DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
This Office Action is in response to communications filed on 04/09/2025. Claims 1-16 are pending for examination.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Higuchi et al. (U.S. Patent Application Pub. 2022/0136847) in view of Meister (U.S. Patent Application Pub. US 2022/0044198).
Regarding claim 1, Higuchi discloses an evaluation method of locations (Figs 1 & 4-5), comprising:
obtaining sensing data (¶014; scores may be determined based on a variety of factors including sensor data from vehicles);
determining a parking state according to the sensing data (¶019; vehicles transmit sensor data to server 102 which server 102 may use to determine live parking availability);
determining a parking location category corresponding to the sensing data under the parking state (¶015; after determining adjusted utility scores for each of the available parking spaces, parking assistance system may select available parking space with highest adjusted utility score and direct vehicle to selected parking space);.
Higuchi is silent on training a location suggestion model according to the parking location category and the sensing data, wherein the location suggestion model is used for suggesting a parking location. Meister from an analogous parking space art discloses an approach for providing dynamic parking recommendations based on on-site parking availability (Abstract) along with a & system for doing so (see Fig 1) and the concept of training a location suggestion model according to the parking location category and the sensing data, wherein the location suggestion model is used for suggesting a parking location (¶044; system 100 can combine various static and dynamic location based sensor data to train a dynamic parking and package delivery load recommendation model (e.g., a machine learning model) in order to generate a model which minimizes the delivery time for the vehicle 101 and/or the driver. The system 100 then recommends the best stopping location and a respective delivery item list based on the following attributes). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the evaluation method of locations of Higuchi with the concept of training a location suggestion model according to the parking location category and the sensing data, wherein the location suggestion model is used for suggesting a parking location, as taught by Meister in order to recommend the best stopping / parking location.
Regarding claim 9, Higuchi discloses driver management system (Figs 1-5), comprises:
a server (Fig 1, server 102, ¶005, ¶034, ¶039 etc.) communicatively connected to an on-board device (see Fig 1; dotted lines represents connections), wherein the server obtains a parking location category corresponding to sensing data (¶014; scores may be determined based on a variety of factors including sensor data from vehicles)of the on-board device under a parking
state (¶019; vehicles transmit sensor data to server 102 which server 102 may use to determine live parking availability).
Higuchi is silent on trains a location suggestion model according to the parking location category and the sensing data, wherein the location suggestion model is used for suggesting a parking location. Meister from an analogous parking space art discloses an approach for providing dynamic parking recommendations based on on-site parking availability (Abstract) along with a & system for doing so (see Fig 1) and the concept of training a location suggestion model according to the parking location category and the sensing data, wherein the location suggestion model is used for suggesting a parking location (¶044; system 100 can combine various static and dynamic location based sensor data to train a dynamic parking and package delivery load recommendation model (e.g., a machine learning model) in order to generate a model which minimizes the delivery time for the vehicle 101 and/or the driver. The system 100 then recommends the best stopping location and a respective delivery item list based on the following attributes). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the evaluation method of locations of Higuchi with the concept of training a location suggestion model according to the parking location category and the sensing data, wherein the location suggestion model is used for suggesting a parking location, as taught by Meister in order to recommend the best stopping / parking location.
Claims 2-5 and 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Higuchi et al. (U.S. Patent Application Pub. 2022/0136847) in view of Meister (U.S. Patent Application Pub. US 2022/0044198) further in view of Yan et al. (U.S. Patent 10,930,151) and still further in view of Shoda et al, (U.S. Patent Application Pub. 2020/0276982).
Regarding claim 2, Higuchi and Meister in combination teach the evaluation method of locations according to claim 1, and Higuchi discloses wherein the sensing data comprises a locating record (¶044 user preference determination module 328 may determine an average safety score of previous parking locations utilized by the user to determine the user's safety sensitivity. In some examples, the user preference determination module 328 may determine that a user's safety sensitivity changes based on the date and time (e.g., a user may be more sensitive to safety at night; Examiner interprets all as indicative of locating record).
