DETAILED ACTION
Status of the Application
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 .
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 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
This action is a Final Action on the merits in response to the application filed on 06/17/2026.
Claims 1-3, 7, and 16-20 have been amended.
Claims 1-20 remain pending in this application.
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 1-7 are directed towards a method, claims 8, 9, 13 are directed towards a system. and claims 15-20 are directed towards a computer-readable medium, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including applying an algorithm to a dataset. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-9, 12, 13, 15-20, the independent claims (claims 1, 17, and 19) are directed to managing broadcasting stream user accounts, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, A method for forecasting parking duration, comprising:
receiving a request for projected vehicular duration at a managed facility during a time range;
determining, at a server, a predicted regional vehicular occupancy within a geographic region encompassing the managed facility during the time range;
retrieving, by the edge device, a set of dynamic environmental parameters corresponding to the managed facility;
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction such as business relations (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim and other independent claims (17 and 19) recites the additional elements of server, edge device, machine learning model, device, signal, processor, computer-readable medium, memory. The claims recite the steps are performed by the server, edge device, machine learning model, device, signal, processor, computer-readable medium, memory.
The limitations of
transmitting, from the server to an edge device at the managed facility, the predicted regional vehicular occupancy, wherein the server is remote from the edge device;
inputting, to a machine learning model by the edge device, the predicted regional vehicular occupancy, the environmental parameters, and the time range
receiving, by the edge device as output from the machine learning model, a predicted measure of vehicular duration at the managed facility during the time range; and
outputting, by the edge device, a control signal instructing a device associated with the managed facility to perform an operation based on the predicted measure of vehicular duration at the managed facility.
are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by server, edge device, machine learning model, device, signal, processor, computer-readable medium, memory. The server, edge device, machine learning model, device, signal, processor, computer-readable medium, memory are recited at a high level of generality. In limitation (a), the machine learning model is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The machine learning model is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites machine learning model. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the server, edge device, machine learning model, device, signal, processor, computer-readable medium, memory. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0019 “The vehicle management system may input this prediction to a machine learning model specific to the managed facility. The machine learning model may be configured to output a prediction of vehicular occupancy or occupancy duration (e.g., how long a vehicle is stopped) at the managed facility for the time period.”; 0089 “The vehicle management server 130 may use this data with machine learning models to predict vehicular occupancy and vehicular duration at a managed facility. In some embodiments, the vehicle management server 130 applies additional machine learning models to predict vehicular occupancy and/or vehicular duration for a geographic region around the managed facility, long-term vehicular occupancy and/or vehicular duration, and short-term vehicular occupancy and/or vehicular duration.”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
transmitting, from the server to an edge device at the managed facility, the predicted regional vehicular occupancy, wherein the server is remote from the edge device;
inputting, to a machine learning model by the edge device, the predicted regional vehicular occupancy, the environmental parameters, and the time range
receiving, by the edge device as output from the machine learning model, a predicted measure of vehicular duration at the managed facility during the time range; and
outputting, by the edge device, a control signal instructing a device associated with the managed facility to perform an operation based on the predicted measure of vehicular duration at the managed facility.
are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a machine learning model, device, signal, processor, computer-readable medium, memory to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-9, 12, 15, 16, 18, and 20 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible. Additionally, the Examiner wants to point out that claim 13 and 12 are not eligible because they are well understood, routine, conventional activity for steps of displaying data and cutting off a machine.
Regarding the dependent claims, dependent claims 2, 3, 6, 7, 9, 16, 18, 20 recite machine learning for analyzing data; claims 2, 3, 18, 20 recite server; claims 7, 16 recite edge device; claim 10, 11 recite control signal instructing a device to block off a section; claim 12 recite control signal to display information; claims 13, 14 recite control signal to interact with chargers, claim 15 recite a mobile application to display. The dependent claims 2-9, 12, 15, 16, 18, and 20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-9, 12, 15, 16, 18, and 20 recites machine learning model, device, signal, processor, computer-readable medium, memory which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-9, 12, 15, 16, 18, and 20 recites machine learning model, device, signal, processor, computer-readable medium, memory, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-9, 12, 15, 16, 18, and 20 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 17, and 19. Therefore claims 2-9, 12, 15, 16, 18, and 20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
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 of this title, 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, 2, 5, 6, 12, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication Number US 8374898, Arquero, et al. to hereinafter Arquero in view of United States Patent Publication US 20150187213, Amir, et al.
Referring to Claim 1, Arquero teaches a method for forecasting parking duration, comprising:
receiving a request for projected vehicular duration at a managed facility during a time range (
Arquero: Sec. 0004, receiving a target destination data set, where the target destination data set includes a route to a target destination for a vehicle;
Arquero: Sec. 0035, GPS mod 314 provides time to park mod 308 with the target destination and a recommended travel route to the target destination from a current location of the vehicle);
retrieving, by the edge device, a set of dynamic environmental parameters corresponding to the managed facility (
Arquero: Sec. 0034, historical data includes historical parking factors data for a plurality of vehicle travel and parking events including: (i) weather; (ii) traffic conditions; (iii) time of day; (iv) day of the week; (v) calendar date; (vi) size of vehicle; (vii) average parking space prices; and (viii) average time to park…As an alternative, any other circumstances with an impact on how long it might take to find a parking space may be considered as parking factors such as: (i) planned or ongoing construction events; (ii) number of parking spaces existing in a region; (iii) currently available parking capacity of designated parking structures and lots; (iv) festivities and sporting events; and/or (v) organized conventions.
Arquero: Sec. 0036, parking factors mod 304 queries parking factors server 112 of FIG. 1 for a parking factors data set and stores the parking factors data set in parking factors data store sub-mod 306 of FIG. 3. In this example embodiment, the parking factors queried include: (i) day of the week (Monday); (ii) calendar date (Nov. 6, 2017); (iii) weather conditions at the target destination (snowing); (iv) traffic conditions at the target destination (30 minutes of congestion or more); );
edge device (
Arquero: Sec. 0013, The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Arquero: Sec. 0022, Parking sub-system 102 is capable of communicating with other computer sub-systems via network 114. Network 114 can be, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, or a combination of the two, and can include wired, wireless, or fiber optic connections. In general, network 114 can be any combination of connections and protocols that will support communications between server and client sub-systems.);
inputting, to a machine learning model by the edge device, the predicted regional vehicular occupancy, the environmental parameters, and the time range receiving, by the edge device, as output from the machine learning model, a predicted measure of vehicular duration at the managed facility during the time range (
Arquero: Sec. 0034, historical data includes historical parking factors data for a plurality of vehicle travel and parking events including: (i) weather; (ii) traffic conditions; (iii) time of day; (iv) day of the week; (v) calendar date; (vi) size of vehicle; (vii) average parking space prices; and (viii) average time to park…As an alternative, any other circumstances with an impact on how long it might take to find a parking space may be considered as parking factors such as: (i) planned or ongoing construction events; (ii) number of parking spaces existing in a region; (iii) currently available parking capacity of designated parking structures and lots; (iv) festivities and sporting events; and/or (v) organized conventions.
