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 .
Response to Amendment
The amendment filed on 06/25/2026 has been entered. Claims 1 and 14 are presently amended. Claims 4-6 and 16 are presently canceled. Claims 21-23 are newly filed. Claims 1-3, 7-15, and 17-23 are pending in the present application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 7-10, 14-15, 17-18, and 21-23 are rejected under 35 U.S.C. 10 as being unpatentable over Ucar et al. (US 20230196917 A1) in view of Agrawal et al. (US 20200143681 A1) and Ghetti et al. (US 12511553 B1).
Regarding claim 1, Ucar teaches a system for parking space management comprising:
one or more processors ([0128]: processor 110); and
a non-transitory computer-readable medium having instructions stored thereon ([0028]: memory 210 stores module 220, i.e. instructions, that are executed by the processor), which when executed by the one or more processors cause the system to perform operations comprising:
determining, based on input from a sensor, a status of a user in relation to a parked vehicle associated with the user, wherein the parked vehicle is parked in a parking spot ([0050]: extracts intrinsic and extrinsic features from the sensor data, which includes human poses and interactions with objects near the vehicle; [0040]: features extracted include “actions associated with people proximate to the vehicle”);
predicting an availability of the parking spot based on the determined status of the user in relation to the parked vehicle ([0022] and [0042]: extracted features, such as a person placing bags into an open trunk, are analyzed to “generate knowledge in the form of a binary determination of whether the vehicle is leaving or not”); and
transmitting a parking availability message based on the predicted availability of the parking spot ([0056]: “provides the status to inform additional vehicles about an availability of a parking spot”).
Ucar does not teach that the prediction of parking availability is also based on user information comprising a user calendar, a user email, a message text, a voicemail, or a combination thereof.
In the same field of endeavor, Agrawal teaches:
predicting parking availability and demand based on user information comprising a user calendar, a user email, a message text, a voicemail, or a combination thereof ([0020], [0023], and [0031]: various voice and text message inputs along with calendar data are used to predict the availability of parking, such as parking demand corresponding to scheduled events).
It would have been obvious to one of ordinary skill at the effective date of filing to modify Ucar with the teachings of Agrawal based on a reasonable expectation of success and motivation, as taught by Agrawal, to allow parking to be able to be predicted with more than just surface/vehicle sensors as said solutions "are costly" and "only partially address the problem" ([0003]).
Agrawal teaches that the model is a random forest ([0020]).It also teaches that the input may come from multiple thiird party applications ([0034]: "Computation component 120 may receive or access input data 118 from other applications 124 on system 100, e.g., a calendar application such as Outlook® or personal assistant application such as Cortana®. For example, computation component 120 may access a destination from an Outlook® calendar entry for a meeting or an appointment in which the destination of the meeting is indicated.") However, the prior combination does not teach using a neural network comprising a large langage model (LLM) algorithm to perform the prediction.
In the field of predicting a user’s intent and future actions based on data gathered across a plurality of applications, Ghetti teaches:
using a neural network comprising a large langage model (LLM) algorithm to perform a prediction of a user’s intent and preferences (Col. 4, lines 37-43 and Col 15, lines 50-65: “one or more large language models (LLMs)… are preconfigured and specifically trained for a specific use case (such as identifying context within an email)”).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination with the teachings of Ghetti based on a reasonable expectation of success and motivation, as taught by Ghetti, of obtaining user’s data and using it to predict behavior and preferences across a plurality of applications to be used in “providing maximal utility to individual users” in a manner that is safe and compliant with laws and user settings pertaining to data privacy (Col. 1, lines 30-59).
Regarding claim 2, Ucar teaches:
wherein the status of the user in relation to the parked vehicle comprises an approaching status, a departing status, an entering status, an existing status, a waiting status, a searching status, a loading items status, or a combination thereof ([0040]: actions analyzed as part of the status of people proximate to the vehicle “may involve loading cargo/items into the vehicle, loading children into the vehicle, entering/exiting the vehicle, and so on”).
Regarding claim 3, Ucar teaches:
wherein the status of the user is at least partially determined based on a relative distance between the user and the parked vehicle ([0040] and [0063-0064]: “actions of the person… proximate to an open trunk” are analyzed, such as seen in Figs. 6-8, where the persons are at a relative distance that is “proximate” the parked vehicle).
