DETAILED ACTION
Claims 1-9, 11-12 are considered in this office action. Claims 1-9 and 11-12 are pending examination.
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 Arguments
Applicant's arguments filed 12/23/2024 have been fully considered but they are not persuasive. The applicant argues that “As best understood by the Applicant, Arditi in view of Qawami does not disclose or render obvious at least "adjust an environmental parameter of the vehicle, including air quality" as recited in revised claim 1. It is submitted that air-conditioning setting, windows, etc., does not render obvious adjusting "air quality" as environmental parameters of the vehicle as recited in revised claim 1.”
The office respectfully disagrees with the applicant’s assertion. The prior art Arditi teaches adjust at least one operating parameter of a plurality of operating parameters of the vehicle to improve the state of the rider: and adjust an environmental parameter of the vehicle, including air quality, based on the determined state of the rider(Para [0022] Line 4-8 : “For example, currently detected sensor data and/or previously known information about the ride requestor may be used to predict the ride requestor's likes and dislikes, which in turn may be used to configure the vehicle (e.g., entertainment, air-conditioning setting, lighting, windows, etc.).” here the examiner is interpreting configuring the vehicle windows to be opened or closed based on the user need of needing fresh air. The applicant specification as indicated by the applicant in Para [0355] also provide such suggestion.).
The examiner believes he has responded to all the arguments presented by the applicant at this time. However, if the applicant believes that the examiner has missed any arguments to respond, the applicant is invited to call the examiner directly to expedite the process.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claims 1-9, 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Arditi (US2019/0197430A1) in view of Qawami et al. (US2019/0049969A1) and herein after will be referred as Arditi and Qawami.
Regarding Claim 1, Arditi teaches a system for operating a vehicle based on a state of a rider, the system comprising (Para [0023] Fig.1 #transportation management system #130):
Arditi also teaches indicate a change in the state of the rider in the vehicle via recognition of patterns of the wearable sensor data of the rider in the vehicle indicative of the change when compared with rider state patterns located in stored wearable sensor data; a vehicle control system configured to control an operation of the vehicle by adjusting the plurality of operating parameters of the vehicle; and a feedback loop through which the indication of the change in the state of the rider in the vehicle is communicated between the vehicle control system and the artificial intelligence system, adjust the at least one operating parameter in response to the indication of the change in the state of the rider in the vehicle: (Para [0043] : “Continuing with the example provided above, even if the internal vehicle temperature is adjusted to 70° F. (i.e., the preferred temperature stated in the ride requestor's 110 personal information), real-time sensor data may reveal that the ride requestor 110 is in fact uncomfortably hot or cold. For example, from the real-time sensor data, the vehicle's computing device may extract various features that are associated with a person being hot, such as body temperature that is higher than a threshold, perspiration, certain body language/gestures (e.g., self-fanning, panting, etc.), and warm clothing. Similarly, the vehicle's computing device may also extract various features that signify that a person is cold, such as body temperature that is lower than a threshold, shivering, goosebumps, certain body language/gestures (e.g., self-hugging, rubbing hands together, breathing into hands, etc.), and light clothing. Based on such information extracted from the real-time sensor data, the vehicle 540 may further adjust its internal temperature accordingly. As another example, even if the currently playing music is set to the ride requestor's 110 preference as indicated in the requestor's 110 personal information, the requestor 110 may in fact not be enjoying the music. The ride requestor's 110 negative reactions may be signified by real-time sensor data capturing the ride requestor 110 putting on headphones or cringing when a song begins. In response, the vehicle 540 may turn off the music, lower the volume, or switch to a different genre. If instead the vehicle 540 detects real-time positive reactions from the ride requestor 110 (e.g., the ride requestor bobbing to the music), then the vehicle 540 may continue to play the music, play similar music, and/or raise the volume. Real-time sensor data may also reveal that the ride requestor 110 may currently be stressed (e.g., nail-biting, frowning, sighing, etc.) or tired (e.g., closed eyes, head resting on the head rest, etc.). While such information may not directly indicate whether the ride requestor likes or dislikes the music, the information may nevertheless be used to as evidence or corroborating evidence that music is likely unwelcomed and should be turned off.” And Figure 11 for the Feedback loop).
