Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 33 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 33 recites the limitation "the ground" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine
grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a non-statutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-9, 13-17, 27-33 are rejected on the ground of non-statutory double patenting over claims 1-34 of U.S. Patent No. 12361811 since the claims, if allowed, would improperly extend the “right to exclude” already granted in the patent.
The subject matter claimed in the instant application is fully disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows:
Claims 1, 13-17 are rejected on the ground of non-statutory double patenting as being unpatentable over claim 1-34 of U.S. Patent No. 12361811 (hereinafter referred to as Jones)
Regarding claim 1, Jones teaches a system for monitoring vehicle riders, the system comprising: a plurality of sensors configured to detect a plurality of parameters associated with a rider, and further configured to generate a first set of signals indicative of the plurality of parameters, wherein the plurality of parameters represents at least one condition or performance of the rider (Claim1)
A system for monitoring vehicle riders, the system comprising: a plurality of sensors configured to detect a plurality of parameters associated with a rider, and further configured to generate a first set of signals indicative of the plurality of parameters, wherein the plurality of parameters represents at least one condition or performance of the rider, wherein the plurality of sensors is placed on at least one of elbow protective gear, knee protective gear, chest protective gear, and boot protective gear that engage with torso or limbs of the rider, wherein the elbow, knee, chest, and boot protective gear refers to specific impact protective members that are utilized by the rider to get protection from different external effects at the specific location of the respective protective gear; an electronic device communicatively coupled to the plurality of sensors, the electronic device comprising a processing unit and a transceiver, the electronic device configured to receive the first set of signals from the plurality of sensors, and further configured to generate a second set of signals; and a microphone and a speaker for facilitating a communication of the rider with another person using one or more user devices via a communication network, the other person having real-time impact and biometric data of the rider from the plurality of sensors.[Cl-1]
Jones then teaches wherein the plurality of sensors is placed on at least one of elbow protective gear, knee protective gear, chest protective gear, and boot protective gear that engage with torso or limbs of the rider, wherein the elbow, knee, chest, and boot protective gear refers to specific impact protective members that are utilized by the rider to get protection from different external effects at the specific location of the respective protective gear (Claim 1)
A system for monitoring vehicle riders, the system comprising: a plurality of sensors configured to detect a plurality of parameters associated with a rider, and further configured to generate a first set of signals indicative of the plurality of parameters, wherein the plurality of parameters represents at least one condition or performance of the rider, wherein the plurality of sensors is placed on at least one of elbow protective gear, knee protective gear, chest protective gear, and boot protective gear that engage with torso or limbs of the rider, wherein the elbow, knee, chest, and boot protective gear refers to specific impact protective members that are utilized by the rider to get protection from different external effects at the specific location of the respective protective gear; an electronic device communicatively coupled to the plurality of sensors, the electronic device comprising a processing unit and a transceiver, the electronic device configured to receive the first set of signals from the plurality of sensors, and further configured to generate a second set of signals; and a microphone and a speaker for facilitating a communication of the rider with another person using one or more user devices via a communication network, the other person having real-time impact and biometric data of the rider from the plurality of sensors.[Cl-1]
Jones teaches an electronic device communicatively coupled to the plurality of sensors, the electronic device comprising a processing unit and a transceiver, the electronic device configured to receive the first set of signals from the plurality of sensors, and further configured to generate a second set of signals; and a network for real-time communication of rider biometric data from at least one of the sensors transmitted to another person via at least one user device. (Claim 1)
A system for monitoring vehicle riders, the system comprising: a plurality of sensors configured to detect a plurality of parameters associated with a rider, and further configured to generate a first set of signals indicative of the plurality of parameters, wherein the plurality of parameters represents at least one condition or performance of the rider, wherein the plurality of sensors is placed on at least one of elbow protective gear, knee protective gear, chest protective gear, and boot protective gear that engage with torso or limbs of the rider, wherein the elbow, knee, chest, and boot protective gear refers to specific impact protective members that are utilized by the rider to get protection from different external effects at the specific location of the respective protective gear; an electronic device communicatively coupled to the plurality of sensors, the electronic device comprising a processing unit and a transceiver, the electronic device configured to receive the first set of signals from the plurality of sensors, and further configured to generate a second set of signals; and a microphone and a speaker for facilitating a communication of the rider with another person using one or more user devices via a communication network, the other person having real-time impact and biometric data of the rider from the plurality of sensors.[Cl-1]
Here, Jones teaches an electronic device communicatively coupled to the plurality of sensors, the electronic device comprising a processing unit and a transceiver, the electronic device configured to receive the first set of signals from the plurality of sensors, and further configured to generate a second set of signals.