Regarding claim 7, Ucar teaches:
wherein the availability of the parking spot is predicted further based on an activity of the user, the activity of the user comprising paying a parking ticket, moving an item to be loaded into the parked vehicle, putting on sunglasses, remotely starting the parked vehicle, or a combination thereof ([0022] and [0042]: the extracted features that are analyzed include a “person placing bags into [an] open trunk”).
Regarding claim 8, Ucar does not teach wherein the availability of the parking spot is predicted further based on an activity of the parked vehicle, the activity of the parked vehicle comprising automatic start, automatic stop, autopilot, or a combination thereof.
While the analyzed activity of the vehicle does not explicitly include an automatic start, automatic stop, or autopilot, Ucar teaches that the lights of a vehicle are analyzed to predict the activity of a vehicle ([0040]: “By way of example, the characteristics of the parked vehicles can include whether lights of the vehicles are presently active (e.g., brake lights, reverse lights, running lights, etc.”).
One of ordinary skill in the art would have recognized that automatic start operations turn on the lights of the vehicle as is well understood in the art. Therefore, it would have been obvious that parking spot availability is predicted based on activity of the vehicle including an automatic start as the lights of a vehicle are well understood to be activated when a vehicle is remotely started, and analyzing the lights of the vehicle advantageously offers a more accurate prediction of when a vehicle may depart from a parking spot.
Regarding claim 9, Ucar teaches:
wherein the sensor comprises a vision sensor, a position sensor, or a combination thereof ([0076] and [0079]: sensor system includes “a global navigation satellite system (GNSS), a global positioning system (GPS), a navigation system 147” … “and/or one or more cameras 126”).
Regarding claim 10, Ucar teaches:
generating a route to the parking spot for an ego vehicle ([0023] and [0060]: the parking system may assign a space and generate a specific route to the space); and
instructing the ego vehicle to operate according to the route ([0023] and [0060]: the specific route is provided to the ego vehicle).
Regarding claim 14, Ucar teaches a method for parking space management comprising:
determining, based on input from a sensor comprising a vision sensor, a position sensor, or a combination thereof ([0076] and [0079]: sensor system includes “a global navigation satellite system (GNSS), a global positioning system (GPS), a navigation system 147” … “and/or one or more cameras 126”),
a status of a user in relation to a parked vehicle associated with the user , wherein the parked vehicle is parked in a parking spot ([0050]: extracts intrinsic and extrinsic features from the sensor data, which includes human poses and interactions with objects near the vehicle; [0040]: features extracted include “actions associated with people proximate to the vehicle”);
predicting an availability of the parking spot based on the determined status of the user in relation to the parked vehicle ([0022] and [0042]: extracted features, such as a person placing bags into an open trunk, are analyzed to “generate knowledge in the form of a binary determination of whether the vehicle is leaving or not”); and
transmitting a parking availability message based on the predicted availability of the parking spot ([0056]: “provides the status to inform additional vehicles about an availability of a parking spot”).
Ucar does not teach that the prediction of parking availability is also based on user information comprising a user calendar, a user email, a message text, a voicemail, or a combination thereof.
In the same field of endeavor, Agrawal teaches:
predicting parking availability and demand based on user information comprising a user calendar, a user email, a message text, a voicemail, or a combination thereof ([0020], [0023], and [0031]: various voice and text message inputs along with calendar data are used to predict the availability of parking, such as parking demand corresponding to scheduled events).
It would have been obvious to one of ordinary skill at the effective date of filing to modify Ucar with the teachings of Agrawal based on a reasonable expectation of success and motivation, as taught by Agrawal, to allow parking to be able to be predicted with more than just surface/vehicle sensors as said solutions "are costly" and "only partially address the problem" ([0003]).
Agrawal teaches that the model is a random forest ([0020]).It also teaches that the input may come from multiple thiird party applications ([0034]: "Computation component 120 may receive or access input data 118 from other applications 124 on system 100, e.g., a calendar application such as Outlook® or personal assistant application such as Cortana®. For example, computation component 120 may access a destination from an Outlook® calendar entry for a meeting or an appointment in which the destination of the meeting is indicated.") However, the prior combination does not teach using a neural network comprising a large langage model (LLM) algorithm to perform the prediction.
