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 .
Claim Status
Claims 1-2, 4-5, and 8-9 have been amended. Claims 10-12 have been added.
Claims 1-12 are pending.
Response to Arguments
Applicant’s arguments, see page 6, filed 07/21/2026, with respect to claims 1, 2, and 9 interpretations under 35 USC 112(f) have been fully considered and are persuasive. The 35 USC 112(f) interpretation of claims 1, 2, and 9 have been withdrawn.
Applicant's arguments with respect to claims 1-9 rejections under 35 USC 102(a)(1) have been fully considered but they are not persuasive. Applicant’s arguments pertain to newly amended limitations not addressed in the prior Office Action of record.
However, based on Applicant’s amendments, Omari et al. (20200406906; hereinafter Omari, already of record) does disclose of providing a driver of the vehicle with guidance regarding at least one passenger, generating feedback in natural language to the driver, and providing this feedback after driving has initiated. Omari discloses:
“a predicted comfort model 230 may be used to provide guidance to a human driver of a vehicle, so as to optimize the experience of a passenger of the vehicle. For example, the predicted comfort model 230 may be used to provide notifications to a vehicle operator to slow down, speed up, brake less aggressively, etc., as appropriate” ¶ 35,
“Generating all proposed vehicle actions at once may allow the predicted comfort level of the vehicle actions to be considered at once. This may allow for the optimal vehicle action to be found and executed. Depending on the presented scenario, proposed vehicle actions may differ significantly” ¶ 53,
Fig. 500 Element 510,
“Ride providers may include, e.g., human operators” ¶ 65,
“Identity management services 704 may also manage and control access to provider and requestor data maintained by the transportation management system 702, such as driving and/or ride histories include ride evaluations, vehicle data, personal data, preferences, usage patterns as a ride provider and as a ride requestor, profile pictures, linked third-party accounts (e.g., credentials for music or entertainment services, social-networking systems, calendar systems, task-management systems, etc.), vehicle action intensity preferences, and any other associated information” ¶ 66
Wherein it can be seen that based on passenger preferences, guidance can be given to ride providers (drivers), generating natural language (notifications) feedback to drivers, and that based on the current driving scenario, constantly generate feedback to notify the driver. As such, Omari does disclose of the claimed invention, and the 35 USC 102(a)(1) rejections are maintained. A detailed rejection follows below.
Claim Rejections - 35 USC § 102
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Omari et al. (20200406906; hereinafter Omari, already of record).
Regarding claim 1, Omari teaches a passenger experience optimization system comprising a vehicle including a vehicle sensor and a vehicle processor and a server device including a server device processor (Omari: Abstract), wherein
the vehicle comprises a first machine learning model configured to interact with at least one passenger of the vehicle (Omari: “The model generator system 220 may receive one or more evaluations 214 of each vehicle action. In particular embodiments, passengers of a vehicle may be afforded the opportunity to provide feedback about the quality or comfort of their experience while riding in a vehicle” ¶ 23, “the model generator system 220 may generate or train a predictive rider preference model 240. The predictive rider preference model 240 may be used for classifying passenger preferences for qualities of the ride during vehicle action responses, such as intensity, comfort, elegance, or experience” ¶ 31),
wherein the server device processor is configured to (Omari: “computations associated with a service task may be performed by a combination of the transportation management system 702 (including any number of servers, databases, etc.) ...” ¶ 65):
generate information regarding the at least one passenger based on a result of interaction with the at least one passenger (Omari: “the model generator system 220 may generate or train a predictive rider preference model 240. The predictive rider preference model 240 may be used for classifying passenger preferences for qualities of the ride during vehicle action responses, such as intensity, comfort, elegance, or experience” ¶ 31, “The model may also be configured to receive, as input, information about one or more passengers of a vehicle. The information may be expressed or inferred passenger profiles (e.g., inferred by a rider preference model 240) that comprise information about a passenger's comfort or vehicle action preferences” ¶ 50),