Arquero: Sec. 0052, (iv) at 520 the program pulls current factors such as current time of day, weather information, traffic information, and other relevant factors; (v) at 525 the program feeds the current factors into the machine learning model and outputs an estimated time to find parking; (vi) at 530 the program displays to the driver the estimated time to find parking at the destination; and (vii) at 535 the program verifies the actual amount of time it took to the driver to find parking and retrains the machine learning model based on the result.); and
outputting, by the edge device, a control signal instructing a device associated with the managed facility to perform an operation based on the predicted measure of vehicular duration at the managed facility (
Arquero: Sec. 0052, (vi) at 530 the program displays to the driver the estimated time to find parking at the destination; and (vii) at 535 the program verifies the actual amount of time it took to the driver to find parking and retrains the machine learning model based on the result.
Arquero: Sec. 0038, the estimated time to park determined by park time estimator algorithm sub-mod would be closer to ten minutes than the fifteen minutes that was determined at S270. This estimated time to park of 15 minutes can be communicated to a user via a user interface, such as shown in user interface 400 of FIG. 4.).
Arquero describes outputting a messages based on predict measures
Arquero does not explicitly teach determining, at a server, a predicted regional vehicular occupancy within a geographic region encompassing the managed facility during the time range; transmitting, from the server to an edge device at the managed facility, the predicted regional vehicular occupancy, wherein the server is remote from the edge device.
However, Amir teaches these limitations.
determining, at a server, a predicted regional vehicular occupancy within a geographic region encompassing the managed facility during the time range (
Amir: Sec. 0023 The facility in turn determines this probability for each of these segments based upon earlier observations of space availability in the segment at the same or similar times and days of week. In various embodiments, these observations of space availability may come from dedicated surveyors, and/or from users using the facility to find parking. Where the facility has received a number of applicable availability observations for a segment that is inadequate for directly determining the segment's probability, the facility determines the segment's probability using a statistical model that predicts an expected number of available spots in a segment based upon attributes of the segment, such as total number of spots, geographic location, link, and number of businesses; context information such as date, time, date week, and current weather; and information about events that may impact parking such as sporting events, school attendance, and holidays… The facility then uses these clusters to construct a classification tree to predict to which cluster a segment should belong based upon its attributes. The facility proceeds to apply the constructed classification tree to assign each segment, on the basis of its attributes, to one of a number of classes that correspond one-to-one with clusters, irrespective of the number of availability observations received for the segment.
Amir: Sec. 0065, The display 1500 includes a map 1510 that shows the user's present location 1521 and the destination 1522. The display further includes an indication 1571 of the amount of time the facility predicts it will take the user to find parking without using a route recommended by the facility, and having indication 1572 of the amount of time the facility predicts it will take for the user-defined parking using a route recommended by the facility. In some embodiments, indication 1572 is a button that the user can press in order to obtain a route recommended by the facility.
Amir: Sec. 0023, In some embodiments, the facility provides a wireless client application having a special user interface by use by surveyors to record the results of their surveys of the availability of parking spots in particular segments at particular times.
Amir: Sec. 0027, A parker client application 121 executing on a number of parker clients 120—such as smartphones, GPS receivers, automobile computers, laptops, tablet computers, or similar mobile devices—implicitly generates availability observations based upon attempts by parkers to park. These availability observations are transmitted wirelessly, such as via a wireless base station 130, and then via the Internet 140 or other network, to a facility server 150. In the facility server, the observations are stored among facility backend data 152 by facility backend code 151.);
Amir teaches the predicting of available parking spacing within area during a time period, which the Examiner is interpreting as determining predicted regional vehicular occupancy.
transmitting, from the server to an edge device at the managed facility, the predicted regional vehicular occupancy, wherein the server is remote from the edge device (
Amir: Sec. 0027, A parker client application 121 executing on a number of parker clients 120—such as smartphones, GPS receivers, automobile computers, laptops, tablet computers, or similar mobile devices—implicitly generates availability observations based upon attempts by parkers to park. These availability observations are transmitted wirelessly, such as via a wireless base station 130, and then via the Internet 140 or other network, to a facility server 150. In the facility server, the observations are stored among facility backend data 152 by facility backend code 151.
Amir: Sec. 0034, In some embodiments, in order to collect segment observations from surveyor clients, the facility receives explicit segment availability reports from surveyors, such as via mobile wireless clients. In various embodiments, the surveyors enter these reports into a web interface, a database front end, a spreadsheet, etc.);
Amir teaches the collecting and transmitting the actual and predicted information from user, base station, severs, and other entities remotely.
Arquero and Amir are both directed to the analysis of vehicle use (See Arquero at 0034, 0051; Amir at 0004, 0027, 0030). Arquero discloses that additional elements, such as the calculating vehicle parking can be considered (See Arquero at 0042). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Arquero, which teaches detecting and repairing parking system information problems in view of Amir, to efficiently apply the analysis of vehicle use to improving the collecting and processing of information to include the outputting of predicting information based on locations. (See Lofty at 0036, 0037, 0076, 0023, 0056, 0059).
