination tags, and wherein the expected set of destinations comprises a first destination associated with a first destination tag, of the plurality of destination tags, and a second destination associated with a second destination tag, of the plurality of destination tags; identify an optimal set of destinations, from the plurality of destinations, based on the user preferences and the routine travel behavior, wherein the optimal set of destinations comprises a third destination associated with the first destination tag and a fourth destination associated with the second destination tag; further wherein the optimal set of destinations are determined by at least: determining most visited destinations, from the plurality of destinations, associated with the vehicle based on the routine travel behavior; estimating a probability of parking the vehicle at each of the most visited destinations at each time based on the routine travel behavior; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; generate a recommendation based on the optimal set of destinations; and output the recommendation comprising the information associated with the optimal set of destinations.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “…determine a routine travel behavior of the vehicle”, “further wherein the optimal set of destinations are determined by at least: determining most visited destinations, from the plurality of destinations, associated with the vehicle based on the routine travel behavior;”, and “determine an expected set of destinations from a plurality of destinations that the vehicle is expected to visit in a preset time duration” in the context of this claim encompasses a person determining a behavior of the driver and destinations of the driver based on the behavior, which can be done with a human’s mental ability as this is simply determining a way of moving the vehicle based on past travel data. For example also see “; identify an optimal set of destinations”, and “generate a recommendation based on the optimal set of destinations” in the context of this claim these limitation encompass a person identifying a desired destination and recommending to the driver a destination based on the desired destination identification, which can all be done within a human mind as it is reasonable to assume a human can identify a choice destination and recommend said destination based on the identification. Again, “estimating a probability of parking the vehicle at each of the most visited destinations at each time based on the routine travel behavior; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold” in the context of this claim these limitations are merely using an mathematical formulation to generate a probability of a parking event and checking the generated number with a set point to best understand the situation of the vehicle which can be done by a human mind with the aid of pen and paper. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
a transceiver configured to receive historical inputs associated with a vehicle and user preferences associated with a vehicle user; and a processor communicatively coupled to the transceiver, wherein the processor is configured to: determine a routine travel behavior of the vehicle based on the historical inputs, wherein the routine travel behavior comprises a travel pattern, a charging pattern, and a parking pattern; determine an expected set of destinations from a plurality of destinations that the vehicle is expected to visit in a preset time duration based on the routine travel behavior, wherein the plurality of destinations is associated with a plurality of destination tags, and wherein the expected set of destinations comprises a first destination associated with a first destination tag, of the plurality of destination tags, and a second destination associated with a second destination tag, of the plurality of destination tags; identify an optimal set of destinations, from the plurality of destinations, based on the user preferences and the routine travel behavior, wherein the optimal set of destinations comprises a third destination associated with the first destination tag and a fourth destination associated with the second destination tag; further wherein the optimal set of destinations are determined by at least: determining most visited destinations, from the plurality of destinations, associated with the vehicle based on the routine travel behavior; estimating a probability of parking the vehicle at each of the most visited destinations at each time based on the routine travel behavior; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; generate a recommendation based on the optimal set of destinations; and output the recommendation comprising the information associated with the optimal set of destinations.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of, “…receive historical inputs associated with a vehicle and user preferences associated with a vehicle user”, “based on the historical inputs, wherein the routine travel behavior comprises a travel pattern, a charging pattern, and a parking pattern”, “based on the routine travel behavior, wherein the plurality of destinations is associated with a plurality of destination tags, and wherein the expected set of destinations comprises a first destination associated with a first destination tag, of the plurality of destination tags, and a second destination associated with a second destination tag, of the plurality of destination tags”, “from the plurality of destinations, based on the user preferences and the routine travel behavior, wherein the optimal set of destinations comprises a third destination associated with the first destination tag and a fourth destination associated with the second destination tag;”, and “and output the recommendation comprising the information associated with the optimal set of destinations.” the examiner submits that these limitations are insignificant extra-solution activities that merely use a processor to perform the mental process. Each of the above cited limitations are simply further defining the mental process and explaining a type of data being gathered or outputting at each step of the mental process (i.e., as a general means of outputting or receiving data in response to determining the travel pattern of the driver), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Additionally the limitations of “a transceiver configured to” and “and a processor communicatively coupled to the transceiver, wherein the processor is configured to:” the examiner submits that these limitations are insignificant extra-solution activities that merely use generic computer components to perform the mental process. As the system used has generic components (‘processor’ and ‘transceiver’) and the applicant does not make an attempt to improve the functioning of a computer with specialized components. The system, is recited at a high level of generality and merely automates recommending destinations to the driver.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond
generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above, the additional limitations of “acquiring a charge request”, and “transmitting a power reception instruction” the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in
Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood,
routine, conventional activity in the field. The additional limitations of “receive”, “acquiring”, and “based on” are well-understood, routine, and conventional activities because the specification recites that the components are all conventional computer components mounted on the vehicle, and the specification does not provide any indication that the system is anything other than what a conventional computer does within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner.
Dependent claims 2-18, do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional
aspects of the judicial exception and/or well-understood, routine and conventional additional
elements that do not integrate the judicial exception into a practical application. Claim 2 mentions “…historical inputs comprise…”, which would fail under Step 2A Prong 2 as a mere receipt of collecting data thus would not make claims 2 to be considered patent eligible subject matter. Claim 3 and 4 mention “… user preferences comprise…”, which would fail under Step 2A Prong 2 as a mere receipt of collecting data thus would not make claims 3 or 4 to be considered patent eligible subject matter. Claim 5 mentions “... identify a destination…” which would fail under Step 2A Prong one as a mental process as the method is just further configured to recognize a destination type for a driver which is something a human mind is capable of doing which would not make claim 5 to be considered patent eligible subject matter. Claim 6 mentions “…obtain the mapping…” which would fail under Step 2A Prong 2 as a mere receipt of collecting data thus would not make claims 6 to be considered patent eligible subject matter. Claim 7 mentions “…obtain the information…” which would fail under Step 2A Prong 2 as a mere receipt of collecting data thus would not make claims 7 to be considered patent eligible subject matter. Claim 8 mentions “…wherein the first destination is different…” which would fail under Step 2A Prong 2 as a mere receipt of collecting data thus would not make claims 8 to be considered patent eligible subject matter. Claim 11 mentions “... estimate the expected set of destinations…” which would fail under Step 2A Prong one as a mental process as the method is just further configured to make a determination for a the destination of the driver which is something a human mind is capable of doing which would not make claim 11 to be considered patent eligible subject matter. Claim 12 mentions “... identify the fourth destination…” which would fail under Step 2A Prong one as a mental process as the method is just further configured to identify a the destination for the driver which is something a human mind is capable of doing which would not make claim 12 to be considered patent eligible subject matter. Claim 13 mentions “... determine a routine charging destination…” which would fail under Step 2A Prong one as a mental process as the method is just further configured to identify a the destination for the driver which is something a human mind is capable of doing which would not make claim 13 to be considered patent eligible subject matter. Claim 14 mentions “... generate the recommendation…” which would fail under Step 2A Prong one as a mental process as the method is just further configured to make a recommendation for the driver which is something a human mind is capable of doing which would not make claim 14 to be considered patent eligible subject matter. Claim 15 mentions “... determine a route…” which would fail under Step 2A Prong one as a mental process as the method is just further configured to determine a path for the driver to take which is something a human mind is capable of doing which would not make claim 15 to be considered patent eligible subject matter. Claim 16 mentions “... identify a parking location…” which would fail under Step 2A Prong one as a mental process as the method is just further configured to determine a parking spot for the driver which is something a human mind is capable of doing which would not make claim 16 to be considered patent eligible subject matter. Claim 17 mentions “... determine one or more additional routes…” which would fail under Step 2A Prong one as a mental process as the method is just further configured to determine a path for the driver to take which is something a human mind is capable of doing which would not make claim 17 to be considered patent eligible subject matter. Claim 18 mentions “…display the information…”, which would fail under Step 2A Prong 2 as a mere receipt of collecting and outputting data thus would not make claims 18 to be considered patent eligible subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 15-16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maeda et al. (US 11,740,098 B2) in view of Vengroff (WO 2008/134595 A1) and in further view of Duym. (US 2021/0341302 Al)
Claims 1-4, 15-16, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being Anticipated by Maeda et al. (US 11,740,098 B2).
Regarding Claim 1 Maeda teaches A system (Pg. 1 – Abstract – “A system and method for providing charging options based on electric vehicle operator daily activities”) comprising: a transceiver configured to receive historical inputs associated with a vehicle and user preferences associated with a vehicle user; (Pg. 18 – Col. 20 – lines 14-19 – “In additional embodiments, the operator may selectively input threshold preferences related to price schemes, queue/ wait times, price incentives, charging types, and the like that may be utilized to pin point one or more charging stations 112 on the planner user interface 500” & See Also Pg. 19 – Col. 21 – lines 8-12 – “planner user interface 500 may be utilized by the operator to schedule the charging of the EV 102 at one or more charging stations(s) 112 that may take place in the midst of the operator's daily routines, tasks, and/or activities.” & See Also Pg. 11 – Col. 5 – lines 26- 29. –“ EV 102 may transmit and receive data ( e.g., state of charge data, energy cost data, charging commands/signals) to and from the remote server 108,”(equates to comprising: a transceiver configured to receive historical inputs associated with a vehicle and user preferences associated with a vehicle user as the first quote shows a variety of user preferences being taken into consideration, and the second quote showing the planner working with the daily routine of the user or thew historical input being utilized to determine a time to charge the vehicle. Last quote shows the transceiver via the EV having communication capabilities with a server to attain said information. ) ) and a processor communicatively coupled to the transceiver, (Pg. 12 – Col. 7 – lines 64-66 – “The vehicle computing device 202 may include a processor 204, a memory 206, a data store 208,” & See Also Pg. 12 – [Col. 7 – lines 64-67 and Pg. 12 – Col. 8 lines 1-4 ] – “The vehicle computing device 202 may include a processor 204, a memory 206, a data store 208, a position determination device 210 (GPS), a plurality of vehicle systems 212 ( e.g., including the electric motor 104, the battery 106) and a communication interface 214. The components of the architecture 200, including the vehicle computing device 202, may be operably connected for computer communication via a bus 216”) wherein the processor is configured to: determine a routine travel behavior of the vehicle based on the historical inputs, (Pg. 12 – Col. 8 – lines 46 – 55 – “As discussed below, the location log 224 may be analyzed by the smart charge application 118 with respect to the one or more stored geo-locations of the EV 102 and/or the one or more stored geo-locations of the portable device 222. In particular, the one or more stored geo-locations of the EV 102 and/or the one or more stored geo-locations of the portable device 222 may be compared to location data that may be provided by the station computing infrastructure 116 and stored on the remote server 108 to determine one or more travel routines that may apply to the operator of the EV” (equates to wherein the processor is configured to: determine a routine travel behavior of the vehicle based on the historical inputs as the quote shows a location log being used to determine a travel routine of a vehicle and thus the historical inputs of a location log are used to determine a routine.)) wherein the routine travel behavior comprises a travel pattern, (Pg. 15 – [Co1. 13 – lines 40-42] – “location log 224 to determine one or more point of interest 40 locations that may be frequently and/or routinely traveled to by the EV.” (equates to wherein the routine travel behavior comprises a travel pattern as the quote shows a frequency of travelling be logged in the locations log thus establishing a pattern)) a charging pattern, (Pg. 15 – [Co1. 13 – lines 40-42] – “location log 224 to determine one or more point of interest locations that may be frequently and/or routinely traveled to by the EV.” & See Also Pg. 15 – Col. 13 – lines 36 -38 – “the travel plan module 402 may be configured to analyze the log of locations at which the EV 102 is driven, parked, and/or charged.”) and a parking pattern; (Pg. 15 – [Co1. 