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 under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/07/2026 has been entered.
Status of Claims
This is a Non-Final Action in response to the communications filed on 01/07/2026.
Claim 14 has been amended;
Claims 1 – 6, and 8 – 13, have been cancelled;
Claims 21 – 27, are new claims;
Claims 14 – 16, and 19 – 27, are currently pending in this application.
Response to Remarks
Examiner’s Response to Remarks
B. The claims recite patentable subject matter;
C. Independent claim 14 is allowable over proposed Seagraves-Ran combination.
Examiner’s response to B. The claims recite patentable subject matter.
Applicant argues claims 14 – 16, 19, and 20 are not directed to an abstract idea under Prong One of the revised Step 2A analysis.
Claim 14 is certain methods of organizing human activity as the claim merely recites insignificant extra solution activity. For example the claim merely gathers data and transmit signals where the claim recites determining that a rider is traveling to a destination; estimating an arrival time of the rider to the destination; based upon predicted probabilities using a flow rate, determining availability for docking; responsive to determining the flow rate satisfies a criteria selecting a station; and automatically transmitting control signals. However, this is merely gathering data and transmitting that data over a network and amounts to insignificant extra-solution activity. Accordingly, claim 14 recites certain methods of organizing human activity.
The additional elements of claim 14 are a computing system, GPS signals, a client device, a rider, a map, a user interface, locking mechanism, sensor signals, control signals, automatically transmitting, via a wireless network, a personal mobility vehicle, geographic region, and a docking station are generic computer components and do not integrate the judicial exception into a practical application, as the claim recites the additional elements at a high level of generality and thus are mere instructions to apply the judicial exception. The claim does not recite additional elements individually nor in combination that amount to significantly more than the judicial exception, as the additional elements merely utilize an intermediary computer to forward information where the claim limitations are gathering data and transmitting the data. There is no technological improvement here. This is merely instructions to implement the judicial exception on a computer, and the claim is not eligible under 35 U.S.C. § 101. The dependent claims are rejected by virtue of depending on the independent claim. Accordingly, all pending claims are rejected under 35 U.S.C. § 101.
Examiner’s Response to C. Independent Claim 14 is allowable over the proposed Seagraves-Ran combination.
Applicant argues claim 14, under 35 U.S.C. § 103(a), is patentable over WO Publication No. 2019/177620 "Seagraves" in view of U.S. Patent No. 6,317,686 "Ran".
Examiner respectfully disagrees. Applicant’s claim 14 is unpatentable over Seagraves in view of Ran. Although, Applicant has amended claim 14 to recite "automatically transmitting, via a wireless network, control signals to the locking mechanism associated with the first docking station that is available to lock out the first docking station from use by other personal mobility vehicles upon the estimated arrival time of the rider," Seagraves teaches this claim limitation. For example, Seagraves teaches in ¶ 0035, the detector on the bicycle may be a proximity based sensor, which may detect a signal-based token within range and automatically unlock the bicycle. In addition, Seagraves further teaches authenticating the request and transmit instructions to the bicycle to be unlocked. Seagraves teaches in ¶ 0038, receiving the data via wireless network; as well as the compute environment 306 may include one or more servers with one or more processors and storage elements for storing and processing the data received from the bicycle stations and performing various functions utilizing the data, such as authenticating a user based on provided credentials, performing transactions, recording and analyzing bicycle usage data, tracking a location of a bicycle, among other computer functions. Thus the bicycle station nor the bicycle will be available or unlocked except where authenticated by the bicycle station and user. Ran cures any deficiencies of Seagraves. Accordingly, claim 14 is unpatentable over Seagraves in view of Ran and all pending claims are rejected under 35 U.S.C. § 103.
Claim Rejections – 35 U.S.C. § 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 14 – 16, and 19 – 27, are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
determining that a rider is traveling to a destination;
estimating an arrival time of the rider to the destination;
based upon predicted probabilities using a flow rate, determining availability for docking;
responsive to determining the flow rate satisfies a criteria, selecting a station;
and automatically transmitting control signals
Claim 1 recites certain methods of organizing human activity, and particularly business relations where the claim involves commercial interactions between a human and a computer. For example the claim merely gathers data and transmit signals where the claim recites determining that a rider is traveling to a destination; estimating an arrival time of the rider to the destination; based upon predicted probabilities using a flow rate, determining availability for docking; responsive to determining the flow rate satisfies a criteria, selecting a station; and automatically transmitting control signals. However, this is merely gathering data and transmitting that data over a network and amounts to insignificant extra-solution activity. Accordingly, claim 14 recites certain methods of organizing human activity.