Referring to Claim 2, Arquero teaches the system of claim 1, wherein determining the predicted regional vehicular occupancy within the geographic region encompassing the managed facility during the time range comprises:
inputting, to a second machine learning model by the server, regional environmental parameters and the time range, wherein the regional environmental parameters correspond to the region (
Arquero: Sec. 0048, Some embodiments of the present invention use historical data to train a machine learning model on factors such as weather, time of day, etc. and then feeds in real-time factors to estimate how long it took users to find parking in the past under similar condition
Arquero: Sec. 0004, (i) receiving a historical parking factors data set; (ii) training a time to park algorithm based, at least in part, on the historical parking factors data set; (iii) receiving a target destination data set, where the target destination data set includes a route to a target destination for a vehicle; (iv) querying, from a parking factors server, a parking factors data set, where the parking factors data set includes parking factors along the route to the target destination and at the target destination;
Arquero: Sec. 0036, parking factors can be queried from a plurality of locations and compiled together in the parking factors data store sub-mod.); and
Arquero describes using regional factors (traffic, events) around a destination location and time.
receiving, at the server as output from the second machine learning model, the predicted regional vehicular occupancy of the geographic region during the time range (
Arquero: Sec. 0048, Some embodiments of the present invention use historical data to train a machine learning model on factors such as weather, time of day, etc. and then feeds in real-time factors to estimate how long it took users to find parking in the past under similar condition
Arquero: Sec. 0003, the facility determines the segment's probability using a statistical model that predicts an expected number of available spots in a segment based upon attributes of the segment, such as total number of spots.
Arquero: Sec. 0045, also consider weather as a prominent factor in estimating time to park (for example: if at time t1, traffic/weather/etc. conditions are similar to time t2 in the past for a given location, estimate the length of time it will take a user to find parking at time t1 based on statistical analysis of time t2. If a user will not arrive at the location until time t1+5, some embodiments of the present invention can base the analysis off of time t2+5 in the past).).
Referring to Claim 5, Arquero teaches the system of claim 2, wherein the regional environmental parameters dynamically update to reflect current conditions of the geographic region and include one or more of:
traffic data representative of vehicles routing to the geographic region (
Arquero: Sec. 0036, Processing proceeds to operation S265, where parking factors mod 304 queries parking factors server 112 of FIG. 1 for a parking factors data set and stores the parking factors data set in parking factors data store sub-mod 306 of FIG. 3. In this example embodiment, the parking factors queried include: (i) day of the week (Monday); (ii) calendar date (Nov. 6, 2017); (iii) weather conditions at the target destination (snowing); (iv) traffic conditions at the target destination (30 minutes of congestion or more); ),
Arquero describes using parking factors include "traffic conditions" along the route to the destination.
event data representative of events scheduled to occur within the geographic region during at least a portion of the time range,
points of interest located within the geographic region, and
historical visit data representative of vehicles that visited a location in the geographic region, each visit associated with a dwell time at the visited location.
Referring to Claim 6, Arquero teaches the system of claim 2, further comprising:
filtering the regional environmental parameters to correspond to a location of the managed facility within the geographic region; inputting the filtered environmental parameters to the machine learning model with the predicted regional vehicular occupancy and the time range (
Arquero: Sec. 0004, querying, from a parking factors server, a parking factors data set, where the parking factors data set includes parking factors along the route to the target destination and at the target destination…the time to park algorithm using the parking factors data set as input; ); and
Arquero describes uses target destination data, route, time information, and parking factors as inputs to the trained time to park algorithm.
Referring to Claim 12, Arquero teaches the system of claim 1, further comprising:
outputting the control signal to a sign configured to display information about the managed facility, wherein control signal causes the sign to display an indication of the predicted measure of vehicular duration at the managed facility (
Arquero: Sec. 0004, FIG. 4 is a screenshot view
Arquero: Sec. 0038, the estimated time to park determined by park time estimator algorithm sub-mod would be closer to ten minutes than the fifteen minutes that was determined at S270. This estimated time to park of 15 minutes can be communicated to a user via a user interface, such as shown in user interface 400 of FIG. 4.).
Referring to Claim 15, Arquero teaches the system of claim 1, further comprising:
sending, to a mobile application, an instruction to display an indication of the predicted measure of vehicular duration of the managed facility via a user interface presented by the mobile application (
Arquero: Sec. 0052, system as shown in flowchart 500 of FIG. 5, including the following steps: (i) at 505, a machine learning model is trained using historical data such as time of day, weather, traffic, etc. to estimate the amount of time it will take a driver to find parking; (ii) at 510, the driver has a program including a software implementation of the present invention installed in their car, phone, or Global Positioning System (GPS) device; (iii) at 515 the driver specifies a destination; (iv) at 520 the program pulls current factors such as current time of day, weather information, traffic information, and other relevant factors; (v) at 525 the program feeds the current factors into the machine learning model and outputs an estimated time to find parking; (vi) at 530 the program displays to the driver the estimated time to find parking at the destination; and (vii) at 535 the program verifies the actual amount of time it took to the driver to find parking and retrains the machine learning model based on the result.).
Referring to Claim 16, Arquero teaches the system of claim 1, further comprising:
inputting, to a second machine learning model at the edge device, the predicted regional vehicular duration, the environmental parameters, and the time range (
Arquero: Sec. 0048, Some embodiments of the present invention use historical data to train a machine learning model on factors such as weather, time of day, etc. and then feeds in real-time factors to estimate how long it took users to find parking in the past under similar condition
Arquero: Sec. 0004, (i) receiving a historical parking factors data set; (ii) training a time to park algorithm based, at least in part, on the historical parking factors data set; (iii) receiving a target destination data set, where the target destination data set includes a route to a target destination for a vehicle; (iv) querying, from a parking factors server, a parking factors data set, where the parking factors data set includes parking factors along the route to the target destination and at the target destination;
Arquero: Sec. 0036, parking factors can be queried from a plurality of locations and compiled together in the parking factors data store sub-mod.); and
Arquero describes using regional factors (traffic, events) around a destination location and time.
receiving, as output from the second machine learning model, a predicted measure of occupancy of vehicles at the managed facility during the time range (
Arquero: Sec. 0048, Some embodiments of the present invention use historical data to train a machine learning model on factors such as weather, time of day, etc. and then feeds in real-time factors to estimate how long it took users to find parking in the past under similar condition
Arquero: Sec. 0003, the facility determines the segment's probability using a statistical model that predicts an expected number of available spots in a segment based upon attributes of the segment, such as total number of spots.