13 – lines 40-42] – “location log 224 to determine one or more point of interest locations that may be frequently and/or routinely traveled to by the EV.” & See Also Pg. 15 – Col. 13 – lines 36 -38 – “the travel plan module 402 may be configured to analyze the log of locations at which the EV 102 is driven, parked, and/or charged.”) determine an expected set of destinations from a plurality of destinations that the vehicle is expected to visit in a preset time duration based on the routine travel behavior, (Pg. 15 – Col. 13 – lines 23-32 – “The travel routines and/or prospective travel plans may be applicable at one or more points of time during one or more periods of time. In one embodiment, the travel plan module 402 may be configured to determine one or more travel routines that may be followed by the operator of the EV 102. The one or more travel routines may include one or more trips of the EV 102, tasks, and/or activities of the operator of the EV 102 that may routinely take place during a particular day, particular week, and/or one or more particular timeframes.”) wherein the plurality of destinations is associated with a plurality of destination tags, (Pg. 16 – Col.15 – lines 33 – 39 – “The one or more predicted points of interest may include, but may not be limited to, a store, a restaurant, a home location, a workplace location, a location to complete a task, 35 a location to shop for an item(s), a school, and the like that may be determined based on one or more routine trips of the EV 102 and/or one or more prospective travel plans of the operator of the EV 102” (equates to wherein the plurality of destinations is associated with a plurality of destination tags as the quote shows a plurality of destination tags as individual locations about a path of travel are determined by type of location and thus tagged.)) and wherein the expected set of destinations comprises a first destination associated with a first destination tag, (Pg. 16 – [Col. 16 ] – “query the station database 314 to determine one or more charging stations 112 that may be located within a predetermined distance (e.g., 5 miles) of” (equates to and wherein the expected set of destinations comprises a first destination associated with a first destination tag as the first destination can be the charging station identified wherein the tag is the fact that the system recognize the location as a charging station and thus establishing a type of location..)) of the plurality of destination tags, and a second destination associated with a second destination tag, of the plurality of destination tags; (Pg. 16 – Col.15 – lines 33 – 39 – “The one or more predicted points of interest may include, but may not be limited to, a store, a restaurant, a home location, a workplace location, a location to complete a task, 35 a location to shop for an item(s), a school, and the like that may be determined based on one or more routine trips of the EV 102 and/or one or more prospective travel plans of the operator of the EV 102” (equates to of the plurality of destination tags, and a second destination associated with a second destination tag, of the plurality of destination tags as the quote shows a plurality of nearby locations of the travel path and the second tag being a restaurant wherein the system can identify he type of destination and thus categorize the location as a location with the type of destination.)) identify an optimal set of destinations, from the plurality of destinations, based on the user preferences and the routine travel behavior, (Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to identify an optimal set of destinations, from the plurality of destinations, based on the user preferences and the routine travel behavior, as the quote shows identification of optimal destination as the geolocations for travel paths presented to users are determined based on a routine and thus an optimal destination is configured based on the user, a plurality of destinations are considered as the end of the quote how’s one or more predicted points of interest. )) wherein the optimal set of destinations comprises a third destination associated with the first destination tag (Pg. 16 – [Col. 16 ] – “query the station database 314 to determine one or more charging stations 112 that may be located within a predetermined distance (e.g., 5 miles) of” (equates to wherein the optimal set of destinations comprises a third destination associated with the first destination tag as the quote shows an amount of charging station sot be identified as more than one and thus a second destination of a type of the destinations (in this case charging stations ) can be identified by the system. )) and a fourth destination associated with the second destination tag; (Pg. 14 – [Col.11 - lines] – “The location data repository 316 may be configured as a relational database/data store that may include various records that may each include stored data that pertains to one or more particular point of interest locations (e.g., stores, 10 restaurants, schools, home, etc.) and associated geo-location coordinates of the one or more particular point of interest locations. Each record of the location data repository 316 may be updated with a description of point of interest locations that may include names, maps, sub-points of 15 interest names, sub-location names, and the like” (equates to and a fourth destination associated with the second destination tag as the quote shows a plurality of destinations being associated with the point of interest locations wherein a plurality of restaurant information can be attained based on the travel path and thus the category of restaurants is the second destination tag and the different names that can be associated with the restaurant tag would inherently include a second destination within the type of destination based on the travel path. )) from the plurality of destinations, (Pg. 12 – Col. 8 – lines 57-60 – “one embodiment, the data store 208 may additionally store an operator dataset 226 that may be utilized to store the one or more predicted point of interests that may be pre- dieted as potential destinations of the operator of the EV 102” (equates to from the plurality of destinations as the quote shows a one or more and thus plurality POI.)) generate a recommendation based on the optimal set of destinations; (Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to generate a recommendation based on the optimal set of destinations; as the quote shows a presentation of planner that includes routines of the user and thus considers recommending an optimal destination based on the user.)) and output the recommendation comprising the information associated with the optimal set of destinations. (Pg. 7 – [Fig. 7 ] – 708 – “PRESENTING A PLANNER USER INTERFACE WITH ONE OR MORE CHARGING STATIONS AND / OR THE ONE OR MORE ADDITIONAL ELECTRIC VEHICLES THAT MAY BE UTILIZED TO CHARGE THE EV IN THE '—708 MIDST OF THE OPERATOR'S DAILY ROUTINES, TASKS, AND/ OR ACTIVITIES” & See Also Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to output the recommendation comprising the information associated with the optimal set of destinations as the quote shows a routine and predictive plan of travel thus an optimal set of destinations being considered for outputting to a user as a number of prospective geo-locations are considered.))
Maeda fails to teach further wherein the optimal set of destinations are determined by at least: determining most visited destinations, associated with the vehicle based on the routine travel behavior; estimating a probability of parking the vehicle at each of the most visited destinations at each time based on the routine travel behavior; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold;
Vengroff teaches further wherein the optimal set of destinations are determined by at least: determining most visited destinations, (Pg. 1 – abstract – “automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest” (equates to determine most visited destinations as a frequency of visits is taken in account when determining a destination of interest and thus a most visited location would be determined based on the highest frequency of visits done by the user.)) associated with the vehicle based on the routine travel behavior. (Pg. 1 – Abstract – “After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).” & See Also Pg. 2 – [0016] – “In addition, in at least some embodiments, the described techniques include automatically identifying user visits to the determined locations of identified points of interest (e.g., for locations and points of interest automatically identified using at least some of the visualization techniques), such as based on location-related data from a single GPS track log of a user's device. As described in greater detail below, the identification of user visits may include various activities in various embodiments, including the following: identifying that a particular visit to a particular point of interest's location has occurred; comparing an identified visit to other visits to the same or other locations, such as to quantify the visit relative to 'typical' visits to the location and/or to categorize a type of the visit relative to one or more other parameters of interest (e.g., a duration; a purpose of the visit and/or activity performed, such as to visit a Starbucks to take out coffee versus to meet with a friend; etc.);” (equates to determine the expected set of destinations from the most visited destinations based on the routine travel behavior as the quote shows how the system is learning based on the user visiting certain POI and thus when the system of this cited art is determining POI to recommend based on a frequency of visit to a first location the system is expecting the user to act in a certain way based on past behavior and similarly will recommend poi of expected interest. )) at each of the most visited destinations at each time based on the routine travel behavior (Pg. 1 – Abstract – “After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).” & See Also Pg. 2 – [0016] – “In addition, in at least some embodiments, the described techniques include automatically identifying user visits to the determined locations of identified points of interest (e.g., for locations and points of interest automatically identified using at least some of the visualization techniques), such as based on location-related data from a single GPS track log of a user's device. As described in greater detail below, the identification of user visits may include various activities in various embodiments, including the following: identifying that a particular visit to a particular point of interest's location has occurred; comparing an identified visit to other visits to the same or other locations, such as to quantify the visit relative to 'typical' visits to the location and/or to categorize a type of the visit relative to one or more other parameters of interest (e.g., a duration; a purpose of the visit and/or activity performed, such as to visit a Starbucks to take out coffee versus to meet with a friend; etc.);”)
Yet both Maeda-Vengroff fail to teach estimating a probability of parking the vehicle; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold;
Duym teaches estimating a probability of parking the vehicle (Pg. 10 – [0007] – “and extending the computed route beyond the route destination in an area around the route destination until a route parking probability of a parking route,” (equates to wherein the processor is further configured to: estimate a probability of parking the vehicle as the quote showing the probability of parking being calculated throughout the route.)) setting a threshold to ascertain a parking event at each destination; ((Pg. 1 – Abstract – “The route parking probability is the probability that parking at some point along the extended computed route will be possible and wherein one or more links of the parking route are suitable for parking.” & See Also Pg. 10 – [0009] – “According to an embodiment, the desired route parking probability is a threshold.” & See Also Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. (equates to set a threshold to ascertain a parking event at each destination as the first quote shows a determination of probability of parking at each point that is along a route of travel. The second quote shows the probability to park is equivalent to a threshold and thus the threshold is set for each point or destination as seen by the first quote. The last quote shows the sub route finishing at a destination in which the parking would take place and thus the parking event at a destination is shown.))) determining that the probability of parking at the first destination at a future time slot is greater than the threshold; (Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. And such methods still further include extending the computed route beyond the route destination in area around the route destination until route parking probability of parking route, including links of the determined sub-route and/or of the extension of the computed route, is equal to or greater than predetermined threshold whilst total expected travel time, associated with the extended computed route and comprising driving and walking times, is minimized” & See Also Pg. 10 – [0009] – “respective distance from a predecessor of the corresponding link in the computed route to the route destination are computed, wherein the link-wise moving back is terminated and the corresponding link is selected as an appropriate start link of the sub-route if the respective route parking probability is greater than or equal to a desired route parking probability” (equates to determining that the probability of parking at the first destination at a future time slot is greater than the threshold; as the quote shows the probability of parking being determined to be greater than a threshold that is accounting for travel time or a destination arrival time to be considered and minimized thus a future time slot is considered when determining the probability of parking at the destination. The quote specifically shows the sub-route terminating at a destination position in which the parking probability is used to be calculated. )) and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; (Pg. 10 – [0009] – “respective distance from a predecessor of the corresponding link in the computed route to the route destination are computed, wherein the link-wise moving back is terminated and the corresponding link is selected as an appropriate start link of the sub-route if the respective route parking probability is greater than or equal to a desired route parking probability” & See Also Pg. 10 – [0009] – “probability that parking at some point along the extended computed route will be possible and wherein one or more links of the parking route are suitable for parking.” (equates to and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; as the quote shows the determination of future links to park at is terminated once the probability of being at an appropriate start link is greater than a desired route parking probability.)) It would have been an advantageous addition to the system disclosed by Maeda-Vengroff to include estimating a probability of parking the vehicle; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; as these limitations allow the system to understand the likelihood of parking at a destination being analyzed allowing for an ability to terminate future recommendations while it is already seen that the vehicle has parked at a particular destination.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include estimating a probability of parking the vehicle; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; as these limitation allow for an understanding of where and when did the vehicle park allowing the system to better understand the users interaction with the destination.
Regarding Claim 2 Maeda-Vengroff-Duym teaches (Maeda teaches the following limitations:) The system of claim 1, wherein the historical inputs comprise historical travel pattern, historical charging pattern, and historical parking pattern. ( Pg. 15 – Col. 13 – lines 36 -38 – “the travel plan module 402 may be configured to analyze the log of locations at which the EV 102 is driven, parked, and/or charged.” & See Also Pg. 1 – Abstract – “analyzing data associated with daily activities of an operator of an electric vehicle and determining at least one travel routine” (equates to wherein the historical inputs comprise historical travel pattern, historical charging pattern, and historical parking pattern as the quote shows the pattern established via routine of tracking the user data and each parking charging and travelling patterns being analyzed within a location log))
Regarding Claim 3 Maeda-Vengroff-Duym teaches (Maeda teaches the following limitations:) The system of claim 1, wherein the user preferences comprise at least one of a preference associated with a high charging speed, (Pg. 13 – Col. 10 – lines 16 – 23 – “The station database 314 may also include records that may pertain to one or more particular charging stations 112 and one or more pricing schemes that may be implemented by the respective charging stations 112. The one or more pricing schemes may include a price per kWh that may include a dynamic value that may change over time based on a time of day, a season, a region, a time zone, charging power requirements, a charging speed,” (equates to wherein the user preferences comprise at least one of a preference associated with a high charging speed, as the quote shows the charging speeds for the charging station being considered within the station database for user experience)) reduced charging rates, a charger availability, a charger reliability, or an incentive associated with vehicle charging.
Regarding Claim 4 Maeda-Vengroff-Duym teaches (Maeda teaches the following limitations:) The system of claim 1, wherein the user preferences comprise a preference to visit a predefined destination. (Pg. 16 – Col. 16 – lines 7-17 – “In some configurations, the travel path module 408 may analyze additional data included within the station database 314 that may pertain to one or more pricing schemes that may be implemented by one or more charging stations that may be located within the predetermined distance of the current geo-location of the EV 102 and/or the predetermined distance from one or more predicted points of interest that may be visited by the EV 102. As discussed above, the station database 314 may also include records that pertain to particular charging stations 112 and current utilization of the charging stations 112.” (equates to wherein the user preferences comprise a preference to visit a predefined destination as the quote shows a predetermined way of recommending POI data that can be based on a particular or predetermined location.))