Higuchi and Meister are silent on an in-vehicle image, and
a step of determining the parking state according to the sensing data comprises:
determining a stay time of a vehicle according to the locating record, an in-vehicle image, and determining whether a passenger in the vehicle leaves a seat according to the in-vehicle image; and determining the parking state according to the stay time and a determination result of the in-vehicle image; wherein the parking state represents whether the vehicle is parked or not. Yan from an analogous parking management art discloses a step of determining the parking state according to the sensing data comprises: determining a stay time of a vehicle according to the locating record (claim 4; processor is further configured to: calculate, when the vehicle crosses a parking line and enters the parking space in the parking space area from the entry detection area, a second time, wherein the vehicle stays in the parking space for the second time, calculate, when the license plate crosses the parking line and enters the parking space in the parking space area from the entry detection area, a third time, wherein the license plate stays in the parking space for the third time, determine, when the second time exceeds a second threshold, that the vehicle is entering vehicle and the parking event of the vehicle is an entry parking event, determine and record the entry parking event information in the on-the-spot vehicle information table, and determine, when the third time exceeds the second threshold, that the vehicle identified by the license plate is the entering vehicle and the parking event of the vehicle identified by the license plate is the entry parking event, CN 110121738 WISBRUN ; characteristic of the system is that, at least one supply unit, by means of a plurality of vehicles within a time period comprises the parking time of stay in the parking area for providing parking area information and a selecting unit for selecting the parking space in the parking area according to vehicle parameters); wherein the parking state represents whether the vehicle is parked (claim 4; a second time, wherein the vehicle stays in the parking space for the second time. Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the evaluation method of locations of Higuchi with the concept of a step of determining the parking state according to the sensing data comprises: determining a stay time of a vehicle according to the locating record and wherein the parking state represents whether the vehicle is parked, as taught by Yan in order to have info on when vehicle stays in the parking space for a given time.
Higuchi, Meister and Yan are all silent on an in-vehicle image, and determining whether a passenger in the vehicle leaves a seat according to the in-vehicle image; and determining the parking state according to a determination result of the in-vehicle image.
Shoda from an analogous recognizing a peripheral environment of a vehicle art discloses the concepts of an in-vehicle image (image captured by the in-vehicle camera 45), and determining whether a passenger in the vehicle leaves a seat according to the in-vehicle image (¶059; boarding state estimator 175 may analyze an image captured by the in-vehicle camera 45, estimate that the occupant U is on board in a case where feature information (for example, the contour of eyes, nose, mouth, or face) of the face, contour information of the body (the upper half of the body), or the like is extracted from the captured image, and estimate that the occupant U is not on board in a case where the feature information of the face, the contour information, or the like is not extracted); and determining a parking state according to a determination result of the in-vehicle image (¶086; parking controller 142 parks vehicle M within parking space of parking area PA, for example, on basis of information acquired from the parking lot management device 500.. i.e, occupant U is not on board). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the evaluation method of locations of Higuchi with the concept of an in-vehicle image, and determining whether a passenger in the vehicle leaves a seat according to the in-vehicle image; and determining the parking state according to a determination result of the in-vehicle image, as taught by Shoda in order to have info on when vehicle should stay in a parking space.
Regarding claim 3, Higuchi, Meister, Yan and Shoda, in combination, teach the evaluation method of locations according to claim 1, and Higuchi further teaches wherein the sensing data comprises a locating record (¶044) and defining the sensing data under the parking state according to a current location in the locating record (¶015) and a timestamp in response to the parking state (¶019; Examiner notes that system determines live parking availability inferring the system knows locations taken, as well as locations available in real time). Shoda further teaches an out-vehicle image (¶059). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the evaluation method of locations of Higuchi with the concept of further defining the sensing data under a parking state according to an out-vehicle image, as taught by Shoda in order to have info on when vehicle should move and stay in a parking space.
Regarding claim 4, Higuchi and Meister in combination teach the evaluation method of locations according to claim 1, and Higuchi discloses wherein the parking location category comprises a suggested parking category (¶015; parking assistance system may select available parking space with highest adjusted utility score and direct vehicle to selected parking space), and Shoda further teaches the evaluation method further comprises: determining a first parking location of the suggested parking category corresponding to a stay point in a route through the location suggestion model (¶050; first controller 120 concurrently realizes a function based on artificial intelligence (AI) and a function based on a model imparted in advance… a function of “recognizing a point of intersection” may be realized by the recognition of a point of intersection based on deep learning or the like and recognition based on conditions (such as a signal or a road sign on which pattern matching is possible) imparted in advance being concurrently executed, and being comprehensively evaluated by performing scoring on both); and providing the first parking location and a street view image thereof in response to the vehicle being located within a recommended range of the stay point (Figs 4-5, ¶045; points of interest (POI) information, or the like). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the evaluation method of locations of Higuchi with the concept of further determining a first parking location of the suggested parking category corresponding to a stay point in a route through the location suggestion model; and providing the first parking location and a street view image thereof in response to the vehicle being located within a recommended range of the stay point, as taught by Shoda in order to have info on how the vehicle should move in a parking area.