In the field of predicting a user’s intent and future actions based on data gathered across a plurality of applications, Ghetti teaches:
using a neural network comprising a large langage model (LLM) algorithm to perform a prediction of a user’s intent and preferences (Col. 4, lines 37-43 and Col 15, lines 50-65: “one or more large language models (LLMs)… are preconfigured and specifically trained for a specific use case (such as identifying context within an email)”).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination with the teachings of Ghetti based on a reasonable expectation of success and motivation, as taught by Ghetti, of obtaining user’s data and using it to predict behavior and preferences across a plurality of applications to be used in “providing maximal utility to individual users” in a manner that is safe and compliant with laws and user settings pertaining to data privacy (Col. 1, lines 30-59).
Regarding claim 15, Ucar teaches:
wherein the status of the user in relation to the parked vehicle comprises an approaching status, a departing status, an entering status, an existing status, a waiting status, a searching status, a loading items status, or a combination thereof ([0040]: actions analyzed as part of the status of people proximate to the vehicle “may involve loading cargo/items into the vehicle, loading children into the vehicle, entering/exiting the vehicle, and so on”); and
the status of the user is at least partially determined based on a relative distance between the user and the parked vehicle ([0040] and [0063-0064]: “actions of the person… proximate to an open trunk” are analyzed, such as seen in Figs. 6-8, where the persons are at a relative distance that is “proximate” the parked vehicle).
Regarding claim 17, Ucar teaches:
predicting the availability of the parking spot based on an activity of the user, the activity of the user comprising paying a parking ticket, moving an item to be loaded into the parked vehicle, putting on sunglasses, remotely starting the parked vehicle, automatic start, automatic stop, autopilot, or a combination thereof ([0022] and [0042]: the extracted features that are analyzed include a “person placing bags into [an] open trunk”).
Regarding claim 18, Ucar teaches:
generating a route to the parking spot for an ego vehicle ([0023] and [0060]: the parking system may assign a space and generate a specific route to the space); and
instructing the ego vehicle to operate according to the route ([0023] and [0060]: the specific route is provided to the ego vehicle).
Regarding claim 21, Agrawal teaches:
the algorithm analyzes data from the user information to identify contextual indicators of departure ([0023], [0031], and [0034]: determines when an event, such as a sporting event or a meeting, is occurring such that a crowd index is set accordingly).
Ghetti further teaches:
an LLM algorithm that analyzes unstructured textual data (Col. 12, lines 21-25 and Col. 15, lines 50-65: external components such as calendar data and emails are analyzed so as to identify context).
While Agrawal does not explicitly teach that these events are indicators of departure, it does “predict the availability of the identified parking venues or parking spaces based on crowd index I” ([0030]), with said crowd index being directly calculated from events that are occurring ([0033]). Therefore, it is implicit that the event ending is recognized as an indicator of departure as it necessarily flows from the disclosure of Agrawal that the crowd index will decrease when an event ends due to the parking availability increasing subsequent to the event ending. The event ending and not being recognized as an indicator of departure would render the invention nonfunctional as the crowd index would otherwise not decrease following the event.
Regarding claim 22, Agrawal teaches:
wherein predicting the availability of the parking spot comprises combining multiple types of the user information to generate a contextual representation of the user's anticipated activity ([0023] and [0034]: multiple types of input for user information allowed, including text/calendar inputs via Outlook® and voice inputs via Cortana®. For example, a user may input an address via a text message input, and event data is obtained and analyzed via calendar data in accordance).
Regarding claim 23, Agrawal teaches:
the algorithm analyzes data from the user information to identify contextual indicators of departure ([0023], [0031], and [0034]: determines when an event, such as a sporting event or a meeting, is occurring such that a crowd index is set accordingly);
the user information comprises two or more types selected from the user calendar, the email content, the text message content, and voicemail content ([0023] and [0034]: multiple types of input for user information allowed, including text/calendar inputs via Outlook® and voice inputs via Cortana®. For example, a user may input an address via a text message input, and event data is obtained and analyzed via calendar data in accordance),
and the two or more types of user information are combined by correlating scheduled events from the user calendar with textual indicators extracted from at least one of the email content, the text message content, or the voicemail content ([0023], [0031], and [0034]: a user can search an event with a search text message or voicemail with a personal assistance application. This then checks the user's calendar, such as an Outlook® calendar, to see if there is another scheduled event near the time and place of the meeting).
