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 .
DETAILED ACTION
Claims 1-2,6-7, 9-13,17-18,20-26 have been examined. Claims 3-5, 8, 14-16, 19 are canceled.
Terminal Disclaimer
The terminal disclaimer filed on July 22, 2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US 18,925,847 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-2,6-7, 9-13,17-18,20-26 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.
Response to Amendment
Examiner acknowledges the changes made to the claims by applicant via amendment. The rejections of claims 7 and 18 from previous office action under 35 U.S.C. 112 have been overcome as a result of amendment and remarks.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 1-2,6-7,9-10,12,17-18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hampiholi (US2016/0267335A1) in view of Salomonsson et al. (US2014/0222280A1), further in view of Seow et al. (US2014/0003710A1).
Claim 1:
Hampiholi discloses an in-vehicle computing system having an external device interface communicatively connecting the in-vehicle computing system to a mobile device, an inter-vehicle system communication module connecting the in-vehicle computing system to vehicle systems, a processor, and a storage device storing instructions executable by the processor. See Hampiholi, ¶¶0021-0034.
With respect to the limitation “A computing system of a vehicle, comprising:”, Hampiholi shows an “in-vehicle computing system” of a vehicle. Hampiholi describes the in-vehicle computing system as including processing and storage components and communication with vehicle systems and external devices. See Hampiholi, ¶¶0021-0034. Thus, Hampiholi shows “A computing system of a vehicle, comprising:”.
“vehicle monitoring system comprising at least one sensor;”, Hampiholi shows vehicle and sensor monitoring components. In particular, Hampiholi discloses a distraction monitoring system including one or more sensors and an outward-facing camera, and further discloses receiving sensor data and image data representing objects in a vehicle environment. See Hampiholi, ¶¶0032-0034 and ¶¶0046-0049. Hampiholi therefore shows “vehicle monitoring system comprising at least one sensor.”
“network communication interface;”, Hampiholi shows an external device interface and communication functionality for communicatively connecting the in-vehicle computing system with a mobile device and other vehicle systems. Hampiholi further discloses communication through wireless communication links, including Bluetooth, Wi-Fi, NFC, and cellular communication. See Hampiholi, ¶¶0023-0025 and ¶¶0034-0037. Thus, Hampiholi shows “network communication interface.”
“processors;”, Hampiholi expressly shows a processor of the in-vehicle computing system. Hampiholi states that the in-vehicle computing system comprises a processor and that the processor executes stored instructions to process received data and determine vehicle, driver, and object states. See Hampiholi, ¶¶0021-0034. Thus, Hampiholi shows “processors.”
With respect to the limitation “memory storing instructions that, when executed, cause:”, Hampiholi shows a storage device storing instructions executable by the processor. Hampiholi expressly describes a first storage device storing instructions executable by a first processor and further describes instructions for receiving sensor/image data and determining driver, object, and vehicle states. See Hampiholi, ¶¶0021-0034 and ¶¶0056-0058. Thus, Hampiholi shows “memory storing instructions that, when executed, cause:”.
“wirelessly connect, over one or more networks, with a computing device of a user of the vehicle while the vehicle is in operation;”, Hampiholi shows wireless communication between the in-vehicle computing system and a mobile computing device. Hampiholi discloses that an external device interface communicatively connects the in-vehicle computing system to a mobile device and that the communication link may be wireless, including Bluetooth, Wi-Fi, NFC, or cellular communication. See Hampiholi, ¶¶0023-0025. Hampiholi further describes the mobile device as associated with the driver and the in-vehicle computing system as operating in connection with vehicle operation. See Hampiholi, ¶¶0034-0037. Thus, Hampiholi shows “wirelessly connect, over one or more networks, with a computing device of a user of the vehicle while the vehicle is in operation.”
“while vehicle is moving, process sensor data, using vehicle monitoring system, to detect an object ahead;”, Hampiholi shows processing sensor and image data representing objects in the vehicle environment. In particular, Hampiholi discloses an outward-facing camera and other sensors that provide object data concerning a vehicle environment, and further discloses processing the object data to determine object states and other information concerning objects in the vicinity of the vehicle. See Hampiholi, ¶¶0046-0049 and ¶¶0056-0058. Hampiholi further discloses that vehicle state information includes an indication of an operating condition of the vehicle. See Hampiholi, ¶¶0054-0055. Thus, Hampiholi shows “while vehicle is moving, process sensor data, using vehicle monitoring system, to detect an object ahead.”
