DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claims 1, 11 and 18 are amended.
Claim 24 is canceled.
Claims 1-23, 25 are pending.
Response to arguments
With respect to Applicant’s remarks filed on 07/16/2026; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
The office reintroduced the same 101 rejection with details explanation:
The claimed invention of claim 1 is directed to processes to Identify available parking spot based on the parking information, grade the parking spot based on parking difficulty, determine driver skill level, compare the graded difficulty associated with the available parking spot and driver skill level, determine/assign parking spot with financial incentive (which is associated with parking difficulty), select parking spot with graded difficulty and skill of the driver associated with the financial incentive which can be performed in the human mind, or by a human using a pen and paper. All the processes claimed on claim 1 mentioned above performed using a controller. According to MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer as a tool to perform the abstract idea. For the reasons, claims 1-23 and 25 recite a mental process, which directed to an abstract idea.
Examiner notes that MPEP 2106.04(a)(2)(III): The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘Mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Here, determination is a form of making evaluation (grading difficulty and driver skill) and judgement based on observation (driver behavior and parking difficulty).
Proceeding to eligibility step 2B, this judicial exception is not integrated into a practical application. In particular, claim 1 only recites one additional element – “receiving, from a parking database entering a parking area, map data associated with the parking area”, “displaying, using the controller and via a display associated with the controller, the selected available parking spot”, it amounts to no more than insignificant extra solution activity of data gathering and/or outputting.
Regarding the additional limitations of receiving……, and displaying…, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform data gathering, displaying, sending and receiving steps. In particular, the receiving and displaying steps are recited at a high level of generality (i.e. as a general means of receiving information and performing communications for use in the next steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The device(s) and processor(s) are recited at a high level of generality and merely automate the steps.
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 impost any meaningful limits on practicing the abstract idea.
Dependent claims do not recite any further limitations that causes the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. Dependent claims limitation of “displaying…grading parking spot…driver’s skill level condition…are not recite any further limitations that causes the claims to be patent eligible.
Regarding arguments about 35 U.S.C. 103 rejection:
Applicant arguments:
Publicover does not disclose financial incentive associated with the graded difficulty of a parking spot and instead relies on the “Implication” derived from disparate portions.
Iwata’s incentives are not associated with the Parking-Spot difficulty
Additional citations on the last office action do not cure the deficiency.
The claimed nexus between financial incentive and graded difficulty remains undisclosed.
Displaying a visual representation of the selected available parking spot.
Display a route to the selected available parking spot.
Claim 15, 2D map with graphical indicator
Graphical indication of graded difficulty
Post display request for the alternative parking spot, Graphical indication with post display alternative spot.
Displaying plurality of the available parking spot.
Office response:
Iwata’s reception information related to incentive information (discount price for the parking spot) from user terminal and Publicover’s difficulty (depends on drivers’ skill) associated with the parking do not teach exactly the claimed subject matter, but it is obvious to ordinary skilled person to relate/associate a parking spot’s financial incentive with parking difficulties, drivers’ skill.
Although the office believes that the argument is persuasive by combining Akavaram with Iwata and Publicover, Office provides new mapping with new art with motivation specifically the mapping for the association of the financial incentive with parking difficulty (congested area).
Please see the motivation for the graphical representation of the graded parking spot in the display unit.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 1 is directed toward non-statutory subject matter, as shown below:
STEP 1: Does claim 1 fall within one of the statutory categories? Yes. Claim 1 is directed towards a method, claim 11 is directed towards a system and claim 18 is directed towards system. Therefore, claims 1, 11 and 18 are within at least one of the four statutory categories.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim is directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claim 1:
A real-time computer-implemented method comprising:
receiving, by a controller from a parking database upon the controller entering a parking area, map data associated with the parking area, wherein the parking area includes a plurality of parking spots, and wherein the map data comprises parking information and incentive information associated with the plurality of parking spots;
identifying, by the controller and based on the parking information, an available parking spot of the plurality of parking spots of the parking area;
grading, using the controller and based on the parking information, a difficulty associated with the available parking spot;
determining, using the controller, a skill level of a driver associated with the controller;
comparing, using the controller, the graded difficulty associated with the available parking spot and the skill level of the driver;
determining, using the controller and based on the incentive information, that the available parking spot is associated with a financial incentive, wherein the financial incentive is associated with the graded difficulty associated with the available parking spot, and wherein the financial incentive is dynamically updated to facilitate reduced congestion in the parking area;
selecting, by the controller and based on the comparison of the graded difficulty and the skill level of the driver and the determination that the available parking spot is associated with the financial incentive, the available parking spot; and displaying, using the controller and via a display associated with the controller, the selected available parking spot.
Claim 11:
A real-time parking guidance system for a vehicle comprising:
a display;
a parking database, wherein the parking database comprises map data associated with a parking area, wherein the parking area includes a plurality of parking spots, and wherein the map data includes parking information and incentive information associated with the plurality of parking spots; and
a parking spot identifier in communication with the parking database, the parking spot identifier comprising a controller configured to in real-time: receive, from the parking database upon the controller entering the parking area, the map data;
identify, based on the parking information, an available parking spot of the plurality of parking spots of the parking area;
grade, based on the parking information, a difficulty associated with the available parking spot;
determine a skill level of a driver associated with the vehicle and the parking spot identifier;
compare the graded difficulty associated with the available parking spot and the skill level of the driver;
determine, based on the incentive information, that the available parking spot is associated with a financial incentive, wherein the financial incentive is associated with the graded difficulty associated with the available parking spot, and wherein the financial incentive is dynamically updated to facilitate reduced congestion in the parking area;
select, based on the comparison of the graded difficulty and the skill level of the driver and the determination that the available parking spot is associated with the financial incentive, the available parking spot; and
display, via the display, the selected available parking spot.
