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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination (RCE) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after allowance. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 12/27/2024 has been entered.
Status of the Claims
Claims 1-12 of U.S. Application No. 17/809,404 filed on 06/28/2022 have been examined. Examiner filed a non-final rejection on 06/05/2024.
Applicant filed remarks and amendments on 08/08/2024. Claims 1-5, 7 and 10-12 were amended and claim 9 was cancelled. Claims 1-8 and 10-12 have been examined. Examiner filed a final rejection on 11/05/2024.
Applicant filed an RCE on 12/27/2024. Claims 1 and 10-12 were amended. Claims 1-8 and 10-12 have been examined. Examiner filed a non-final rejection on 04/04/2025.
Applicant filed remarks and amendments on 06/24/2025. Claim 1 was amended and claims 1-9, 11-12 were cancelled and claims 13-14 were newly added. Claims 10 and 13-14 are presently pending and presented for examination.
Response to Arguments
Regarding the claim rejections under 35 USC 103: Applicant's arguments filed 06/24/2025 with respect to Takato (US 20210150422 A1) in view of PANG et al. (US 20240092344 A1) and in further view of Vander Helm et al. (US 10262467 B2) have been fully considered but they are not persuasive.
Applicant argues that, The cited references fail to disclose features related to ridesharing, particularly the feature where exit requests specifying destinations in the same direction and the same exit time zone are received from a plurality of users that uses an automatic parking service and a ride share service, and an alternative solution proposal unit proposes carpooling to each of the users when the congestion degree of the platform is high.
However, the examiner respectfully disagrees this argument is not persuasive. Takato discloses a system for managing automatic valet parking and includes techniques for usage of a boarding and alighting area having a low degree of congestion, which can be extended to include ride-sharing scenarios. Additionally, PANG [0055]) describes a platform that proposes carpooling based on user requests and congestion levels. When combined, these teachings suggest an automatic parking service integrated with a ride-sharing service, where carpooling is proposed based on congestion, as claimed.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 10 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Takato (US 20210150422 A1) in view of PANG et al. (US 20240092344 A1) and in further view of Vander Helm et al. (US 10262467 B2), hereinafter referred to as Takato, PANG and Vander respectively.
Regarding claims 10, Takato discloses An automatic entry-exit system comprising an entry-exit control server that controls entry and exit so as to provide an automatic parking service that causes a vehicle that has arrived at a platform to enter one parking space among a plurality of the parking spaces by autonomous driving and that causes the vehicle parked in the parking space to exit to the platform by autonomous driving (“ The management device is configured to manage automated valet parking in which, in a parking facility having a plurality of boarding and alighting areas, entry of a vehicle from a used boarding and alighting area selected from the boarding and alighting areas to a parking space of the parking facility or exit of the vehicle from the parking space to the used boarding and alighting area is performed through autonomous traveling of the vehicle.” [0006]),
determine, when exit requests specifying destinations in the same direction and the same exit time zone are received from a plurality of users that uses an automatic parking service and a ride share service (“ The method includes: a reception step of receiving facility usage information including a scheduled time of entry or exit of the vehicle by the automated valet parking; an acquisition step of acquiring facility management information indicating a congestion status of the parking facility at the scheduled time of entry or exit, based on the facility usage information; a generation step of generating boarding and alighting area candidate information in which usage incentive information corresponding to the congestion status is provided for each of candidates of the boarding and alighting areas, based on the facility management information; and a display step of displaying the boarding and alighting area candidate information on a display device of a terminal device associated with the vehicle.” [0024]), a congestion degree of the platform in the exit time zone for which the exit requests are received (“The disclosure provides a management device, a management system, and a management method for automated valet parking that can guide, in a parking facility offering an automated valet parking service and having a plurality of boarding and alighting areas, a user to a recommended boarding and alighting area depending on a congestion status of the parking facility.” [0005]),
and propose carpooling to each of the users when the congestion degree of the platform is high, and wherein the entry-exit control server is configured to control the vehicle based on the proposal (“The usage incentive here is an incentive provided to the usage of the applicable boarding and alighting area. Examples of the usage incentive include proposal of reducing the usage fee related to the automated valet parking service, or an offer of fee discount coupons for the next usage of the automated valet parking service, discount coupons and various points that can be used at stores in the commercial facility 2. The management device 22 transmits, to the user terminal 30, information in which the usage incentive information including the presence/absence of the usage incentive and content of the incentive is provided for each of candidates of the boarding and alighting areas 6. In the following description, this information is referred to as “boarding and alighting area candidate information”. The user refers to the usage incentive information included in the boarding and alighting area candidate information displayed on the display device 34, and selects, from the boarding and alighting areas 6, a used boarding and alighting area that the user desires to use.” [0045]);