With respect to the limitation “based on detected object, generate an alert communicated over one or more networks to computing device of user;”, Hampiholi shows determining an action based on object state and vehicle state and performing a selected action. Hampiholi further discloses an in-vehicle computing system communicatively connected to a mobile device and other devices and providing warnings or other actions responsive to determined conditions. See Hampiholi, ¶¶0056-0058 and ¶¶0061-0063. Accordingly, Hampiholi shows the claimed vehicle monitoring, object-state determination, and responsive warning/action architecture. However, to the extent that the limitation requires the particular combination of “based on detected object, generate an alert communicated over one or more networks to computing device of user,” Hampiholi is Not Disclosed as expressly teaching that exact combination.
Salomonsson et al. supplies the missing vehicle-specific relationship. With respect to the limitation “based on detected object, generate an alert communicated over one or more networks to computing device of user;”, Salomonsson shows a vehicle vision system using image processing of image data captured by a forward-facing camera to determine conditions involving another vehicle ahead and a traffic light, and generating an alert or notification to the driver. Salomonsson further discloses communicating the alert or notification to a driver's PDA, cell phone, smartphone, or other communication device. See Salomonsson et al., ¶¶0005-0007 and ¶¶0024-0029. Thus, Salomonsson shows “based on detected object, generate an alert communicated over one or more networks to computing device of user.”
Hampiholi, however, does not disclose the limitation “wherein instructions comprise a machine learning model that processes the sensor data to determine a pattern for the detected object.” Although Hampiholi discloses processing sensor and image data and determining object states, the identified Hampiholi disclosure does not expressly disclose a machine learning model that processes the sensor data to determine a pattern for the detected object. Accordingly, this limitation is Not Disclosed by Hampiholi.
Seow et al. supplies the missing machine-learning limitation. With respect to the limitation “wherein instructions comprise a machine learning model that processes the sensor data to determine a pattern for the detected object,” Seow discloses a video analysis system that analyzes sequences of video frames and uses machine learning to learn behavior from streaming video data. Seow discloses identifying an object in a video frame, tracking the identified object through successive frames, extracting features associated with the object, and using the extracted features for machine-learning analysis. See Seow et al., US2014/0003710A1, ¶¶0020-0024.
Seow further shows the machine-learning processing of object features. With respect to “processes the sensor data to determine a pattern for the detected object,” Seow discloses determining features associated with an object, including appearance and kinematic characteristics, and applying machine-learning techniques to the feature information to identify and learn patterns associated with objects and their behavior. See Seow et al., ¶¶0023-0030. Seow's system uses feature vectors and clustering to identify relationships among observed object features and to learn expected object behavior. See Seow et al., ¶¶0023-0030. Thus, Seow shows the claimed machine-learning model/pattern-processing functionality.
Seow, however, does not disclose the limitation “A computing system of a vehicle”, nor does Seow disclose “wirelessly connect, over one or more networks, with a computing device of a user of the vehicle while the vehicle is in operation,” “while vehicle is moving, process sensor data, using vehicle monitoring system, to detect an object ahead,” or “based on detected object, generate an alert communicated over one or more networks to computing device of user.” Those vehicle-specific limitations are therefore Not Disclosed by Seow.
Salomonsson further shows the claimed forward-looking vehicle sensing and alerting relationship. With respect to the limitation “while vehicle is moving, process sensor data, using vehicle monitoring system, to detect an object ahead,” Salomonsson discloses a forward-facing camera and image processing for determining when another vehicle is ahead of the equipped vehicle and for determining traffic-light conditions. See Salomonsson et al., ¶¶0005-0007. Salomonsson therefore shows “while vehicle is moving, process sensor data, using vehicle monitoring system, to detect an object ahead.”
Salomonsson, however, does not disclose the limitation “wherein instructions comprise a machine learning model that processes the sensor data to determine a pattern for the detected object.” Accordingly, the machine-learning-model limitation is Not Disclosed by Salomonsson.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle computing and sensor-processing system of Hampiholi to incorporate the machine-learning object-feature and pattern-processing techniques of Seow et al., and to use the forward-object detection and user-device alerting arrangement of Salomonsson, because Hampiholi already provides a vehicle computing system having sensors, processors, storage, vehicle-environment object processing, and wireless communication with a mobile device, while Seow provides a known machine-learning technique for extracting features from object data and learning object patterns, and Salomonsson provides a vehicle-specific implementation in which forward-looking image processing identifies an object or traffic condition and an alert is communicated to a driver's computing device.