The dependent claims encompass the same abstract ideas as well. For instance, claim 15 is directed towards observing the flow rate; claim 16 is directed towards sending instructions for presenting a notification; claim 19 is directed towards evaluating based on a user profile associated with the rider, the rider is eligible for reserving a docking station from the evaluated docking stations; claim 20 is directed towards observing the predicted probabilities are evaluated further based on contextual data associated with the rider; claim 21 is directed towards receiving a request to dock, and transmitting a signal; claim 22 is directed towards observing a flow rate; claim 23 is directed towards observing a docking location; claim 24 is directed towards evaluating a number of docking stations, and observing a number of docking locations; claim 25 is directed towards observing a route to the docking location; claim 26 is directed towards evaluating a level of activity with the destination, and observing an element corresponding to the level of activity; and claim 27 is directed towards evaluating predicted probabilities based on contextual data. Thus, the dependent claims further limit the abstract ideas.
These judicial exceptions are not integrated into a practical application. Claim 14 recites the additional elements of a computing system, GPS signals, a client device, a rider, a map, a user interface, locking mechanism, sensor signals, control signals, automatically transmitting, via a wireless network, a personal mobility vehicle, geographic region, and a docking station. However, these are all generic computer components performing generic computer functions as per Applicant’s Specification shown below:
[90] This disclosure contemplates any suitable number of computer systems 1100. This disclosure contemplates computer system 1100 taking any suitable physical form. As example and not by way of limitation, computer system 1100 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented/virtual reality device, or a combination of two or more of these. Where appropriate, computer system 1100 may include one or more computer systems 1100; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, one or more computer systems 1100 may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example and not by way of limitation, one or more computer systems 1100 may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems 1100 may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.
and thus are not practically integrated nor significantly more.
Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., processor). See MPEP 2106.05(f). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception, as the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea, and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Thus, the claims are directed to an abstract idea.
Dependent claims 15 – 16, and 19 – 27, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 14 – 16, and 19 – 27 are not patent eligible under 35 U.S.C. § 101.
Claim Rejections – 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 14 – 16, and 19 – 27, are rejected under 35 U.S.C. § 103 as being unpatentable over Seagraves, Jamel et al. (WO 2019/177620) in view of Ran, Bin (U.S. Patent No. 6,317,686).
Claim 14:
determining, based on GPS signals from a personal mobility vehicle, that a rider associated with the personal mobility vehicle is traveling to a destination; Seagraves teaches in ¶ 0023, a user may want to return their bicycle to a station when they arrive at their destination but find that the station is full and has no docking spots available. Seagraves teaches in ¶ 0033, the GPS unit tracks the geographic location of the bicycle, allowing the current location as well as a travel path of the bicycle to be known;
estimating an arrival time of the rider to the destination; Seagraves teaches ¶ 0055, estimated arrival time may be determined;
associated with at least one docking station associated with the destination, determining that a plurality of docking stations within a threshold distance of the destination and during the estimated arrival time will be available for docking the personal mobility vehicle, wherein the flow rate is calculated based on (i) an outflow of personal mobility vehicles unlocked from the at least one docking station, determined based on sensor signals from a locking mechanism of the at least one docking station, and (ii) an inflow of personal mobility vehicles locked to the at least one docking station, determined based on sensor signals from the locking mechanism of the at least one docking station; Seagraves teaches in ¶ 0017, providing an intelligent networked bicycle sharing system that is instrumented with specialized sensors; Seagraves teaches in ¶ 0043, a current number of available docking spots at each docking station may be displayed and a route to the best docking station may be provided on the map view. The best docking station may be determined based on proximity to the selected location and a docking spot availability prediction for the docking station. In some embodiments, the docking spot availability prediction based be determined using a neural network trained on historical docking spot availability data across various times and locations. The docking spot availability prediction may also take into consideration currently observed conditions, such as a number of currently available spots and a number of users currently enroute to the location either to dock a vehicle in a docking spot or remove (e.g., check out) a vehicle from a docking spot. In some embodiments, as the user is enroute to the location, the current conditions may change, and the docking sport availability prediction may be recalculated based on the updated current conditions. Thus, in some embodiments, the optimal docking station for the user may change and the map view is updated to direct the user to the updated optimal docking station. Seagraves teaches in ¶ 0044, providing resource availability predictions for a vehicle sharing environment, in accordance with various embodiments. Resource may refer to vehicles, docking spots, or any other such resources that may have an available state and an unavailable state. In various embodiments, a user device may be used by a user to request and obtain a resource availability prediction or recommendation for a desired time and location. Seagraves teaches in ¶ 0053, an individual vehicle docking spot associated with one of a plurality of locations and having either an available state or an unavailable state at a given time and is likened to personal mobility vehicles unlocked and locked; Seagraves teaches in ¶ 0047, the prediction model includes probabilistic models. Seagraves teaches in ¶ 0051, the resource availability prediction will be based on the time at which the user is expected to arrive at the location. Seagraves teaches above in claim 1, a user operating the user device selects a certain time and a certain location for which they'd like to check out a vehicle, and thus checks the availability prediction. Flow rate is taught above in claim 1.