Claim 18:
A real-time parking guidance system for a vehicle comprising:
a display;
a parking database, wherein the parking database comprises map data associated with a parking area, wherein the parking area includes a plurality of parking spots, and wherein the map data includes parking information and incentive information associated with the plurality of parking spots; and
a parking spot identifier in communication with the parking database, the parking spot identifier comprising a controller configured to in real-time: receive, from the parking database upon the controller entering the parking area, the map data;
identify, based on the parking information, an available parking spot of the plurality of parking spots of the parking area;
grade, based on the parking information, a difficulty associated with the available parking spot;
determine a skill level of a driver associated with the vehicle and the parking spot identifier;
compare the graded difficulty associated with the available parking spot and the skill level of the driver;
determine, based on the incentive information, that the available parking spot is associated with a financial incentive, wherein the financial incentive is associated with the graded difficulty associated with the available parking spot, and wherein the financial incentive is dynamically updated to facilitate reduced congestion in the parking area;
select, based on the comparison of the graded difficulty and the skill level of the driver and the determination that the available parking spot is associated with the financial incentive, the available parking spot; and
display, via the display, the selected available parking spot.
The method in claim 1 ,11 and 18 are a mental process that can be practicably performed in the human mind and, therefore, an abstract idea and a certain method of organizing human activity. Specifically, the limitations of claim 1, highlighted above merely consist of (in bold) Identifying an available parking spot, grading difficulty associated with the available parking spot, determine skilled level of driver, compare the graded difficulty with the skill level of the driver, determine the available parking spot is associated with a financial incentive, selecting based on the graded difficulty and skill level of the driver available parking spot, are merely a mental process (except for the generic linking to a controller).
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
Regarding Step 2B of the Revised Guidance, 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 with respect to integration of the abstract idea into a practical application, the additional element of for claim 1,
A real-time computer-implemented method comprising:
receiving, by a controller from a parking database upon the controller entering a parking area, map data associated with the parking area, wherein the parking area includes a plurality of parking spots, and wherein the map data comprises parking information and incentive information associated with the plurality of parking spots; displaying, using the controller and via a display associated with the controller, the selected available parking spot. For claim 11, “A real-time parking guidance system for a vehicle comprising:
a display;
a parking database, wherein the parking database comprises map data associated with a parking area, wherein the parking area includes a plurality of parking spots, and wherein the map data includes parking information and incentive information associated with the plurality of parking spots; and
a parking spot identifier in communication with the parking database, the parking spot identifier comprising a controller configured to in real-time: receive, from the parking database upon the controller entering the parking area, the map data“, amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to access profile to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Therefore, claims 1-23, 25 are ineligible under 35 USC §101.
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.
Claims 1-7,11-13,16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatented over US20160117925 of Akavaram (herein after “Akavaram”) in view of CN114511937A to Sun et al. (herein after “Sun”)
Regarding claim 1, Akavaram discloses A real-time computer-implemented method comprising (see Akavaram para[0037] real-time parking availability data 385 to intelligent parking system 300 ):[[R]]receiving, by a controller from a parking database (see Akavaram at least para [0060] At step 620, the process receives a preferred parking space location (e.g., GPS coordinates) from intelligent parking system 300) upon the controller (see Akavaram at least para [0025] Graphics controller 125 also connects to Northbridge 115 . In one embodiment, PCI Express bus 118 connects Northbridge 115 to graphics controller 125. Graphics controller 125 connects to display device 130, such as a computer monitor) entering a parking area, map data associated with the parking area (see Akavaram at least para [0070] FIG. 9 is an exemplary diagram depicting a vehicle that includes an onboard system and sensors to capture parking space properties of available parking spaces), wherein the parking area includes a plurality of parking spots (see Akavaram figure 4) ,
identifying, by the controller and based on the parking information (see abstract available parking spaces to factors such as driver skill data corresponding to the driver or vehicle properties corresponding to the vehicle), an available parking spot of the plurality of parking spots of the parking area;(see Akavaram figure 6, [0035] When intelligent parking system 300 receives parking space request 320 , intelligent parking system 300 identifies an area for which to search for an available parking space (search area 340 ) based upon the drivers target destination; Abstract “The intelligent parking system identifies available parking spaces and selects a preferred parking space from the available parking spaces based upon comparing dynamic parking space properties corresponding to the available parking spaces to factors such as driver skill data corresponding to the driver or vehicle properties corresponding to the vehicle”);
grading, using the controller and based on the parking information, a difficulty associated with the available parking spots; ( see Akavaram at least para [0003] the available parking spaces based upon comparing dynamic parking space properties corresponding to the available parking spaces to factors such as driver skill data corresponding to the driver or vehicle properties corresponding to the vehicle.).
determining, using the controller, a skill level of a driver associated with the controller; (see Akavaram para[0038] In addition, intelligent parking system 300 considers the skill level of the driver (included in driver data 315 ). For example, if the driver is a highly skilled driver, intelligent parking system 300 may place a higher weighting over a “tight fitting” parking space close to the target destination over a larger parking space that is further from the target destination.)
comparing, using the controller, the graded difficulty associated with the available parking spot and the skill level of the driver; (see Akavaram para[0002] a driver's skill level contributes to the success or failure of parking the vehicle without damage to the vehicle or surrounding objects, such as other vehicles.)
; and, displaying, using the controller and via a display associated with the controller, the selected available parking spot (see Akavaram at least para[0025] Graphics controller 125 connects to display device 130 , such as a computer monitor, displaying the parking spot associated with difficulty and financial incentive is a post processing step which is obvious to add information such as financial incentive with difficulty(congested) parking spot for the encourage).