Arditi also teaches adjust at least one operating parameter of a plurality of operating parameters of the vehicle to improve the state of the rider: and adjust an environmental parameter of the vehicle, including air quality, based on the determined state of the rider(Para [0022] Line 4-8 : “For example, currently detected sensor data and/or previously known information about the ride requestor may be used to predict the ride requestor's likes and dislikes, which in turn may be used to configure the vehicle (e.g., entertainment, air-conditioning setting, lighting, windows, etc.).” here the examiner is interpreting configuring the vehicle he windows settings to be opened or closed based on the user need of needing fresh air. The applicant specification as indicated by the applicant in Para [0355] also provide such suggestion.).
Arditi teaches an artificial intelligence system and processing the data and predicting the state of the rider but may not expressly teaches the sensors to be wearable sensors as claimed by the applicant, so the office is bringing in another reference Qawami from the similar art to teach those limitations.
Qawami teaches an artificial intelligence system configured to: process a sensory input received as wearable sensor data from a wearable device in the vehicle to determine the state of the rider in the vehicle (Para [0055] Line 1-7: “In some embodiments, occupant assessment subsystem 204 may include a pre-trained neural network 900 to assess condition of an occupant. The input variables (x) 902 may include objects recognized in images of the inward-looking cameras of the vehicle, sensor data, such as heart rate, GSR readings from sensors on mobile or wearable devices carried or worn by the occupant.” And Para [0040]: “Occupant condition assessment subsystems 204 may be configured to process the images collected by inward looking camera sensors to determine whether any of the occupants are injured. Occupant condition assessment subsystems 204 may also be configured to process the ECG and GSR data to determine the health and emotional (stress) state of the occupants, including the occupants heart rate, blood pressure, stress levels and so forth.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Arditi to incorporate the teachings of Qawami to include an artificial intelligence system configured to: process a sensory input received as wearable sensor data from a wearable device in the vehicle to determine the state of the rider in the vehicle. Doing so would optimize the rider experience while using the rideshare.
Regarding Claim 2, Arditi in view of Qawami teaches the system of claim 1.
Arditi also teaches wherein the artificial intelligence system is further configured to classify the state of the rider into one of a plurality of predefined categories (Para [0021] Line 20-29: “In particular embodiments, sensors in the vehicle may be used to capture real-time sensor data associated with the ride requestor. Real-time sensor data may include, for example, video, image, audio, infrared, temperature, 3D modeling, and any other suitable types of data that capture the ride requestor's current state. In particular embodiments, the real-time sensor data may be processed using one or more machine-learning models trained based on similar types of data to predict real-time features of the ride requestor. The real-time features may include, for example, the ride requestor's current mood (e.g., happy, angry, sad, etc.), stress level, comfort level with respect to vehicle amenities (e.g., temperature, audio, entertainment, etc.), health condition, and/or any other features that may characterize or represent the requestor's current state.”).
Regarding Claim 3, Arditi in view of Qawami teaches the system of claim 2.
Arditi also teaches wherein the plurality of predefined categories includes at least one of[[:]] relaxed, stressed, alert, [[and]]or fatigued (Para [0021] Line 20-29: “In particular embodiments, sensors in the vehicle may be used to capture real-time sensor data associated with the ride requestor. Real-time sensor data may include, for example, video, image, audio, infrared, temperature, 3D modeling, and any other suitable types of data that capture the ride requestor's current state. In particular embodiments, the real-time sensor data may be processed using one or more machine-learning models trained based on similar types of data to predict real-time features of the ride requestor. The real-time features may include, for example, the ride requestor's current mood (e.g., happy, angry, sad, etc.), stress level, comfort level with respect to vehicle amenities (e.g., temperature, audio, entertainment, etc.), health condition, and/or any other features that may characterize or represent the requestor's current state.”).
Regarding Claim 4, Arditi in view of Qawami teaches the system of claim 1.
Arditi also teaches wherein the artificial intelligence system is further configured to predict a future state of the rider based on historical data related to the state of the rider under similar conditions (Para [0096]: “Using historical data, the system 1360 in particular embodiments may predict and provide ride suggestions in response to a ride request. In particular embodiments, the system 1360 may use machine-learning, such as neural networks, regression algorithms, instance-based algorithms (e.g., k-Nearest Neighbor), decision-tree algorithms, Bayesian algorithms, clustering algorithms, association-rule-learning algorithms, deep-learning algorithms, dimensionality-reduction algorithms, ensemble algorithms, and any other suitable machine-learning algorithms known to persons of ordinary skill in the art. The machine-learning models may be trained using any suitable training algorithm, including supervised learning based on labeled training data, unsupervised learning based on unlabeled training data, and/or semi-supervised learning based on a mixture of labeled and unlabeled training data.”).