Jones’ disclosure includes a microphone and a speaker for facilitating a communication of the rider with another person using one or more user devices (additional limitations) via a communication network, the other person having real-time impact (additional limitations) and biometric data of the rider from the plurality of sensors.
Despite, Jones’ disclosure having additional limitations such as a microphone and speaker facilitating communication, as well as real-time impact data of the rider, the narrower element do not affect the influence of the overall teaching of the pending applications, “an electronic device communicatively coupled to the plurality of sensors, the electronic device comprising a processing unit and a transceiver, the electronic device configured to receive the first set of signals from the plurality of sensors, and further configured to generate a second set of signals; and a network for real-time communication of rider biometric data from at least one of the sensors transmitted to another person via at least one user device”
Therefore, it is obvious to one of ordinary skill in the art that despite Jones additional limitations, the scope of the pending application is still taught by Jones, for similar operational benefits such as improved vehicle operation, and rea-time emergency detection and response.
Regarding claim 13, Jones teaches a microphone and a speaker for facilitating a communication of the rider with a third person using the one or more user devices via the communication network (Claim 1).
A system for monitoring vehicle riders, the system comprising: a plurality of sensors configured to detect a plurality of parameters associated with a rider, and further configured to generate a first set of signals indicative of the plurality of parameters, wherein the plurality of parameters represents at least one condition or performance of the rider, wherein the plurality of sensors is placed on at least one of elbow protective gear, knee protective gear, chest protective gear, and boot protective gear that engage with torso or limbs of the rider, wherein the elbow, knee, chest, and boot protective gear refers to specific impact protective members that are utilized by the rider to get protection from different external effects at the specific location of the respective protective gear; an electronic device communicatively coupled to the plurality of sensors, the electronic device comprising a processing unit and a transceiver, the electronic device configured to receive the first set of signals from the plurality of sensors, and further configured to generate a second set of signals; and a microphone and a speaker for facilitating a communication of the rider with another person using one or more user devices via a communication network, the other person having real-time impact and biometric data of the rider from the plurality of sensors.[Cl-1]
In regards to claim 14, Jones teaches a haptic device having one or more haptic sensors for detecting one or more parameters and a haptic indicator, the haptic device being configured to be in communication with the electronic device to transmit the one or more detected parameters.(Claim 13)
The system of claim 1, comprises a haptic device having one or more haptic sensors for detecting one or more parameters, the haptic device being configured to be in communication with the electronic device to transmit the one or more detected parameters.[Cl-1]
Regarding claim 15, Jones teaches the electronic device is configured to detect a condition of any one of the rider and a corresponding vehicle, generate a haptic alert signal corresponding to the detected condition, and is further configured to transmit the generated haptic alert signal to the haptic indicator in real-time (Claim 14)
The system of claim 13, wherein the electronic device is configured to detect a condition of any one of the rider and a corresponding vehicle, generate a haptic alert signal corresponding to the detected condition, and is further configured to transmit the generated haptic alert signal to the haptic indicator in real-time[Cl-14]
Regarding claim 16, Jones teaches the haptic indicator is a vibrator secured to any of the protective gear or helmet of the rider, the haptic indicator being configured to indicate the generated haptic alert signal, and consequently the detected condition to the rider in real-time. (Claims 1, 15)
The system of claim 14, wherein the haptic indicator is a chinstrap vibrator, the haptic indicator being configured to indicate the generated haptic alert signal, and consequently the detected condition to the rider in real-time.[Cl-15]
Being that the vibrator is secure in a component of the helmet such as the chinstrap, thereby it would be obvious that the chinstrap is a part of the worn helmet.