Regarding Claim 15 Maeda-Vengroff-Duym teaches (Maeda teaches the following limitations:) The system of claim 1, wherein the processor is further configured to: determine a route that the vehicle is expected to travel in the preset time duration based on the expected set of destinations; (Pg. 15 – Col. 13 – lines 28-32 – “The one or more travel routines may include one or more trips of the EV 102, tasks, and/or activities of the operator of the EV 102 that may routinely take place during a particular day, particular week, and/or one or more particular timeframes… In particular, the travel plan module 402 may be configured to access the location log 224 that may be stored on the data store 208 of the vehicle computing device 202. Upon accessing the location log 224” (equates to determine a route that the vehicle is expected to travel in the preset time duration based on the expected set of destinations as the quote shows a determination of a trip or route taking place that would occur over a timeframe. Also the end of the quote shows how the location log would be used and thus a set of destinations would be included in the route. ) ) and identify the optimal set of destinations in the route based on the user preferences and the routine travel behavior. Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to and identify the optimal set of destinations in the route based on the user preferences and the routine travel behavior as the quote shows identification of optimal destination as the geolocations for travel paths presented to users are determined based on a routine and thus an optimal destination is configured based on the user, a plurality of destinations are considered as the end of the quote how’s one or more predicted points of interest. ))
Regarding Claim 16 Maeda-Vengroff-Duym teaches (Maeda teaches the following limitations:) The system of claim 15, wherein the processor is further configured to: identify a parking location in the route based on the user preferences; (Pg. 16 – Col. 15 – lines 49-58 – “The location log 224 may be configured to store the geo-locations of the point(s) of interest visited by the operator of the EV 102 based on the location(s) at which the EV 102 is parked which may be a pre-determined distance from one or more particular points of interest. Additionally, the travel path module 408 may access the map data 318 stored upon the data store 308 of the computing device 302 55 to determine the geo-locations of one or more points of interest (that may have not been previously visited by the EV 102).” (equates to identify a parking location in the route based on the user preferences as the quote shows the EV parking location being used to determine the route and location data around the location wherein the EV being parked is a user preference itself.)) and generate the recommendation based on the parking location. (Pg. 16 – Col. 15 – lines 49-58 – “The location log 224 may be configured to store the geo-locations of the point(s) of interest visited by the operator of the EV 102 based on the location(s) at which the EV 102 is parked which may be a pre-determined distance from one or more particular points of interest. Additionally, the travel path module 408 may access the map data 318 stored upon the data store 308 of the computing device 302 55 to determine the geo-locations of one or more points of interest (that may have not been previously visited by the EV 102).” & See Also Pg. 13 – Col. 9 – lines 20-24 – “With continued reference to FIG. 2, the communication interface 214 of the EV 102 may provide software, firmware and/or hardware to facilitate data input and output between the components of the vehicle computing device 202 and other components, networks and data sources.” (equates to and generate the recommendation based on the parking location as the first quote shows a determination of POI data based on where the EV is parked and thus the second quote shows how information can be output which in turn would include the POI data from the parking location determination. ))
Regarding Claim 19 Maeda- teaches A method comprising: (Pg. 1 – Abstract – “A system and method for providing charging options based on electric vehicle operator daily activities”) obtaining, by a processor, historical inputs associated with a vehicle and user preferences associated with a vehicle user; (Pg. 18 – Col. 20 – lines 14-19 – “In additional embodiments, the operator may selectively input threshold preferences related to price schemes, queue/ wait times, price incentives, charging types, and the like that may be utilized to pin point one or more charging stations 112 on the planner user interface 500” & See Also Pg. 19 – Col. 21 – lines 8-12 – “planner user interface 500 may be utilized by the operator to schedule the charging of the EV 102 at one or more charging stations(s) 112 that may take place in the midst of the operator's daily routines, tasks, and/or activities.” & See Also Pg. 11 – Col. 5 – lines 26- 29. –“ EV 102 may transmit and receive data ( e.g., state of charge data, energy cost data, charging commands/signals) to and from the remote server 108,”(equates to comprising: a transceiver configured to receive historical inputs associated with a vehicle and user preferences associated with a vehicle user as the first quote shows a variety of user preferences being taken into consideration, and the second quote showing the planner working with the daily routine of the user or thew historical input being utilized to determine a time to charge the vehicle. Last quote shows the transceiver via the EV having communication capabilities with a server to attain said information. ) ) determining, by the processor, (Pg. 12 – Col. 7 – lines 64-66 – “The vehicle computing device 202 may include a processor 204, a memory 206, a data store 208,”) a routine travel behavior of the vehicle based on the historical inputs, (Pg. 12 – Col. 8 – lines 46 – 55 – “As discussed below, the location log 224 may be analyzed by the smart charge application 118 with respect to the one or more stored geo-locations of the EV 102 and/or the one or more stored geo-locations of the portable device 222. In particular, the one or more stored geo-locations of the EV 102 and/or the one or more stored geo-locations of the portable device 222 may be compared to location data that may be provided by the station computing infrastructure 116 and stored on the remote server 108 to determine one or more travel routines that may apply to the operator of the EV” (equates to wherein the processor is configured to: determine a routine travel behavior of the vehicle based on the historical inputs as the quote shows a location log being used to determine a travel routine of a vehicle and thus the historical inputs of a location log are used to determine a routine.)) wherein the routine travel behavior comprises a travel pattern, (Pg. 15 – [Co1. 13 – lines 40-42] – “location log 224 to determine one or more point of interest 40 locations that may be frequently and/or routinely traveled to by the EV.” (equates to wherein the routine travel behavior comprises a travel pattern as the quote shows a frequency of travelling be logged in the locations log thus establishing a pattern)) wherein the routine travel behavior comprises a travel pattern, a charging pattern, and a parking pattern; , (Pg. 15 – [Co1. 13 – lines 40-42] – “location log 224 to determine one or more point of interest locations that may be frequently and/or routinely traveled to by the EV.” & See Also Pg. 15 – Col. 13 – lines 36 -38 – “the travel plan module 402 may be configured to analyze the log of locations at which the EV 102 is driven, parked, and/or charged.”) determining, by the processor, an expected set of destinations from a plurality of destinations that the vehicle is expected to visit in a preset time duration based on the routine travel behavior, (Pg. 15 – Col. 13 – lines 23-32 – “The travel routines and/or prospective travel plans may be applicable at one or more points of time during one or more periods of time. In one embodiment, the travel plan module 402 may be configured to determine one or more travel routines that may be followed by the operator of the EV 102. The one or more travel routines may include one or more trips of the EV 102, tasks, and/or activities of the operator of the EV 102 that may routinely take place during a particular day, particular week, and/or one or more particular timeframes.”) wherein the plurality of destinations is associated with a plurality of destination tags, (Pg. 16 – Col.15 – lines 33 – 39 – “The one or more predicted points of interest may include, but may not be limited to, a store, a restaurant, a home location, a workplace location, a location to complete a task, 35 a location to shop for an item(s), a school, and the like that may be determined based on one or more routine trips of the EV 102 and/or one or more prospective travel plans of the operator of the EV 102” (equates to wherein the plurality of destinations is associated with a plurality of destination tags as the quote shows a plurality of destination tags as individual locations about a path of travel are determined by type of location and thus tagged.)) and wherein the expected set of destinations comprises a first destination associated with a first destination tag, of the plurality of destination tags, (Pg. 16 – [Col. 16 ] – “query the station database 314 to determine one or more charging stations 112 that may be located within a predetermined distance (e.g., 5 miles) of” (equates to and wherein the expected set of destinations comprises a first destination associated with a first destination tag as the first destination can be the charging station identified wherein the tag is the fact that the system recognize the location as a charging station and thus establishing a type of location..)) and a second destination associated with a second destination tag, of the plurality of destination tags; (Pg. 16 – Col.15 – lines 33 – 39 – “The one or more predicted points of interest may include, but may not be limited to, a store, a restaurant, a home location, a workplace location, a location to complete a task, 35 a location to shop for an item(s), a school, and the like that may be determined based on one or more routine trips of the EV 102 and/or one or more prospective travel plans of the operator of the EV 102” (equates to of the plurality of destination tags, and a second destination associated with a second destination tag, of the plurality of destination tags as the quote shows a plurality of nearby locations of the travel path and the second tag being a restaurant wherein the system can identify he type of destination and thus categorize the location as a location with the type of destination.)) identifying, by the processor, an optimal set of destinations, from the plurality of destinations, based on the user preferences and the routine travel behavior, (Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to identify an optimal set of destinations, from the plurality of destinations, based on the user preferences and the routine travel behavior, as the quote shows identification of optimal destination as the geolocations for travel paths presented to users are determined based on a routine and thus an optimal destination is configured based on the user, a plurality of destinations are considered as the end of the quote how’s one or more predicted points of interest. )) wherein the optimal set of destinations comprises a third destination associated with the first destination tag (Pg. 16 – [Col. 16 ] – “query the station database 314 to determine one or more charging stations 112 that may be located within a predetermined distance (e.g., 5 miles) of” (equates to wherein the optimal set of destinations comprises a third destination associated with the first destination tag as the quote shows an amount of charging station sot be identified as more than one and thus a second destination of a type of the destinations (in this case charging stations ) can be identified by the system. )) and a fourth destination associated with the second destination tag; (Pg. 14 – [Col.11 - lines] – “The location data repository 316 may be configured as a relational database/data store that may include various records that may each include stored data that pertains to one or more particular point of interest locations (e.g., stores, 10 restaurants, schools, home, etc.) and associated geo-location coordinates of the one or more particular point of interest locations. Each record of the location data repository 316 may be updated with a description of point of interest locations that may include names, maps, sub-points of 15 interest names, sub-location names, and the like” (equates to and a fourth destination associated with the second destination tag as the quote shows a plurality of destinations being associated with the point of interest locations wherein a plurality of restaurant information can be attained based on the travel path and thus the category of restaurants is the second destination tag and the different names that can be associated with the restaurant tag would inherently include a second destination within the type of destination based on the travel path. )) from the plurality of destinations, (Pg. 12 – Col. 8 – lines 57-60 – “one embodiment, the data store 208 may additionally store an operator dataset 226 that may be utilized to store the one or more predicted point of interests that may be pre- dieted as potential destinations of the operator of the EV 102” (equates to from the plurality of destinations as the quote shows a one or more and thus plurality POI.)) generating, by the processor, a recommendation based on the optimal set of destinations; (Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to generate a recommendation based on the optimal set of destinations; as the quote shows a presentation of planner that includes routines of the user and thus considers recommending an optimal destination based on the user.)) and outputting, by the processor, the recommendation comprising the information associated with the optimal set of destinations. (Pg. 7 – [Fig. 7 ] – 708 – “PRESENTING A PLANNER USER INTERFACE WITH ONE OR MORE CHARGING STATIONS AND / OR THE ONE OR MORE ADDITIONAL ELECTRIC VEHICLES THAT MAY BE UTILIZED TO CHARGE THE EV IN THE '—708 MIDST OF THE OPERATOR'S DAILY ROUTINES, TASKS, AND/ OR ACTIVITIES” & See Also Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to output the recommendation comprising the information associated with the optimal set of destinations as the quote shows a routine and predictive plan of travel thus an optimal set of destinations being considered for outputting to a user as a number of prospective geo-locations are considered.))
Maeda fails to teach further wherein the optimal set of destinations are determined by at least: determining most visited destinations, associated with the vehicle based on the routine travel behavior; estimating a probability of parking the vehicle at each of the most visited destinations at each time based on the routine travel behavior; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold;
Vengroff teaches further wherein the optimal set of destinations are determined by at least: determining most visited destinations, (Pg. 1 – abstract – “automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest” (equates to determine most visited destinations as a frequency of visits is taken in account when determining a destination of interest and thus a most visited location would be determined based on the highest frequency of visits done by the user.)) associated with the vehicle based on the routine travel behavior. (Pg. 1 – Abstract – “After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).” & See Also Pg. 2 – [0016] – “In addition, in at least some embodiments, the described techniques include automatically identifying user visits to the determined locations of identified points of interest (e.g., for locations and points of interest automatically identified using at least some of the visualization techniques), such as based on location-related data from a single GPS track log of a user's device. As described in greater detail below, the identification of user visits may include various activities in various embodiments, including the following: identifying that a particular visit to a particular point of interest's location has occurred; comparing an identified visit to other visits to the same or other locations, such as to quantify the visit relative to 'typical' visits to the location and/or to categorize a type of the visit relative to one or more other parameters of interest (e.g., a duration; a purpose of the visit and/or activity performed, such as to visit a Starbucks to take out coffee versus to meet with a friend; etc.);” (equates to determine the expected set of destinations from the most visited destinations based on the routine travel behavior as the quote shows how the system is learning based on the user visiting certain POI and thus when the system of this cited art is determining POI to recommend based on a frequency of visit to a first location the system is expecting the user to act in a certain way based on past behavior and similarly will recommend poi of expected interest. )) at each of the most visited destinations at each time based on the routine travel behavior (Pg. 1 – Abstract – “After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).” & See Also Pg. 2 – [0016] – “In addition, in at least some embodiments, the described techniques include automatically identifying user visits to the determined locations of identified points of interest (e.g., for locations and points of interest automatically identified using at least some of the visualization techniques), such as based on location-related data from a single GPS track log of a user's device. As described in greater detail below, the identification of user visits may include various activities in various embodiments, including the following: identifying that a particular visit to a particular point of interest's location has occurred; comparing an identified visit to other visits to the same or other locations, such as to quantify the visit relative to 'typical' visits to the location and/or to categorize a type of the visit relative to one or more other parameters of interest (e.g., a duration; a purpose of the visit and/or activity performed, such as to visit a Starbucks to take out coffee versus to meet with a friend; etc.);”)
Yet both Maeda-Vengroff fail to teach estimating a probability of parking the vehicle; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold;
Duym teaches estimating a probability of parking the vehicle (Pg. 10 – [0007] – “and extending the computed route beyond the route destination in an area around the route destination until a route parking probability of a parking route,” (equates to wherein the processor is further configured to: estimate a probability of parking the vehicle as the quote showing the probability of parking being calculated throughout the route.)) setting a threshold to ascertain a parking event at each destination; ((Pg. 1 – Abstract – “The route parking probability is the probability that parking at some point along the extended computed route will be possible and wherein one or more links of the parking route are suitable for parking.” & See Also Pg. 10 – [0009] – “According to an embodiment, the desired route parking probability is a threshold.” & See Also Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. (equates to set a threshold to ascertain a parking event at each destination as the first quote shows a determination of probability of parking at each point that is along a route of travel. The second quote shows the probability to park is equivalent to a threshold and thus the threshold is set for each point or destination as seen by the first quote. The last quote shows the sub route finishing at a destination in which the parking would take place and thus the parking event at a destination is shown.))) determining that the probability of parking at the first destination at a future time slot is greater than the threshold; (Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. And such methods still further include extending the computed route beyond the route destination in area around the route destination until route parking probability of parking route, including links of the determined sub-route and/or of the extension of the computed route, is equal to or greater than predetermined threshold whilst total expected travel time, associated with the extended computed route and comprising driving and walking times, is minimized” & See Also Pg. 10 – [0009] – “respective distance from a predecessor of the corresponding link in the computed route to the route destination are computed, wherein the link-wise moving back is terminated and the corresponding link is selected as an appropriate start link of the sub-route if the respective route parking probability is greater than or equal to a desired route parking probability” (equates to determining that the probability of parking at the first destination at a future time slot is greater than the threshold; as the quote shows the probability of parking being determined to be greater than a threshold that is accounting for travel time or a destination arrival time to be considered and minimized thus a future time slot is considered when determining the probability of parking at the destination. The quote specifically shows the sub-route terminating at a destination position in which the parking probability is used to be calculated. )) and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; (Pg. 10 – [0009] – “respective distance from a predecessor of the corresponding link in the computed route to the route destination are computed, wherein the link-wise moving back is terminated and the corresponding link is selected as an appropriate start link of the sub-route if the respective route parking probability is greater than or equal to a desired route parking probability” & See Also Pg. 10 – [0009] – “probability that parking at some point along the extended computed route will be possible and wherein one or more links of the parking route are suitable for parking.” (equates to and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; as the quote shows the determination of future links to park at is terminated once the probability of being at an appropriate start link is greater than a desired route parking probability.)) It would have been an advantageous addition to the system disclosed by Maeda-Vengroff to include estimating a probability of parking the vehicle; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; as these limitations allow the system to understand the likelihood of parking at a destination being analyzed allowing for an ability to terminate future recommendations while it is already seen that the vehicle has parked at a particular destination.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include estimating a probability of parking the vehicle; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; as these limitation allow for an understanding of where and when did the vehicle park allowing the system to better understand the users interaction with the destination.