However, Akavaram does not expressly disclose or otherwise teach wherein the map data comprises parking information and incentive information associated with the plurality of parking spots, determining, using the controller and based on the incentive information, that the available parking spot is associated with a financial incentive. Nevertheless, in a related field of invention, Sun teaches wherein the map data comprises parking information and incentive information associated with the plurality of parking spots (See Sun high-price area, low-price area, abstract the parking space turnover rate of a high-price area and the parking space utilization rate of a low-price area are improved, and the system trip cost is minimized, para[0003] Some low-price areas far from the destination have low parking space utilization., para[0071] The parking charging method proposed by the present invention can induce travelers who have parked for a long time to park in the parking spaces farther from the destination, thereby improving the parking space turnover rate in the high land price area and the parking space utilization rate in the low land price area, so as to minimize the travel cost of the system)
determining, using the controller and based on the incentive information, that the available parking spot is associated with a financial incentive (see Sun’s method provides a higher turnover where the best spots are, Sun para[0003] The current parking space allocation method mostly follows the first-come, first-served (FIFO) principle, that is, travelers who arrive early choose the parking space close to the destination, which may lead to the unreasonable long-term occupation of parking spaces in some areas with high land prices. Some low-price areas far from the destination have low parking space utilization)
selecting, by the controller and based on the comparison of the graded difficulty (See Sun para[0002] At the same time, the shortage of land in big cities, high land prices, and the standard of parking facilities lag behind the growth of car ownership, resulting in parking demand far greater than the supply of parking facilities, resulting in problems such as difficult parking and random parking) and the skill level (Akavaram’s skilled level of driver) of the driver and the determination that the available parking spot is associated with the financial incentive, the available parking spot. (See Sun para[0147] That is to say, the congestion cost of the traveler includes the congestion cost (time cost) of the person (the traveler itself) and the congestion cost of the vehicle , the optimal system achieved here can eliminate the queuing of travelers themselves during early commuting )
wherein the financial incentive is associated with the graded difficulty associated with the available parking spot or a location of the available parking spot in the parking area (See Sun abstract Under the method, a traveler with long parking time can select a parking space far away from a destination, and a user with short parking time can select a parking space near the destination, so that the parking space turnover rate of a high-price area and the parking space utilization rate of a low-price area are improved, and the system trip cost is minimized) and wherein the financial incentive is dynamically updated to facilitate reduced congestion in the parking area; (See Sun para[0018] β is the penalty for arriving early per unit time, γ is the penalty for being late per unit time, t * is the prescribed work time, w is the energy consumption per unit distance of the vehicle, τ is the congestion cost per unit time of the vehicle, and λ is the energy consumption per unit. The value of the fuel needed, p d is the parking time, x(p d ) is the distance between the vehicle parking position and the destination, a is the reduced parking fee per unit distance away from the destination, b is the vehicle parked at the destination (i.e. x(p d )=0) parking fee, σ is the proportional coefficient between the distance between the vehicle parking position and the destination (i.e. x(p d )) and the traveler’s parking time (i.e. p d ) , α is the traveler’s cost per unit time due to queuing delay, which implies that the parking fee’s is the proportional to distance between the vehicle parking position and the destination).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Sun’s parking difficulty(far distance from the destination) associated with the high and low price parking fee’s in order to allow to solve early commuting process, congestion and querying due to the inability to meet the demand of parking (see Sun para[0003]).
Regarding claim 2, Akavaram and Sun remain applies to claim 1. Akavaram discloses wherein outputting the selection comprises: displaying a visual representation of the selected parking spot on a display (see Akavaram at least para[0025] Graphics controller 125 connects to display device 130 , such as a computer monitor).
Regarding claim 3, Akavaram and Sun remain applies to claim 1. Akavaram discloses wherein outputting the selection comprises: displaying a route to the selected parking spot on the display (see Akavaram at least para[0068] A driver enters destination information into driver device 705 , such as with a touch screen keyboard depicted on display 810) .
Regarding claim 4, Akavaram and Sun remain applies to claim 1. However, Akavaram teaches a display unit, where it is possible to add available parking spot with Sun’s financial incentive (See Sun abstract Under the method, a traveler with long parking time can select a parking space far away from a destination, and a user with short parking time can select a parking space near the destination, so that the parking space turnover rate of a high-price area and the parking space utilization rate of a low-price area are improved, and the system trip cost is minimized.), wherein displaying the selected available parking spot further comprises: displaying the financial incentive, including at least a portion of the incentive information associated with the financial incentive (see Akavaram at least para[0025] Graphics controller 125 connects to display device 130 , such as a computer monitor, displaying the parking spot associated with difficulty and financial incentive is a post processing step which is obvious to add information such as financial incentive with difficulty(congested) parking spot for the encourage).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Sun’s parking difficulty(far distance from the destination) associated with the high and low price parking fee’s in order to allow to solve early commuting process, congestion and querying due to the inability to meet the demand of parking (see Sun para[0003]).
Regarding claim 5, Akavaram and Sun remain applies to claim 1. Akavaram discloses wherein the skill level of the driver is a setting input by the driver (see Akavaram at least para [0059] n this example, intelligent parking system 300 may query the driver's device for the driver's data. In turn, intelligent parking system 300 locates a preferred parking space based upon the driver's data, such as the driver's skill level and other parking preferences; para[0067] or other computing device capable of receiving driver input and providing the driver input to intelligent parking system 300).
Regarding claim 6, Akavaram and Sun remain applies to claim 1. Akavaram discloses wherein the skill level of the driver comprises preferences of the driver (see Akavaram at least para[0059] In this example, intelligent parking system 300 may query the driver's device for the driver's data. In turn, intelligent parking system 300 locates a preferred parking space based upon the driver's data, such as the driver's skill level and other parking preferences).