selecting a first docking station from the determined plurality of docking stations that is to be reserved for the rider; Seagraves teaches in ¶ 0044, a selection of the type of resource (e.g., vehicle or docking spot); Seagraves teaches in ¶ 0044, a selection of the desired time and location the user would like to utilize the resource, among other options and information where a user is likened to rider;
wherein the predicted probability associated with the first docking station indicates the first docking station is available for docking the personal mobility vehicle upon the estimated arrival time of the rider; Seagraves teaches in ¶ 0015, a user may request, using a user device (e.g., smartphone), a bicycle availability prediction for a certain time and at a certain location. Upon receiving the request, the time and location may be processed using a trained model to determine the bicycle availability prediction for that time and location. The bicycle availability prediction may be further processed and presented to the user in a variety of forms. For example, the bicycle availability prediction may be presented as a likelihood of there being at least one bicycle available at that time and location. In another example, the bicycle availability prediction may be presented as an estimated number of bicycles that will be available at that time and location. Additionally, in various embodiments, a recommendation can be made to the user to optimize their chance of reserving a bicycle. For example, if the bicycle availability prediction for the requested time and location is relatively low or unfavorable, the recommendation may include a suggestion for an alternate time (e.g., 20 minutes later than the initially requested time) or alternate location (nearby location) that has a more favorable availability prediction. In various embodiments, the bicycle sharing system may also include a finite number of docking spots. Thus, the bicycle sharing system is also able to predict availability of docking spots based on historical data, and provide recommendations, using similar techniques as described above with respect to predicting availability of bicycles.
and automatically transmitting, via a wireless network, control signals to the locking mechanism associated with the first docking station that is available to lock out the first docking station from use by other personal mobility vehicles upon the estimated arrival time of the rider; Seagraves teaches in ¶ 0017, various other features and application can be implemented based on, and thus practice, the above described technology and presently disclosed techniques. Accordingly, approaches in accordance with various embodiments improve the technology of bicycle sharing systems. Traditional bicycle sharing technology includes mechanisms for checking out (e.g., unlocking) bicycles from docking stations based user authentication or payment authentication. The present disclosure provides an intelligent networked bicycle sharing system that is instrumented with specialized sensors, network interfacing devices, and other electronics that enable users to receive up to date information and even future availability predictions that can enable them to better plan their commute. Various other applications, processes, and uses are presented below with respect to the various embodiments, each of which improves the operation and performance of the computing device(s) on which they are implemented. Seagraves teaches in ¶ 0030, a compute environment 214 may receive the data and the metadata collected from bicycle stations via the one or more networks 204. The at least one network 204 can include any appropriate network, including an intranet, the Internet, a cellular network, a local area network (LAN), or any other such network or combination, and communication over the network can be enabled via wired and/or wireless connections. Seagraves teaches in ¶ 0035, a bicycle 302 may include an interface, such as a human-machine interface that may include a combination of user interfacing components, such as a keypad or touch screen through which a user may enter credentials (e.g., username, password, pin number). In some embodiments, the credentials may be in the form of biometric data such as fingerprint, retina scan, and the like. In some embodiments, the bicycle 302 may include detectors or readers for accepting cards (e.g., credit cards, debit cards, account cards, or other types of membership/identification cards) or other signal-based tokens (e.g., key fob, smart phone, wearable device, RFID devices). The detectors or readers on the bicycle 302 may include near-field communication (NFC) readers, Bluetooth, among various other wireless communication interfaces and devices. The interface on the bicycle 302 may enables user to unlock or otherwise check out a bicycle by performing one or more actions, such as entering account information, swiping, tapping, or holding a card or at the card reader, presenting a smart phone or other user device, among others. If the user is successfully authenticated, the bicycle 302 may be unlocked and the user can use the bicycle. In some embodiments, the detector on the bicycle 302 may be a proximity based sensor, which may detect a signal-based token within range and automatically unlock the bicycle. The identity of the user may also be identified through the token. Seagraves teaches in ¶ 0036, the bicycles may include a wireless communication interface that does not include human interfacing components. Rather, in certain such embodiments, the bicycles 302 may communicate with a user device 308 through a wireless communication protocol. In other such embodiments, the bicycles 302 may communication with a compute environment 306 over the one or more networks 304 rather than directly with the user device 308. For example, the user device may include a mobile device carried by a user. The user device 308 may have a specific software application (i.e., “app”) installed thereon for providing a user interface between the user and the bicycles 