Ghetti further teaches:
an LLM algorithm that analyzes unstructured textual data (Col. 12, lines 21-25 and Col. 15, lines 50-65: external components such as calendar data and emails are analyzed so as to identify context).
While Agrawal does not explicitly teach that these events are indicators of departure, it does “predict the availability of the identified parking venues or parking spaces based on crowd index I” ([0030]), with said crowd index being directly calculated from events that are occurring ([0033]). Therefore, it is implicit that the event ending is recognized as an indicator of departure as it necessarily flows from the disclosure of Agrawal that the crowd index will decrease when an event ends due to the parking availability increasing subsequent to the event ending. The event ending and not being recognized as an indicator of departure would render the invention nonfunctional as the crowd index would otherwise not decrease following the event.
Claims 11-13 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ucar in view of Agrawal and Ghetti as applied to claims 1 and 14 above, and further in view of Tomer (US 20170200320 A1).
Regarding claim 11, Ucar teaches:
transmitting the parking availability message of the parking spot to a plurality of waiting vehicles ([0056]).
Ucar is not privy to how this system handles multiple vehicles waiting for a parking spot, and does not teach selecting a first waiting vehicle out of the waiting vehicles to park in the parking spot.
In the same field of endeavor, Tomer teaches:
selecting a first waiting vehicle out of the waiting vehicles to park in the parking spot ([0010] and [0092]: a vehicle is selected so that the arriving vehicle arrives to the parking spot when the parked vehicle is departing from said parking spot).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Ucar with this vehicle selection operation of Tomer based on a reasonable expectation of success and motivation, as taught by Tomer, of selecting vehicles for parking so as to minimize the time a vehicle spends waiting for a parking spot ([0010]).
Regarding claim 12, Ucar teaches:
estimating an available time of the parking spot ([0042]: time when the spot will be available, i.e. departure time, is predicted).
Tomer further teaches:
selecting the first waiting vehicle based on an arrival time of the first waiting vehicle and the available time of the parking spot ([0010]: a vehicle is selected so that the arriving vehicle arrives to the parking spot when the parked vehicle is departing from said parking spot).
Regarding claim 13, Tomer teaches:
selecting the first waiting vehicle based on a queue of a waiting list associated with the waiting vehicles ([0092]: a first vehicle on the waiting list is “matched with departing vehicle 221 to occupy the parking space”).
Regarding claim 19, Ucar teaches:
transmitting the parking availability message of the parking spot to a plurality of waiting vehicles ([0056]).
Ucar is not privy to how this system handles multiple vehicles waiting for a parking spot, and does not teach selecting a first waiting vehicle out of the waiting vehicles to park in the parking spot.
In the same field of endeavor, Tomer teaches:
selecting a first waiting vehicle out of the waiting vehicles to park in the parking spot ([0010] and [0092]: a vehicle is selected so that the arriving vehicle arrives to the parking spot when the parked vehicle is departing from said parking spot).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Ucar with this vehicle selection operation of Tomer based on a reasonable expectation of success and motivation, as taught by Tomer, of selecting vehicles for parking so as to minimize the time a vehicle spends waiting for a parking spot ([0010]).
Regarding claim 20, Ucar teaches:
estimating an available time of the parking spot ([0042]: time when the spot will be available, i.e. departure time, is predicted).
Tomer further teaches:
selecting the first waiting vehicle based on an arrival time of the first waiting vehicle and the available time of the parking spot or based on a queue of a waiting list associated with the waiting vehicles ([0010] and [0092]: a vehicle is selected so that the arriving vehicle arrives to the parking spot when the parked vehicle is departing from said parking spot).
Response to Arguments
Applicant’s arguments with respect to the amendments to the independent claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/JACK R BREWER/Examiner, Art Unit 3663
/ADAM D TISSOT/Primary Examiner, Art Unit 3663