Arquero: Sec. 0036, parking factors can be queried from a plurality of locations and compiled together in the parking factors data store sub-mod
Arquero: Sec. 0045, also consider weather as a prominent factor in estimating time to park (for example: if at time t1, traffic/weather/etc. conditions are similar to time t2 in the past for a given location, estimate the length of time it will take a user to find parking at time t1 based on statistical analysis of time t2. If a user will not arrive at the location until time t1+5, some embodiments of the present invention can base the analysis off of time t2+5 in the past).);
inputting, by the edge device to a third machine learning model, the predicted measure of occupancy of vehicles at the managed facility and the predicted measure of vehicular duration (
Arquero: Sec. 0034, historical data includes historical parking factors data for a plurality of vehicle travel and parking events including: (i) weather; (ii) traffic conditions; (iii) time of day; (iv) day of the week; (v) calendar date; (vi) size of vehicle; (vii) average parking space prices; and (viii) average time to park…As an alternative, any other circumstances with an impact on how long it might take to find a parking space may be considered as parking factors such as: (i) planned or ongoing construction events; (ii) number of parking spaces existing in a region; (iii) currently available parking capacity of designated parking structures and lots; (iv) festivities and sporting events; and/or (v) organized conventions.
Arquero: Sec. 0052, (iv) at 520 the program pulls current factors such as current time of day, weather information, traffic information, and other relevant factors; (v) at 525 the program feeds the current factors into the machine learning model and outputs an estimated time to find parking; (vi) at 530 the program displays to the driver the estimated time to find parking at the destination; and (vii) at 535 the program verifies the actual amount of time it took to the driver to find parking and retrains the machine learning model based on the result); and
receiving, by the edge device an output from the third machine learning model, wherein the edge device uses the output to determine an aspect of the control signal (
Arquero: Sec. 0052, (vi) at 530 the program displays to the driver the estimated time to find parking at the destination; and (vii) at 535 the program verifies the actual amount of time it took to the driver to find parking and retrains the machine learning model based on the result.
Arquero: Sec. 0038, the estimated time to park determined by park time estimator algorithm sub-mod would be closer to ten minutes than the fifteen minutes that was determined at S270. This estimated time to park of 15 minutes can be communicated to a user via a user interface, such as shown in user interface 400 of FIG. 4.).
Arquero describes outputting a messages based on predict measures
Claims 17 and 18 recite limitations that stand rejected via the art citations and rationale applied to claims 1 and 2. Regarding, a non-transitory computer-readable medium comprising memory with instructions encoded thereon, the instructions comprising instructions to cause one or more processors to perform steps comprising (
Arquero: Sec. 0011, The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.):
Claims 19 and 20 recite limitations that stand rejected via the art citations and rationale applied to claims 1 and 2. Regarding, a system comprising:
memory with instructions encoded thereon; and
one or more processors that, when executing the instructions, are caused to perform operations (
Arquero: Sec. 0011, The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
Arquero: Sec. 0012, computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing):
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 of this title, 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 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication Number US 8374898, Arquero, et al. to hereinafter Arquero in view of United States Patent Publication US 20150187213, Amir, et al. to hereinafter Amir in view of United States Patent Number US 10169993, Dance, et al.
Referring to Claim 3, Arquero teaches the system of claim 2, wherein the second machine learning model is trained by:
receiving, at the server vehicle data associated with a plurality of vehicles previously located within the geographic region (
Arquero: Sec. 0034, historical data includes historical parking factors data for a plurality of vehicle travel and parking events including: (i) weather; (ii) traffic conditions; (iii) time of day; (iv) day of the week; (v) calendar date; (vi) size of vehicle; (vii) average parking space prices; and (viii) average time to park…As an alternative, any other circumstances with an impact on how long it might take to find a parking space may be considered as parking factors such as: (i) planned or ongoing construction events; (ii) number of parking spaces existing in a region; (iii) currently available parking capacity of designated parking structures and lots; (iv) festivities and sporting events; and/or (v) organized conventions.);
Arquero describes parking factors such as traffic, events, and in some embodiments parking information associated with vehicles attempting to park in a destination location and time.
Arquero in view of Amir does not explicitly teach determining, by the server for each of one or more locations associated with the plurality of vehicles, a number of vehicles parked at the location during each of a set of time windows; generating by the server training data by labeling, for each location during each time window, vehicle data associated with the location with the number of vehicles parked at the location during the time window; and training by the server the second machine learning model on the training data.
However, Dance teaches these limitations
determining, by the server for each of one or more locations associated with the plurality of vehicles, a number of vehicles parked at the location during each of a set of time windows (
Dance: Col. 6 Ln. 20-34, These sensors S accurately observe the state X(t)∈
PNG
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38
25
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n+1 (so there are n+1 states) which represents the number of vehicles parked at time t.);
generating by the server training data by labeling, for each location during each time window, vehicle data associated with the location with the number of vehicles parked at the location during the time window; and training by the server the second machine learning model on the training data (
Dance: Col. 8 Ln. 18-42, With reference to FIG. 2, sensor training data 100 is used to train the model parameters 102. In the illustrative embodiment employing the random variable ξ having a single gamma distribution as its probability density function, the trained model parameters include the matrix parameters θ and the gamma variance parameter v. In other embodiments employing a different random variable, the appropriate parameters of the probability density function of the employed random variable are suitably trained; e.g., if the mixture of of nm gamma random variables with unit mean of Equation (4) is used then the training is performed to train the means μk, variances vk, and weights wk for k=1, . . . , nm subject to the constraint of Equation (4b). The training process of FIG. 2 may, for example, be performed by the forecasting server 80 (see FIG. 1). To perform the training, the training sensor data 100 are converted in an operation 104 to training observations (X(t),X(t+s)). This is straightforward since the X(t) and X(t+s) are simply the number of occupied parking spaces measured by the sensors S at times t and t+s, respectively, in the training sensor data 100. In an operation 106, these observations (X(t),X(t+s)) are then used to train the model parameters 102. Some non-limiting illustrative examples of ways to perform the training operation 106 are described next.).
Arquero, Amir, and Dance are all directed to the analysis of vehicle use (See Arquero at 0034, 0051; Amir at 0004, 0027, 0030; Dance at Col. 4 Ln. 1-25). Arquero discloses that additional elements, such as the calculating vehicle parking can be considered (See Arquero at 0042). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Arquero in view of Amir, which teaches detecting and repairing parking system information problems in view of Dance, to efficiently apply the analysis of vehicle use to enhancing the capability to use various parking tools. (See Dance at Col. 5 Ln. 23-45).