Regarding claim 17, Jones teaches the haptic device is placed on any one of one or more accessories of the rider and a portion of a corresponding vehicle.(claim 17)
The system of claim 1, wherein the one or more body impact-protective accessories comprise at least one of boots, armor suits, chest protectors, pads, elbow pads, or combination thereof.[Cl-17]
Claims 2-5, 7-9, 27, 30-33 are rejected on the ground of non-statutory double patenting as being unpatentable over claim 1-34 of U.S. Patent No. 12361811 (hereinafter referred to as Jones) in view of Cella (US 20210272394 A1)
Regarding claim 2, Jones teaches the plurality of sensors is configured to detect a plurality of parameters associated with a corresponding vehicle of the rider, the plurality of parameters associated with the corresponding vehicle represent at least one condition or performance of the vehicle (Claim 2)
The system of claim 1, wherein the plurality of sensors is configured to detect a plurality of engine parameters associated with a corresponding vehicle of the rider, the plurality of parameters associated with the corresponding vehicle represent at least one condition or performance of the vehicle.[Cl-2]
Jones fails to teach the performance of the vehicle including the orientation of the vehicle
Cella, however, teaches the performance of the vehicle including the orientation of the vehicle (Paragraphs 160, 375, 377)
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
In would therefore be obvious to one of ordinary skill in the art during the time of the filing date of the said invention to combine Cella’s teaching with Jones teaching in order to enable a safer and more optimized method to operate a motor vehicle
Regarding claim 3, Jones modified teaches one or more user devices communicatively coupled to the electronic device using a communication network, wherein the one or more user devices are configured to receive the second set of signals, and further configured to analyze the received second set of signals to determine the at least one condition or the performance of the rider (Claim 3, Jones)
The system of claim 1, further comprises one or more user devices communicatively coupled to the electronic device using a communication network, wherein the one or more user devices are configured to receive the second set of signals, and further configured to analyze the received second set of signals to determine the at least one condition or the performance of the rider.[Cl-3]
Cella further teaches the performance of the rider, including the orientation of at least a portion of the rider relative to the orientation of the vehicle. (Paragraphs 160, 375, 377, 379, Cella)
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
An aspect provided herein includes a motorcycle helmet augmented reality system comprising: a display 44175 disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet; a circuit 4488 for registering at least one of location and orientation of a motorcycle that the rider is riding; a machine learning circuit 44179 that determines at least one augmentation parameter 44156 by processing at least one input relating to at least one of the rider 44163 and the motorcycle 44180; and a reality augmentation circuit 4488 that, responsive to the registered at least one of a location and orientation of the motorcycle generates an augmentation element 44177 for presenting in the display 44175, the generating based at least in part on the determined at least one augmentation parameter 44156.[P-379]
Regarding claim 4, Jones modified teaches the plurality of sensors is placed at least on one or more accessories of the rider and on the vehicle to detect the plurality of parameters. (Claim 4, Jones)
The system of claim 1, wherein the plurality of sensors is further placed on the vehicle to detect the plurality of parameters.[Cl-4]
Cella further teaches the plurality of sensors on the vehicle includes one or more of accelerometers, gyroscopes, and inclinometers. is placed at least on one or more accessories of the rider and on the vehicle to detect the plurality of parameters. (Paragraph 169, Cella)
In embodiments, the platform described herein may include, integrate with, or connect with a system for robotic process automation (RPA), whereby an artificial intelligence/machine learning system may be trained on a training set of data that consists of tracking and recording sets of interactions of humans as the humans interact with a set of interfaces, such as graphical user interfaces (e.g., via interactions with mouse, trackpad, keyboard, touch screen, joystick, remote control devices); audio system interfaces (such as by microphones, smart speakers, voice response interfaces, intelligent agent interfaces (e.g., Siri and Alexa) and the like); human-machine interfaces (such as involving robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wrist bands, glasses, arm bands, torso bands, belts, rings, necklaces and other accessories); physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers, handles, steering systems, wheels, and many others); optical interfaces (including ones triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, and the like); sensor-enabled interfaces (such as ones involving cameras, EEG or other electrical signal sensing (such as for brain-computer interfaces), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR and other sensor sets that are capable of recognizing thoughts, gestures (facial, hand, posture, or other), utterances, and the like, and others. In addition to tracking and recording human interactions, the RPA system may also track and record a set of states, actions, events and results that occur by, within, from or about the systems and processes with which the humans are engaging. For example, the RPA system may record mouse clicks on a frame of video that appears within a process by which a human review the video, such as where the human highlights points of interest within the video, tags objects in the video, captures parameters (such as sizes, dimensions, or the like), or otherwise operates on the video within a graphical user interface. The RPA system may also record system or process states and events, such as recording what elements were the subject of interaction, what the state of a system was before, during and after interaction, and what outputs were provided by the system or what results were achieved. Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision, such as by having a human correct the RPA system as it attempts in a set of trials to undertake the action that the human would have undertaken (e.g., tagging the right object, labeling an item correctly, selecting the correct button to trigger a next step in a process, or the like), such that over a set of trials the RPA system becomes increasingly effective at replicating the action the human would have taken. Learning may include deep learning, such as by reinforcing learning based on outcomes, such as successful outcomes (such as based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, an RPA system may be seeded during a learning phase with a set of expert human interactions, such that the RPA system begins to be able to replicate expert interaction with a system. For example, an expert driver's interactions with a robotic system, such as a remote-controlled vehicle or a UAV, may be recorded along with information about the vehicles state (e.g., the surrounding environment, navigation parameters, and purpose), such that the RPA system may learn to drive the vehicle in a way that reflects the same choices as an expert driver. After being taught to replicate the skills or expertise of an expert human, the RPA system may be transitioned to a deep learning mode, where the system further improves based on a set of outcomes, such as by being configured to attempt some level of variation in approach (e.g., trying different navigation paths to optimize time of arrival, or trying different approaches to deceleration and acceleration in curves) and tracking outcomes (with feedback), such that the RPA system can learn, by variation/experimentation (which may be randomized, rule-based, or the like, such as using genetic programming techniques, random-walk techniques, random forest techniques, and others) and selection, to exceed the expertise of the human expert. Thus, the RPA system learns from a human expert, acquires expertise in interacting with a system or process, facilitates automation of the process (such as by taking over some of the more repetitive tasks, including ones that require consistent execution of acquired skills), and provides a very effective seed for artificial intelligence, such as by providing a seed model or system that can be improved by machine learning with feedback on outcomes of a system or process.[P-169]
Regarding claim 5, Jones teaches the electronic device is configured to generate a third set of signals indicative of one or more conditions of the rider based on the first set of signals. (Claim 5, Jones)
The system of claim 1, wherein the electronic device is configured to generate a third set of signals indicative of one or more biometric conditions of the rider based on the first set of signals.[Cl-5]
Jones fails to teach the plurality of sensors are placed adjacent a head and torso of a rider, and the plurality of sensors are placed adjacent at least one of the arms or legs of the rider
Cella teaches the plurality of sensors are placed adjacent a head and torso of a rider, and the plurality of sensors are placed adjacent at least one of the arms or legs of the rider. (Paragraph 169, Cella)
In embodiments, the platform described herein may include, integrate with, or connect with a system for robotic process automation (RPA), whereby an artificial intelligence/machine learning system may be trained on a training set of data that consists of tracking and recording sets of interactions of humans as the humans interact with a set of interfaces, such as graphical user interfaces (e.g., via interactions with mouse, trackpad, keyboard, touch screen, joystick, remote control devices); audio system interfaces (such as by microphones, smart speakers, voice response interfaces, intelligent agent interfaces (e.g., Siri and Alexa) and the like); human-machine interfaces (such as involving robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wrist bands, glasses, arm bands, torso bands, belts, rings, necklaces and other accessories); physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers, handles, steering systems, wheels, and many others); optical interfaces (including ones triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, and the like); sensor-enabled interfaces (such as ones involving cameras, EEG or other electrical signal sensing (such as for brain-computer interfaces), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR and other sensor sets that are capable of recognizing thoughts, gestures (facial, hand, posture, or other), utterances, and the like, and others. In addition to tracking and recording human interactions, the RPA system may also track and record a set of states, actions, events and results that occur by, within, from or about the systems and processes with which the humans are engaging. For example, the RPA system may record mouse clicks on a frame of video that appears within a process by which a human review the video, such as where the human highlights points of interest within the video, tags objects in the video, captures parameters (such as sizes, dimensions, or the like), or otherwise operates on the video within a graphical user interface. The RPA system may also record system or process states and events, such as recording what elements were the subject of interaction, what the state of a system was before, during and after interaction, and what outputs were provided by the system or what results were achieved. Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision, such as by having a human correct the RPA system as it attempts in a set of trials to undertake the action that the human would have undertaken (e.g., tagging the right object, labeling an item correctly, selecting the correct button to trigger a next step in a process, or the like), such that over a set of trials the RPA system becomes increasingly effective at replicating the action the human would have taken. Learning may include deep learning, such as by reinforcing learning based on outcomes, such as successful outcomes (such as based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, an RPA system may be seeded during a learning phase with a set of expert human interactions, such that the RPA system begins to be able to replicate expert interaction with a system. For example, an expert driver's interactions with a robotic system, such as a remote-controlled vehicle or a UAV, may be recorded along with information about the vehicles state (e.g., the surrounding environment, navigation parameters, and purpose), such that the RPA system may learn to drive the vehicle in a way that reflects the same choices as an expert driver. After being taught to replicate the skills or expertise of an expert human, the RPA system may be transitioned to a deep learning mode, where the system further improves based on a set of outcomes, such as by being configured to attempt some level of variation in approach (e.g., trying different navigation paths to optimize time of arrival, or trying different approaches to deceleration and acceleration in curves) and tracking outcomes (with feedback), such that the RPA system can learn, by variation/experimentation (which may be randomized, rule-based, or the like, such as using genetic programming techniques, random-walk techniques, random forest techniques, and others) and selection, to exceed the expertise of the human expert. Thus, the RPA system learns from a human expert, acquires expertise in interacting with a system or process, facilitates automation of the process (such as by taking over some of the more repetitive tasks, including ones that require consistent execution of acquired skills), and provides a very effective seed for artificial intelligence, such as by providing a seed model or system that can be improved by machine learning with feedback on outcomes of a system or process.[P-169]
In would therefore be obvious to one of ordinary skill in the art during the time of the filing date of the said invention to combine Cella’s teaching with Jones teaching in order to enable a safer and more optimized method to operate a motor vehicle
Regarding claim 7, Jones teaches the electronic device is configured to receive, a fourth set of signals from one or more sources via the communication network, and further configured to generate a fifth set of signals, the fourth set of signals indicative of one or more unsafe surrounding conditions.(Claim 7)
The system of claim 1, wherein the electronic device is configured to receive a fourth set of signals from one or more sources via the communication network, and further configured to generate a fifth set of signals, the fourth set of signals indicative of one or more unsafe surrounding conditions external to the rider.[Cl-7]
Jones fails to teach data from a communication network having data regarding conditions in the area of the rider
Cella then teaches data from a communication network having data regarding conditions in the area of the rider (Paragraphs 305, 320)
Referring to FIG. 31, in embodiments, provided herein are transportation systems having an artificial intelligence system for processing a voice of a rider in a vehicle to determine an emotional state and optimizing at least one operating parameter of the vehicle to improve the rider's emotional state. A voice-analysis module may take voice input and, using a training set of labeled data where individuals indicate emotional states while speaking and/or whether others tag the data to indicate perceived emotional states while individuals are talking, a machine learning system (such as any of the types described herein) may be trained (such as using supervised learning, deep learning, or the like) to classify the emotional state of the individual based on the voice. Machine learning may improve classification by using feedback from a large set of trials, where feedback in each instance indicates whether the system has correctly assessed the emotional state of the individual in the case of an instance of speaking. Once trained to classify the emotional state, an expert system (optionally using a different machine learning system or other artificial intelligence system) may, based on feedback of outcomes of the emotional states of a set of individuals, be trained to optimize various vehicle parameters noted throughout this disclosure to maintain or induce more favorable states. For example, among many other indicators, where a voice of an individual indicates happiness, the expert system may select or recommend upbeat music to maintain that state. Where a voice indicates stress, the system may recommend or provide a control signal to change a planned route to one that is less stressful (e.g., has less stop-and-go traffic, or that has a higher probability of an on-time arrival). In embodiments, the system may be configured to engage in a dialog (such as on on-screen dialog or an audio dialog), such as using an intelligent agent module of the system, that is configured to use a series of questions to help obtain feedback from a user about the user's emotional state, such as asking the rider about whether the rider is experiencing stress, what the source of the stress may be (e.g., traffic conditions, potential for late arrival, behavior of other drivers, or other sources unrelated to the nature of the ride), what might mitigate the stress (route options, communication options (such as offering to send a note that arrival may be delayed), entertainment options, ride configuration options, and the like), and the like. Driver responses may be fed as inputs to the expert system as indicators of emotional state, as well as to constrain efforts to optimize one or more vehicle parameters, such as by eliminating options for configuration that are not related to a driver's source of stress from a set of available configurations.[P-305]