302. The user may perform certain actions on the user device through the app to check out and/or check in a bicycle. In some embodiments, the app may be associated with an account for the user and/or be connected to a form of payment such as credit card credentials (e.g., credit card number) or bank account credentials (e.g., account number, routing number), or other third party payment platforms. In some embodiments, authentication and user identification may be performed passively, such as through proximity based sensing. For example, a device may emit a user carrying such a device may approach a bicycle station, and when the device is within a signal detection range of the bicycle station, the bicycle station may detect the device and receive a signal emitting from the device. The signal may include authentication parameters, thereby causing the user to be authenticated and a bicycle to become unlocked. In other embodiments, the user device may submit a request to the compute environment 306, including credentials and location or a specific bicycle the user would like to unlock. The computer environment may authenticate the request and transmit instructions to the bicycle to be unlocked. Seagraves teaches in ¶ 0040, the present disclosure provides an intelligent vehicle sharing system, such as the bicycle sharing systems of Figs. 2 and 3, that is able to provide helpful vehicle availability predictions based on historical data, including various utilization statistics. Historical data can be collected over time as users use the vehicle sharing system. For example, the historical data may include the number of available vehicle at various locations and times, as well as contextual data associated with the locations and times. In some embodiments, a vehicle may be available if it has been checked in and not yet checked out, and a vehicle may be unavailable if it has been checked out but not yet checked in. A vehicle may also be unavailable if it is checked out for maintenance and the like. Seagraves further teaches in ¶ 0038, the compute environment 306 may receive the data and the metadata collected from the bicycles via the one or more networks 304. The at least one network 304 can include any appropriate network, including an intranet, the Internet, a cellular network, a local area network (LAN), or any other such network or combination, and communication over the network can be enabled via wired and/or wireless connections. In various embodiments, the compute environment 306 may include one or more servers with one or more processors and storage elements for storing and processing the data received from the bicycle stations and performing various functions utilizing the data, such as authenticating a user based on provided credentials, performing transactions, recording and analyzing bicycle usage data, tracking a location of a bicycle, among other computer functions. In various embodiments, one or more data analysis models (e.g., trained machine learning based model) may be stored in the compute environment and used to make determinations or predictions based on various data. In some embodiments, the compute environment may include a distributed computing system, or "cloud computing" environment, in which computing and storage may be distributed across a network of resources, such as servers and storage, which may be rapidly provisioned as needed.
Although Seagraves teaches a live map, geographic regions, requested resource availability prediction, and a bicycle availability prediction for a certain time, Seagraves does not explicitly teach determining a vehicle flow rate. However, Ran teaches the following:
based upon predicted probabilities using a flow rate; Ran teaches in col. 4, lines 66 – 67, and col. 5, lines 1 – 3, forecasting traffic conditions is an inherently less deterministic system in the sense that predictions about the future based on present conditions are better than predictions based on historical average conditions based on time-of-day, day-of-week, etc., for only about one hour into the future;
responsive to determining the flow rate satisfies a criteria; Ran teaches in col. 12, lines 58 – 59, determining a vehicle flow rate measured as the number of vehicles per time; Ran teaches in col. 22, lines 50 – 59; the address-to-address routing and alert 12 provide three types of information: 1) traffic prediction and prediction confidence probability for user-defined routes 69; 2) routing, departure time, arrival time, destination recommendations and prediction confidence probability based on route choice criteria of major roads, minimum time, minimum cost, alternative routes 610; and 3) alert for incident, construction, event, abnormal travel times, and abnormal departure time/arrival times for user-specified routes and recommended routes 611. Ran teaches in col. 23, lines 55 – 67, when a traveler is at his/her origin 83, his/her current location and destination information 86 will be provided to the personalized multi-modal travel prediction and decision support system 117 via wireline or wireless devices 82. Furthermore, the traveler is required to input his/her personalized profile and parameters 87, including preferred devices, account number, and password, (the account number and password constituting a user identifier) to receive personalized predictive traffic information. Subsequently, the user is required to input his/her data on traveler behavior, vehicle type, mode preference 119, departure time or arrival time preference 89, and route/mode selection criteria and preference;
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine an intelligent bicycle sharing system, or other vehicle sharing system, is able to provide helpful bicycle availability predictions based on historical data, including various utilization statistics of Seagraves with a traffic information system for predicting travel times of Ran to assist businesses in developing and using statistical models on flow conditions to provide accurate estimates of traffic flow (Ran, Spec. col. 19, lines 3 – 7).