Referring to Claim 11, Arquero teaches the system of claim 1, Arquero in view of Amir does not explicitly teach further comprising:
estimating, based on the predicted measure of vehicular duration, that the managed facility will run out of vehicular occupancy available for a time window; and outputting the control signal to a mechanical barrier within the managed facility, wherein the control signal causes the mechanical barrier to block off a section of the managed facility from vehicles.
However, Dance teaches estimating, based on the predicted measure of vehicular duration, that the managed facility will run out of vehicular occupancy available for a time window; and outputting the control signal to a mechanical barrier within the managed facility, wherein the control signal causes the mechanical barrier to block off a section of the managed facility from vehicles (
Dance: Col. 10 Ln. 15-25, In an operation 114 the parking occupancy states at time t+s are forecast,
Dance: Col. 10 Ln. 60-67, operation 120, it is determined whether the parking occupancy state forecast for future time t+s is within a threshold of being full.
Dance: Col. 11 Ln. 1-23, the operation 120 concludes the parking facility 60 will likely be full at the future time t+s then in an operation 122 the “lot full” sign 76 is activated (encompassing keeping it activated if already activated); whereas, if the operation 120 concludes the parking facility 60 will likely not be full at the future time t+s then in an operation 124 the “lot full” sign 76 is deactivated (encompassing keeping it deactivated if already deactivated).
Dance: Col. 4 Ln. 1-25, Such a time frame is useful, for example, in operating the “Lot full” sign 76, since it is desirable to predict when the lot will be full a few minutes into the future so as to turn the “Lot full” 76 on a few minutes before the parking facility 60 is actually full in order to provide the last vehicles entering the facility time to park.)
Dance describes using occupancy forecasts to generate parking guidance information and transmit to devices, in which the occupancy state probabilities for future times are computed; from these, high occupancy or full occupancy are interpreted.
Arquero, Amir, and Dance are all directed to the analysis of vehicle use (See Arquero at 0034, 0051; Amir at 0004, 0027, 0030; Dance at Col. 4 Ln. 1-25). Arquero discloses that additional elements, such as the calculating vehicle parking can be considered (See Arquero at 0042). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Arquero in view of Amir, which teaches detecting and repairing parking system information problems in view of Dance, to efficiently apply the analysis of vehicle use to enhancing the capability to use various parking tools. (See Dance at Col. 5 Ln. 23-45).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication Number US 8374898, Arquero, et al. to hereinafter Arquero in view of United States Patent Publication US 20150187213, Amir, et al. to hereinafter Amir in view of United States Patent Number US 10169993, Dance, et al. to hereinafter Dance in view of United States Patent Publication US 20220414567, Whitaker, et al.
Referring to Claim 4, Arquero teaches the system of claim 3, wherein the vehicle data for each vehicle of the plurality includes one or more of:
one or more routes of the vehicle (
Arquero: Sec. 0035, FIG. 1, inputs a target destination of the federal courthouse in Chicago, Ill. GPS mod 314 provides time to park mod 308 with the target destination and a recommended travel route to the target destination from a current location of the vehicle.),
one or more locations within the geographic region that the vehicle was parked at (
Arquero: Sec. 0038, machine learning sub-mod 312 updates the park time estimator algorithm sub-mod based on how long it took to park and prevailing parking factors prior to parking. In this example embodiment, the vehicle parks ten minutes after arriving near the target destination.),
one or more time ranges the vehicle was parked within the geographic region (
Arquero: Sec. 0044, make that the actual arrival location; and/or (vi) cognitive parking time estimation, parking time via traffic analysis, and an algorithm to estimate parking time.),
Arquero describes event data typically includes timestamps of arrival and departure, enabling computation of dwell times.
one or more dates the vehicle was parked within the geographic region (
Arquero: Sec. 0034, FIG. 3 trains park time estimator algorithm sub-mod 310 using historical data from historical data store module (“mod”) 302. In this example embodiment, historical data includes historical parking factors data for a plurality of vehicle travel and parking events including: (i) weather; (ii) traffic conditions; (iii) time of day; (iv) day of the week; (v) calendar date; (vi) size of vehicle; (vii) average parking space prices; and (viii) average time to park.
Arquero: Sec. 0036, Processing proceeds to operation S265, where parking factors mod 304 queries parking factors server 112 of FIG. 1 for a parking factors data set and stores the parking factors data set in parking factors data store sub-mod 306 of FIG. 3. In this example embodiment, the parking factors queried include: (i) day of the week (Monday); (ii) calendar date (Nov. 6, 2017); (iii) weather conditions at the target destination (snowing); (iv) traffic conditions at the target destination (30 minutes of congestion or more); and (v) size of vehicle (standard car). Other size of vehicle options can include: (i) motorcycles; (ii) large trucks; (iii) tractor-trailers; (iv) bicycles; and (v) compact cars.), and
Arquero in view of Amir in view of Dance does not explicitly teach a number of times the vehicle has visited each of the one or more locations.
However, Whitaker teaches a number of times the vehicle has visited each of the one or more locations (
Whitaker: Claim 3, the server is further programmed to, responsive to verifying a user associated with the user profile visits the one of the business entities, increase a value of a user credibility parameter of the user profile).
Whitaker describe storing and updating a per user historical measure (credibility) when visits occur at a location.
Arquero, Amir, Dance, and Whitaker are all directed to the analysis of vehicle use (See Arquero at 0034, 0051; Amir at 0004, 0027, 0030; Dance at Col. 4 Ln. 1-25; Whitaker at 0029, 0031). Arquero discloses that additional elements, such as the calculating vehicle parking can be considered (See Arquero at 0042). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Arquero in view of Amir in view of Dance, which teaches detecting and repairing parking system information problems in view of Whitaker, to efficiently apply the analysis of vehicle use to improving how to calculate the use of vehicles. (See Whitaker at 0023, 0029, 0031).
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication Number US 8374898, Arquero, et al. to hereinafter Arquero in view of United States Patent Publication US 20150187213, Amir, et al. to hereinafter Amir in view of United States Patent Publication US 20240257627, Sohlberg, et al.