Regarding Claim 5, Arditi in view of Qawami teaches the system of claim 4.
Arditi also teaches wherein the artificial intelligence system is further configured to adjust the operating parameter of the vehicle based on the predicted future state of the rider (Para [0064] : “The current contextual information provided by the sensor data may be used to improve personalization predictions ………Such signals may include, for example, movement speed (e.g., based on video data capturing the speed in which the ride requestor opened the vehicle's door and entered the vehicle), the force used in closing the door (e.g., based on sound, speed, and vibration), the requestor's speech patterns (e.g., based on tone, pitch, rate), breathing pattern (e.g., based on sound), whether the requestor is engaged in conversation with another passenger or via a phone (e.g., based on voices, speech pattern, video data), the requestor's expression (e.g., based on images and voice data), attire or hair style (e.g., based on video data), body temperature (e.g., based on infrared sensor data), the outside weather or temperature (e.g., based on external light sensor, water sensor, and thermostat data)”) .
Regarding Claim 6, Arditi in view of Qawami teaches the system of claim 1.
Arditi also teaches wherein the operating parameter of the vehicle includes at least one of[[:]] temperature, lighting, seat position, music volume, [[and]]or driving speed (Para [0022] : “For example, currently detected sensor data and/or previously known information about the ride requestor may be used to predict the ride requestor's likes and dislikes, which in turn may be used to configure the vehicle (e.g., entertainment, air-conditioning setting, lighting, windows, etc.).”).
Regarding Claim 7, Arditi in view of Qawami teaches the system of claim 1.
Qawami teaches wherein the wearable device includes sensors for monitoring physiological responses of the rider, including at least one of heart rate, skin conductivity, or respiratory rate (Para [0040]: “Sensor data collected by other sensors 220, e.g., sensors of mobile or wearable devices carried or worn by occupants of the vehicle, may include electrocardiography (ECG) data collected by ECG sensors, galvanic skin response (GSR) data collected by GSR sensors and so forth. Occupant condition assessment subsystems 204 may be configured to process the images collected by inward looking camera sensors to determine whether any of the occupants are injured. Occupant condition assessment subsystems 204 may also be configured to process the ECG and GSR data to determine the health and emotional (stress) state of the occupants, including the occupants heart rate, blood pressure, stress levels and so forth.”).
Regarding Claim 8, Arditi in view of Qawami teaches the system of claim 1.
Arditi also teaches wherein the vehicle control system is further configured to adjust the plurality of vehicle operating parameters in a sequence prioritized based on an urgency of the change in the state of the rider (Para [0022]: “As yet another example, interior sensor data may be used to detect and respond to emergencies appropriately (e. g., urgent health conditions, etc.). The various embodiments described herein, therefore, enable vehicles dispatched for transporting ride requestors to be dynamically configured to suit the preferences of the ride requestors and provide additional services such as emergency detection. Such enhancements enable the dispatched vehicles to provide ride requestors with improved ride experience and safety.”).
Regarding Claim 9, Arditi in view of Qawami teaches the system of claim 1.
Arditi also teaches wherein the artificial intelligence system is further configured to learn and adapt its pattern recognition over time based on accumulated wearable sensor data and feedback received from the rider ( Para [0052] : “By iteratively training in this manner, the machine-learning model 720 may “learn” from the different training data samples and become better at generating predictions 730 that are similar to the known “ground truth” represented by the training labels 719.”).
Regarding Claim 10, Arditi in view of Qawami teaches the system of claim 1.
Arditi also teaches wherein the artificial intelligence system is further configured to adjust environmental parameters of the vehicle, including at least one of air quality or scent diffusion, based on the determined state of the rider (Para [0022] Line 4-8 : “For example, currently detected sensor data and/or previously known information about the ride requestor may be used to predict the ride requestor's likes and dislikes, which in turn may be used to configure the vehicle (e.g., entertainment, air-conditioning setting, lighting, windows, etc.).”).