before driving of the vehicle is initiated, provide a driver of the vehicle with guidance regarding the at least one passenger based on the information regarding the at least one passenger (Omari: Fig. 500 Element 510, “Ride providers may include, e.g., human operators” ¶ 65, “Identity management services 704 may also manage and control access to provider and requestor data maintained by the transportation management system 702, such as driving and/or ride histories include ride evaluations, vehicle data, personal data, preferences, usage patterns as a ride provider and as a ride requestor, profile pictures, linked third-party accounts (e.g., credentials for music or entertainment services, social-networking systems, calendar systems, task-management systems, etc.), vehicle action intensity preferences, and any other associated information” ¶ 66, see also ¶ 74 Note: Wherein it can be seen in Fig. 5 that before the vehicle performs an action (such as driving) the passenger information is first accessed and provided to a driver of the vehicle),
generate a second machine learning model for each passenger based on the information regarding the at least one passenger (Omari: “the model generator system 220 may generate a predicted comfort model 230 for predicting perceived comfort for a given vehicle action to be performed by an autonomous vehicle” ¶ 29, “A ride preference classification 245 for a passenger of a vehicle may be output by the rider preference model 240 and passed to the predicted comfort model 230 as an additional input to use when determining a predicted comfort level for a proposed vehicle action” ¶ 31), and
wherein the vehicle processor is configured to:
generate input information to be input to the second machine learning model for the second machine learning model to predict an experience of the at least one passenger based on a detection result of the vehicle sensor (Omari: “The model may be used by an autonomous vehicle to evaluate possible vehicle actions while selecting which action of a plurality of proposed actions to perform. In use, the predicted comfort model 230 may receive as input information such as an urgency of the vehicle response, information describing the scenario and environment of a vehicle, and one or more possible vehicle actions ... the scenario may comprise sensor data for the scenario, an environmental representation of the scenario, passenger data, communication data, any other suitable information or combination thereof” ¶ 29), and
generate feedback in natural language to be provided to a driver of the vehicle for improving the experience of the at least one passenger based on at least the experience of the at least one passenger predicted by the second machine learning model (Omari: “a predicted comfort model 230 may be used to provide guidance to a human driver of a vehicle, so as to optimize the experience of a passenger of the vehicle. For example, the predicted comfort model 230 may be used to provide notifications to a vehicle operator to slow down, speed up, brake less aggressively, etc., as appropriate” ¶ 35, see also ¶ 65, 66, 67, Note: Wherein the Examiner notes that the “natural language” is described in the Specification as providing “voice output, display on the speedometer, display on a navigation system, etc.” ¶ 65, “for example, that it is necessary to select a driving course having few curves” ¶ 35, “for example, the need to suppress sudden acceleration and deceleration of the vehicle 12” ¶ 36, which is what is disclosed by Omari, as seen in the citations above)
wherein the feedback is provided to the driver after the driving of the vehicle has initiated (Omari: Fig. 5 Elements 540-570, “a predicted comfort model 230 may be used to provide guidance to a human driver of a vehicle, so as to optimize the experience of a passenger of the vehicle. For example, the predicted comfort model 230 may be used to provide notifications to a vehicle operator to slow down, speed up, brake less aggressively, etc., as appropriate” ¶ 35, “Generating all proposed vehicle actions at once may allow the predicted comfort level of the vehicle actions to be considered at once. This may allow for the optimal vehicle action to be found and executed. Depending on the presented scenario, proposed vehicle actions may differ significantly” ¶ 53, see also ¶ 55, 62).