Claim 15:
Seagraves and Ran teach claim 14. Seagraves further teaches the following limitations:
wherein the flow rate is based on at least one of (i) a historical flow rate associated with the at least one docking station, (ii) the plurality of docking stations within the threshold distance of the destination, or (iii) a historical flow rate associated with the plurality of docking stations within the threshold distance of the destination; Seagraves teaches in Fig. 2 a plurality of docking stations; Seagraves teaches in ¶ 0040, an intelligent vehicle sharing system, such as the bicycle sharing systems of Figs. 2 and 3, that is able to provide helpful vehicle availability predictions based on historical data, including various utilization statistics. Historical data can be collected over time as users use the vehicle sharing system. Flow rate is taught above in claim 1.
Claim 16:
Seagraves and Ran teach claim 14. Seagraves further teaches the following limitations:
sending, to a client device associated with the rider, instructions for presenting a notification indicating the first docking station is reserved for the rider; Seagraves teaches in ¶ 0036, the computer environment may authenticate the request and transmit instructions to the bicycle to be unlocked.
7. Claim 19 is rejected under 35 U.S.C. § 103 as being unpatentable over Seagraves, Jamel et al. (WO 2019/177620) in view of Ran, Bin (U.S. Patent No. 6,317,686) in view of Suzuki, Daisuke (U.S. Publication No. 2021/0001744).
While Seagraves teaches a live map, geographic regions, authenticating requests, requested resource availability prediction, and a bicycle availability prediction for a certain time, and Ran teaches a flow rate, and Seagraves and Ran are similar Suzuki where Suzuki teaches a locking and unlocking device, charging electric vehicles such as an electric bicycle, electric scooter, or electric automobile, controlling a battery station for charging electric vehicles; and Suzuki further teaches the following:
Claim 19:
determining, based on a user profile associated with the rider, the rider is eligible for reserving a docking station from the determined docking stations; Suzuki teaches in ¶ 0091, the management server restricts the electric vehicle reserved by a certain user such that other users are not capable of using the electric vehicle until the usage start reserved date and time.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine an intelligent bicycle sharing system, or other vehicle sharing system, is able to provide helpful bicycle availability predictions based on historical data, including various utilization statistics of Seagraves and a traffic information system for predicting travel times of Ran with a battery management system of an electric vehicle suitable for a sharing service of Suzuki to assist businesses in implementing transmission of reservations of stations for electric vehicles (Suzuki, Spec. ¶ 0091).
Claim 20:
Seagraves and Ran teach claim 14. Seagraves further teaches the following limitations:
wherein the predicted probabilities are determined further based on contextual data associated with the rider; Seagraves teaches in ¶ 0047, the prediction model includes one or more neural networks trained to determine a resource availability prediction for a selected time and location. As mentioned, the model may be trained on historical data which may include, for example, a record of resource availability statistics across many times and locations. Additionally, the historical data may also include contextual data associated with respective times and locations.
8. Claim 21 is rejected under 35 U.S.C. § 103 as being unpatentable over Seagraves, Jamel et al. (WO 2019/177620) in view of Ran, Bin (U.S. Patent No. 6,317,686) in view of Yoshikawa, Yukitaka et al. (JP2017151946A)
Claim 21:
Seagraves and Ran teach claim 14. Seagraves further teaches the following limitations:
receiving, from a client device associated with the rider, a request to dock the personal mobility vehicle at the first docking station; Seagraves teaches in ¶ 0044, resource availability predictions for a vehicle sharing environment, in accordance with various embodiments. Resource may refer to vehicles, docking spots, or any other such resources that may have an available state and an unavailable state. In various embodiments, a user device 602 may be used by a user to request and obtain a resource availability prediction or recommendation for a desired time and location.