Regarding claim 7, Akavaram and Sun remain applies to claim 1. Akavaram teaches a parking spot with accessibility features; or a parking spot with an energy transfer system ( see Akavaram at least para [0049] Parking Preferences: Parking Distance Threshold: Within 500 Feet Of Target Destination, Parking Cost Preferences: Under $3 Per Hour, Safety Preferences: Within 30 Feet Of Street Light, Additional Area Preference: Adjacent To Mid-Size Cars And Smaller, Parking Space Type Preference: Angled Parking).
However, Akavaram does not expressly disclose or otherwise teach wherein the preferences of the driver comprise at least one of: a parking spot associated with a financial incentive. Nevertheless, in a related field of invention, Sun teaches a parking spot associated with a financial incentive; (See Sun abstract Under the method, a traveler with long parking time can select a parking space far away from a destination, and a user with short parking time can select a parking space near the destination, so that the parking space turnover rate of a high-price area and the parking space utilization rate of a low-price area are improved, and the system trip cost is minimized., see Sun’s high and low price area)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Sun’s parking difficulty(far distance from the destination) associated with the high and low price parking fee’s in order to allow to solve early commuting process, congestion and querying due to the inability to meet the demand of parking (see Sun para[0003]).
Regarding claim 11, Akavaram discloses A real-time computer-implemented method comprising (see Akavaram para[0037] real-time parking availability data 385 to intelligent parking system 300 ): a display (see Akavaram at least para[0025] Graphics controller 125 connects to display device 130 , such as a computer monitor, displaying the parking spot associated with difficulty and financial incentive is a post processing step which is obvious to add information such as financial incentive with difficulty(congested) parking spot for the encourage).; a parking database (see Akavaram at least para [0060] At step 620, the process receives a preferred parking space location (e.g., GPS coordinates) from intelligent parking system 300) wherein the parking database comprises map data associated with a parking area, (see Akavaram at least para [0070] FIG. 9 is an exemplary diagram depicting a vehicle that includes an onboard system and sensors to capture parking space properties of available parking spaces), wherein the parking area includes a plurality of parking spots (see Akavaram figure 4) ,
a parking spot identifier in communication with the parking database, the parking spot identifier comprising a controller configured to in real-time: receive, from the parking database upon the controller entering the parking area, the map data;
identify, by the controller and based on the parking information (see abstract available parking spaces to factors such as driver skill data corresponding to the driver or vehicle properties corresponding to the vehicle), an available parking spot of the plurality of parking spots of the parking area;(see Akavaram figure 6, [0035] When intelligent parking system 300 receives parking space request 320 , intelligent parking system 300 identifies an area for which to search for an available parking space (search area 340 ) based upon the drivers target destination; Abstract “The intelligent parking system identifies available parking spaces and selects a preferred parking space from the available parking spaces based upon comparing dynamic parking space properties corresponding to the available parking spaces to factors such as driver skill data corresponding to the driver or vehicle properties corresponding to the vehicle”);
grade, using the controller and based on the parking information, a difficulty associated with the available parking spots; ( see Akavaram at least para [0003] the available parking spaces based upon comparing dynamic parking space properties corresponding to the available parking spaces to factors such as driver skill data corresponding to the driver or vehicle properties corresponding to the vehicle.).
determine, a skill level of a driver associated with the vehicle and the parking spot identifier;; (see Akavaram para[0038] In addition, intelligent parking system 300 considers the skill level of the driver (included in driver data 315 ). For example, if the driver is a highly skilled driver, intelligent parking system 300 may place a higher weighting over a “tight fitting” parking space close to the target destination over a larger parking space that is further from the target destination.)
compare, using the controller, the graded difficulty associated with the available parking spot and the skill level of the driver; (see Akavaram para[0002] a driver's skill level contributes to the success or failure of parking the vehicle without damage to the vehicle or surrounding objects, such as other vehicles.)
; and, displaying, using the controller and via a display associated with the controller, the selected available parking spot (see Akavaram at least para[0025] Graphics controller 125 connects to display device 130 , such as a computer monitor, displaying the parking spot associated with difficulty and financial incentive is a post processing step which is obvious to add information such as financial incentive with difficulty(congested) parking spot for the encourage).
However, Akavaram does not expressly disclose or otherwise teach wherein the map data comprises parking information and incentive information associated with the plurality of parking spots, determining, using the controller and based on the incentive information, that the available parking spot is associated with a financial incentive. Nevertheless, in a related field of invention, Sun teaches wherein the map data comprises parking information and incentive information associated with the plurality of parking spots (See Sun high-price area, low-price area, abstract the parking space turnover rate of a high-price area and the parking space utilization rate of a low-price area are improved, and the system trip cost is minimized, para[0003] Some low-price areas far from the destination have low parking space utilization., para[0071] The parking charging method proposed by the present invention can induce travelers who have parked for a long time to park in the parking spaces farther from the destination, thereby improving the parking space turnover rate in the high land price area and the parking space utilization rate in the low land price area, so as to minimize the travel cost of the system)
determining, using the controller and based on the incentive information, that the available parking spot is associated with a financial incentive (see Sun’s method provides a higher turnover where the best spots are, Sun para[0003] The current parking space allocation method mostly follows the first-come, first-served (FIFO) principle, that is, travelers who arrive early choose the parking space close to the destination, which may lead to the unreasonable long-term occupation of parking spaces in some areas with high land prices. Some low-price areas far from the destination have low parking space utilization)
select, by the controller and based on the comparison of the graded difficulty (See Sun para[0002] At the same time, the shortage of land in big cities, high land prices, and the standard of parking facilities lag behind the growth of car ownership, resulting in parking demand far greater than the supply of parking facilities, resulting in problems such as difficult parking and random parking) and the skill level of the driver (Akavaram’s skilled level of driver) and the determination that the available parking spot is associated with the financial incentive, the available parking spot. . (See Sun para[0147] That is to say, the congestion cost of the traveler includes the congestion cost (time cost) of the person (the traveler itself) and the congestion cost of the vehicle , the optimal system achieved here can eliminate the queuing of travelers themselves during early commuting ).