Referring to FIG. 34 and FIG. 35, in embodiments, the vehicle 3410 comprises a system for automating at least one control parameter 34153 of the vehicle 3410. In embodiments, the vehicle 3410 is at least a semi-autonomous vehicle. In embodiments, the vehicle 3410 is automatically routed. In embodiments, the vehicle 3410 is a self-driving vehicle. In embodiments, the at least one Internet-of-things device 34150 is disposed in an operating environment 34154 of the vehicle. In embodiments, the at least one Internet-of-things device 34150 that captures the data about the vehicle 3410 is disposed external to the vehicle 3410. In embodiments, the at least one Internet-of-things device is a dashboard camera. In embodiments, the at least one Internet-of-things device is a mirror camera. In embodiments, the at least one Internet-of-things device is a motion sensor. In embodiments, the at least one Internet-of-things device is a seat-based sensor system. In embodiments, the at least one Internet-of-things device is an IoT enabled lighting system. In embodiments, the lighting system is a vehicle interior lighting system. In embodiments, the lighting system is a headlight lighting system. In embodiments, the at least one Internet-of-things device is a traffic light camera or sensor. In embodiments, the at least one Internet-of-things device is a roadway camera. In embodiments, the roadway camera is disposed on at least one of a telephone phone and a light pole. In embodiments, the at least one Internet-of-things device is an in-road sensor. In embodiments, the at least one Internet-of-things device is an in-vehicle thermostat. In embodiments, the at least one Internet-of-things device is a toll booth. In embodiments, the at least one Internet-of-things device is a street sign. In embodiments, the at least one Internet-of-things device is a traffic control light. In embodiments, the at least one Internet-of-things device is a vehicle mounted sensor. In embodiments, the at least one Internet-of-things device is a refueling system. In embodiments, the at least one Internet-of-things device is a recharging system. In embodiments, the at least one Internet-of-things device is a wireless charging station [P-320]
In would therefore be obvious to one of ordinary skill in the art during the time of the filing date of the said invention to combine Cella’s teaching with Jones teaching in order to enable a safer and more optimized method to operate a motor vehicle
Regarding claim 8, Jones modified via Cella teaches conditions in the area of the rider include other riders and objects in the path of the rider. (Paragraphs 12, 21, Cella)
In embodiments, the detected satisfaction state of the rider user is a detected emotional state of the rider user. In embodiments, the favorable satisfaction state of the rider user is a favorable emotional state of the rider user. In embodiments, the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. In embodiments, at least one of the neural networks is a hybrid neural network and includes a convolutional neural network. In embodiments, the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider satisfaction state of the rider user. In embodiments, the second neural network optimizes the operational parameter in real time responsive to the detecting of the detected satisfaction state of the rider user by the first neural network. In embodiments, the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.[P-12]
In embodiments, the method includes forming, using the first neural network, one or more connected nodes that form a directed cycle. In embodiments, the first neural network further facilitates bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.[P-21]
Regarding claim 9, Jones modified via Cella teaches one or more indicators are configured to signal the rider of unsafe conditions in the area of the rider (Paragraphs 47, 431, Cella)
In embodiments, the interface for the digital twin system provides a fleet monitoring view to the owner for tracking and monitoring movement/route/condition of one or more vehicles. In embodiments, the interface for the digital twin system provides a driver behavior monitoring view to the owner for allowing the owner to monitor instances of unsafe or dangerous driving by a driver. In embodiments, the interface for the digital twin system provides an insurance view to the owner for assisting the owner in determining an insurance policy quote of a vehicle based on a vehicle condition. In embodiments, the interface for the digital twin system provides a compliance view to the owner for showing compliance status with respect to emission/pollution and other regulatory norms based on a condition of the vehicle. In embodiments, the interface provides a performance tuning view to the owner for modifying or tuning characteristics of one or more components to personalize the performance of the vehicle based on a preference of the owner.[P-47]
An artificial intelligence-based control system 5136 may be trained on a set of outcomes (of various types described herein) to provide a level of variation of a user experience that achieves desired outcomes, including selection of the timing and extent of such variations. As another example, an audio system may be varied to preserve hearing (such as based on tracking accumulated sound pressure levels, accumulated dosage, or the like), to promote alertness (such as by varying the type of content), and/or to improve health (such as by providing a mix of stimulating and relaxing content). In embodiments, such an artificial intelligence system 5136 may be fed sensor data 51444, such as from a wearable device 51157 (including a sensor set) or a physiological sensing system 51190, which includes a set of systems and/or sensors capable of providing physiological monitoring within a vehicle 5110 (e.g., a vison-based system 51186 that observes a user, a sensor 5125 embedded in a seat, a steering wheel, or the like that can measure a