Regarding claim 5, Higuchi and Meister in combination teach the evaluation method of locations according to claim 1, and Shoda further teaches comprising: obtaining a street view image within an evaluation range of a stay point in a route (Fig 4);
obtaining the parking location category corresponding to the street view image within the evaluation range (¶045); and
providing a second parking location determined as the suggested parking category
from the street view image within the evaluation range (see Fig 4; several parking locations suggested). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the evaluation method of locations of Higuchi with the concept of further obtaining a street view image within an evaluation range of a stay point in a route; and providing the first parking location and a street view image thereof in response to the vehicle being located within a recommended range of the stay point, obtaining the parking location category corresponding to the street view image within the evaluation range and providing a second parking location determined as the suggested parking category
from the street view image within the evaluation range, as taught by Shoda in order to have info on how the vehicle should move in a parking area.
Regarding claim 10, Higuchi and Meister in combination teach the driver management system according to claim 9, wherein the sensing data comprises a locating record (¶044 user preference determination module 328 may determine an average safety score of previous parking locations utilized by the user to determine the user's safety sensitivity. In some examples, the user preference determination module 328 may determine that a user's safety sensitivity changes based on the date and time (e.g., a user may be more sensitive to safety at night; Examiner interprets all as indicative of locating record).
Higuchi and Meister are silent on an in-vehicle image, and the server determines the parking state by: determining a stay time of a vehicle according to the locating record, an in-vehicle image, and determining whether a passenger in the vehicle leaves a seat according to the in-vehicle image; and determining the parking state according to the stay time and a determination result of the in-vehicle image; wherein the parking state represents whether the vehicle is parked or not. Yan from an analogous parking management art discloses a step of determining the parking state according to the sensing data comprises: determining a stay time of a vehicle according to the locating record (claim 4; processor is further configured to: calculate, when the vehicle crosses a parking line and enters the parking space in the parking space area from the entry detection area, a second time, wherein the vehicle stays in the parking space for the second time, calculate, when the license plate crosses the parking line and enters the parking space in the parking space area from the entry detection area, a third time, wherein the license plate stays in the parking space for the third time, determine, when the second time exceeds a second threshold, that the vehicle is entering vehicle and the parking event of the vehicle is an entry parking event, determine and record the entry parking event information in the on-the-spot vehicle information table, and determine, when the third time exceeds the second threshold, that the vehicle identified by the license plate is the entering vehicle and the parking event of the vehicle identified by the license plate is the entry parking event); wherein the parking state represents whether the vehicle is parked (claim 4; a second time, wherein the vehicle stays in the parking space for the second time. Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to combine the driver management system of Higuchi with the concept of determining the parking state according to the sensing data comprises: determining a stay time of a vehicle according to the locating record and wherein the parking state represents whether the vehicle is parked, as taught by Yan in order to have info on when vehicle stays in the parking space for a given time.
Higuchi, Meister and Yan are all silent on an in-vehicle image, and determining whether a passenger in the vehicle leaves a seat according to the in-vehicle image; and determining the parking state according to a determination result of the in-vehicle image.
Shoda from an analogous recognizing a peripheral environment of a vehicle art discloses the concepts of an in-vehicle image (image captured by the in-vehicle camera 45), and determining whether a passenger in the vehicle leaves a seat according to the in-vehicle image (¶059; boarding state estimator 175 may analyze an image captured by the in-vehicle camera 45, estimate that the occupant U is on board in a case where feature information (for example, the contour of eyes, nose, mouth, or face) of the face, contour information of the body (the upper half of the body), or the like is extracted from the captured image, and estimate that the occupant U is not on board in a case where the feature information of the face, the contour information, or the like is not extracted); and determining a parking state according to a determination result of the in-vehicle image (¶086; parking controller 142 parks vehicle M within parking space of parking area PA, for example, on basis of information acquired from the parking lot management device 500.. i.e, occupant U is not on board). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the evaluation method of locations of Higuchi with the concept of an in-vehicle image, and determining whether a passenger in the vehicle leaves a seat according to the in-vehicle image; and determining the parking state according to a determination result of the in-vehicle image, as taught by Shoda in order to have info on when vehicle should stay in a parking space
Regarding claim 11, Higuchi, Meister, Yan and Shoda, in combination, teach the driver management system according to claim 9, and Higuchi further teaches wherein the sensing data comprises a locating record (¶044) and defining the sensing data under the parking state according to a current location in the locating record (¶015) and a timestamp in response to the parking state (¶019; Examiner notes that system determines live parking availability inferring the system knows locations taken, as well as locations available in real time). Shoda further teaches an out-vehicle image (¶059). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the driver management system of Higuchi with the concept of further defining the sensing data under a parking state according to an out-vehicle image, as taught by Shoda in order to have info on when a vehicle should move and stay in a parking space.