wherein the financial incentive is associated with the graded difficulty associated with the available parking spot or a location of the available parking spot in the parking area (See Sun abstract Under the method, a traveler with long parking time can select a parking space far away from a destination, and a user with short parking time can select a parking space near the destination, so that the parking space turnover rate of a high-price area and the parking space utilization rate of a low-price area are improved, and the system trip cost is minimized) , and wherein the financial incentive is dynamically updated to facilitate reduced congestion in the parking area; (See Sun para[0018] β is the penalty for arriving early per unit time, γ is the penalty for being late per unit time, t * is the prescribed work time, w is the energy consumption per unit distance of the vehicle, τ is the congestion cost per unit time of the vehicle, and λ is the energy consumption per unit. The value of the fuel needed, p d is the parking time, x(p d ) is the distance between the vehicle parking position and the destination, a is the reduced parking fee per unit distance away from the destination, b is the vehicle parked at the destination (i.e. x(p d )=0) parking fee, σ is the proportional coefficient between the distance between the vehicle parking position and the destination (i.e. x(p d )) and the traveler’s parking time (i.e. p d ) , α is the traveler’s cost per unit time due to queuing delay, which implies that the parking fee’s is the proportional to distance between the vehicle parking position and the destination).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Sun’s parking difficulty(far distance from the destination) associated with the high and low price parking fee’s in order to allow to solve early commuting process, congestion and querying due to the inability to meet the demand of parking (see Sun para[0003]).
Regarding claim 12, Akavaram and Sun remain applies to claim 11. Akavaram discloses wherein the parking system further comprises: an input device in communication with the parking spot identifier, wherein the input device comprises driver input relating to the skill level of the driver (see Akavaram at least para [0067] FIG. 8 is an exemplary diagram depicting a driver device that requests a parking space and receives a preferred parking space location based upon driver data. As discussed earlier, driver device 705 may be a computing device such as a smart phone, laptop, desktop, tablet, onboard vehicle system (e.g., computer system, navigation system), wearable device, or other computing device capable of receiving driver input and providing the driver input to intelligent parking system 300).
Regarding claim 13, Akavaram and Sun remain applies to claim 11. Akavaram discloses wherein the parking system further comprises: a global positioning system (GPS), wherein the GPS determines a path from an entrance to the one selected parking spot, and wherein the display updates to graphically depict the path (see Akavaram at least para [0060] At step 620, the process receives a preferred parking space location (e.g., GPS coordinates) from intelligent parking system 300. At step 630, in one embodiment, the process changes the target destination on the navigation system to the preferred parking space location to direct the driver directly to the preferred parking space instead of the target destination (e.g., restaurant)).
Regarding claim 16, Akavaram and Sun remain applies to claim 11. Akavaram discloses wherein the display comprises graphical indicators based on the grade of one or more available parking spots (See Akavaram at least para [[0042] FIG. 4 shows three available parking spaces 410 , 420 , and 440 , which are detected by three different mobile devices0 ; and Figure 4).
Regarding claim 17, Akavaram and Sun remain applies to claim 11. However, Akavaram does not expressly disclose or otherwise teach wherein the financial incentive associated with the selected available parking spot comprises at least one of: a reduction in price for an energy transfer station; a reduction in parking rates; or redeemable points. Nevertheless, in a related field of invention, Sun teaches wherein the financial incentive associated with the selected available parking spot comprises at least one of: a reduction in price for an energy transfer station (See Sun para[0002] Management and other aspects pose serious challenges to city managers. In addition, the process of finding a parking space is time-consuming and labor-intensive for drivers, creating additional problems such as pollution, energy consumption and traffic congestion) ; a reduction in parking rates (See Sun’s reduction in parking fee) ; or redeemable points (See Sun “Five special time points during the early commute, Table 1”.).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Sun’s parking difficulty(far distance from the destination) associated with the high and low price parking fee’s in order to allow to solve early commuting process, congestion and querying due to the inability to meet the demand of parking (see Sun para[0003]).
Regarding claim 18, Akavaram discloses A real-time incentive-based parking system for a vehicle comprising: a parking database (see Akavaram at least para [0060] At step 620, the process receives a preferred parking space location (e.g., GPS coordinates) from intelligent parking system 300), wherein the parking database comprises map data associated with a parking area (see Akavaram at least para [0070] FIG. 9 is an exemplary diagram depicting a vehicle that includes an onboard system and sensors to capture parking space properties of available parking spaces),, wherein the parking area includes a plurality of parking spots(see Akavaram figure 4) , wherein the parking database is in communication with a parking spot (see Akavaram figure 4) ,
identifier, and wherein the parking spot identifier comprises a controller; and the controller, the controller being configured to in real-time:
receive, from the parking database upon the controller entering the parking area, the map data;
determine, based on the parking information (see abstract available parking spaces to factors such as driver skill data corresponding to the driver or vehicle properties corresponding to the vehicle), that the plurality of parking spots of the parking area include an available parking spots;
grade, based on the parking information, a difficulty associated with the available parking spots (see Akavaram para[0038] In addition, intelligent parking system 300 considers the skill level of the driver (included in driver data 315 ). For example, if the driver is a highly skilled driver, intelligent parking system 300 may place a higher weighting over a “tight fitting” parking space close to the target destination over a larger parking space that is further from the target destination.);
determine a skill level of a driver associated with the vehicle and the parking spot identifier ( see Akavaram at least para [0003] the available parking spaces based upon comparing dynamic parking space properties corresponding to the available parking spaces to factors such as driver skill data corresponding to the driver or vehicle properties corresponding to the vehicle, (see Akavaram para[0038] ).