The modification would use Seow's machine-learning processing as the object-data processing technique within Hampiholi's existing vehicle computing architecture. Seow expressly teaches processing video/object data, extracting object features, and using machine learning to learn patterns associated with objects. See Seow et al., ¶¶0020-0030. Hampiholi already provides sensor and image data concerning objects in a vehicle environment and an in-vehicle processor and storage architecture for processing such data. See Hampiholi, ¶¶0021-0034 and ¶¶0046-0058. Salomonsson further demonstrates that vehicle forward-looking image processing can be used to detect an object or traffic condition and generate a notification to a driver's mobile computing device. See Salomonsson et al., ¶¶0005-0007 and ¶¶0024-0029.
The proposed combination would retain the intended vehicle monitoring and alert functions of Hampiholi while applying Seow's machine-learning technique to the vehicle sensor/image data to provide learned object-pattern processing and using Salomonsson's known vehicle-to-user-device alert pathway for communicating the resulting condition to the user. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results.
Claim 2:
“wherein detecting the object comprises detecting traffic light changing states,” Hampiholi is Not Disclosed. Salomonsson, however, expressly teaches this limitation. Salomonsson discloses detecting the object comprising detecting a traffic light changing states See Salomonsson, ¶[0020 and 0029 and 0014-0015].
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle computing and sensor system of Hampiholi to incorporate the machine-learning object-pattern processing taught by Seow and the traffic-light state detection and driver-device alerting taught by Salomonsson. Hampiholi, Seow, and Salomonsson are directed to systems that process sensor or camera information concerning a vehicle environment and provide information or warnings based on detected conditions. Hampiholi provides the vehicle computing, sensor, processor, memory, mobile-device communication, and object-detection framework; Seow provides a specifically disclosed machine-learning technique for processing object data and learning object patterns; and Salomonsson provides a specifically disclosed vehicle vision technique for detecting traffic-light state changes and communicating an alert to a driver's communication device. The proposed combination would therefore have placed the respective teachings into the same vehicle safety-monitoring architecture without requiring a change in the basic function of the respective components.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. In particular, applying Seow's disclosed machine-learning object-pattern analysis to sensor/image data in Hampiholi's vehicle computing system would provide pattern-based processing of detected objects, while applying Salomonsson's disclosed traffic-light detection and driver-device notification to the same vehicle monitoring architecture would provide detection of traffic-light state changes and notification of the user. The combination would have had a reasonable expectation of success because each reference expressly discloses the relevant sensing, processing, communication, and alerting operations and the proposed combination uses those operations for their disclosed purposes.
Claim 6:
“wherein the sensor comprises one or more vehicle cameras,” Hampiholi expressly discloses that the sensor subsystem 210 may include a camera, including a rear-view camera for assisting a user in parking the vehicle and a cabin camera for identifying a user. See Hampiholi, ¶[0034]. Hampiholi further discloses one or more outward-facing cameras that provide image data of objects in the vehicle environment. See Hampiholi, ¶¶[0064]. Salomonsson also discloses the at least one sensor comprises one or more cameras of the vehicle ¶¶[0029].
Claim 7:
“wherein the alert indicates a change in at least one condition external to the vehicle,” Salomonsson expressly discloses this limitation. Salomonsson describes a vehicle vision/alert system that processes image data captured by a forward-facing camera to determine traffic-light conditions and determine when a traffic light changes from red to green. See Salomonsson, ¶¶[0014]-[0015], ¶¶[0020]-[0029]. Salomonsson further expressly states that the system may process image data to detect when the traffic or intersection light changes from red to green and may generate an alert responsive to detection of the traffic light changing to green. See Salomonsson, ¶¶[0014]-[0015], ¶¶[0020]-[0029]. Thus, Salomonsson specifically teaches an alert indicating a detected change in an external condition of the vehicle, namely a change in the state of a traffic light.
Salomonsson further confirms the relationship between the external-condition change and the alert by disclosing that, responsive to detection of the leading vehicle moving forward, indicative of the traffic light changing to green, an alert or notification may be communicated to the driver's PDA, cell phone, smartphone, or other communication device. See Salomonsson, ¶¶[0024]-[0026]. Thus, the same reference teaches both detection of a change in an external vehicle condition and generation/communication of an alert indicative of that change.
Therefore, it would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle computing and monitoring system of Hampiholi to incorporate Seow's machine-learning object-pattern processing and Salomonsson's external-condition-change alerting and communication to a user device. Hampiholi provides the vehicle computing system, vehicle sensors, processors, memory, communication architecture, object/environment processing, and alert functionality; Seow provides a specifically disclosed machine-learning technique for processing object data and learning object patterns; and Salomonsson provides a specifically disclosed vehicle vision technique for detecting a change in an external condition and generating an alert indicative of that change, including communicating the alert to a driver's mobile communication device.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. Specifically, applying Seow's disclosed machine-learning object-pattern processing to sensor/image data in Hampiholi's vehicle computing system would provide pattern-based processing of detected objects, while applying Salomonsson's disclosed external-condition detection and alerting technique would enable Hampiholi's vehicle system to identify a change in an external condition and communicate an alert indicative of that change to the user.