physiological parameter, or the like). For example, a vehicle interface 51188 (such as a steering wheel or any other interface described herein) can measure a physiological parameter (e.g., galvanic skin response, such as to indicate a stress level, cortisol level, or the like of a driver or other user), which can be used to indicate a current state for purposes of control or can be used as part of a training data set to optimize one or more parameters that may benefit from control, including control of variation of user experience to achieve desired outcomes. In one such example, an artificial intelligence system 5136 may vary parameters, such as driving experience, music and the like, to account for changes in hormonal systems of the user (such as cortisol and other adrenal system hormones), such as to induce healthy changes in state (consistent with evidence that varying cortisol levels over the course of a day are typical in healthy individuals, but excessively high or low levels at certain times of day may be unhealthy or unsafe). Such a system may, for example, “amp up” the experience with more aggressive settings (e.g., more acceleration into curves, tighter suspension, and/or louder music) in the morning when rising cortisol levels are healthy and “mellow out” the experience (such as by softer suspension, relaxing music and/or gentle driving motion) in the afternoon when cortisol levels should be dropping to lower levels to promote health. Experiences may consider both health of the user and safety, such as by ensuring that levels vary over time, but are sufficiently high to assure alertness (and hence safety) in situations where high alertness is required. While cortisol (an important hormone) is provided as an example, user experience parameters may be controlled (optionally with random or configured variation) with respect to other hormonal or biological systems, including insulin-related systems, cardiovascular systems (e.g., relating to pulse and blood pressure), gastrointestinal systems, and many others.[P-431]
Regarding claim 27, Jones teaches a system for monitoring riders relative to vehicles ridden, the system comprising: a plurality of rider position sensors secured to rider gear and configured to detect rider body orientation (Claim 27)
A method for monitoring vehicle riders, the method being performed on a system comprising a plurality of sensors and an electronic device including a processing unit and a transceiver, the method comprising: detecting a plurality of parameters by the plurality of sensors, the plurality of parameters being associated with a rider, thereby generating a first set of signals indicative of the plurality of parameters, wherein the plurality of parameters represents at least one condition or performance of the rider, wherein the plurality of sensors is placed on at least one of elbow protective gear, knee protective gear, chest protective gear, and boot protective gear that engage with torso or limbs of the rider, wherein the elbow, knee, chest, and boot protective gear refers to specific impact protective members that are utilized by the rider to get protection from different external effects at the specific location of the respective protective gear; receiving the first set of signals by the electronic device; generating a second set of signals by the electronic device based on the first set of signals; generating a third set of signals by the electronic device, the third set of signals indicative of one or more conditions of the rider at the rider's knee, elbow, or chest based on the first set of signals; receiving the third set of signals by one or more indicators from the electronic device; and generating a corresponding indication on the one or more indicators, the indication being associated with the one or more biometric conditions of the rider.[Cl-27]
Jones fails to teach at least one vehicle position sensor secured to the vehicle and configured to detect vehicle orientation and position relative to rider body position.
Cella on the other hand teaches at least one vehicle position sensor secured to the vehicle and configured to detect vehicle orientation and position relative to rider body position. (Paragraphs 160, 375, 377)
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
In would therefore be obvious to one of ordinary skill in the art during the time of the filing date of the said invention to combine Cella’s teaching with Jones teaching in order to enable a safer and more optimized method to operate a motor vehicle
Regarding claim 30, Jones modified teaches the plurality of rider position sensors includes sensors on at least one of a chest protector, an elbow guard, a knee guard, a protective boot, and a helmet.(claim 27, Jones)
A method for monitoring vehicle riders, the method being performed on a system comprising a plurality of sensors and an electronic device including a processing unit and a transceiver, the method comprising: detecting a plurality of parameters by the plurality of sensors, the plurality of parameters being associated with a rider, thereby generating a first set of signals indicative of the plurality of parameters, wherein the plurality of parameters represents at least one condition or performance of the rider, wherein the plurality of sensors is placed on at least one of elbow protective gear, knee protective gear, chest protective gear, and boot protective gear that engage with torso or limbs of the rider, wherein the elbow, knee, chest, and boot protective gear refers to specific impact protective members that are utilized by the rider to get protection from different external effects at the specific location of the respective protective gear; receiving the first set of signals by the electronic device; generating a second set of signals by the electronic device based on the first set of signals; generating a third set of signals by the electronic device, the third set of signals indicative of one or more conditions of the rider at the rider's knee, elbow, or chest based on the first set of signals; receiving the third set of signals by one or more indicators from the electronic device; and generating a corresponding indication on the one or more indicators, the indication being associated with the one or more biometric conditions of the rider.[Cl-27]