Regarding Claim 20 Maeda teaches A non-transitory computer-readable storage medium having instructions stored thereupon which, (Pg. 22 – Col. 27 – lines 51-53 – “Furthermore, various exemplary embodiments may be implemented as instructions stored on a non-transitory machine-readable storage medium”) when executed by a processor, cause the processor to: obtain historical inputs associated with a vehicle and user preferences associated with a vehicle user; (Pg. 18 – Col. 20 – lines 14-19 – “In additional embodiments, the operator may selectively input threshold preferences related to price schemes, queue/ wait times, price incentives, charging types, and the like that may be utilized to pin point one or more charging stations 112 on the planner user interface 500” & See Also Pg. 19 – Col. 21 – lines 8-12 – “planner user interface 500 may be utilized by the operator to schedule the charging of the EV 102 at one or more charging stations(s) 112 that may take place in the midst of the operator's daily routines, tasks, and/or activities.” & See Also Pg. 11 – Col. 5 – lines 26- 29. –“ EV 102 may transmit and receive data ( e.g., state of charge data, energy cost data, charging commands/signals) to and from the remote server 108,”(equates to comprising: a transceiver configured to receive historical inputs associated with a vehicle and user preferences associated with a vehicle user as the first quote shows a variety of user preferences being taken into consideration, and the second quote showing the planner working with the daily routine of the user or thew historical input being utilized to determine a time to charge the vehicle. Last quote shows the transceiver via the EV having communication capabilities with a server to attain said information. ) determine a routine travel behavior of the vehicle based on the historical inputs, (Pg. 12 – Col. 8 – lines 46 – 55 – “As discussed below, the location log 224 may be analyzed by the smart charge application 118 with respect to the one or more stored geo-locations of the EV 102 and/or the one or more stored geo-locations of the portable device 222. In particular, the one or more stored geo-locations of the EV 102 and/or the one or more stored geo-locations of the portable device 222 may be compared to location data that may be provided by the station computing infrastructure 116 and stored on the remote server 108 to determine one or more travel routines that may apply to the operator of the EV” (equates to wherein the processor is configured to: determine a routine travel behavior of the vehicle based on the historical inputs as the quote shows a location log being used to determine a travel routine of a vehicle and thus the historical inputs of a location log are used to determine a routine.)) wherein the routine travel behavior comprises a travel pattern, a charging pattern, and a parking pattern; , (Pg. 15 – [Co1. 13 – lines 40-42] – “location log 224 to determine one or more point of interest locations that may be frequently and/or routinely traveled to by the EV.” & See Also Pg. 15 – Col. 13 – lines 36 -38 – “the travel plan module 402 may be configured to analyze the log of locations at which the EV 102 is driven, parked, and/or charged.”) determine an expected set of destinations from a plurality of destinations that the vehicle is expected to visit in a preset time duration based on the routine travel behavior, (Pg. 15 – Col. 13 – lines 23-32 – “The travel routines and/or prospective travel plans may be applicable at one or more points of time during one or more periods of time. In one embodiment, the travel plan module 402 may be configured to determine one or more travel routines that may be followed by the operator of the EV 102. The one or more travel routines may include one or more trips of the EV 102, tasks, and/or activities of the operator of the EV 102 that may routinely take place during a particular day, particular week, and/or one or more particular timeframes.”) wherein the plurality of destinations is associated with a plurality of destination tags, (Pg. 16 – Col.15 – lines 33 – 39 – “The one or more predicted points of interest may include, but may not be limited to, a store, a restaurant, a home location, a workplace location, a location to complete a task, 35 a location to shop for an item(s), a school, and the like that may be determined based on one or more routine trips of the EV 102 and/or one or more prospective travel plans of the operator of the EV 102” (equates to wherein the plurality of destinations is associated with a plurality of destination tags as the quote shows a plurality of destination tags as individual locations about a path of travel are determined by type of location and thus tagged.)) wherein the plurality of destinations is associated with a plurality of destination tags, (Pg. 16 – Col.15 – lines 33 – 39 – “The one or more predicted points of interest may include, but may not be limited to, a store, a restaurant, a home location, a workplace location, a location to complete a task, 35 a location to shop for an item(s), a school, and the like that may be determined based on one or more routine trips of the EV 102 and/or one or more prospective travel plans of the operator of the EV 102” (equates to wherein the plurality of destinations is associated with a plurality of destination tags as the quote shows a plurality of destination tags as individual locations about a path of travel are determined by type of location and thus tagged.)) and wherein the expected set of destinations comprises a first destination associated with a first destination tag, of the plurality of destination tags, (Pg. 16 – [Col. 16 ] – “query the station database 314 to determine one or more charging stations 112 that may be located within a predetermined distance (e.g., 5 miles) of” (equates to and wherein the expected set of destinations comprises a first destination associated with a first destination tag as the first destination can be the charging station identified wherein the tag is the fact that the system recognize the location as a charging station and thus establishing a type of location..)) and a second destination associated with a second destination tag, of the plurality of destination tags; (Pg. 16 – Col.15 – lines 33 – 39 – “The one or more predicted points of interest may include, but may not be limited to, a store, a restaurant, a home location, a workplace location, a location to complete a task, 35 a location to shop for an item(s), a school, and the like that may be determined based on one or more routine trips of the EV 102 and/or one or more prospective travel plans of the operator of the EV 102” (equates to of the plurality of destination tags, and a second destination associated with a second destination tag, of the plurality of destination tags as the quote shows a plurality of nearby locations of the travel path and the second tag being a restaurant wherein the system can identify he type of destination and thus categorize the location as a location with the type of destination.)) identify an optimal set of destinations, from the plurality of destinations, based on the user preferences and the routine travel behavior, (Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to identify an optimal set of destinations, from the plurality of destinations, based on the user preferences and the routine travel behavior, as the quote shows identification of optimal destination as the geolocations for travel paths presented to users are determined based on a routine and thus an optimal destination is configured based on the user, a plurality of destinations are considered as the end of the quote how’s one or more predicted points of interest. )) wherein the optimal set of destinations comprises a third destination associated with the first destination tag (Pg. 16 – [Col. 16 ] – “query the station database 314 to determine one or more charging stations 112 that may be located within a predetermined distance (e.g., 5 miles) of” (equates to wherein the optimal set of destinations comprises a third destination associated with the first destination tag as the quote shows an amount of charging station sot be identified as more than one and thus a second destination of a type of the destinations (in this case charging stations ) can be identified by the system. )) and a fourth destination associated with the second destination tag; (Pg. 14 – [Col.11 - lines] – “The location data repository 316 may be configured as a relational database/data store that may include various records that may each include stored data that pertains to one or more particular point of interest locations (e.g., stores, 10 restaurants, schools, home, etc.) and associated geo-location coordinates of the one or more particular point of interest locations. Each record of the location data repository 316 may be updated with a description of point of interest locations that may include names, maps, sub-points of 15 interest names, sub-location names, and the like” (equates to and a fourth destination associated with the second destination tag as the quote shows a plurality of destinations being associated with the point of interest locations wherein a plurality of restaurant information can be attained based on the travel path and thus the category of restaurants is the second destination tag and the different names that can be associated with the restaurant tag would inherently include a second destination within the type of destination based on the travel path. )) from the plurality of destinations, (Pg. 12 – Col. 8 – lines 57-60 – “one embodiment, the data store 208 may additionally store an operator dataset 226 that may be utilized to store the one or more predicted point of interests that may be pre- dieted as potential destinations of the operator of the EV 102” (equates to from the plurality of destinations as the quote shows a one or more and thus plurality POI.)) generate a recommendation based on the optimal set of destinations; (Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to generate a recommendation based on the optimal set of destinations; as the quote shows a presentation of planner that includes routines of the user and thus considers recommending an optimal destination based on the user.)) and output the recommendation comprising the information associated with the optimal set of destinations. (Pg. 7 – [Fig. 7 ] – 708 – “PRESENTING A PLANNER USER INTERFACE WITH ONE OR MORE CHARGING STATIONS AND / OR THE ONE OR MORE ADDITIONAL ELECTRIC VEHICLES THAT MAY BE UTILIZED TO CHARGE THE EV IN THE '—708 MIDST OF THE OPERATOR'S DAILY ROUTINES, TASKS, AND/ OR ACTIVITIES” & See Also Pg. 21 – [Col. 26 – lines 23-38 ]– “The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. As discussed above, the planner user interface may include one or more prospective travel paths that may be presented to the operator of the EV 102 to reach from a current geo-location of the EV 102 and/or a prospec- tive future geo-location of the EV 102 to one or more predicted points of interest that may be predicted based on one or more travel routines and/or one or more predicted travel plans.” (equates to output the recommendation comprising the information associated with the optimal set of destinations as the quote shows a routine and predictive plan of travel thus an optimal set of destinations being considered for outputting to a user as a number of prospective geo-locations are considered.))