Claim 9:
“wherein the executed instructions classify the detected object based on the determined pattern.” Hampiholi does not expressly disclose classifying the detected object based on a machine-learning-determined pattern.
Seow et al. expressly teaches this additional limitation. Seow teaches that its computer vision engine identifies appearance and kinematic features used by the machine-learning engine to derive object classifications. See Seow et al., ¶0022. Seow further teaches that the machine-learning engine learns patterns regarding object behavior and builds models of different observed object types. See Seow et al., ¶0023. More specifically, Seow teaches that a micro-feature classifier evaluates feature vectors for foreground objects, creates clusters representing distinct object types, and assigns an object-type identifier to each cluster. See Seow et al., ¶¶0036-0038. Thus, Seow teaches classifying a detected object using feature information and learned pattern/cluster information generated by the machine-learning system.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle computing, sensor, object-detection, and alert architecture of Hampiholi to incorporate the machine-learning object-pattern and object-classification techniques of Seow, and to use the remote-driver-device alert communication taught by Salomonsson, because Hampiholi, Seow, and Salomonsson are directed to processing sensor or image information concerning a vehicle environment, identifying objects or conditions, and providing safety-related information or alerts to a user. Seow specifically provides a known machine-learning architecture for extracting object features, learning object patterns, building object models, and assigning object classifications, while Hampiholi provides the vehicle-mounted sensors, processors, memory, object detection, and vehicle safety architecture into which such machine-learning processing may be incorporated. Salomonsson further provides the corresponding technique of communicating a vehicle-detection alert to a driver's mobile computing device.
The combination would have provided the vehicle system of Hampiholi with learned object-pattern classification and remote user notification while retaining the respective functions of the vehicle sensors, machine-learning processing, object detection, and alert communication. The references therefore provide a specific technical reason for the combination rather than merely relying on a statement that the claimed features were known in the art. The resulting system would have been expected to operate predictably because Seow expressly teaches machine-learning processing of video/object feature data to generate object classifications and learned behavioral patterns, while Hampiholi expressly teaches processing vehicle camera/sensor data and generating safety-related actions based on detected objects and environmental risks.
Accordingly, it would have been an implementation of applying a known technique to a known device ready for improvement to yield predictable results. The combination would have had a reasonable expectation of success because the proposed modification merely applies Seow's disclosed machine-learning feature extraction, pattern learning, object modeling, and classification techniques to the vehicle camera/sensor data already processed by Hampiholi, while using the driver-device communication and alert technique expressly disclosed by Salomonsson.
Claim 10:
Seow et al. teaches the claimed machine-learning object classification. Seow teaches that the computer vision engine identifies appearance and kinematic features used by the machine-learning engine to derive object classifications. See Seow et al., ¶0022. Seow further teaches that the machine-learning engine analyzes received data, clusters objects having similar visual and/or kinematic features, learns expected patterns of behavior for objects mapping to a cluster, and builds a model of different observed object types. See Seow et al., ¶0023. Seow additionally teaches a micro-feature classifier that creates clusters from micro-feature vectors, with each cluster representing a distinct object type. See Seow et al., ¶0036. Thus, Seow teaches classifying a detected object based on feature information and learned pattern/cluster information.
Claim 10 further recites “wherein the executed instructions classify the detected object as a person ahead of the vehicle.” Hampiholi teaches detection of objects in the vehicle environment and objects in the path or ahead of the vehicle, but Hampiholi does not expressly disclose classifying the detected object specifically as a person.
Seow et al. expressly discloses classification of a detected object as a person. In particular, Seow describes object-recognition systems in which a group of pixels corresponding to an object is classified as a particular object, including a person or vehicle, and further describes a foreground object being classified as a person. See Seow et al., ¶0005. Seow therefore expressly teaches classification of an image-detected object as a person.