Regarding claim 2, Omari teaches the passenger experience optimization system according to claim 1, wherein the vehicle is configured to receive information regarding the driver of the vehicle (Omari: “Ride providers may include, e.g., human operators or operating autonomous vehicles ... computations associated with a service task may be performed by a combination of the transportation management system 702 (including any number of servers, databases, etc.), one or more devices associated with the provider (e.g., devices integrated with the managed vehicles 714, provider's computing devices 716 and tablets 720” ¶ 65), and
the vehicle processor is configured to generate the feedback based on the experience of the at least one passenger and the information regarding the driver of the vehicle (Omari: “a predicted comfort model 230 may be used to provide guidance to a human driver of a vehicle, so as to optimize the experience of a passenger of the vehicle. For example, the predicted comfort model 230 may be used to provide notifications to a vehicle operator to slow down, speed up, brake less aggressively, etc., as appropriate” ¶ 35, “the passenger may be asked to rate their perceived level of comfort, whether the response of the vehicle was what they would have done or would have expected another driver to do, or provide other feedback related to any particular vehicle action, trajectory, or maneuver. The evaluation may be associated with and stored in conjunction with the vehicle action to be used by the model generator system 220” ¶ 23, see also ¶ 74),
wherein the information regarding the driver of the vehicle comprises at least one of driving skill, driving tendencies, and characteristics of the driver (Omari: “Ride providers may include, e.g., human operators ... ” ¶ 65, “perform authorization services for requestors and providers and manage their interactions and data with the transportation management system 702. This may include, e.g., authenticating the identity of providers and determining that they are authorized to provide services through the transportation management system 702” ¶ 66, Note: Wherein under BRI, the plain language of characteristics of the driver can encompass nearly any trait regarding a driver and thus a driver’s identity meets this description because it expresses a trait that can distinguish the driver).
Regarding claim 3, Omari teaches the passenger experience optimization system according to claim 1, wherein the server device processor is configured to generate the second machine learning model for each passenger based on the information regarding the at least one passenger and the detection result of the vehicle sensor after the at least one passenger boards the vehicle and before the interaction with the at least one passenger is performed (Omari: “The sensors may include sensors to measure cabin conditions, such as the number, approximate weight, age, or status of passengers, whether one or more windows are open, etc.” ¶ 21, “a model 230 for predicting perceived comfort of a vehicle action or a model 240 for predicting or inferring rider preferences” ¶ 22, “the predicted comfort model may be used to score a variety of different proposed trajectories to determine and select the proposed trajectory that has a highest comfort or elegance score for human passengers overall, the subset of passengers currently in the vehicle, or a specific passenger currently in the vehicle” ¶ 29).
Regarding claim 4, Omari teaches the passenger experience optimization system according to claim 1, wherein the server device processor is configured to:
search for one or more base passenger models which match the at least one passenger from a model pool based on the information regarding the at least one passenger (Omari: “a transportation system to be able to determine the passenger's vehicle action intensity preferences based on actual evaluations submitted by the passenger or passengers with one or more commonalities to the passenger” ¶ 32, “passengers may be classified according to their preference for vehicle responses (e.g., “aggressive,” “moderate,” or “conservative”). The expected comfort level may be personalized and determined for the individual passenger or group of passengers” ¶ 55), and
generate the second machine learning model using the one or more base passenger models (Omari: “the vehicle control system may evaluate the predicted comfort level of each proposed vehicle action (e.g., by use of a predicted comfort model 230)” ¶ 54, see also ¶ 58), and
the model pool includes at least one pretrained machine learning model which corresponds to various types of passengers (Omari: “the expected comfort level may be based on the particular passenger or group of passengers in the vehicle. For example, passengers may be classified according to their preference for vehicle responses (e.g., “aggressive,” “moderate,” or “conservative”). The expected comfort level may be personalized and determined for the individual passenger or group of passengers” ¶ 55, see also ¶ 56).
Regarding claim 5, Omari teaches the passenger experience optimization system according to claim 4, wherein the server device processor is configured to generate the second machine learning model by executing fine-tuning of the one or more base passenger models based on the information regarding the at least one passenger (Omari: “the received evaluation may be used to train or update a model for predicting a comfort level of a vehicle action, to train or update a model for inferring a preferred vehicle action intensity of a passenger, or for any other suitable purpose of the vehicle control system. The received evaluation may be stored in a record of the performed vehicle action in conjunction with data regarding the environment of the vehicle before, during, or after the vehicle action is performed, operational data of the vehicle, and other suitable information” ¶ 58).