Maeda fails to teach further wherein the optimal set of destinations are determined by at least: determining most visited destinations, associated with the vehicle based on the routine travel behavior; estimating a probability of parking the vehicle at each of the most visited destinations at each time based on the routine travel behavior; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold;
Vengroff teaches further wherein the optimal set of destinations are determined by at least: determining most visited destinations, (Pg. 1 – abstract – “automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest” (equates to determine most visited destinations as a frequency of visits is taken in account when determining a destination of interest and thus a most visited location would be determined based on the highest frequency of visits done by the user.)) associated with the vehicle based on the routine travel behavior. (Pg. 1 – Abstract – “After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).” & See Also Pg. 2 – [0016] – “In addition, in at least some embodiments, the described techniques include automatically identifying user visits to the determined locations of identified points of interest (e.g., for locations and points of interest automatically identified using at least some of the visualization techniques), such as based on location-related data from a single GPS track log of a user's device. As described in greater detail below, the identification of user visits may include various activities in various embodiments, including the following: identifying that a particular visit to a particular point of interest's location has occurred; comparing an identified visit to other visits to the same or other locations, such as to quantify the visit relative to 'typical' visits to the location and/or to categorize a type of the visit relative to one or more other parameters of interest (e.g., a duration; a purpose of the visit and/or activity performed, such as to visit a Starbucks to take out coffee versus to meet with a friend; etc.);” (equates to determine the expected set of destinations from the most visited destinations based on the routine travel behavior as the quote shows how the system is learning based on the user visiting certain POI and thus when the system of this cited art is determining POI to recommend based on a frequency of visit to a first location the system is expecting the user to act in a certain way based on past behavior and similarly will recommend poi of expected interest. )) at each of the most visited destinations at each time based on the routine travel behavior (Pg. 1 – Abstract – “After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).” & See Also Pg. 2 – [0016] – “In addition, in at least some embodiments, the described techniques include automatically identifying user visits to the determined locations of identified points of interest (e.g., for locations and points of interest automatically identified using at least some of the visualization techniques), such as based on location-related data from a single GPS track log of a user's device. As described in greater detail below, the identification of user visits may include various activities in various embodiments, including the following: identifying that a particular visit to a particular point of interest's location has occurred; comparing an identified visit to other visits to the same or other locations, such as to quantify the visit relative to 'typical' visits to the location and/or to categorize a type of the visit relative to one or more other parameters of interest (e.g., a duration; a purpose of the visit and/or activity performed, such as to visit a Starbucks to take out coffee versus to meet with a friend; etc.);”)
Yet both Maeda-Vengroff fail to teach estimating a probability of parking the vehicle; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold;
Duym teaches estimating a probability of parking the vehicle (Pg. 10 – [0007] – “and extending the computed route beyond the route destination in an area around the route destination until a route parking probability of a parking route,” (equates to wherein the processor is further configured to: estimate a probability of parking the vehicle as the quote showing the probability of parking being calculated throughout the route.)) setting a threshold to ascertain a parking event at each destination; ((Pg. 1 – Abstract – “The route parking probability is the probability that parking at some point along the extended computed route will be possible and wherein one or more links of the parking route are suitable for parking.” & See Also Pg. 10 – [0009] – “According to an embodiment, the desired route parking probability is a threshold.” & See Also Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. (equates to set a threshold to ascertain a parking event at each destination as the first quote shows a determination of probability of parking at each point that is along a route of travel. The second quote shows the probability to park is equivalent to a threshold and thus the threshold is set for each point or destination as seen by the first quote. The last quote shows the sub route finishing at a destination in which the parking would take place and thus the parking event at a destination is shown.))) determining that the probability of parking at the first destination at a future time slot is greater than the threshold; (Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. And such methods still further include extending the computed route beyond the route destination in area around the route destination until route parking probability of parking route, including links of the determined sub-route and/or of the extension of the computed route, is equal to or greater than predetermined threshold whilst total expected travel time, associated with the extended computed route and comprising driving and walking times, is minimized” & See Also Pg. 10 – [0009] – “respective distance from a predecessor of the corresponding link in the computed route to the route destination are computed, wherein the link-wise moving back is terminated and the corresponding link is selected as an appropriate start link of the sub-route if the respective route parking probability is greater than or equal to a desired route parking probability” (equates to determining that the probability of parking at the first destination at a future time slot is greater than the threshold; as the quote shows the probability of parking being determined to be greater than a threshold that is accounting for travel time or a destination arrival time to be considered and minimized thus a future time slot is considered when determining the probability of parking at the destination. The quote specifically shows the sub-route terminating at a destination position in which the parking probability is used to be calculated. )) and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; (Pg. 10 – [0009] – “respective distance from a predecessor of the corresponding link in the computed route to the route destination are computed, wherein the link-wise moving back is terminated and the corresponding link is selected as an appropriate start link of the sub-route if the respective route parking probability is greater than or equal to a desired route parking probability” & See Also Pg. 10 – [0009] – “probability that parking at some point along the extended computed route will be possible and wherein one or more links of the parking route are suitable for parking.” (equates to and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; as the quote shows the determination of future links to park at is terminated once the probability of being at an appropriate start link is greater than a desired route parking probability.)) It would have been an advantageous addition to the system disclosed by Maeda-Vengroff to include estimating a probability of parking the vehicle; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; as these limitations allow the system to understand the likelihood of parking at a destination being analyzed allowing for an ability to terminate future recommendations while it is already seen that the vehicle has parked at a particular destination.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include estimating a probability of parking the vehicle; setting a threshold to ascertain a parking event at each destination; determining that the probability of parking at the first destination at a future time slot is greater than the threshold; and determining that the vehicle is expected to park at the first destination during the future time slot based on a determination that the probability is greater than the threshold; as these limitation allow for an understanding of where and when did the vehicle park allowing the system to better understand the users interaction with the destination.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Maeda-Vengroff-Duym as cited above, and in view of Hansen et al. (CN106897788A).
Regarding Claim 5 Maeda-Vengroff-Duym teaches (Maeda teaches the following limitations:) The system of claim 1, wherein the processor is further configured to: identify a destination type of each of the plurality of destinations; (Pg. 15 – Col. 13 – lines 36 -38 – “the travel plan module 402 may be configured to analyze the log of locations at which the EV 102 is driven, parked, and/or charged.” (equates to identify a destination type of each of the plurality of destinations as the quote shows a variety of destination types being recognized within a dataset.))
Yet Maeda fails to teach categorize the plurality of destinations into the plurality of destination tags based on the destination type; and store a mapping of the plurality of destinations with the plurality of destination tags in a system memory.
Hansen teaches categorize the plurality of destinations into the plurality of destination tags based on the destination type (Pg. 3 – [0035] – “In the case where the rating value is assigned to a POI group ("cluster" or "superior"), a combined POI rating value is calculated for a set of POIs that includes information for identifying, in substantially the same place Highway exit), at least one POI of a specified set of POI categories (eg, gas stations, food, grocery stores, hotels, etc.).” (equates to categorize the plurality of destinations into the plurality of destination tags based on the destination type as the quote shows how a grouping of POI data is made based on the POI type and that a group is establish for each type of data, hotels, food, etc. )) and store a mapping of the plurality of destinations with the plurality of destination tags in a system memory. (Pg. 16 – [106] – “The path point display module associates each current scheduled path point using map coordinates for describing the map on the graphical display output, including: (1) a travel path through all scheduled path points, and (2) a distance corresponding to each currently scheduled A set of nodes in a path. Path point display module 290 also generates an image / control corresponding to an optional suggested POI overlay associated with the identified run” (equates to and store a mapping of the plurality of destinations with the plurality of destination tags in a system memory as we see the poi data that is previously being tagged into individual groups being associated with mapping points for the assorted data. )) It would have been an advantageous addition to the system disclosed by Maeda to include categorize the plurality of destinations into the plurality of destination tags based on the destination type; and store a mapping of the plurality of destinations with the plurality of destination tags in a system memory as these limitations allow for a group of POI data to be considered together and allow for pulling of data from certain groups rather than one continuous list of data.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include categorize the plurality of destinations into the plurality of destination tags based on the destination type; and store a mapping of the plurality of destinations with the plurality of destination tags in a system memory as these limitations allow for a small grouping of data to be considered at a time and thus ensure lower processing power is needed to recommend data from predetermined smaller set of locations.
Regarding Claim 6 Maeda-Vengroff-Duym-Hansen teaches (Maeda teaches the following limitations:) The system of claim 5, wherein the processor is further configured to: obtain the mapping from the system memory; (Pg. 4 – Fig. 3 & See Also Pg. 14 – Col. 11 lines 42-44 – “In one embodiment, the data store 308 may also be configured to store map data 318 that may be accessed by the smart charge application 118.” Pg. 14 – Col. 11 – lines 35-37 – “As discussed below, the smart charge application 118 may be configured to analyze one or more geo-locations of the EV 102,” (equates to obtain the mapping from the system memory as the mapping data is within a data store of this application and is obtained via an application within the EV.))
Yet Maeda fails to teach determine a first group of destinations from the plurality of destinations associated with the first destination tag, and a second group of destinations from the plurality of destinations associated with the second destination tag based on the mapping, wherein the first destination tag is different from the second destination tag; and determine the third destination from the first group of destinations and the fourth destination from the second group of destinations based on the user preferences and the routine travel behavior.
Hansen teaches determine a first group of destinations from the plurality of destinations associated with the first destination tag (Pg. 21 – [134] – “During dispensing, the proposed POI module 270 categorizes the obtained rated POI examples according to a set of categories. Examples of these categories are: gas stations, restaurants, hotels, roadside public resting areas, etc.” (equates to determine a first group of destinations from the plurality of destinations associated with the first destination tag as the quote shows a grouping of POI data done and the first group of this list would be gas stations.)) and a second group of destinations from the plurality of destinations associated with the second destination tag based on the mapping (Pg. 21 – [134] – “During dispensing, the proposed POI module 270 categorizes the obtained rated POI examples according to a set of categories. Examples of these categories are: gas stations, restaurants, hotels, roadside public resting areas, etc.” (equates to and a second group of destinations from the plurality of destinations associated with the second destination tag based on the mapping as the quote shows a list of groups of POI data and in this example the second group could be restaurants.)) wherein the first destination tag is different from the second destination tag; (Pg. 21 – [134] – “During dispensing, the proposed POI module 270 categorizes the obtained rated POI examples according to a set of categories. Examples of these categories are: gas stations, restaurants, hotels, roadside public resting areas, etc.” (equates to wherein the first destination tag is different from the second destination tag; as the quote shows a variety of groups or tags and the grouping for restaurants and gas stations is shown to be different as each are within their own set of categories.)) and determine the third destination from the first group of destinations and the fourth destination from the second group of destinations based on the user preferences and the routine travel behavior. (Pg. 10- [75] – “The information provided in connection with a particular identified occupant and itinerary (or a particular waypoint within the itinerary) can be used in various ways. For example, the name of a particular restaurant / coffee chain associated with a waypoint that an identified occupant actually visited may be used to determine the closeness between a particular identified vehicle occupant and the identified POI (or POI type) to determine A set of recommended POIs for the journey in the vehicle on which the occupant rides is used by the waypoint server 145” & See Also Pg. 10 – [75] – “Pg. 21 – [134] – “During dispensing, the proposed POI module 270 categorizes the obtained rated POI examples according to a set of categories. Examples of these categories are: gas stations, restaurants, hotels, roadside public resting areas, etc.”” (equates to and determine the third destination from the first group of destinations and the fourth destination from the second group of destinations based on the user preferences and the routine travel behavior as the quote shows how a third destination, the restaurant, and the fourth destination, coffee shop, can be identified based on the vehicle occupant and thus establish a user preference ad routine based on identified POI data from the vehicle occupant. And the last quote shows how poi can be grouped and then selected from said group. A third destination is had as a particular place can be selected from a grouping of POI data.)) It would have been an advantageous addition to the system disclosed by Maeda to include determine a first group of destinations from the plurality of destinations associated with the first destination tag, and a second group of destinations from the plurality of destinations associated with the second destination tag based on the mapping, wherein the first destination tag is different from the second destination tag; and determine the third destination from the first group of destinations and the fourth destination from the second group of destinations based on the user preferences and the routine travel behavior as these limitations allow for a variety of locations to be considered for recommendation based on a grouping of the POI data and ensure the data selected is based on action that the user would actually commit to.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include determine a first group of destinations from the plurality of destinations associated with the first destination tag, and a second group of destinations from the plurality of destinations associated with the second destination tag based on the mapping, wherein the first destination tag is different from the second destination tag; and determine the third destination from the first group of destinations and the fourth destination from the second group of destinations based on the user preferences and the routine travel behavior as these limitations ensure data relevant to the user is selected and that the data is presorted into a variety of tags for the system to filter out POI data from.
Claim 7, is rejected under 35 U.S.C. 103 as being unpatentable over Maeda-Vengroff-Duym -Hansen as cited above, and in view of Lee (KR 102751622 B1).
Regarding Claim 7 Maeda-Vengroff-Duym -Hansen teaches (Maeda discloses the following limitations:) The system of claim 6, wherein the processor is further configured to: correlate the information associated with charging services with the user preferences; (Pg. 12 – Col. 7 – lines 28-32) – “In other words, the smart charge application 118 may provide planning functionality that may take into account electric vehicle charging requirements and/or preferences 30 with respect to the EV 102 in addition to daily routines, tasks, and/or activities of the operator of the EV 102” & See Also Pg. 11 Col. 6 – lines 65-67 & Pg. 12 – Col. 7 – lines – “some embodiments, the station computing infrastructure 116 may determine a price per kilowatt-hour of energy (price per kWh) that may be communicated to the EV 102 . Control the remote server 108, and/or the charging station(s) 112 based on utility rates that are received from the one or more energy providers”(equates to correlate the information associated with charging services with the user preferences as the quotes show the EV preferences which are used to determine the charging station to recommend and the second quote showing the price per KWh to be inputted as information to allows the user to make an informed choice of the charging station and the user preference would be whether or not they are willing to pay the price to charge at a location.))
Yet Maeda-Hansen both fail to teach obtain the information associated with charging services of the first group of destinations and the second group of destinations and determine the third destination and the fourth destination based on the correlation.