Referring to Claim 7, Arquero teaches the system of claim 1, further comprising:
receiving at the server vehicle data associated with a plurality of parking instances of vehicles previously located at the managed facility for a continuous period of time (
Arquero: Sec. 0042, (i) a sufficient data gathering period that takes into account factors such as time of day, day of the week, weather, and current traffic when determining the length of time it took users to find parking; (ii) a user will choose a destination; (iii) the application will look at similar cases from the data gathering period and create a bell curve model of times it took for users to find parking in that area; (iv) a range of time will be determined using a probability of 80% on the bell curve; (v) the time range will be output to the user; (vi) the data of the current user will be added to the current database of previous scenarios; and/or (vii) a possible extension is that a user's estimated time of arrival from their current location will be taken into account when calculating average parking time (for example, where a user is 10 minutes away from their destination the current factors of the destination will be gathered, and the average amount of time to find parking will be calculated based on the average amount of time to find parking 10 minutes after the time of the similar case).);
determining, by the edge device for each parking instance of each vehicle, a dwell time of how long the vehicle (See Sohlberg) was continuously located at the managed facility during the parking instance (
Arquero: Sec. 0042, (vii) a possible extension is that a user's estimated time of arrival from their current location will be taken into account when calculating average parking time (for example, where a user is 10 minutes away from their destination the current factors of the destination will be gathered, and the average amount of time to find parking will be calculated based on the average amount of time to find parking 10 minutes after the time of the similar case).
Arquero: Sec. 0044, (iii) an extension to a mapping application that allows a user to select an “include parking time” option, which will then include the estimated parking time in the estimated arrival time via the algorithm described above; );
generating by the edge device training data by labeling, for each parking instance of each vehicle, vehicle data associated with the parking instance with the determined dwell time (See Sohlberg); and training by the edge device the machine learning model on the training data (
Arquero: Sec. 0004, Processing proceeds to operation S275, where machine learning sub-mod 312 updates the park time estimator algorithm sub-mod based on how long it took to park and prevailing parking factors prior to parking. In this example embodiment, the vehicle parks ten minutes after arriving near the target destination. Machine learning sub-mod 312 updates the park time estimator algorithm sub-mod to reflect that the parking factors used as input at S270 yielded a time to park five minutes greater than how long it actually took to park, such that the next time identical or substantially similar inputs are provided, the estimated time to park determined by park time estimator algorithm sub-mod would be closer to ten minutes than the fifteen minutes that was determined at S270. This estimated time to park of 15 minutes can be communicated to a user via a user interface, such as shown in user interface 400 of FIG. 4.).
Arquero in view of Amir does not explicitly teach determined dwell time, dwell time of how long the vehicle.
However, Sohlberg teaches determined dwell time, dwell time of how long the vehicle (
Sohlberg: Abstract, The set of areas, including the at least one area, may dynamically change over time based on a time spent within respective locations.
Sohlberg: Claim 1, obtain a dwell time for the at least one area out of the set of areas, the dwell time being indicative of an actual time spent by the at least one first vehicle in the at least one area.)
Arquero, Amir, and Sohlberg are all directed to the analysis of vehicle use (See Arquero at 0034, 0051; Amir at 0004, 0027, 0030; Sohlberg at Col. 4 Ln. 1-25). Arquero discloses that additional elements, such as the calculating vehicle parking can be considered (See Arquero at 0042). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Arquero in view of Amir, which teaches detecting and repairing parking system information problems in view of Sohlberg, to efficiently apply the analysis of vehicle use to refining the analyzed data to include vehicle time spent in an area. (See Sohlberg at Col. 5 Ln. 23-45).
Referring to Claim 8, Arquero teaches the system of claim 7, wherein the vehicle data for each vehicle of the plurality includes one or more of:
one or more routes of the vehicle(
Arquero: Sec. 0035, FIG. 1, inputs a target destination of the federal courthouse in Chicago, Ill. GPS mod 314 provides time to park mod 308 with the target destination and a recommended travel route to the target destination from a current location of the vehicle.),
one or more time ranges the vehicle was located at the managed facility,
one or more dates the vehicle was located at the managed facility (
Arquero: Sec. 0004, receiving a target destination data set, where the target destination data set includes a route to a target destination for a vehicle;
Arquero: Sec. 0034, historical data includes historical parking factors data for a plurality of vehicle travel and parking events including: (i) weather; (ii) traffic conditions; (iii) time of day; (iv) day of the week; (v) calendar date; (vi) size of vehicle; (vii) average parking space prices; and (viii) average time to park…As an alternative, any other circumstances with an impact on how long it might take to find a parking space may be considered as parking factors such as: (i) planned or ongoing construction events; (ii) number of parking spaces existing in a region; (iii) currently available parking capacity of designated parking structures and lots; (iv) festivities and sporting events; and/or (v) organized conventions.), and
a number of times the vehicle has visited the managed facility.
Referring to Claim 9, Arquero teaches the system of claim 1, wherein the machine learning model includes a first sub- model and a second sub-model, the method further comprising:
receiving, from the first sub-model, a predicted location duration for managed facilities within the geographic region, the first sub-model trained on a plurality of sets of vehicle data, wherein each set of vehicle data is labeled with dwell time (See Sohlberg) at an associated managed facility of a plurality of managed facilities (
Arquero: Sec. 0003, for each of a number of street parking regions called “parking segments” or “segments,” a probability that at least one suitable street parking spot will be available in the segment . . . . The facility in turn determines this probability for each of these segments based upon earlier observations of space availability in the segment at the same or similar times and days of week . . . the facility determines the segment's probability using a statistical model that predicts an expected number of available spots in a segment based upon attributes of the segment, such as total number of spots, geographic location, link, and number of businesses; context information such as date, time, date week, current weather; and information about events that may impact parking such as sporting events, school attendance, and holidays.
Arquero: Sec. 0004, querying, from a parking factors server, a parking factors data set, where the parking factors data set includes parking factors along the route to the target destination and at the target destination…the time to park algorithm using the parking factors data set as input;
Arquero: Sec. 0049, instead relies on historical factors that are relatively easy to gather (time of day, day of week, weather, traffic) which can then be translated into an estimated parking time more easily using historical data. Likewise, some embodiments of the present invention can be expanded to easily incorporate any other relevant data points. Some embodiments of the present invention can also be extended to include predictions for future parking situations by predicting the values of the inputs into an algorithm that tries to predict the future parking situation in itself.);
Arquero describes uses target destination data, route, time information, and parking factors as inputs to the trained time to park algorithm.
and
receiving, from the second sub-model, a predicted vehicle duration at managed facilities, the second sub-model trained on a plurality of sets of vehicle data, wherein each set of vehicle data is labeled with dwell time (See Sohlberg) of a vehicle at a managed facility of the plurality of managed facilities; wherein the predicted measure of vehicular duration at the managed facility includes the predicted location duration and the predicted vehicle duration (
Arquero: Sec. 0034, here machine learning sub-module (“sub-mod”) 312 of FIG. 3 trains park time estimator algorithm sub-mod 310 using historical data from historical data store module (“mod”) 302. In this example embodiment, historical data includes historical parking factors data for a plurality of vehicle travel and parking events including: (i) weather; (ii) traffic conditions; (iii) time of day; (iv) day of the week; (v) calendar date; (vi) size of vehicle; (vii) average parking space prices; and (viii) average time to park. The machine learning sub-mod uses the historical parking factors data to train an algorithm to determine an approximate amount of time it would take on average to find a parking space upon reaching a destination in a plurality of circumstances when provided a set of parking factors and a target destination.