; compare the graded difficulty associated with the available parking spot and the skill level of the driver(see Akavaram para[0002] a driver's skill level contributes to the success or failure of parking the vehicle without damage to the vehicle or surrounding objects, such as other vehicles.)
and, displaying, using the controller and via a display associated with the controller, the selected available parking spot (see Akavaram at least para[0025] Graphics controller 125 connects to display device 130 , such as a computer monitor, displaying the parking spot associated with difficulty and financial incentive is a post processing step which is obvious to add information such as financial incentive with difficulty(congested) parking spot for the encourage).
However, Akavaram does not expressly disclose or otherwise teach wherein the map data comprises parking information and incentive information associated with the plurality of parking spots, determining, using the controller and based on the incentive information, that the available parking spot is associated with a financial incentive. Nevertheless, in a related field of invention, Sun teaches wherein the map data comprises parking information and incentive information associated with the plurality of parking spots (See Sun high-price area, low-price area, abstract the parking space turnover rate of a high-price area and the parking space utilization rate of a low-price area are improved, and the system trip cost is minimized, para[0003] Some low-price areas far from the destination have low parking space utilization., para[0071] The parking charging method proposed by the present invention can induce travelers who have parked for a long time to park in the parking spaces farther from the destination, thereby improving the parking space turnover rate in the high land price area and the parking space utilization rate in the low land price area, so as to minimize the travel cost of the system)
determining, using the controller and based on the incentive information, that the available parking spot is associated with a financial incentive (see Sun’s method provides a higher turnover where the best spots are, Sun para[0003] The current parking space allocation method mostly follows the first-come, first-served (FIFO) principle, that is, travelers who arrive early choose the parking space close to the destination, which may lead to the unreasonable long-term occupation of parking spaces in some areas with high land prices. Some low-price areas far from the destination have low parking space utilization)
selecting, by the controller and based on the comparison of the graded difficulty (See Sun para[0002] At the same time, the shortage of land in big cities, high land prices, and the standard of parking facilities lag behind the growth of car ownership, resulting in parking demand far greater than the supply of parking facilities, resulting in problems such as difficult parking and random parking) and the skill level of the driver (Akavaram’s skilled level of driver) and the determination that the available parking spot is associated with the financial incentive, the available parking spot. (See Sun para[0147] That is to say, the congestion cost of the traveler includes the congestion cost (time cost) of the person (the traveler itself) and the congestion cost of the vehicle , the optimal system achieved here can eliminate the queuing of travelers themselves during early commuting )
wherein the financial incentive is associated with the graded difficulty associated with the available parking spot or a location of the available parking spot in the parking area (See Sun abstract Under the method, a traveler with long parking time can select a parking space far away from a destination, and a user with short parking time can select a parking space near the destination, so that the parking space turnover rate of a high-price area and the parking space utilization rate of a low-price area are improved, and the system trip cost is minimized) and wherein the financial incentive is dynamically updated to facilitate reduced congestion in the parking area; (See Sun para[0018] β is the penalty for arriving early per unit time, γ is the penalty for being late per unit time, t * is the prescribed work time, w is the energy consumption per unit distance of the vehicle, τ is the congestion cost per unit time of the vehicle, and λ is the energy consumption per unit. The value of the fuel needed, p d is the parking time, x(p d ) is the distance between the vehicle parking position and the destination, a is the reduced parking fee per unit distance away from the destination, b is the vehicle parked at the destination (i.e. x(p d )=0) parking fee, σ is the proportional coefficient between the distance between the vehicle parking position and the destination (i.e. x(p d )) and the traveler’s parking time (i.e. p d ) , α is the traveler’s cost per unit time due to queuing delay, which implies that the parking fee’s is the proportional to distance between the vehicle parking position and the destination).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Sun’s parking difficulty(far distance from the destination) associated with the high and low price parking fee’s in order to allow to solve early commuting process, congestion and querying due to the inability to meet the demand of parking (see Sun para[0003]).
Regarding claim 20, Akavaram and Sun remain applies to claim 18. Akavaram discloses wherein grading of the skill level of the driver is based on at least one of: vehicle history of the driver; information provided by the driver; or settings input by the driver (see Akavaram at least para [0067] FIG. 8 is an exemplary diagram depicting a driver device that requests a parking space and receives a preferred parking space location based upon driver data. As discussed earlier, driver device 705 may be a computing device such as a smart phone, laptop, desktop, tablet, onboard vehicle system (e.g., computer system, navigation system), wearable device, or other computing device capable of receiving driver input and providing the driver input to intelligent parking system 300).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatented over Akavaram in view of Sun and JP2023007850A Iwata (herein after “Iwata”).
Regarding claim 19, Akavaram and Sun remain applies to claim 18. However, Akavaram does not expressly disclose or otherwise teach wherein the parking spot identifier is configured to transmit a request to a device that the driver receives the financial incentive. Nevertheless, in a related field of invention, Iwata teaches wherein the parking spot identifier is configured to transmit a request to a device that the driver receives the financial incentive. (see Iwata para[0083] Further, in the above-described embodiment, as an incentive given to the user, a discount price for a parking lot has been described as an example, but the content of the incentive is not particularly limited to the discount price).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Iwata’s incentive information associated with parking spot to improve management efficiency (see Iwata para[0003]).