Regarding claim 12, Higuchi and Meister in combination teach the driver management system according to claim 9, and Higuchi discloses wherein the parking location category comprises a suggested parking category (¶015; parking assistance system may select available parking space with highest adjusted utility score and direct vehicle to selected parking space), and Shoda further teaches the evaluation method further comprises: determining a first parking location of the suggested parking category corresponding to a stay point in a route through the location suggestion model (¶050; first controller 120 concurrently realizes a function based on artificial intelligence (AI) and a function based on a model imparted in advance… a function of “recognizing a point of intersection” may be realized by the recognition of a point of intersection based on deep learning or the like and recognition based on conditions (such as a signal or a road sign on which pattern matching is possible) imparted in advance being concurrently executed, and being comprehensively evaluated by performing scoring on both); and providing the first parking location and a street view image thereof in response to the vehicle being located within a recommended range of the stay point (Figs 4-5, ¶045; points of interest (POI) information, or the like). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the driver management system of Higuchi with the concept of further determining a first parking location of the suggested parking category corresponding to a stay point in a route through the location suggestion model; and providing the first parking location and a street view image thereof in response to the vehicle being located within a recommended range of the stay point, as taught by Shoda in order to have info on how the vehicle should move in a parking area.
Regarding claim 13, Higuchi and Meister in combination teach the driver management system according to claim 9, and Shoda further teaches comprising: obtaining a street view image within an evaluation range of a stay point in a route (Fig 4);
obtaining the parking location category corresponding to the street view image within the evaluation range (¶045); and
providing a second parking location determined as the suggested parking category
from the street view image within the evaluation range (see Fig 4; several parking locations suggested). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the evaluation method of locations of Higuchi with the concept of further obtaining a street view image within an evaluation range of a stay point in a route; and providing the first parking location and a street view image thereof in response to the vehicle being located within a recommended range of the stay point, obtaining the parking location category corresponding to the street view image within the evaluation range and providing a second parking location determined as the suggested parking category
from the street view image within the evaluation range, as taught by Shoda in order to have info on how the vehicle should move in a parking area.
Claim 6-8 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Higuchi et al. (U.S. Patent Application Pub. 2022/0136847) in view of Meister (U.S. Patent Application Pub. US 2022/0044198) further in view of Havercamp (U.S. Patent Application Pub. 20190096254).
Regarding claim 6, Higuchi and Meister in combination teach the evaluation method of locations according to claim 1, and Higuchi discloses wherein a step of determining the parking location category corresponding to the sensing data under the parking state comprises:
determining an initial category corresponding to the sensing data according to a
classification rule (¶044 user preference determination module 328 may determine an average safety score of previous parking locations utilized by the user to determine the user's safety sensitivity. In some examples, the user preference determination module 328 may determine that a user's safety sensitivity changes based on the date and time (e.g., a user may be more sensitive to safety at night; Examiner interprets all as indicative of locating record), wherein the classification rule is related to a parking legality;
obtaining a review result of the initial category (Fig 5; elements 504 - 518); and
determining the parking location category according to the review result (Fig 5; element 520).
Higuchi and Meister are silent on wherein the classification rule is related to a parking legality. Havercamp from an analogous vehicle parking art teaches a parking app (also referred to as “ParkParkGoose” herein) executable on a mobile device (e.g. smartphone, vehicle navigation unit etc.) that enables users to tap, park and go with ease, knowing that they have safely parked their car each and every time and/or acted in accordance with local laws and regulations (¶053). Havercamp teaches wherein the classification rule is related to a parking legality (¶055; By using GPS location, time and day, and user variables like permits and placards, compared to regulations in the area, the parking app/technology may provide parking/stopping/standing legality and payment guidance in real-time in order to check legality of manual parking and/or automated parking and/or commercial stopping and standing. Further, the parking information may be critical for autonomous vehicle navigation as well as rideshare companies operating legally by city and state rules and regulations. The parking information may be provided through the parking app, an API, or built in car technology). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the evaluation method of locations of Higuchi with the concept of wherein the classification rule is related to a parking legality, as taught by Havercamp in order to have info about vehicle parking operating legally by city and state rules and regulations.