Arquero: Sec. 0038, Processing proceeds to operation S275, where machine learning sub-mod 312 updates the park time estimator algorithm sub-mod based on how long it took to park and prevailing parking factors prior to parking. In this example embodiment, the vehicle parks ten minutes after arriving near the target destination. Machine learning sub-mod 312 updates the park time estimator algorithm sub-mod to reflect that the parking factors used as input at S270 yielded a time to park five minutes greater than how long it actually took to park, such that the next time identical or substantially similar inputs are provided, the estimated time to park determined by park time estimator algorithm sub-mod would be closer to ten minutes than the fifteen minutes that was determined at S270.
Arquero: Sec. 0050, one, or more, of the following features, characteristics and/or advantages: (i) using a variety of factors to train a machine learning model; (ii) feeding in real-time factors to estimate how long it took users to find parking in the past under similar conditions; (iii) accounting for a greater number of variations that may impact the amount of time it takes a user to find parking, such as weather and traffic; (iv) easily expanding to incorporate any other relevant data points; (v) using weather as one of a number of parameters in generating a machine learning model to calculate an approximate amount of time to find parking; (vi) creating a model for estimating the amount of time it will take to find parking in a given location; and/or (vii) including an algorithm that relies on known machine learning techniques used to estimate values, such as training models, regressions, etc.).
Arquero describes a park algorithm trained on historical parking factors for predicting parking situations that can include parking facilities/garages
Arquero in view of Amir does not explicitly teach dwell time.
However, Sohlberg teaches dwell time (
Sohlberg: Abstract, The set of areas, including the at least one area, may dynamically change over time based on a time spent within respective locations.
Sohlberg: Claim 1, obtain a dwell time for the at least one area out of the set of areas, the dwell time being indicative of an actual time spent by the at least one first vehicle in the at least one area.)
Arquero, Amir, and Sohlberg are all directed to the analysis of vehicle use (See Arquero at 0034, 0051; Amir at 0004, 0027, 0030; Sohlberg at Col. 4 Ln. 1-25). Arquero discloses that additional elements, such as the calculating vehicle parking can be considered (See Arquero at 0042). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Arquero in view of Amir, which teaches detecting and repairing parking system information problems in view of Sohlberg, to efficiently apply the analysis of vehicle use to refining the analyzed data to include vehicle time spent in an area. (See Sohlberg at Col. 5 Ln. 23-45).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication Number US 8374898, Arquero, et al. to hereinafter Arquero in view of United States Patent Publication US 20150187213, Amir, et al. to hereinafter Amir in view of United States Patent Publication US 20240257627, Sohlberg, et al. to hereinafter Sohlberg in view of United States Patent Publication US 20170309177, Hoffmann, et al.
Referring to Claim 10, Arquero teaches the system of claim 9, wherein outputting the control signal instructing the device associated with the managed facility to perform the operation based on the predicted measure of vehicular duration at the managed facility comprises:
in response to determining a short-term predicted duration is less than a long-term predicted duration,
Arquero: Sec. 0052, at 525 the program feeds the current factors into the machine learning model and outputs an estimated time to find parking; (vi) at 530 the program displays to the driver the estimated time to find parking at the destination; and (vii) at 535 the program verifies the actual amount of time it took to the driver to find parking and retrains the machine learning model based on the result.).
Arquero in view of Amir does not explicitly teach outputting the control signal instructing a mechanical barrier within the managed facility to block off a section of the managed facility from vehicles.
However, Hoffmann teaches outputting the control signal instructing a mechanical barrier within the managed facility to block off a section of the managed facility from vehicles (
Hoffmann: Sec. 0007, a method for operating a parking lot includes: ascertaining a route for the autonomous travel of a vehicle in the parking lot to a target position within the parking lot; and blocking off at least one section of the route to other vehicles before the vehicle travels the section, so that the vehicle is able to drive autonomously through the blocked-off section of the route.
Hoffmann: Sec. 0026, the blocking off includes a control of one or more signal generators and/or a control of a barrier. A signal generator, for example, emits a red signal light for a blocking operation. The release includes a control of the signal generator in such a way that it emits a green signal light. A control of a barrier for blocking off the section particularly includes a barrier closure. A release of the blocked-off section particularly includes a control of the barrier such that it is opened.)
Hoffmann describes controlling a barrier to block off at least one section of the route within a parking lot. That maps to outputting the control signal instructing a mechanical barrier to block off a section of the managed facility from vehicles.)
Arquero, Amir, Sohlberg, and Hoffmann are all directed to the analysis of vehicle use (See Arquero at 0034, 0051; Amir at 0004, 0027, 0030; Sohlberg at Col. 4 Ln. 1-25; Hoffmann at 0040, 0044). Arquero discloses that additional elements, such as the calculating vehicle parking can be considered (See Arquero at 0042). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Arquero in view of Amir in view of Sohlberg, which teaches detecting and repairing parking system information problems in view of Hoffmann, to efficiently apply the analysis of vehicle use to creating policies for controlling parking lots . (See Hoffmann at 0012, 0068).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication Number US 8374898, Arquero, et al. to hereinafter Arquero in view of United States Patent Publication US 20150187213, Amir, et al. to hereinafter Amir in view of United States Patent Publication US 20200369175, Rajabally, et al.
Referring to Claim 13, Arquero teaches the system of claim 1, further comprising:
Arquero in view of Amir does explicitly teach responsive to determining the predicted measure of vehicular duration is below a threshold amount, outputting the control signal to a set of vehicle chargers ,wherein control signal causes one or more of the vehicle chargers to turn off.