Lee teaches obtain the information associated with charging services of the first group of destinations and the second group of destinations (Pg. 1 – Abstract – “identifies a category list of infrastructure adjacent to the charging stations within each group, and outputs a representative category of the adjacent infrastructure and a group-specific icon related to the charging stations within each group through the display in relation to the recommended charging section” (equates to obtain the information associated with charging services of the first group of destinations and the second group of destinations as the quote shows a category of infrastructure associated with each charging station being located and recommended to the user wherein a first group and second group exist via the each group from each charging station recommended.)) and determine the third destination and the fourth destination based on the correlation. (Pg. 2 – “identifies a category list of infrastructure adjacent to the charging stations in each group,” & See Also Pg. 5 – “therefore, users are searching for and using nearby infrastructure (e.g. restaurants, coffee shops) to use the waiting time more efficiently” (equates to and determine the third destination and the fourth destination based on the correlation as the first quote shows the grouping of infrastructure and thus locations available to the user nearby wherein the various groups of locations sent to the user can include a restaurant in one grouping of locations and a coffee shop in another grouping.)) It would have been an advantageous addition to the system disclosed by Maeda-Hansen to include obtain the information associated with charging services of the first group of destinations and the second group of destinations and determine the third destination and the fourth destination based on the correlation as these limitations allow for locations specific to the various charging stations to be provided to the user allowing the user to do another activity while waiting for their vehicle to charge.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include obtain the information associated with charging services of the first group of destinations and the second group of destinations and determine the third destination and the fourth destination based on the correlation as these limitation ensure the user knows what available alternative services are within the area of the charging station allowing the user to feel well informed about what exists around them while charging their vehicle.
Claims 8 and 13, are rejected under 35 U.S.C. 103 as being unpatentable over Maeda-Vengroff-Duym as cited above, and in view of Lee (KR 102751622 B1).
Regarding Claim 8 Maeda-Vengroff-Duym teaches The system of claim 1, as previously mapped above.
Yet Maeda fails to teach wherein the first destination is different from the third destination, or the second destination is different from the fourth destination.
Lee teaches wherein the first destination is different from the third destination, or the second destination is different from the fourth destination. (Pg. 2 – “identifies a category list of infrastructure adjacent to the charging stations in each group,” & See Also Pg. 5 – “therefore, users are searching for and using nearby infrastructure (e.g. restaurants, coffee shops) to use the waiting time more efficiently”(equates to wherein the first destination is different from the third destination, or the second destination is different from the fourth destination as the first quote shows how a grouping of infrastructure can be determined adjacent to a charging statin and thus a first tag of information is had, wherein the grouping of information in the tag may include different places to travel to as seen by the second quote wherein the first location would be a restaurant and the third a coffee shop.)) It would have been an advantageous addition to the system disclosed by Maeda to include wherein the first destination is different from the third destination, or the second destination is different from the fourth destination as this allows a variety of location types or varieties to be included in a grouping of locations allowing the user to select from a variety of locations.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein the first destination is different from the third destination, or the second destination is different from the fourth destination as this limitation ensures a variety of user experiences that they would want to be presented with when the specific action the user is looking for may not follow their routine behavior.
Regarding Claim 13 Maeda-Vengroff-Duym teaches (Maeda discloses the following limitations:) The system of claim 1, wherein the processor is further configured to: determine a routine charging destination from the expected set of destinations based on the routine travel behavior; (Pg. 14 – Col. 11 – lines 35-41 – “As discussed below, the smart charge application 118 may be configured to analyze one or more geo-locations of the EV 102, one or more geo-locations of the portable device 222, and/or one or more geo-locations of one or more additional electric vehicles 120 to provide charging options to charge the EV 102 that may take place in the midst of the 40 operator's daily routines, tasks, and/or activities.” (equates to determine a routine charging destination from the expected set of destinations based on the routine travel behavior as the quote shows a provision of charging options and thus a determination of charging station wherein the user’s routine is taken into consideration before determining such information.)) determine an updated charging destination from the optimal set of destinations based on the user preferences; (Pg. 16 – Col. 15 – lines 40-48 - “Upon predicting one or more points of interest that may 40 be predicted as potential destinations of the operator of the EV 102 at one or more timeframes, the travel path module 408 may access the location log 224 stored on the data store 208 to determine the geo-location(s) of the point(s) of interest. In some configurations, the location log 224 may be updated to keep a log of locations at which the EV 102 is driven, parked, and/or charged and/or the operator visits at one or more points in time during one or more periods of time.” & See Also Pg. 19 – Col. 21 – lines 60-64 – “The one or more travel routines may include one or 60 more trips of the EV 102, tasks, and/or activities of the operator of the EV 102 that may routinely take place during a particular day particular week, and/or one or more particular timeframes.”(equates to determine an updated charging destination from the optimal set of destinations based on the user preferences as the first quote shows a time frame being used to update a location log with charging station information, wherein the second quote shows the timeframe being associated with routine user behavior and thus user preference.. ))
Yet Maeda fails to teach and generate the recommendation comprising the information associated with the updated charging destination.
Lee teaches generate the recommendation comprising the information associated with the updated charging destination. (Pg. 7 – “According to the above-described embodiment, some of the recommended charging section and the detailed information (e.g., number of people waiting) may be information that changes in real time. Therefore, the electronic device (130) may provide the recommended charging section and the charging station-related information related thereto after the initial route search and then update and provide it. The electronic device (130) may display the updated information on the route guidance screen - for example, on the right side of the driving route.” (equates to generate the recommendation comprising the information associated with the updated charging destination as the quote shows an updated charging station being attained and poi or information associated with the charging station being gathered and recommended to the user.)) It would have been an advantageous addition to the system disclosed by Maeda to include generate the recommendation comprising the information associated with the updated charging destination as this limitation allows for Poi or other data to be associated with an updated charging station and similarly recommend to the user based on perceived interest.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include generate the recommendation comprising the information associated with the updated charging destination as this limitation allows for a variety of information to be recommended to the user that they might have otherwise missed out on.
Claims 11 and 12, are rejected under 35 U.S.C. 103 as being unpatentable over Maeda-Vengroff-Duym as cited above, and in view of Chun et al. (US 2021/0164795 Al) and in further view of Tseng et al. (US 2020/0151264 Al).
Regarding Claim 11 Maeda-Vengroff-Duym teaches The system of claim 1, (Maeda discloses the following limitations:) based on the routine travel behavior; (Pg. 14 – Col. 11 – lines 35-41 – “As discussed below, the smart charge application 118 may be configured to analyze one or more geo-locations of the EV 102, one or more geo-locations of the portable device 222, and/or one or more geo-locations of one or more additional electric vehicles 120 to provide charging options to charge the EV 102 that may take place in the midst of the 40 operator's daily routines, tasks, and/or activities.”)
Yet Maeda-Vengroff fails to teach wherein the processor is further configured to: predict the second destination and the second destination tag that the vehicle is expected to visit after the first destination and estimate the expected set of destinations based on the prediction of the second destination and the determination that the vehicle is expected to park at the first destination.
Duym teaches and the determination that the vehicle is expected to park at the first destination. ((Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. And such methods still further include extending the computed route beyond the route destination in area around the route destination until route parking probability of parking route, including links of the determined sub-route and/or of the extension of the computed route, is equal to or greater than predetermined threshold whilst total expected travel time, associated with the extended computed route and comprising driving and walking times, is minimized” (equates to and the determination that the vehicle is expected to park at the first destination as the quote shows a determination that a vehicle is parking at a parking route or first destination. )))
Yet Maeda-Vengroff-Duym fail to teach wherein the processor is further configured to: predict the second destination and the second destination tag that the vehicle is expected to visit after the first destination and estimate the expected set of destinations based on the prediction of the second destination
Chun teaches wherein the processor is further configured to: predict the second destination and the second destination tag that the vehicle is expected to visit after the first destination, (Pg. 14 – [0009] – “The controller calculates a location similarity probability between the current location and a plurality of points of interest (POis) when the plurality of POis is collected based on the driving pattern information. The controller calculates a visit probability of the plurality of POis at the current location based on the location similarity probability and the visit probability of the POis.” & See Also Pg. 19 – [0080] – “In S120, it may be understood that the location similarity probability indicates how similar the location of each of the collected plurality of POis is to the current location. calculates a visit probability of the plurality of POis at the current location based on the location similarity probability and the visit probability of the POis.” (equates to wherein the processor is further configured to: predict the second destination and the second destination tag that the vehicle is expected to visit after the first destination, as the first quote shows a first destination be arrived and a calculation taking place that is predicting the probability of the user visiting a neighboring POI or a second destination.. The last quote shows how similarity between the POI is calculated and thus a tag is determined based on the similarity of the poi it decides to recommend to the user. ) )
Yet all fail to teach and estimate the expected set of destinations based on the prediction of the second destination
Tseng teaches and estimate the expected set of destinations based on the prediction of the second destination (Pg. 7 – Fig. 6 & See Also Pg. 17 – [0079] – “FIG. 6 depicts a flow chart 600 for a sequence of operations that may be implemented by the vehicle controller 103. At operation 602, the vehicle controller 103 may learn locations or destinations as described previously herein. The locations may be user identified and/or learned at key-off events over time” & See Also Pg. 17 – [0080] – “At operation 604, the vehicle controller 103 may execute instructions to predict a destination for the next trip. For example, this operation may be performed at key on.” & See Also pg. 17 – [0081] – “At operation 606, the vehicle controller 103 may execute instructions to generate tag phrases for the predicted destinations.” & See Also Pg. 16 – [0078] – “The content display 500 may be displayed on the user interface 104 in response to arriving at a location (e.g., turning ignition off)” & See Also Pg. 1 – Abstract – “A vehicle includes a controller programmed to identify tag phrases for locations that express a relationship between the location and a learned location tag” (equates to and estimate the expected set of destinations based on the prediction of the second destination as the first quote shows the location being determined which is equivalent to a first destination as the fourth quote shows the location is a place arrived at. The second quote shows a destination being predicted for a next trip or for a second destination. The third quote shows that a plurality of tag phrases are generated and presented to the driver and the fifth quote goes to shows the group of tag phrase or set of destination are associated with locations. )) It would have been an advantageous addition to the system disclosed by Maeda-Vengroff-Duym-Chun to include and estimate the expected set of destinations based on the prediction of the second destination as the system is working a step ahead of the user and allowing for a plurality of POI to be considered and recommended to the user based on a first destination and expected second destination allowing the user to see more potential places to visit after the second trip.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include and estimate the expected set of destinations based on the prediction of the second destination as this limitation allows for a set of destinations to be considered in advance of a second location of arrival ensuring the user can plan ahead with the recommendation system.
Regarding Claim 12 Maeda-Vengroff-Duym-Chun-Tseng teaches (Maeda discloses the following limitations:) The system of claim 11, wherein the processor is further configured to: identify the fourth destination associated with the second destination tag based on the user preferences and the routine travel behavior in real-time, (Pg. 14 – [Col.11 – lines 7 -16 ] – “The location data repository 316 may be configured as a relational database/data store that may include various records that may each include stored data that pertains to one or more particular point of interest locations (e.g., stores, 10 restaurants, schools, home, etc.) and associated geo-location coordinates of the one or more particular point of interest locations. Each record of the location data repository 316 may be updated with a description of point of interest locations that may include names, maps, sub-points of 15 interest names, sub-location names, and the like” & See Also Pg. 14 – [Col. 11 – lines 25-28] – “In one or more embodiments, the location data repository 25 316 may be updated in real-time to provide geo-locational coordinates associated with one or point of interest locations ( e.g., current point of interest location of the EV 102),” & See Also Pg. 14 – Col. 11 – lines 35-41 – “As discussed below, the smart charge application 118 may be configured to analyze one or more geo-locations of the EV 102, one or more geo-locations of the portable device 222, and/or one or more geo-locations of one or more additional electric vehicles 120 to provide charging options to charge the EV 102 that may take place in the midst of the 40 operator's daily routines, tasks, and/or activities.” (equates to : identify the fourth destination associated with the second destination tag based on the user preferences and the routine travel behavior in real-time as the first quote shows the second tag in this case being restaurants wherein a fourth location is identified within the tag as the end of the quote shows how one or more POI can be identified wherein the one or more and thus fourth location would include a name and position via a subpoint on the map. The second quote shows how the geo location are provided in real time and the last quote shows how POI is provided based on user routine. )) and generate the recommendation to visit the fourth destination responsive to identifying the fourth destination; (Pg. 14 – [Col.11 – lines 7 -16 ] – “The location data repository 316 may be configured as a relational database/data store that may include various records that may each include stored data that pertains to one or more particular point of interest locations (e.g., stores, 10 restaurants, schools, home, etc.) and associated geo-location coordinates of the one or more particular point of interest locations. Each record of the location data repository 316 may be updated with a description of point of interest locations that may include names, maps, sub-points of 15 interest names, sub-location names, and the like” & See Also Pg. 13 – Col. 9 – lines 34-36 – “and the portable device 222 that may include the display and/or input/output devices that may be used to operate various functions of the EV 102 and/or the smart charge application 118” & See Also The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. (equates to and generate the recommendation to visit the fourth destination responsive to identifying the fourth destination as the first quote shows the identification of a fourth location via a one or more geo location being identified within a prescribed region in this case a restaurant and the second quote showing a display unit allow for outputting of information wherein the third quote shows the ability of a planner to be used within the display unit. )) and output the recommendation comprising the information associated with the fourth destination. (Pg. 14 – [Col.11 – lines 7 -16 ] – “The location data repository 316 may be configured as a relational database/data store that may include various records that may each include stored data that pertains to one or more particular point of interest locations (e.g., stores, 10 restaurants, schools, home, etc.) and associated geo-location coordinates of the one or more particular point of interest locations. Each record of the location data repository 316 may be updated with a description of point of interest locations that may include names, maps, sub-points of 15 interest names, sub-location names, and the like” & See Also Pg. 13 – Col. 9 – lines 34-36 – “and the portable device 222 that may include the display and/or input/output devices that may be used to operate various functions of the EV 102 and/or the smart charge application 118” & See Also The method 700 may proceed to block 708, wherein the method 700 may include presenting a planner user interface with one or more charging stations and/or the one or more additional electric vehicles 120 that may be utilized to charge the EV 102 in the midst of the operator's daily routines, tasks, and/or activities. In an exemplary embodiment, the planner presentation module 410 of the smart charge application 118 may be configured to present the planner user interface. (equates to and output the recommendation comprising the information associated with the fourth destination as the first quote shows the identification of a fourth location via a one or more geo location being identified within a prescribed region in this case a restaurant, which has information associated with it (including name and subpoint location on map) and the second quote showing a display unit allow for outputting of information wherein the third quote shows the ability of a planner to be used within the display unit. ))
Yet Maeda-Vengroff -Chun-Tseng fails to teach responsive to determining that the vehicle is parked at the first destination.