wherein the congestion degree is predicted based on at least scheduled numbers of entries and exits per unit time and a day of a week, and the prediction is further performed using a congestion degree prediction model created based on past data, and a dataset that comprises a list of basic parameters that directly affect the congestion degree, auxiliary parameters that have a large effect on the congestion degree, and the actual congestion degree (“Examples of such facility management information include the number, the density, etc. of people in each of the first area 201 and the second area 202 at the scheduled time of entry or exit. In the commercial facility 2, a plurality of motion sensors and surveillance cameras are installed. The acquisition unit 222 of the management device 22 analyzes past data obtained from the sensors and the cameras and sorted by time, so as to acquire the facility management information at the scheduled time of entry or exit. The acquired facility management information is stored in the memory of the management device 22.” [0074] and “The management device 22 stores, in the memory, various usage incentives and basic information such as position information of the boarding and alighting areas 6. Based on the facility management information, the generation unit 224 generates the boarding and alighting area candidate information in which the usage incentive information is provided for information of each candidate of the boarding and alighting areas 6. Typically, when the facility management information is information indicating the degree of congestion of each boarding and alighting area 6 at the scheduled time of entry or exit, the generation unit 224 generates the boarding and alighting area candidate information such that the usage incentive provided for the second boarding and alighting area 62 having a low degree of congestion is more favorable than the usage incentive provided for the first boarding and alighting area 61 having a high degree of congestion” [0051]);
Takato does not explicitly teach wherein the entry-exit control server includes a processor configured to execute program instructions that cause the entry-exit control server to
and wherein the prediction model is created using a neural network comprising an input layer, a hidden layer, an output layer and a softmax layer
However, PANG does teach wherein the entry-exit control server includes a processor configured to execute program instructions that cause the entry-exit control server to (“As shown in FIG. 8, the server 800 may vary greatly due to different configurations or performances. The server 800 may include one or more central processing units (CPUs) 822 (for example, one or more processors) and a memory 832, and one or more storage media 830 (for example, one or more mass storage devices) storing an application program 842 or data 844. The memory 832 and the storage medium 830 may perform temporary storage or persistent storage. The program stored in the storage medium 830 may include one or more devices (not shown in FIG. 8), and each of the devices may include a series of instruction operations in the server. Further, the central processor unit 822 may be configured to communicate with the storage medium 830 and perform, on the server 800, the series of instruction operations stored in the storage medium 830. “ [0088] and “After training the parking space detection model, the server 101 transmits model parameters of the parking space detection model to the parking system 102 of the vehicle, and the parking system 102 may detect a parking space by using the parking space detection model.” [0032]):
and wherein the prediction model is created using a neural network comprising an input layer, a hidden layer, an output layer and a softmax layer (“Therefore, a method for detecting a parking place and a direction angle of the parking place is provided according to the embodiments of the present disclosure. With the method, parking space detection is performed by using a pre-trained parking space detection model. The detection of a parking space and the detection of a direction angle of the parking space are fused into one deep neural network, improving computational performance. In addition, the type of the parking space and the direction angle of the parking space are detected simultaneously, it is only required for the parking space classification network to determine whether the parking space is a real parking space without determining the type of the parking space. The type of the parking space is determined by the assistance of the direction angle of the parking space.” [0069] and “ For example, the weight of the regression loss may be set to 1. In the loss function of the parking space detection model, the classification loss may be expressed as a classification loss function such as softmax, and the regression loss may be expressed as a regression loss function L_regression, such as an absolute value loss L1 loss, a square loss L2 loss, or a smoothed mean absolute error loss Huber loss.” [0048]). Both Takato and PANG teach methods for autonomous vehicle parking management. However, only PANG explicitly teaches wherein the entry-exit control server includes a processor configured to execute program instructions and wherein the prediction model is created using a neural network comprising an input layer, a hidden layer, an output layer and a softmax layer.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle parking management method of Takato to also include the entry-exit control server includes a processor configured to execute program instructions and wherein the prediction model is created using a neural network comprising an input layer, a hidden layer, an output layer and a softmax layer, as in PANG. Doing so improves safety and efficiency of automated parking services (With regard to this reasoning, see at least [PANG, 0003-0005]).