However, Rajabally teaches responsive to determining the predicted measure of vehicular duration is below a threshold amount, outputting the control signal to a set of vehicle chargers ,wherein control signal causes one or more of the vehicle chargers to turn off (
Rajabally: Sec. 0024, forecast future occupation of the parking bays by electric vehicles based on expected arrival times and expected stay durations of electric vehicles entering the parking area;
Rajabally: Sec. 0009, forecasting, on the basis of the forecast of future occupation and the expected charge and discharge capabilities, a total electric vehicle state of charge and total electric vehicle energy storage capacity in the parking area;
Rajabally: Sec. 0010, recording actual arrivals and departures of electric vehicles at the parking area;
Rajabally: Sec. 0011, adjusting, on the basis of the recorded arrivals and departures, the forecast of future occupation and the forecast of total electric vehicle state of charge and total electric vehicle energy storage capacity in the parking area.).
Rajabally describes forecast expected stay durations and uses them to manage total SoC and capacity; this is a basis for deciding when to make chargers inactive/available, when duration below threshold.
Arquero, Amir, and Rajabally are all directed to the analysis of vehicle use (See Arquero at 0034, 0051; Amir at 0004, 0027, 0030; Rajabally at 0012, 0017, 0021). Arquero discloses that additional elements, such as the calculating vehicle parking can be considered (See Arquero at 0042). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Arquero in view of Amir, which teaches detecting and repairing parking system information problems in view of Rajabally, to efficiently apply the analysis of vehicle use to enhancing the capability to use various parking tools. (See Rajabally at 0018, 0023, 0036, 0042).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication Number US 8374898, Arquero, et al. to hereinafter Arquero in view of United States Patent Publication US 20150187213, Amir, et al. to hereinafter Amir in view of United States Patent Publication US 20190217737, Lofty.
Referring to Claim 14, Arquero teaches the system of claim 1, Arquero in view of Amir does not explicitly teach further comprising:
responsive to determining the predicted measure of vehicular duration is above a threshold amount, outputting the control signal to a set of robotic vehicle chargers, wherein control signal causes one or more of the robotic vehicle chargers to move to a designated location within the managed facility.
However, Lofty teaches responsive to determining the predicted measure of vehicular duration is above a threshold amount, outputting the control signal to a set of robotic vehicle chargers, wherein control signal causes one or more of the robotic vehicle chargers to move to a designated location within the managed facility (
Lofty: Sec. 0073, An autonomous robotic charger can include an onboard energy storage system, a navigation unit, a transportation unit, and a power exchange unit.
Lofty: Claim 10, A control station for robotic chargers, comprising one or more computing devices, configured to:
communicate with the one or more autonomous robotic chargers according to claim 1;
receive a charge request;
select a robotic charger to be dispatched;
select an electric vehicle (EV) to be charged; and
command a selected robotic charger to charge a selected EV.).
Lofty describes that a control station sends commands to robotic chargers (e.g., select and dispatch a charger to a location), which corresponds to control signal causes one or more of the robotic vehicle chargers to move to a designated location.
Arquero, Amir, and Lofty are both directed to the analysis of vehicle use (See Arquero at 0034, 0051; Amir at 0004, 0027, 0030; Lofty at 0009, 0059, 0068). Arquero discloses that additional elements, such as the calculating vehicle parking can be considered (See Arquero at 0042). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Arquero in view of Amir, which teaches detecting and repairing parking system information problems in view of Lofty, to efficiently apply the analysis of vehicle use to enhancing the capability to use various parking tools. (See Lofty at 0036, 0037, 0076, 0077, 0096, 0097).
Response to Arguments
Applicant’s arguments filed 06/17/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 06/17/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 11-13 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
A method for forecasting parking duration, comprising:
receiving a request for projected vehicular duration at a managed facility during a time range;
determining, at a server, a predicted regional vehicular occupancy within a geographic region encompassing the managed facility during the time range;
transmitting, from the server to an edge device at the managed facility, the predicted regional vehicular occupancy, wherein the server is remote from the edge device;
retrieving, by the edge device, a set of dynamic environmental parameters corresponding to the managed facility;
inputting, to a machine learning model by the edge device, the predicted regional vehicular occupancy, the environmental parameters, and the time range
receiving, by the edge device as output from the machine learning model, a predicted measure of vehicular duration at the managed facility during the time range; and
outputting, by the edge device, a control signal instructing a device associated with the managed facility to perform an operation based on the predicted measure of vehicular duration at the managed facility.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing broadcasting stream user accounts, which constitutes methods related to commercial interactions such as business relations which are still considered an abstract idea under the 2019 PEG. The server and devices are comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 12 and 13 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible.
In regards to Ex Parte Desjardins, the instant claims are not similar to Ex Parte Desjardins, Examiner finds the Board determined the improvements in Desjardins to be directed to addressing problems arising in the context of a technical improvements to machine learning systems, which overcome a problem specifically arising in the realm of AI and machine learning inventions. There is no similar technological problem or solution here, as the current claims are just using typically known actions/steps of a machine learning model and no improvements.
Furthermore, the Examiner respectfully disagrees because the steps/arguments at pg. 13:
“This architecture partitions computation between the server and edge device, thus enabling the edge device to generate timely, locally informed control signals without depending on a continuous connection to the server.”
“The particular distributed architecture described in claim 1 improves the functioning of the computing system itself by reducing redundant computation, minimizing data transfer, and enabling low-latency, autonomous edge-level control.”
the Examiner wants to point out that claims do not show or break down how this is actually executed, at this point it’s just an aspirational statement.
Additionally, the Examiner would like to point the Applicant to the 2019 PEG, in which managing broadcasting stream user accounts will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Regarding the 35 U.S.C. 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matthiesenet al., U.S. Pub. 20180308191, (discussing the operation and managing of an autonomous vehicle).
Iga et al., J.P. Pub. JP2016126405, (discussing the detecting of service for a taxi based on the position of a user).
Le et al., Supply, Demand, Operations, And Management Of Crowd-Shipping Services: A Review And Empirical Evidence, https://www.sciencedirect.com/science/article/pii/S0968090X18314700, Transportation Research Part C: Emerging Technologies, 2019 (discussing the transportation using automated vehicles for shipping).
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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/UCHE BYRD/Examiner, Art Unit 3624
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