Regarding Claim 11, Arditi in view of Qawami teaches the system of claim 1.
Arditi also teaches wherein the artificial intelligence system is further configured to adjust the audio system of the vehicle to play content, wherein the content is conducive to improving the state of the rider, based on the determined state of the rider (Para [0064] : “the sensors may capture signals from the ride requestors indicative of their mood, whether they are stressed or relaxed, whether they are occupied or engrossed in something or with someone, whether they feel hot or cold, etc. The contextual state of the ride requestor may be relevant to the requestor's current preferences. For example, if the requestor is stressed or in a bad mood, soothing music may be welcomed but not conversation with the driver; if the requestor is in a good mood, upbeat music may be appropriate rather than news; if the requestor is engaged in conversation, any distraction such as audio or video may be unwanted; and if the requestor is hot or cold, the vehicle's air conditioning unit may adjust the interior temperature. It should be appreciated that, in particular embodiments, the system may not need to determine the exact categorical context (e.g., mood, stress level, etc.) and/or how it correlates to particular personalization preferences. Rather, machine-learning may be used to learn the relationship between the sensor signals and personalization preferences. Such signals may include, for example, movement speed (e.g., based on video data capturing the speed in which the ride requestor opened the vehicle's door and entered the vehicle), the force used in closing the door (e.g., based on sound, speed, and vibration), the requestor's speech patterns (e.g., based on tone, pitch, rate), breathing pattern (e.g., based on sound), whether the requestor is engaged in conversation with another passenger or via a phone (e.g., based on voices, speech pattern, video data), the requestor's expression (e.g., based on images and voice data), attire or hair style (e.g., based on video data), body temperature (e.g., based on infrared sensor data), the outside weather or temperature (e.g., based on external light sensor, water sensor, and thermostat data), traffic congestion (e.g., based on ultrasound, LiDAR, odometer, and accelerometer data), and any other contextual signals that may be relevant to the ride requestor's personalization preference.”).
Regarding Claim 12, Arditi in view of Qawami teaches the system of claim 1.
Arditi teaches wherein the artificial intelligence system is further configured to control the navigation system of the vehicle to suggest route changes to improve the state of the rider, including avoiding traffic congestion in response to determining the rider is stressed (Para [0064]: “The current contextual information provided by the sensor data may be used to improve personalization predictions. Conceptually, for example, the sensors may capture signals from the ride requestors indicative of their mood, whether they are stressed or relaxed, whether they are occupied or engrossed in something or with someone, whether they feel hot or cold, etc. The contextual state of the ride requestor may be relevant to the requestor's current preferences. For example, if the requestor is stressed or in a bad mood, soothing music may be welcomed but not conversation with the driver; if the requestor is in a good mood, upbeat music may be appropriate rather than news; if the requestor is engaged in conversation, any distraction such as audio or video may be unwanted; and if the requestor is hot or cold, the vehicle's air conditioning unit may adjust the interior temperature. It should be appreciated that, in particular embodiments, the system may not need to determine the exact categorical context (e.g., mood, stress level, etc.) and/or how it correlates to particular personalization preferences. Rather, machine-learning may be used to learn the relationship between the sensor signals and personalization preferences. Such signals may include, for example, movement speed (e.g., based on video data capturing the speed in which the ride requestor opened the vehicle's door and entered the vehicle), the force used in closing the door (e.g., based on sound, speed, and vibration), the requestor's speech patterns (e.g., based on tone, pitch, rate), breathing pattern (e.g., based on sound), whether the requestor is engaged in conversation with another passenger or via a phone (e.g., based on voices, speech pattern, video data), the requestor's expression (e.g., based on images and voice data), attire or hair style (e.g., based on video data), body temperature (e.g., based on infrared sensor data), the outside weather or temperature (e.g., based on external light sensor, water sensor, and thermostat data), traffic congestion (e.g., based on ultrasound, LiDAR, odometer, and accelerometer data), and any other contextual signals that may be relevant to the ride requestor's personalization preference.”).
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDHESH K JHA whose telephone number is (571)272-6218. The examiner can normally be reached M-F:0800-1700.
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/ABDHESH K JHA/Primary Examiner, Art Unit 3668
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