While Seagraves teaches a live map, geographic regions, authenticating requests, requested resource availability prediction, and a bicycle availability prediction for a certain time, and Ran teaches a flow rate, and Seagraves and Ran are similar to Yoshikawa where Yoshikawa teaches requesting and returning vehicles to stations and Yoshikawa further teaches the following:
and automatically transmitting an unlock signal to the locking mechanism to unlock the locking mechanism; Yoshikawa teaches in ¶ 7, Pg. 5, a technique for managing each station by communicating with an external management center in order to rent a bicycle. In this technique, a key is used for each bicycle, and when the management center receives a vehicle number of a bicycle that the user wants to borrow from the user's portable communication device, the bicycle is unlocked to unlock the key of the bicycle. A signal is transmitted to the key, and the key is automatically unlocked when the unlock signal is received.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine an intelligent bicycle sharing system, or other vehicle sharing system, is able to provide helpful bicycle availability predictions based on historical data, including various utilization statistics of Seagraves and a traffic information system for predicting travel times of Ran with a technique for centrally managing multiple stations for rental vehicles Yoshikawa to assist businesses in implementing unlocking and locking mechanism for vehicles at stations (Yoshikawa, Spec. ¶ 4, Pg. 6).
Claim 22:
wherein the flow rate is further based on at least one of (i) a historical flow rate associated with the at least one docking station, (ii) one or more additional docking stations at the destination, or (iii) a historical flow rate associated with one or more additional docking stations at another location of the destination; Seagraves teaches in ¶ 0043, the docking spot availability prediction based be determined using a neural network trained on historical docking spot availability data across various times and locations.
Claim 23:
presenting, in a map rendered by a user interface on a client device associated with the rider, a geolocation of the first docking station; Seagraves teaches in ¶ 0042, the interface may provide a live map view 508 including the selected location. In some embodiments, the live map view may indicate one or more docking stations 510 or zones at or close to the selected location.
Claim 24:
determining a number of the plurality of docking stations; Seagraves teaches in ¶ 0025, a bicycle sharing station 202 of the intelligent bicycle sharing system 200 may include a docking portion 206 for holding a plurality of bicycles 208. In some embodiments, the docking portion 206 may have a specific number of docking spots 210 and thus can hold a maximum number of bicycles.
and presenting, in the map rendered by the user interface on the client device, the number of the plurality of docking stations docking stations; Seagraves teaches in ¶ 0042, the interface may provide a live map view 508 including the selected location. In some embodiments, the live map view may indicate one or more docking stations 510 or zones at or close to the selected location.
Claim 25:
presenting, in the map rendered by the user interface on the client device, a route to the first docking station; Seagraves teaches in ¶ 0043, a current number of available docking spots 512 at each docking station may be displayed and a route to the best docking station may be provided on the map view.
Claim 26:
determining a level of activity associated with the destination; Seagraves teaches in ¶ 0055, there may be a number of other users currently enroute to the location with intention to utilize a resource of the plurality of resources based on behavior data associated with the users, and the resource availability prediction may be based at least in part on the number of users currently enroute. The intention to utilize a resource may be determine using various data. For example, a user may be currently utilizing a vehicle of the shared vehicle system and has entered the location as their destination, where they will likely dock their vehicle at a docking spot. The estimated arrival time may be determined. Thus, it may be interpreted that a vehicle will become available at the location at the estimated arrival time and a docking spot will become unavailable, which can affect the resource availability prediction.
and presenting, in the map rendered by the user interface on the client device, a user interface element corresponding to the level of activity; Seagraves teaches in ¶ 0043, the docking spot availability prediction may be recalculated based on the updated current conditions. Thus, in some embodiments, the optimal docking station for the user may change and the map view is updated to direct the user to the updated optimal docking station.
Claim 27:
wherein the predicted probabilities are further based on contextual data associated with the rider; Seagraves teaches in ¶ 0047, the prediction model includes one or more neural networks trained to determine a resource availability prediction for a selected time and location. As mentioned, the model may be trained on historical data which may include, for example, a record of resource availability statistics across many times and locations. Additionally, the historical data may also include contextual data associated with respective times and locations.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied.
Note: these are additional references found but not used.
- Reference Hines, George et al. (U.S. Publication No. U.S. 2016/0221627) discloses methods and computer software for use with such bicycle systems or smartphones, and to servers configured to communicate with such bicycle systems or smartphones.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday.
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, Beth Boswell can be reached on 571-272-6737. 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.
/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
03/06/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625