Takato in view of PANG does not explicitly teach wherein the auxiliary parameters include a day of the week, weather forecasts and scheduled events
and wherein the number of scheduled entries, the number of scheduled exits, empty parking spaces, the day of the week, weather forecasts and scheduled events are input to the input layer
However, Vander does teach wherein the auxiliary parameters include a day of the week, weather forecasts and scheduled events (Vander teaches “Illustrated in FIG. 3 is one aspect of a real-time parking availability system 10 of the present invention. Notably, a plurality of individual parking spaces 12 in the real-time parking availability system 10 may be managed and sold as individual units, rather than as in groups of spaces (e.g., bays) as managed and sold in traditional parking models. These individual parking spaces 12 may be marketed and priced separately in real-time using dynamic market conditions. These market conditions may include: demand; location of the parking space; walking distance to street level, stairwell, skywalk, elevator, or attached businesses; time of day; day of the week; holidays; special events; number of car occupants; personal safety; convenience; age of the parking consumer; weather conditions; and/or combinations thereof.” [Col.4 ln 39-53]);
and wherein the number of scheduled entries, the number of scheduled exits, empty parking spaces, the day of the week, weather forecasts (Vander teaches “These individual parking spaces 12 may be marketed and priced separately in real-time using dynamic market conditions. These market conditions may include: demand; location of the parking space; walking distance to street level, stairwell, skywalk, elevator, or attached businesses; time of day; day of the week; holidays; special events; number of car occupants; personal safety; convenience; age of the parking consumer; weather conditions; and/or combinations thereof.” [Col.4 ln 39-53]), and scheduled events are input to the input layer (Vander teaches “The server processor is operable to perform actions under control of electronic program instructions, including processing/executing instructions and managing the flow of data and information through the parking availability system 10.” [Col.9 ln 30-45]). Both Takato in view of PANG and Vander teach methods for autonomous vehicle parking management. However, Vander explicitly teaches wherein the auxiliary parameters include a day of the week, weather forecasts and scheduled events and wherein the number of scheduled entries, the number of scheduled exits, empty parking spaces, the day of the week, weather forecasts and scheduled events are input to the input layer.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle parking management method of Takato in view of Vander to also include wherein the auxiliary parameters include a day of the week, weather forecasts and scheduled events and wherein the number of scheduled entries, the number of scheduled exits, empty parking spaces, the day of the week, weather forecasts and scheduled events are input to the input layer, as in Vander. Doing so improves safety and efficiency of automated parking services (With regard to this reasoning, see at least [Vander, Col.1]).
Regarding claims 13, Takato discloses The automatic entry-exit system of claim 10, wherein when an actual entry- exit time zone is significantly delayed from the entry-exit time zone, the processor proposes to the user to change the entry-exit time zone specified by the user to an entry-exit time zone with a low congestion degree (“The management device 22 stores, in the memory, various usage incentives and basic information such as position information of the boarding and alighting areas 6. Based on the facility management information, the generation unit 224 generates the boarding and alighting area candidate information in which the usage incentive information is provided for information of each candidate of the boarding and alighting areas 6. Typically, when the facility management information is information indicating the degree of congestion of each boarding and alighting area 6 at the scheduled time of entry or exit, the generation unit 224 generates the boarding and alighting area candidate information such that the usage incentive provided for the second boarding and alighting area 62 having a low degree of congestion is more favorable than the usage incentive provided for the first boarding and alighting area 61 having a high degree of congestion” [0051]).
Regarding claims 14, Takato discloses The automatic entry-exit system of claim 10, wherein the congestion degree is predicted based on at least scheduled number of entries and exits per unit time and a day of a week, respectively(“The facility management information includes, for example, a scheduled number of entering or exiting vehicles at each boarding and alighting area 6 at the scheduled time of entry or exit, a ratio of the scheduled number of entering or exiting vehicles to the number of available parking spaces associated with each boarding and alighting area 6 at the scheduled time of entry or exit, a distribution of parked vehicles in each parking area of the parking lot 4 at the scheduled time of entry or exit, and the like.” [0044]).
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED ALKIRSH whose telephone number is (703) 756-4503. The examiner can normally be reached M-F 9:00 am-5:00 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FADEY JABR can be reached on (571) 272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
AHMED ALKIRSHExaminer, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668