Claims 21 -23 are rejected under 35 U.S.C. 103 as being unpatented over Akavaram in view of Sun and US 20200242924 A1 to Publicover et al. (herein after “Publicover”).
Regarding claim 21, Akavaram and Sun remain applies to claim 1. Akavaram teaches further comprising: receiving, via the display associated with the controller, and after displaying the selected available parking spot, a first an input associated with a request (see Publicover request processor 111 and para[0091]and [0092] ) for an alternative available parking spot of the plurality of parking spots (see Akavaram para[0068] In one embodiment, the driver then selects button 830 to send a request to intelligent parking system 300, which instructs intelligent parking system 300 to search for an available parking space in proximity to the target destination based upon the driver's data discussed herein. In another embodiment, driver device 705 automatically sends a parking space request to intelligent parking system 300 when the driver enters a target destination meeting particular criteria such as a location within the driver's city limits) that is different form the selected available parking spot ( see publicover pars[0091]-[0092] based on the queue for the request, the parking space reservation update a parking space reservation of bundled parking space allocations or create a new one).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Publicover’s financial incentive associated with parking difficulty and updated financial incentive to reduce congestion in order to allow to control their speed to avoid arriving at the traffic control device until it permits the passage of traffic, thus avoiding stopping, idling and reaccelerating when reaching the traffic control device (see Publicover abstract).
Regarding claim 22, Akavaram , Sun and Publicover remain applies to claims 1 and 21. Akabvaram teaches further comprising: identifying, by the controller and based on the parking information, a plurality of available parking spots, including the available parking spot (see Akavaram abstract intelligent parking system identifies available parking spaces and selects a preferred parking space from the available parking spaces based upon comparing dynamic parking space properties corresponding to the available parking spaces) and the alternative available parking spot, of the plurality of parking spots (see Akavaram figure 3 Search area 340, para[0036] The example in FIG. 3 shows that search area 340 includes the drivers target destination 350, parking lots 360, 370, 380, and possible street side parking along the roads); and displaying, using the controller and via the display associated with the controller, the plurality of available parking spots (See Akavaram para[0041] FIG. 4 is an exemplary diagram depicting an intelligent parking system receiving parking availability data from mobile devices. FIG. 4 depicts various mobile devices capturing parking availability data within parking lot 360., figure 4).
Regarding claim 23, Akavaram, Sun and Publicover remain applies to claims 1 and 21. Nevertheless, Publicover same field of endeavor teaches wherein displaying the plurality of available parking spots comprises displaying a plurality of graphical indicators associated with the plurality of available parking spots(see Publicover paras[0091], [0138]), the plurality of graphical indicators indicating a graded difficulty associated with each of the plurality of available parking spots, including the graded difficulty of the selected available parking spot (see Publicover 7,8 and 9); and
wherein the method further comprises receiving, via the display associated with the controller, a second input associated with a selection of the alternative available parking spot. (Publicover teaches parking space allocation transmits target location, calculate an arrival time for prospective parking location (can be consider as selected parking since prospective parking location is expected to empty with the vicinity) and also calculate arrival time for alternative location. (para[0007]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Publicover’s arrival time with Akavaram’s display unit in order to allow to control their speed to avoid arriving at the traffic control device until it permits the passage of traffic, thus avoiding stopping, idling and reaccelerating when reaching the traffic control device (see Publicover abstract).
Claims 8-9 and 15 are rejected under 35 U.S.C. 103 as being unpatented over Akavaram in view of Sun and EP2479538A2 of Haleem (herein after” Haleem”).
Regarding claim 8, Akavaram and Sun remain applies to claim 1. Akavaram teaches Intelligent Parking Space Identification system. However, Akavaram does not teach skill level of driver determined based on grade (novice, intermediate and expert). Nevertheless, Haleem – same field of endeavor teaches wherein the skill level of the driver is determined to be one of: a novice, an intermediate, or an expert; wherein the skill level of the novice, the intermediate, or the expert is based on a parking skill of the driver; wherein the skill level of the novice is less than the skill level of the intermediate; and wherein the skill level of the intermediate is less than the skill level of the expert ( see Haleem at least para [0139] The profile module 602 receives driver experience information about the driver of the driver's vehicle 204 of FIG. 2 for generating the maneuver instruction 212 of FIG. 2 . For example, the navigation system 100 can classify a driver as "beginner", "intermediate", or "expert" based on the driver's performance in prior driving sessions) (see Haleem at least para [0039] [0039] A brand new driver, an intermediate level driver, and an expert driver can receive a different version of the maneuver instruction 212 with different content that is customized to the driver's experience level).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Haleem’s grading of driver skill to make the system more efficient and significant.
Regarding claim 9, Akavaram and Sun remain applies to claim 1. Akavaram teaches Intelligent Parking Space Identification system. However, Akavaram does not teach grade the parking space based on skill level of driver (easy, medium, or difficult). Nevertheless, Haleem – same field of endeavor teaches wherein a grade of each of the one or more available parking space is determined to be one of: easy, medium, or difficult; wherein the grade of each of the one or more available parking spaces is based on difficulty to park in the corresponding parking space; wherein the grade of easy is less than the grade of medium; and wherein the grade of medium is less than the grade of difficult such that the grade of easy corresponds to the skill level of the novice, the grade of medium corresponds to the skill level of the intermediate, and the grade of difficult corresponds to the skill level of the expert (See Haleem at least para [0057] A maneuver difficulty 308 is defined as a difficulty rating for the driving maneuver 206. For example, the maneuver difficulty 308 for the driving maneuver 206 of FIG. 2 can be rated or classified as "hard", "medium", and "easy"); (para[[0060] The maneuver difficulty 308 of the three-way stop 306 can be rated or classified as "easy" because the driver's vehicle 204 is protected by the stop sign when turning left. The driving maneuver 206 of parking in a slanted parking spot can also be rated as "easy" for the maneuver difficulty 308 because the maneuver requires less skill to complete than a straight parking spot. The maneuver difficulty 308 of the driving maneuver 206 can also be based on user-generated ratings for the driving maneuver 206 and historical statistics about the driving maneuver 206)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Haleem’s grading of parking spot based on driver skill to make the system more efficient and significant.