Regarding claim 7, Higuchi and Meister in combination teach the evaluation method of locations according to claim 1, and Havercamp teaches further comprising:
counting a number of incidents of a violation event, wherein the sensing data comprises
a locating record, and an occurrence location of the violation event and a location of the locating record are within a statistical range (¶083; method 200 may further include receiving, using the communication device, at least one contextual variable. Further, retrieving the parking data may be further based on the contextual variable. In some embodiments, the contextual variable may include at least one a time of day, a day of week, a lane of a road associated with the location, a level of traffic congestion in a vicinity of the location, a number of vehicles currently seeking parking in the vicinity of the location, at least one characteristic of a user associated with the vehicle, at least one characteristic of the vehicle, at least one permit associated with the vehicle and at least one parking violation associated with one or more of the vehicle and the user);
determining whether the number of incidents r exceeds a number threshold (¶083; at least one permit / parking violation exceeds threshold of 0);
classifying at least one event location corresponding to the violation event in response to the number of incidents exceeding the number threshold (¶083; classifies as permit and/or parking violation), and
defining a violation location according to the at least one event location (¶083; contextual variable may include at least one a time of day, a day of week, a lane of a road associated with the location). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the evaluation method of locations of Higuchi with the concepts listed above, as taught by Havercamp in order to have info about vehicle parking operating legally by city and state rules and regulations.
Regarding claim 8, Higuchi and Meister in combination teach the evaluation method of locations according to claim 1, and Higuchi discloses wherein the parking location category comprises a suggested parking category (¶050; utility score; ¶053; safety score; Examiner interprets the scores to consist of a range from low to high), a cautioned parking category (a mid-grade safety score), and a hazardous parking category (a low safety score).
Havercamp teaches wherein a suggested parking category complies with a traffic regulation, (¶055; By using GPS location, time and day, and user variables like permits and placards, compared to regulations in the area, the parking app/technology may provide parking/stopping/standing legality and payment guidance in real-time). Higuchi, Meister nor Havercamp explicitly mention that the cautioned parking category does not comply with the traffic regulation but a corresponding accident risk is less than a risk threshold, and the hazardous parking category does not comply with the traffic regulation and the corresponding accident risk is not less than the risk threshold. However, a person of ordinary skill in the art, upon reading the reference, would have recognized options whereby the cautioned parking category may or may not comply with the traffic regulation and that a corresponding accident risk is less than a risk threshold, and the hazardous parking category does not comply with the traffic regulation and the corresponding accident risk is not less than the risk threshold because the claimed options all coincide with known user variables being compared to regulations in the area, as cited by Havercamp. One of ordinary skill in the art would have had good reason whereby the cautioned parking category may or may not comply with the traffic regulation and that a corresponding accident risk is less than a risk threshold, while a hazardous parking category may or may not comply with traffic regulations and corresponding accident risk is may or may not be less than the risk threshold. It would require no more than "ordinary skill and common sense," to utilize parking categories as claimed since it appears as though no specific correlations are presented thereby connecting the may or may nots to the claim features in any novel way. Thus, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to try a parking category that may or may not comply with the traffic regulation and corresponding accident risks above or below a given threshold, as a person with ordinary skill would have good reason to pursue known options within his or her technical grasp.
Regarding claim 14, Higuchi and Meister in combination teach the driver management system according to claim 9, and Higuchi discloses wherein the server determines the parking location category corresponding to the sensing data under the parking state comprises:
determining an initial category corresponding to the sensing data according to a
classification rule (¶044 user preference determination module 328 may determine an average safety score of previous parking locations utilized by the user to determine the user's safety sensitivity. In some examples, the user preference determination module 328 may determine that a user's safety sensitivity changes based on the date and time (e.g., a user may be more sensitive to safety at night; Examiner interprets all as indicative of locating record), wherein the classification rule is related to a parking legality;
obtaining a review result of the initial category (Fig 5; elements 504 - 518); and
determining the parking location category according to the review result (Fig 5; element 520).