Duym teaches responsive to determining that the vehicle is parked at the first destination. (Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. And such methods still further include extending the computed route beyond the route destination in area around the route destination until route parking probability of parking route, including links of the determined sub-route and/or of the extension of the computed route, is equal to or greater than predetermined threshold whilst total expected travel time, associated with the extended computed route and comprising driving and walking times, is minimized” (equates to responsive to determining that the vehicle is parked at the first destination as the quote shows a determination that a vehicle is parking at a parking route or first destination. )) It would have been an advantageous addition to the system disclosed by Maeda-Vengroff -Chun-Tseng to include responsive to determining that the vehicle is parked at the first destination as this limitation allows for parking to be incorporated in the POI process thus allowing for an additional layer of data to be considered allowing the system to better understand the user intention.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include responsive to determining that the vehicle is parked at the first destination as this limitation allows for additional user data to be considered when recommending and outputting information to the driver allowing for better POI to be given to the driver, making the system have a potential for more.
Claim 14, is rejected under 35 U.S.C. 103 as being unpatentable over Maeda-Vengroff-Duym as cited above, and in view of Chun et al. (US 2021/0164795 Al).
Regarding Claim 14 Maeda-Vengroff-Duym teaches The system of claim 1, as previously mapped above.
Yet Maeda fails to teach wherein the processor is further configured to: estimate a sequence to visit the expected set of destinations; and generate the recommendation based on the sequence and the user preferences, wherein the recommendation comprises an updated sequence to visit the expected set of destinations.
Chun teaches wherein the processor is further configured to: estimate a sequence to visit the expected set of destinations (Pg. 14 – [0009] – “The controller calculates a location similarity probability between the current location and a plurality of points of interest (POis) when the plurality of POis is collected based on the driving pattern information” (equates to estimate a sequence to visit the expected set of destinations as the quote shows a plurality of POI or destinations that are collected based on a driving pattern or a sequence of visiting locations. )) and generate the recommendation based on the sequence and the user preferences, (Pg. 17 – [0054] – “the controller 140 may control a recommendation destination name and the distance to the destination to be output together in a pop-up form and may control a message for suggesting a guide to the recommendation destination to be output in a pop-up form” & See Also Pg. 10 – Fig. 9 (equates to and generate the recommendation based on the sequence and the user preferences as earlier the system disclosed is always attaining a driving pattern of the user and the quote cited here shows the recommendation being provided only when the driving pattern is of a degree of reliability.) ) wherein the recommendation comprises an updated sequence to visit the expected set of destinations. (Pg. 14 – [0018] – “According to an aspect of the present disclosure, a destination recommending method includes: collecting driving pattern information of a user; calculating a visit probability of a destination at a current location or the visit probability of the destination at a current time, based on the driving pattern information; and predicting the destination based on the visit probability.” & See Also Pg. 14 – [0014] – “The controller calculates location-based recommendation reliability or time-based recommendation reliability depending on whether the user selects the predicted destination and determines whether to recommend the predicted destination, based on the recommendation reliability” (equates to wherein the recommendation comprises an updated sequence to visit the expected set of destinations as a driving pattern is collected by the user and a visit probability is calculated for a set of POI, and calculating the chance that a POI is a destination, wherein the second quote shows how that based on the pattern collected and thus the updated sequence a recommendation is made to the driver based on the updated driving pattern taken in. )) It would have been an advantageous addition to the system disclosed by Maeda to include wherein the processor is further configured to: estimate a sequence to visit the expected set of destinations; and generate the recommendation based on the sequence and the user preferences, wherein the recommendation comprises an updated sequence to visit the expected set of destinations as these limitations allow for the driver’s behavior to be observed and then update POI based on observed behavior allowing for a more accurate POI recommendation system to be attained.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein the processor is further configured to: estimate a sequence to visit the expected set of destinations; and generate the recommendation based on the sequence and the user preferences, wherein the recommendation comprises an updated sequence to visit the expected set of destinations as these limitations allow for sequence to be identified by the driving pattern of the user and output accurate POI data based on the recognize of the driving pattern, thus allow for a more useful recommendation system.
Claim 17, is rejected under 35 U.S.C. 103 as being unpatentable over Maeda-Vengroff-Duym as cited above, and in view of Quint (US 2019/0219417 Al).
Regarding Claim 17 Maeda-Vengroff-Duym teaches (Maeda discloses the following limitations:) The system of claim 15, wherein the processor is further configured to: determine one or more additional routes based on the user preferences and the routine travel behavior, (Pg. 15 – Col. 13 – lines 28-31 – “The one or more travel routines may include one or more trips of the EV 102, tasks, and/or activities of the operator of the EV 102 that may routinely take place during a particular day, particular week, and/or one or more particular timeframes.” (equates to determine one or more additional routes based on the user preferences and the routine travel behavior, as the quote shows one or more trips and thus routes being established based on a variety of timeframes of routine user action.)) wherein each additional route comprises respective destinations in the first destination tag and the second destination tag, (Pg. 16 – Col. 15 – lines 28-36 – “102. Additionally, the one or more predicted points of interest may be predicted as potential destinations of the EV 102 based on analysis of the one or 30 more travel routines and/or the one or more prospective travel plans as determined by the travel plan module 402. The one or more predicted points of interest may include, but may not be limited to, a store, a restaurant, a home location, a workplace location, a location to complete a task, 35 a location to shop for an item(s),” (equates to wherein each additional route comprises respective destinations in the first destination tag and the second destination tag as the quote shows a travel plan or a route that would comprise a plurality of potential POI to be included into the plan wherein the different category tags are seen to be listed where a first could be the stores and second the restaurants.))
Yet Maeda fails to teach and wherein the one or more additional routes are different from the route; and generate the recommendation based on the one or more additional routes.
Quint teaches and wherein the one or more additional routes are different from the route; (Pg. 5 – Fig. 4 & See Also Pg. 21 – [0048] – “The occurrence icons representing the occurrences may be user-selectable. Responsive to an occurrence icon, such as one of the occurrence icons 306, being selected, the horizon navigation GUI 137 may be configured to generate a window including additional information and one or more options related to the occurrence represented by the selected occurrence icon. For example, FIG. 4 illustrates a screen 400 that may be provided by the horizon navigation GUI 137 responsive to a user selecting the occurrence icon 306A of the timeline 304A associated with adverse road conditions. The screen 400 includes an additional information window 402 generated by the horizon navigation GUI 137 responsive to the user selection of the occurrence icon 306A. As shown in the illustrated embodiment, the horizon navigation GUI 137 may display the additional information window 402 over the map 202, and/or adjacent to but not overlapping the on-the-horizon graphic object 302. In this way, the user may use the information and options offered by the additional information window 402 while also having the option to select another tab 310 to view a different timeline 304.” & See Also Pg. 2 1- [0049] – “The map section 404 may further illustrate a detour 406 available to the user to avoid the traffic backup and/or accident” (equates to and wherein the one or more additional routes are different from the route as the figure and first quote show a recommended alternative route to the first route and the second quote shows the detour be added within that screen for the user to take allowing for an additional route to be taken based on user’s wants and needs.)) and generate the recommendation based on the one or more additional routes. (Pg. 2 1- [0049] – “The map section 404 may further illustrate a detour 406 available to the user to avoid the traffic backup and/or accident” & See Also Pg. 24 – [0071] – “FIG. 14 illustrates a screen 1400 that may be automatically generated by the horizon navigation software 136 via the horizon navigation GUI 137 responsive to an upcoming occurrence. Responsive to an upcoming occurrence, such as a traffic backup, the horizon navigation software 136 may be configured to identify and display a recommendation 1402 relating to the occurrence. The recommendation may include advice that aids a user in handling an upcoming adverse occurrence, and may be based on several rules pre-” (equates to and generate the recommendation based on the one or more additional routes as the first quote shows how an additional route is given based on an occurrence and the second quote shows how additional recommendation are given based on the occurrence and thus alternative route provided. )) It would have been an advantageous addition to the system disclosed by Maeda to include and wherein the one or more additional routes are different from the route; and generate the recommendation based on the one or more additional routes as this allows a variety of routes and thus a greater spread of destinations an travels times available to the driver based on currents needs or environmental surroundings.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include and wherein the one or more additional routes are different from the route; and generate the recommendation based on the one or more additional routes as these limitations allow for a widespread look upon the map to be had by the user allowing different passages an routes of travel to be had including new recommendation based on the updated routes that are provided.
Claim 18, is rejected under 35 U.S.C. 103 as being unpatentable over Maeda-Vengroff-Duym -Quint as cited above, and in view of Scofield (US 2020/0013284 Al).
Regarding Claim 18 Maeda-Vengroff-Duym -Quint teaches (Maeda discloses the following limitations:) The system of claim 17, wherein the processor is further configured to: display the information associated with the route and the one or more additional routes on a user interface; (Pg. 13 – Col. 9 – lines 34 – 42 – “that may include the display and/or input/output devices that may be used to operate various functions of the EV 102 and/or the smart charge application 118. In one embodiment, the display 218 of the EV 102 and/or the portable device 222 (e.g., through a display screen of the portable device 222) may be utilized to provide one or more user interfaces that may be included as a human machine interface(s) of the smart charge application 118” & See Also Pg. 14 – Col. 11 – lines 65 – 67 – “In one embodiment, the map data 318 may be accessed by the smart charge application 118 to determine one or more travel routes”(equates to display the information associated with the route and the one or more additional routes on a user interface as the quote shows a display used to display parts of the smart charge application which includes data about the route the vehicle is taking via the mapping information.))
Yet Maeda fails to teach obtain a user selection of a preferred route responsive to displaying the information associated with the route and the one or more additional routes; and cause the vehicle to move in the preferred route responsive to obtaining the user selection.
Quint teaches obtain a user selection of a preferred route responsive to displaying the information associated with the route and the one or more additional routes (Pg. 5 – Fig. 4 & See Also Pg. 24 – [0066] – “Responsive to a user selecting the more tab 310G, the on-the-horizon graphic object 302 may be configured to show a selection bar 1102. The selection bar 1102 may be displayed over the map 202 and adjacent to the on-the-horizon graphic object 302. The selection bar 1102 may show additional category interests 1104 that are not represented by the other tabs 310. The horizon navigation GUI 137 may be configured such that upon user selection of one of the additional category interests 1104” (equates to obtain a user selection of a preferred route responsive to displaying the information associated with the route and the one or more additional routes as the figure shows the ability of the user to select route based on the occurrence detected and the quote further confirms the ability of a user to select a variety of categories one of which may be the alternative route provided in figure 4. ))
Yet both Maeda-Quint fail to teach and cause the vehicle to move in the preferred route responsive to obtaining the user selection.
Scofield teaches and cause the vehicle to move in the preferred route responsive to obtaining the user selection. (Pg. 1 – Abstract – “One or more techniques and/or systems are provided for operating an autonomous vehicle based upon a driving preference. For example, a driving profile, comprising a driving preference (e.g., a speed preference, a route preference, etc.) of a user, may be provided to an automated driving component of the autonomous vehicle. An operational parameter for the autonomous vehicle may be generated based upon the driving preference of the user. The autonomous vehicle may be operated based upon the operational parameter.” & See Also Pg. 14 – [0004] – “Autonomous vehicle may be operated to travel along the route based upon the operational parameter…” & See Also Pg. 15 – [0022] – “In an example of providing the driving preference, the driving profile may be selected, such as manually by the user and/or automatically by the automated driving component” (equates to and cause the vehicle to move in the preferred route responsive to obtaining the user selection as the first quote shows how route preference is used as a driving preference which influences the operation of the autonomous vehicle and the second quote further shows the operating being travelling along the route. The last quote shows how user selects the driving profile (which can be route preference as seen by first quote.) )) It would have been an advantageous addition to the system disclosed by Maeda-Quint to include and cause the vehicle to move in the preferred route responsive to obtaining the user selection as this limitation allows for a route preference specified by the user to directly control the movement of the vehicle via a selection of a route.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include and cause the vehicle to move in the preferred route responsive to obtaining the user selection as this allows for an autonomous driving control component to be integrated into the POI determination enabling the user to only have to worry about selecting the correct POI and the vehicle doing the remainder of the work of travelling to said destination.