Regarding claim 15, Akavaram and Sun remain applies to claim 11. Akavaram teaches Intelligent Parking Space Identification system. However, Akavaram does not teaches display 2D map of the area. Nevertheless, Haleem -same field of endeavor teaches wherein the display comprises a 2-D map of the area and graphical indicators on the 2-D map, wherein the graphical indicators comprise at least one of: available parking spots; the one or more selected parking spots; parking spots with an energy transfer station; or parking spots with a financial incentive (see Haleem at least para [0017] One skilled in the art would appreciate that the format with which navigation information is expressed is not critical to some embodiments of the invention. For example, in some embodiments, navigation information is presented in the format of (X, Y), where X and Y are two ordinates that define the geographic location, i.e., a position of a user).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Haleem’s 2D graphical display to make the system more efficient and accessible.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatented over US20160117925 of Akavaram (herein after “Akavaram”) in view of Sun , Haleem and JP2017090968A of Takenaka (hereinafter “Takenaka”).
Regarding claim 10, Akavaram, Sun and Haleem remain applies to claims 8 and 9. Akavaram teaches Intelligent Parking Space Identification system. However, Akavaram does not teach the grade of parking spot of the parking area must be equal or less than the skill of the driver. Nevertheless, Takenaka -same field of endeavor teaches wherein in order to be selected as the selected parking spot, the grade of the one or more available parking spots of the parking area must be equal to or less than the skill level of the driver (see at least para[0056] where the degree of parking difficulty is three or more, the control unit 31 may determine the parking area 51 where the degree of difficulty of parking is equal to or less than the predetermined difficulty as the easy parking area).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Takenaka’s grading of parking spot based on driver skill to make the system more efficient and significant.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatented over US20160117925 of Akavaram (herein after “Akavaram”) in view of Sun and US 20210018587A1 of Harris et al. (herein after “Harries”).
Regarding claim 14, Akavaram and Sun remain applies to claim 11. Akavaram teaches Intelligent Parking Space Identification system. However, Akavaram does not teach the display in 3D maps. Nevertheless, Harris- same field of endeavor teaches wherein the display shows a 3-D map of the parking area (see Harris at least para [0020] the device 26 may then display the three-dimensional map 28 containing the location of the MaaS asset 14 to the user 24 such that the user 24 can find the MaaS asset 14 and utilize the MaaS asset 14; Figure 3)
It would have been obvious to one of ordinary skilled in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Harris’s three-dimensional display to make the system more efficient and significant.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatented over US20160117925 of Akavaram (herein after “Akavaram”) in view of Sun and US 20160061618 A1 to Benenson et al. (herein after “Benenson”).
Regarding claim 25, Akavaram and Sun remain applies to claim 1. Akavaram teaches receiving, via the controller, a first input associated with a plurality of driver preferences (See Akavaram para[0059]). However, Akavaram does not expressly discloses or otherwise teaches receiving, via the display associated with the controller (See publicover display[0091],[0138]) and after displaying the selected available parking spot, a second input associated with a request for an alternative available parking spot of the plurality of parking spots that is different from the selected available parking spot (Publicover teaches parking space allocation transmits target location, calculate an arrival times for prospective parking location (can be consider as selected parking since prospective parking location is expected to empty with the vicinity) and also calculate arrival time for alternative location. (para[0007])
displaying, using the controller and via the display associated with the controller, the selected alternative available parking spot (Akavaram display).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Publicover’s displaying the selected available parking spot, request for alternative available parking spot in order to allow to control their speed to avoid arriving at the traffic control device until it permits the passage of traffic, thus avoiding stopping, idling and reaccelerating when reaching the traffic control device (see Publicover abstract).
However, Akavaram does not expressly discloses or otherwise teaches determining, via the controller and based on the driver preferences, that the alternative available parking spot is not suitable for selection; receiving, via the controller and in response to the determination that the alternative available parking sport is not suitable for selection, a second input associated with updating the plurality of driver preferences; selecting, by the controller and based on the updated plurality of driver preferences, the alternative available parking spot. Nevertheless, Benenson same field of endeavor teaches wherein selecting the available parking spot is further based on the plurality of driver preferences (see Benenson [0189] Block 83 Selects the portion which accumulates the maximal number of expected vacant parking places PP; that portion will constitute the current portion of the best parking route(s));
determining, via the controller and based on the driver preferences, that the alternative available parking spot is not suitable for selection (See Benenson para[0012] an alternative best parking route satisfying an alternative set of parking preferences)
;receiving, via the controller and in response to the determination that the alternative available parking sport is not suitable for selection, a second input associated with updating the plurality of driver preferences; (see Benenson para[0090] user's parking preferences and update said DB accordingly.)
selecting, by the controller and based on the updated plurality of driver preferences, the alternative available parking spot (see Benenson para[0090] user's parking preferences and update said DB accordingly.)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Akavaram’s Intelligent Parking Space Identification system with Benenson’s updating driver’s preferences based on the non-suitable alternative parking location in order to allow to propose a novel technique of providing guidance to a driver (see Benenson para[0012]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NAZIA AFRIN/ Examiner, Art Unit 3666
/SCOTT A BROWNE/ Supervisory Patent Examiner, Art Unit 3666