The “ahead” aspect of “classify the detected object as a person ahead” is supplied by Hampiholi's vehicle-environment and object-in-path disclosure. Hampiholi teaches processing image data to identify objects in the external environment and determining objects in the path of the vehicle. See Hampiholi, ¶¶0057-0058, 0064-0065. Seow supplies the classification of such an object as a person. See Seow et al., ¶0005. Accordingly, the combination teaches a vehicle system detecting an object ahead and classifying the detected object as a person.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle computing, sensor, object-detection, and alert system of Hampiholi to incorporate the machine-learning pattern-learning and object-classification techniques of Seow, and to incorporate the remote-driver-device alert communication of Salomonsson, because Hampiholi, Seow, and Salomonsson each address processing image/sensor information concerning objects or conditions in a monitored environment and providing information or alerts concerning those detected conditions.
More particularly, Hampiholi provides the vehicle-mounted sensor, processor, memory, object-detection, and vehicle-environment architecture. Seow provides a specific machine-learning implementation that extracts object features, derives object classifications, learns object patterns, builds models of object types, and classifies objects. Salomonsson provides a specific vehicle-alert implementation in which information concerning an object or condition ahead is communicated to a driver's PDA, cellular telephone, smartphone, or other communication device. The proposed combination therefore uses the machine-learning and classification functionality of Seow to process the object data already obtained by Hampiholi and uses the remote communication technique of Salomonsson to communicate the resulting safety alert to the vehicle user's computing device.
The combination would have been an implementation of applying a known technique to a known device ready for improvement to yield predictable results. Specifically, Seow expressly teaches applying machine-learning feature extraction, pattern learning, object modeling, and object classification to video data, while Hampiholi expressly provides a vehicle-based sensor and object-detection system capable of supplying corresponding image/object data. Salomonsson expressly demonstrates vehicle-based detection and communication of an alert to a driver's mobile computing device. Thus, the proposed combination does not require reconstruction of the references or reliance solely on a general assertion that the claimed features were known; the proposed functions and their respective technical relationships are expressly disclosed in the references.
A person of ordinary skill in the art would have had a reasonable expectation of success because the modification would apply Seow's disclosed object-feature and machine-learning classification processes to image/object data of the type already processed by Hampiholi, while using Salomonsson's expressly disclosed driver-device communication for the resulting vehicle alert.
Claim 12 and claim 20:
They correspond to claim 1, they are therefore rejected for the similar reasons set forth.
“a non-transitory computer-readable medium storing instructions that, when executed, cause:”, Hampiholi teaches a storage device storing instructions executable by a processor in an in-vehicle computing system. Specifically, Hampiholi discloses a storage device 208 storing instructions executable by processors 214 and 220. See Hampiholi, ¶0032. Hampiholi therefore teaches a computer-readable storage medium containing executable instructions for performing the vehicle-monitoring operations described therein.
Claims 17-18:
They correspond to claims 6-7; they are therefore rejected for the similar reasons set forth.
Claim(s) 22, 24, 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hampiholi (US20160267335A1) in view of Salomonsson et al. (US20140222280A1), further in view of Seow et al. (US20140003710A1), and further in view of Cullinane et al. (US9547307B1).
Claim 22:
With respect to the additional limitation of claim 22, “wherein the vehicle being in operation includes the vehicle being temporarily stopped to pick up or drop off a passenger of the vehicle,” Hampiholi does not expressly disclose that the claimed vehicle operation includes temporarily stopping the vehicle for passenger pickup or drop-off.
Cullinane et al. expressly describes an autonomous vehicle that receives instructions to pick up a passenger and, when appropriate, stops the vehicle at the pickup location to wait for the passenger. See Cullinane et al., US9547307B1, col. 2, lines 1-20 and col. 4, lines 45-65. Cullinane further expressly states that the described features may be used to implement a drop-off of a passenger and may involve stopping the vehicle in a traffic lane. See Cullinane et al., col. 4, lines 45-65. The reference therefore teaches temporarily stopping the vehicle in connection with passenger pickup and drop-off.
Accordingly, Hampiholi teaches the claimed vehicle monitoring system, sensor, processor, memory, vehicle operation, sensor-data processing, and object-detection architecture; Salomonsson teaches communicating a vehicle-generated alert to a driver's/user's separate computing device; Seow teaches machine-learning processing of object data, feature extraction, learning object patterns, and construction of a model; and Cullinane et al. teaches temporarily stopping a vehicle to pick up or drop off a passenger. The differences between Hampiholi and the claimed subject matter are therefore supplied by the additional references.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle monitoring and object-detection system of Hampiholi to communicate an alert to a user's computing device as taught by Salomonsson, to employ machine-learning processing of object features and learned object patterns as taught by Seow, and to recognize temporary vehicle stopping for passenger pickup or drop-off as an operational condition as taught by Cullinane et al., because these modifications would provide a vehicle safety-monitoring system capable of processing object information using learned patterns, communicating safety information to a user's mobile computing device, and continuing to provide the vehicle monitoring and alert functionality during passenger pickup or drop-off operations.