Response to Arguments
Response to 35 U.S.C. § 101 rejection of claims 1-20 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered but are not persuasive.
Applicant argues on page 2-3, “Applicant respectfully submits that the pending claims are not directed to a mental process. Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. MIPEP 2106.04(a)(2)(III)(A). Applicant submits that amended claim 1 now recites probabilistic, time-slot, and thresholding computations that cannot be practically performed in the human mind. The human mind is not capable, in practice, of calculating and maintaining per-destination, per-time-slot probability distributions, selecting and applying statistical thresholds to ascertain a parking event, and then projecting those values into a future time slot to determine an expected parking occurrence -especially not across the number of destinations and time granularity found in vehicle-scale historical datasets.
Because amended claim 1 recites limitations that cannot practically be performed in the human mind, the claim is patent-eligible under Step 2A, Prong One. Withdrawal of the pending rejections under 35 U.S.C. @ 101 is respectfully requested.
Step 2A -Prong Two
Even assuming, arguendo, that the pending claims are found to be directed to an abstract idea, they are nonetheless integrated into a practical application and therefore patent- eligible under Step 2A, Prong 2. Amended claim 1 now recites steps for "determining most-visited destinations, estimating, for each such destination, a time-indexed probability of parking, setting a statistical threshold for ascertaining a parking event, and determining that, in a future time slot, the probability for a specific destination exceeds that threshold" that go beyond mere gathering or identifying of data. The claims improve the functioning of a computer or another technology or technical field and involve technical problems (e.g., vehicle-behavior modeling) that involve large historical datasets and complex time-dependent travel, charging, and parking patterns. Here, the amended limitations provide a particular solution: a structured, statistical-computational method that identifies routine behaviors, computes probabilistic predictions for future parking events, and uses those predictions to drive destination-recommendation outputs within a preset time duration.
Because amended claim 1 recites limitations that integrate the alleged abstract idea into a practical application, the claim is patent-eligible under Step 2A, Prong Two. Withdrawal of the
pending rejections under 35 U.S.C. § 101 is respectfully requested.
Step 2B
Even assuming, arguendo, that amended claim 1 were found to recite a judicial exception and not to integrate that exception into a practical application under Step 2A, Prong Two, the amended claim nevertheless recites additional elements that amount to significantly more than the alleged abstract idea.
The amended claim requires a system's processor to estimate, for each destination, the probability of parking at each time; to set a threshold to ascertain a parking event; to determine
that the probability of parking at the first destination in a future time slot exceeds the threshold; and to determine that the vehicle is expected to park there during the future time slot. The Office Action does not cite any evidence that these particular operations are well-understood, routine, or conventional in the field. The Office alleges that the claims are merely "collection or receipt of data over a network" performed in a well-understood, routine, and conventional manner (WURC). Applicant submits that the amended claims are not merely WURC activity. At least the amended features of claim 1 are not well-understood, routine, and conventional. As evidence of this, the art of record does not teach or suggest these features, as discussed in greater detail below. As the Office has not presented any evidence that the claims are well-understood, routine, and conventional, the rejection of the claims under Step 2B has been traversed.
Independent claim 9 and 19 are also patent-eligible at least for the reasons discussed above.
Accordingly, Applicant submits that claims 1, 9, and 19 are patent-eligible under 35 U.S.C.§ 101. Withdrawal of the pending rejection of claims 1-20 is, therefore, respectfully requested.” – As to point (B), Examiner respectfully disagrees. Applicant appears to argue that the additional elements evaluated in Step 2A, Prong 2 amount to more than an insignificant extra solution activity involving data gathering because the specification indicates that the embodiments described herein improve the functioning of a computer based on using historical datasets and solve existing issues in vehicle behavior modeling. In particular the limitations: “determining most-visited destinations, estimating, for each such destination, a time-indexed probability of parking, setting a statistical threshold for ascertaining a parking event, and determining that, in a future time slot, the probability for a specific destination exceeds that threshold” are argued. In Step 2A, Prong 2, Examiner evaluates whether the claim recites additional elements that integrate the exception into a practical application of that exception. In Step 2A, Prong 1, Examiner identified how the functions recited could be reasonably performed in the human mind. The additional elements of “determining most-visited destinations, estimating, for each such destination, a time-indexed probability of parking, setting a statistical threshold for ascertaining a parking event, and determining that, in a future time slot, the probability for a specific destination exceeds that threshold” merely use generic computer components to perform the abstract idea and simply makes mental judgements about whether or not parking will occur and then derives a mathematical formulation to generate a probabilistic model being compared toa setpoint all of which a human mind can reasonably perform with the aid of pen and paper.
Response to 35 U.S.C. § 102 & 35 U.S.C. § 103 rejection of claims 1-20 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered but are not persuasive.
Applicant argues on pages 5-6, “As noted in the Office Action at 37-42, Maeda and Vengroff fail to teach or disclose these features. Instead, the Office relies on Duym to allegedly teach or disclose these features. However, Applicant submits that amended claim 1 is non-obvious for at least the following reasons: Duym is generally directed to optimizing a parking route extension after the vehicle has already reached a single, known route destination. Duym computes probabilities at the level of "links" in a digital map i.e., roadway segments-not "destinations" associated with "destination tags" as required to read on claim 1.
Duym at best discloses parking probabilities associated with a "link" or "route link". See Duym at [0017] and [0063]. However, the "route links" of Duym represent individual roadway segments that form part of the directed graph embodying the digital map. Each link constitutes a small unit of traversable roadway and does not independently correspond to a meaningful user destination or endpoint. Within the context of Duym, the probability computed for a given link relates to the probability of finding parking availability somewhere along that segment when the driver is performing an extended parking search at the end of a routing task. The role of a "route link" in Duym is strictly as a micro-unit of a computed path, not as a location that the vehicle is expected to visit as part of its routine travel pattern. Accordingly, even under a broadest reasonable interpretation (BRI), interpreting Duym's route links as "destinations" would require reading the term "destination" in a manner inconsistent with how it is used within the internal logic of the claim. Such an interpretation would effectively collapse the distinction between a traveled path and an endpoint of travel. Since the claim expressly distinguishes destinations as objects derived from the user's routine travel behavior, the term cannot reasonably be stretched to cover generic roadway links devoid of semantic or user-centric significance.
Additionally, Duym does not teach or disclose a time-indexed "future time slot" probability, as recited in amended claim 1. Duym does not compute, predict, or utilize probability as a function of time slots. Instead, Duym at best computes an instantaneous probability model for link-based parking availability during a parking search. Duym emphasizes that the probability model is used during the execution of the best-first parking search - for example, Duym at [0089] discloses:"[w]ith regard to each one of the considered extension candidates it is controlled that the route parking probability... is equal to or greater than a predetermined threshold..." This shows that the probability model is applied at the time a link is being evaluated, not as a long-term historical or destination-based metric. Accordingly, Duym is also silent with regard "determining that the probability of parking at the first destination at a future time slot..." as recited in amended claim 1. Accordingly, amended claim 1 is allowable under 35 U.S.C. §§ 102 and 103 over the art of record. Independent claims 19 and 20 are similarly amended and allowable for at least the reasons discussed above.” – As to point B the examiner respectfully disagrees. Applicant asserts that Duym does not teach “estimating a probability of parking the vehicle at each of the most visited destinations at each time based on the routine travel behavior;”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is to estimate a probability that a vehicle will park at a given location based on frequently visited POI. Duym teaches a probability in which a parking action would occur in an area of a destination and Vengroff teaches storing POI data in regards to most visited locations by the user of the vehicle (as mapped above in claim 1, 19 and 20). Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Duym-Vengroff teaches “estimating a probability of parking the vehicle at each of the most visited destinations at each time based on the routine travel behavior;”. Vengroff teaches: at each of the most visited destinations at each time based on the routine travel behavior (Pg. 1 – Abstract – “After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).” & See Also Pg. 2 – [0016] – “In addition, in at least some embodiments, the described techniques include automatically identifying user visits to the determined locations of identified points of interest (e.g., for locations and points of interest automatically identified using at least some of the visualization techniques), such as based on location-related data from a single GPS track log of a user's device. As described in greater detail below, the identification of user visits may include various activities in various embodiments, including the following: identifying that a particular visit to a particular point of interest's location has occurred; comparing an identified visit to other visits to the same or other locations, such as to quantify the visit relative to 'typical' visits to the location and/or to categorize a type of the visit relative to one or more other parameters of interest (e.g., a duration; a purpose of the visit and/or activity performed, such as to visit a Starbucks to take out coffee versus to meet with a friend; etc.);”)
Duym teaches : estimating a probability of parking the vehicle (Pg. 10 – [0007] – “and extending the computed route beyond the route destination in an area around the route destination until a route parking probability of a parking route,” (equates to wherein the processor is further configured to: estimate a probability of parking the vehicle as the quote showing the probability of parking being calculated throughout the route.))
Similarly, Applicant asserts that Duym does not teach “setting a threshold to ascertain a parking event at each destination;”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is to set a threshold in which an action in which parking would take place is given. Duym teaches a probability in which a parking action would occur along a sub-route in which a destination of parking is included in the sub route information (as mapped above in claim 1, 19 and 20). Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Duym teaches “setting a threshold to ascertain a parking event at each destination;”.
Duym teaches: setting a threshold to ascertain a parking event at each destination; ((Pg. 1 – Abstract – “The route parking probability is the probability that parking at some point along the extended computed route will be possible and wherein one or more links of the parking route are suitable for parking.” & See Also Pg. 10 – [0009] – “According to an embodiment, the desired route parking probability is a threshold.” & See Also Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. (equates to set a threshold to ascertain a parking event at each destination as the first quote shows a determination of probability of parking at each point that is along a route of travel. The second quote shows the probability to park is equivalent to a threshold and thus the threshold is set for each point or destination as seen by the first quote. The last quote shows the sub route finishing at a destination in which the parking would take place and thus the parking event at a destination is shown.)))
Similarly, Applicant asserts that Duym does not teach “; determining that the probability of parking at the first destination at a future time slot is greater than the threshold;”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is to determine if the vehicle being parked has a probability greater than a set point to arrive at a destination at a time set in the future. Duym teaches a system in which a determination about the host vehicle being parked along a sub route which contains the destination information will happen, based on a probabilistic approach, within a given time interval ahead of the vehicle travelling to the destination (as mapped above in claim 1, 19 and 20). Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Duym teaches “determining that the probability of parking at the first destination at a future time slot is greater than the threshold;;”.
Duym teaches: determining that the probability of parking at the first destination at a future time slot is greater than the threshold; (Pg. 1 – [Abstract] – “Methods for determining routes for routing vehicle include computing, in a digital map a route from route source to route destination. Such methods further include determining sub-route of the computed route, which ends at the destination and along which the vehicle could be parked. And such methods still further include extending the computed route beyond the route destination in area around the route destination until route parking probability of parking route, including links of the determined sub-route and/or of the extension of the computed route, is equal to or greater than predetermined threshold whilst total expected travel time, associated with the extended computed route and comprising driving and walking times, is minimized” & See Also Pg. 10 – [0009] – “respective distance from a predecessor of the corresponding link in the computed route to the route destination are computed, wherein the link-wise moving back is terminated and the corresponding link is selected as an appropriate start link of the sub-route if the respective route parking probability is greater than or equal to a desired route parking probability” (equates to determining that the probability of parking at the first destination at a future time slot is greater than the threshold; as the quote shows the probability of parking being determined to be greater than a threshold that is accounting for travel time or a destination arrival time to be considered and minimized thus a future time slot is considered when determining the probability of parking at the destination. The quote specifically shows the sub-route terminating at a destination position in which the parking probability is used to be calculated. ))
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. DE102019213836A1 - A method for operating a navigation system (11) of a motor vehicle (10) when driving from a starting location to a destination (5) selected by the driver, the navigation system (11) having a plurality of possible routes (2, 3, 4) from the starting location to the The destination (5) and / or a target position located in a predetermined environment (6) around the destination (5) is determined and one of these routes (2, 3, 4) selected in particular by the driver is used to guide the driver to the destination (5 ) or to the target position, with at least one of the routes (4) taking into account the position and, in particular, the time-dependent occupancy probability for parking spaces (7, 8) in the specified surroundings (6
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REECE ANTHONY WAKELY whose telephone number is (571)272-3783. The examiner can normally be reached Monday - Friday 8:30am-6:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hitesh Patel can be reached at (571) 270-5442. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.A.W./Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
4/15/26
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