Higuchi and Meister are silent on wherein the classification rule is related to a parking legality. Havercamp from an analogous vehicle parking art teaches a parking app (also referred to as “ParkParkGoose” herein) executable on a mobile device (e.g. smartphone, vehicle navigation unit etc.) that enables users to tap, park and go with ease, knowing that they have safely parked their car each and every time and/or acted in accordance with local laws and regulations (¶053). Havercamp teaches wherein the classification rule is related to a parking legality (¶055; By using GPS location, time and day, and user variables like permits and placards, compared to regulations in the area, the parking app/technology may provide parking/stopping/standing legality and payment guidance in real-time in order to check legality of manual parking and/or automated parking and/or commercial stopping and standing. Further, the parking information may be critical for autonomous vehicle navigation as well as rideshare companies operating legally by city and state rules and regulations. The parking information may be provided through the parking app, an API, or built in car technology). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the driver management system of Higuchi with the concept of wherein the classification rule is related to a parking legality, as taught by Havercamp in order to have info about vehicle parking operating legally by city and state rules and regulations.
Regarding claim 15, Higuchi and Meister in combination teach the driver management system according to claim 9, and Havercamp teaches further comprising:
counting a number of incidents of a violation event, wherein the sensing data comprises
a locating record, and an occurrence location of the violation event and a location of the locating record are within a statistical range (¶083; method 200 may further include receiving, using the communication device, at least one contextual variable. Further, retrieving the parking data may be further based on the contextual variable. In some embodiments, the contextual variable may include at least one a time of day, a day of week, a lane of a road associated with the location, a level of traffic congestion in a vicinity of the location, a number of vehicles currently seeking parking in the vicinity of the location, at least one characteristic of a user associated with the vehicle, at least one characteristic of the vehicle, at least one permit associated with the vehicle and at least one parking violation associated with one or more of the vehicle and the user);
determining whether the number of incidents r exceeds a number threshold (¶083; at least one permit / parking violation exceeds threshold of 0);
classifying at least one event location corresponding to the violation event in response to the number of incidents exceeding the number threshold (¶083; classifies as permit and/or parking violation), and
defining a violation location according to the at least one event location (¶083; contextual variable may include at least one a time of day, a day of week, a lane of a road associated with the location). Therefore, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to further combine the driver management system of Higuchi with the concepts listed above, as taught by Havercamp in order to have info about vehicle parking operating legally by city and state rules and regulations.
Regarding claim 16, Higuchi and Meister in combination teach the driver management system according to claim 9, and Higuchi discloses wherein the parking location category comprises a suggested parking category (¶050; utility score; ¶053; safety score; Examiner interprets the scores to consist of a range from low to high), a cautioned parking category (a mid-grade safety score), and a hazardous parking category (a low safety score).
Havercamp teaches wherein a suggested parking category complies with a traffic regulation, (¶055; By using GPS location, time and day, and user variables like permits and placards, compared to regulations in the area, the parking app/technology may provide parking/stopping/standing legality and payment guidance in real-time). Higuchi, Meister nor Havercamp explicitly mention that the cautioned parking category does not comply with the traffic regulation but a corresponding accident risk is less than a risk threshold, and the hazardous parking category does not comply with the traffic regulation and the corresponding accident risk is not less than the risk threshold. However, a person of ordinary skill in the art, upon reading the reference, would have recognized options whereby the cautioned parking category may or may not comply with the traffic regulation and that a corresponding accident risk is less than a risk threshold, and the hazardous parking category does not comply with the traffic regulation and the corresponding accident risk is not less than the risk threshold because the claimed options all coincide with known user variables being compared to regulations in the area, as cited by Havercamp. One of ordinary skill in the art would have had good reason whereby the cautioned parking category may or may not comply with the traffic regulation and that a corresponding accident risk is less than a risk threshold, while a hazardous parking category may or may not comply with traffic regulations and corresponding accident risk is may or may not be less than the risk threshold. It would require no more than "ordinary skill and common sense," to utilize parking categories as claimed since it appears as though no specific correlations are presented thereby connecting the may or may nots to the claim features in any novel way. Thus, it would have been obvious for one of ordinary skill in the art at the time of filing the invention to try a parking category that may or may not comply with the traffic regulation and corresponding accident risks above or below a given threshold, as a person with ordinary skill would have good reason to pursue known options within his or her technical grasp.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANCIL H LITTLEJOHN JR whose telephone number is (571)270-3718. The examiner can normally be reached M-F 8:30-5 (CST).
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/MANCIL LITTLEJOHN JR/Examiner, Art Unit 2685
/QUAN ZHEN WANG/Supervisory Patent Examiner, Art Unit 2685
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