The combination would have been an implementation of applying a known technique to a known device ready for improvement to yield predictable results. Hampiholi already provides a vehicle computing architecture that receives sensor/object information and determines responsive actions; Salomonsson demonstrates communicating vehicle alerts to a driver's mobile device; Seow demonstrates using machine-learning processing and learned object patterns for detecting events and generating alerts; and Cullinane et al. demonstrates that an autonomous vehicle may temporarily stop for passenger pickup or drop-off. Applying these respective teachings to the vehicle monitoring architecture of Hampiholi would have resulted in the claimed combination without requiring a change in the basic operation of the respective systems.
Claim 24:
“wherein the vehicle being in operation includes the vehicle being temporarily stopped to pick up or drop off a passenger of the vehicle,” Hampiholi, Salomonsson, and Seow do not expressly disclose the claimed temporary stopping of the vehicle for passenger pickup or drop-off.
Cullinane shows an autonomous vehicle having computing devices configured to stop the vehicle at a pickup location for a predetermined period of time to wait for a passenger. Cullinane, col. 2, ll. 1-20. Cullinane further expressly states that the disclosed features may be used to implement a drop-off of a passenger and describes the vehicle stopping for passenger pickup and drop-off. Cullinane, col. 4, ll. 45-65. The disclosure further provides that the vehicle may stop at the pickup location for a brief period to allow the passenger to meet the vehicle. Cullinane, col. 4, ll. 45-55.
Cullinane also expressly discloses a non-transitory computer-readable medium storing instructions executed by one or more processors to perform the vehicle operation, including operations associated with picking up a passenger and stopping the vehicle. Cullinane, col. 4, ll. 68-70; col. 5, ll. 1-20. Thus, Cullinane specifically teaches the additional claimed circumstance that vehicle operation includes temporarily stopping the vehicle for passenger pickup, and further teaches applying the same features to passenger drop-off.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle computing and monitoring system of Hampiholi, as supplemented by the wireless driver-device alerting of Salomonsson and the machine-learning object-pattern processing of Seow, to include the temporary stopping operation for passenger pickup or drop-off taught by Cullinane. Hampiholi, Salomonsson, and Cullinane are directed to computer-controlled vehicle systems that monitor vehicle conditions and perform vehicle-related operations using processors, sensors, and stored instructions. Cullinane specifically teaches that an autonomous vehicle may temporarily stop at a pickup location to wait for a passenger and that the same features may be used for passenger drop-off. Cullinane, col. 4, ll. 45-65. The modification would therefore have provided the vehicle-monitoring and alert system with an additional vehicle-operation condition corresponding to a passenger pickup or drop-off stop.
The motivation to make the foregoing combination is that incorporating Cullinane's passenger pickup/drop-off stopping operation into the vehicle computing architecture of Hampiholi would allow the vehicle system to continue monitoring and responding to detected objects and conditions during a temporary passenger stop, while retaining the wireless user-device communication of Salomonsson and machine-learning object-pattern processing of Seow. It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. The expected result would be a vehicle computing system capable of performing the same monitoring, object detection, machine-learning processing, and alerting functions while the vehicle is temporarily stopped for passenger pickup or drop-off.
A person of ordinary skill in the art would also have had a reasonable expectation of success because Cullinane expressly implements the temporary passenger pickup/drop-off stop using vehicle computing devices and stored processor-executable instructions, while Hampiholi, Salomonsson, and Seow likewise disclose processor-based vehicle monitoring, sensor processing, wireless communication, machine-learning processing, and alert generation.
Claim 26:
“wherein the vehicle being in operation includes the vehicle being temporarily stopped to pick up or drop off a passenger of the vehicle,” is not expressly disclosed by Hampiholi.
Cullinane et al. expressly teaches this additional limitation. Cullinane describes an autonomous vehicle dispatched to a pickup location for a passenger and explains that the vehicle may come to a stop at the pickup location for a brief period to allow the passenger to meet the vehicle. See Cullinane et al., col. 3, ln 46- col. 4, ln 65; col. 10, ln 26 – col. 11, ln 50; col. 12, ln 26-70, particularly the disclosure that the vehicle may come to a stop at the pickup location for a brief period and may stop in the lane at the pickup location to wait for the passenger. Cullinane further expressly states that the described stopping techniques may be used to implement a drop off of a passenger at a destination location. See id., description corresponding to lines 626-627 and 747-749. Cullinane therefore teaches “wherein the vehicle being in operation includes the vehicle being temporarily stopped to pick up or drop off a passenger of the vehicle.”