Regarding claim 6, Omari teaches the passenger experience optimization system according to claim 1, wherein the server device processor is configured to:
execute a passenger experience simulation under various conditions based on the information regarding the at least one passenger (Omari: “a simulated vehicle may include a simulation of driving or passenger experiences provided to passenger, for example, through a computer-based or virtual-reality experience” ¶ 36),
generate a training data set including condition and simulated experience (Omari: “As the vehicles operate in their respective environments, their actions may be recorded by a variety of sensors and computing systems. These sensors and computing systems may store vehicle actions and information about the vehicle actions locally within the vehicle system or may upload them to one or more remote computing systems” ¶ 36), and
train the second machine learning model using the training data set (Omari: “the vehicle computing systems may be configured to identify particular scenarios and vehicle actions that are likely to be novel, provoke strong reactions from passengers, or would otherwise be of more interest when used in a training dataset” ¶ 36, see also ¶ 35).
Regarding claim 7, Omari teaches the passenger experience optimization system according to claim 6, wherein the various conditions include a plurality of positions of the vehicle and a plurality of control parameters including steering, driving, and braking of the vehicle (Omari: “The operations of a simulated vehicle during the “ride” may correspond to operations of a non-virtual vehicle in a similar non-virtual environment” ¶ 36, “The model 240 may be trained to provide a nuanced preference accounting for particular details of the environment (e.g., busy street, weather factors, etc.), the scenario prompting the response (e.g., approaching a stop sign, following a slow-moving truck, etc.), the proposed vehicle action (e.g., braking, or accelerating to overtake), or other suitable details” ¶ 33).
Regarding claim 8, the claim recites analogous limitations to previously rejected claim 1, and is therefore rejected under the same premise.
Regarding claim 9, Omari teaches a passenger experience optimization device provided in a vehicle including a vehicle sensor and a passenger user interface for interacting with at least one passenger of the vehicle using a first machine learning model (Omari: “Embodiments according to the invention are in particular disclosed in the attached claims directed to a method, a storage medium, a system and a computer program product” ¶ 12, “A model generator system 220 may receive sensor data 210 from one or more sensors of the vehicle” ¶ 21, The evaluation system may provide an interface to allow the user to provide feedback regarding their perceived comfort during the course of their time in the vehicle” ¶ 37), wherein
...
In regards to the remainder of claim 9, the claim recites analogous limitations to claim 1, and is therefore rejected under the same premise.
Regarding claim 10, Omari teaches the passenger experience optimization system of claim 1, wherein the generating feedback occurs repeatedly until the driving ceases (Omari: “The predictive rider preference model 240 may be used for classifying passenger preferences for qualities of the ride during vehicle action responses, such as intensity, comfort, elegance, or experience ... . A ride preference classification 245 for a passenger of a vehicle may be output by the rider preference model 240 and passed to the predicted comfort model 230 as an additional input to use when determining a predicted comfort level for a proposed vehicle action” ¶ 31, “a predicted comfort model 230 may be used to provide guidance to a human driver of a vehicle, so as to optimize the experience of a passenger of the vehicle. For example, the predicted comfort model 230 may be used to provide notifications to a vehicle operator to slow down, speed up, brake less aggressively, etc., as appropriate” ¶ 35).
Regarding claim 11, Omari teaches the passenger experience optimization system of claim 1, wherein the first machine learning model is further configured to interact with the at least one passenger using a chat user interface (Omari: “The feedback may be requested and/or provided through a user interface of an application associated with a transportation management system that is executing on a user device of the passenger. The feedback may be requested through a messaging service, such as email or text messaging, associated with or used by the transportation management system” ¶ 58).
Regarding claim 12, Omari teaches the passenger experience optimization system of claim 1 wherein the predicting the experience of the at least one passenger comprises outputting at least one of a satisfaction score (Omari: “The vehicle control system may select the proposed vehicle action that it determines has the greatest likelihood of satisfying an expected comfort level of a passenger of the vehicle” ¶ 55, see also ¶ 56), an anxiety score, a fear score, information indicating motion sickness, information indicating drowsiness of the at least one passenger, information indicating fatigue of the at least one passenger, information indicating hunger of the at least one passenger, information indicating thirst of the at least one passenger, a degree to which the at least one passenger wishes to use the toilet.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Iliffe-Moon (20210240277) is in the similar field of endeavor as the claimed invention of driver and passenger feedback.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLINT V PHAM whose telephone number is (571)272-4543. The examiner can normally be reached M-F 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached at 571-272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/C.P./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
Prosecution Insights