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle-monitoring and object-detection system of Hampiholi with Salomonsson's wireless user-device alert communication, Seow's machine-learning object-pattern processing, and Cullinane's passenger pickup/drop-off stopping operation. The references address related vehicle-operation and vehicle-safety functions. Hampiholi already provides a vehicle computing architecture that processes vehicle sensor information to identify objects and take safety-related actions. Salomonsson provides a specific wireless alerting arrangement in which information associated with detected vehicle conditions is communicated to a driver's personal computing device. Seow provides a machine-learning technique for processing detected-object information to learn object patterns and generate safety-related alerts. Cullinane provides a specific vehicle-operation condition in which a vehicle temporarily stops at a passenger pickup or drop-off location while continuing to operate as a passenger-transport vehicle. Combining these teachings would have maintained the existing vehicle-monitoring and safety architecture while adding known techniques directed to communicating safety information, identifying object patterns, and accommodating passenger pickup or drop-off operations.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. In particular, applying Cullinane's known passenger pickup/drop-off stopping operation to the vehicle-monitoring and alert architecture of Hampiholi, while incorporating Salomonsson's wireless user-device alerting and Seow's machine-learning object-pattern processing, would have predictably allowed the vehicle system to continue monitoring detected objects and providing alerts while the vehicle is temporarily stopped for passenger pickup or drop-off.
Claim(s) 23 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hampiholi (US20160267335A1) in view of Salomonsson et al. (US20140222280A1), further in view of Seow et al. (US20140003710A1), and further in view of Chatham (US9207679B1).
Claim 23:
“determine, utilizing location data from a location module, a distance between the vehicle and the detected object”, Hampiholi in view of the combination does not expressly disclose utilizing location data from a location module to determine a distance between the vehicle and the detected object.
Chatham shows a geographic position component 144 in communication with the vehicle computer for determining the geographic location of the vehicle, wherein the position component may include a GPS receiver determining latitude, longitude, and altitude. See Chatham, col. 4, lines 28-38. Chatham further shows detecting surrounding objects, determining the location of a detected object using ground-marker location data, comparing the detected object's location with position data for the vehicle, and thereby determining the distance of the detected object from the vehicle. See Chatham, col. 5, line 21 – col. 9, line 5. Chatham further describes determining the current location of the autonomous vehicle and, by comparing the vehicle's location with the location of the detected object/ground marker, determining the proximity of the vehicle to the detected object. See Chatham, col. 10, lines 35-50, col. 5, line 21 – col. 9, line 5. Thus, Chatham supplies the location-data-based vehicle/object distance determination not expressly disclosed by Hampiholi.
“and wherein the alert is generated based at least in part on the determined distance”, Hampiholi does not expressly disclose generating the claimed alert based at least in part on a determined distance between the vehicle and the detected object.
Chatham expressly teaches that, after determining the distance of a detected vehicle from the subject vehicle, the subject vehicle may take one or more actions based on that distance. See Chatham, col. 8, lines 55-65, col. 5, line 21 – col. 9, line 5. Chatham also describes determining the proximity of the autonomous vehicle to the detected object and implementing a new or altered control strategy based on the position/proximity of the detected object. See Chatham, col. 10, lines 35-50, col. 5, line 21 – col. 9, line 5. Salomonsson further teaches generating an alert responsive to a detected vehicle moving a threshold distance away from the equipped vehicle and describes the threshold distance as a condition for generating the alert. See Salomonsson (above rejection and reference para. 15,16,18,23,27,29,31-32,34,36-37). Thus, Chatham supplies the distance determination and distance-dependent response, while Salomonsson supplies the alert generation responsive to a distance condition.
Accordingly, Hampiholi teaches the vehicle computing system, stored executable instructions, vehicle sensors, sensor-data processing, and detection of objects ahead; Salomonsson teaches wireless communication with a driver's/user's computing device and generation/communication of vehicle alerts, including alerts responsive to a distance condition; Seow teaches machine-learning processing of video/object data, extraction of object features, learning of object patterns, and construction of object models; and Chatham teaches use of geographic-position information to determine the position and proximity/distance of a detected object relative to a vehicle and taking action based on the determined distance.
it would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle monitoring and object-detection system of Hampiholi to communicate an alert to a user's computing device as taught by Salomonsson, to employ the machine-learning processing and learned object-pattern model of Seow, and to utilize geographic-position information to determine the distance or proximity of a detected object and use that distance in determining a responsive action as taught by Chatham, because such a combination would provide the vehicle monitoring system with additional information concerning the spatial relationship between the vehicle and detected objects and would permit the system to condition a safety notification on the determined proximity of the detected object.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. Hampiholi already provides a vehicle computing architecture that processes sensor and object information and performs responsive actions. Salomonsson demonstrates wireless communication of vehicle alerts to a driver's mobile communication device and generation of alerts responsive to detected vehicle conditions, including distance-related conditions. Seow demonstrates machine-learning processing of object features and learned object patterns for identifying events and generating alerts. Chatham demonstrates determining geographic position and object proximity/distance in an autonomous vehicle and taking responsive action based on the determined distance. Applying these disclosed techniques to the corresponding functions of Hampiholi would have predictably provided distance-sensitive alerting using the existing vehicle computing and sensor architecture.
Claim 25:
With respect to the first additional limitation of claim 25, “determining, utilizing location data from a location module, a distance between the vehicle and the detected object,” Hampiholi does not expressly disclose determining the distance between the vehicle and the detected object utilizing location data from a location module.
Chatham expressly discloses that the vehicle includes a “geographic position component 144” in communication with the vehicle computer for determining the geographic location of the vehicle, and that the geographic position component may include a GPS receiver for determining latitude, longitude, and altitude. See Chatham, col. 4, ll. 45-65. Chatham further discloses that the vehicle uses location data and detected-object information to determine the position and proximity of surrounding objects and to calculate the distance between the vehicle and a detected vehicle. See Chatham, col. 4, lines 28-38; col. 5, line 21 – col. 9, line 5; col. 10, lines 35-50. Chatham's claims likewise expressly recite determining a geographic position of the vehicle and identifying the vehicle's proximity to the object by comparing the object's position with the geographic position of the vehicle.
Thus, although Chatham uses the terminology “geographic position component” rather than the exact phrase “location module,” the geographic position component performs the claimed location-module function by obtaining vehicle location data through a GPS receiver and providing that location data to the vehicle computer. Chatham therefore teaches the claimed functional relationship of utilizing location data from a location component to determine the distance or proximity between the vehicle and the detected object.
With respect to the further limitation “wherein the alert is generated based at least in part on the determined distance,” Hampiholi does not expressly disclose generating the claimed alert based at least in part on a determined vehicle-object distance. Salomonsson expressly teaches an alert system that determines a distance between the equipped vehicle and a leading vehicle and generates an alert after the leading vehicle has moved a threshold distance away from the equipped vehicle. See Salomonsson, above rejection and ¶¶[ (15,16,18,23,27,29,31-32,34,36-37). Salomonsson further states that the alert system reliably detects the distance between the target vehicle and equipped vehicle and uses a threshold setting to determine when the alert is generated. See Salomonsson, ¶¶[ (15,16,18,23,27,29,31-32,34,36-37).
Accordingly, Salomonsson expressly teaches the claimed relationship between a determined distance and generation of an alert, while Chatham teaches obtaining vehicle location data from a geographic-position component and determining vehicle-object distance or proximity from location information.
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to modify the vehicle monitoring and object-detection system of Hampiholi, as supplemented by the wireless user-device alerting of Salomonsson and the machine-learning object-pattern processing of Seow, to determine the distance between the vehicle and the detected object using vehicle location information from a geographic-position component as taught by Chatham, and to generate the alert based at least in part on the determined distance as taught by Salomonsson.
The reason for the combination is expressly supported by the references. Salomonsson teaches that distance information is useful for determining when an alert should be generated, including generating an alert when a detected vehicle moves a threshold distance from the equipped vehicle. See Salomonsson, ¶¶[ (15,16,18,23,27,29,31-32,34,36-37). Chatham teaches obtaining vehicle geographic-position information from a geographic-position component and using position information to determine proximity or distance to a surrounding object. See Chatham, col. 4, lines 28-38; col. 5, line 21 – col. 9, line 5; col. 10, lines 35-50. Applying Chatham's location-based distance determination to the object-detection and alert system of Hampiholi, while using Salomonsson's distance-based alert criterion, would provide a specific vehicle-object distance parameter for determining when an alert should be generated.
It would be an implementation of applying a known technique to a known device ready for improvement to yield predictable results. Specifically, Chatham's disclosed location-based determination of vehicle-object proximity would be applied to Hampiholi's disclosed vehicle sensor and object-detection architecture, and Salomonsson's disclosed threshold-distance alert operation would use the resulting distance determination. The expected result would be an alert system that generates an alert when the detected object reaches a selected distance relative to the vehicle.
Allowable Subject Matter
Claims 11 and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
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/HOI C LAU/Primary Examiner, Art Unit 2689