Regarding claim 31, Jones modified via Cella the at least one vehicle position sensor comprises at least one of a gyroscope, an inclinometer, and an accelerometer (Paragraph 169, Cella)
In embodiments, the platform described herein may include, integrate with, or connect with a system for robotic process automation (RPA), whereby an artificial intelligence/machine learning system may be trained on a training set of data that consists of tracking and recording sets of interactions of humans as the humans interact with a set of interfaces, such as graphical user interfaces (e.g., via interactions with mouse, trackpad, keyboard, touch screen, joystick, remote control devices); audio system interfaces (such as by microphones, smart speakers, voice response interfaces, intelligent agent interfaces (e.g., Siri and Alexa) and the like); human-machine interfaces (such as involving robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wrist bands, glasses, arm bands, torso bands, belts, rings, necklaces and other accessories); physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers, handles, steering systems, wheels, and many others); optical interfaces (including ones triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, and the like); sensor-enabled interfaces (such as ones involving cameras, EEG or other electrical signal sensing (such as for brain-computer interfaces), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR and other sensor sets that are capable of recognizing thoughts, gestures (facial, hand, posture, or other), utterances, and the like, and others. In addition to tracking and recording human interactions, the RPA system may also track and record a set of states, actions, events and results that occur by, within, from or about the systems and processes with which the humans are engaging. For example, the RPA system may record mouse clicks on a frame of video that appears within a process by which a human review the video, such as where the human highlights points of interest within the video, tags objects in the video, captures parameters (such as sizes, dimensions, or the like), or otherwise operates on the video within a graphical user interface. The RPA system may also record system or process states and events, such as recording what elements were the subject of interaction, what the state of a system was before, during and after interaction, and what outputs were provided by the system or what results were achieved. Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision, such as by having a human correct the RPA system as it attempts in a set of trials to undertake the action that the human would have undertaken (e.g., tagging the right object, labeling an item correctly, selecting the correct button to trigger a next step in a process, or the like), such that over a set of trials the RPA system becomes increasingly effective at replicating the action the human would have taken. Learning may include deep learning, such as by reinforcing learning based on outcomes, such as successful outcomes (such as based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, an RPA system may be seeded during a learning phase with a set of expert human interactions, such that the RPA system begins to be able to replicate expert interaction with a system. For example, an expert driver's interactions with a robotic system, such as a remote-controlled vehicle or a UAV, may be recorded along with information about the vehicles state (e.g., the surrounding environment, navigation parameters, and purpose), such that the RPA system may learn to drive the vehicle in a way that reflects the same choices as an expert driver. After being taught to replicate the skills or expertise of an expert human, the RPA system may be transitioned to a deep learning mode, where the system further improves based on a set of outcomes, such as by being configured to attempt some level of variation in approach (e.g., trying different navigation paths to optimize time of arrival, or trying different approaches to deceleration and acceleration in curves) and tracking outcomes (with feedback), such that the RPA system can learn, by variation/experimentation (which may be randomized, rule-based, or the like, such as using genetic programming techniques, random-walk techniques, random forest techniques, and others) and selection, to exceed the expertise of the human expert. Thus, the RPA system learns from a human expert, acquires expertise in interacting with a system or process, facilitates automation of the process (such as by taking over some of the more repetitive tasks, including ones that require consistent execution of acquired skills), and provides a very effective seed for artificial intelligence, such as by providing a seed model or system that can be improved by machine learning with feedback on outcomes of a system or process.[P-169]
Regarding claim 32, Jones modified via Cella teaches the sensors detect the position of the rider's limbs and body relative to the position and orientation of the vehicle as the vehicle is being ridden (Paragraphs 160, 375, 377, 379, Cella)
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
An aspect provided herein includes a motorcycle helmet augmented reality system comprising: a display 44175 disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet; a circuit 4488 for registering at least one of location and orientation of a motorcycle that the rider is riding; a machine learning circuit 44179 that determines at least one augmentation parameter 44156 by processing at least one input relating to at least one of the rider 44163 and the motorcycle 44180; and a reality augmentation circuit 4488 that, responsive to the registered at least one of a location and orientation of the motorcycle generates an augmentation element 44177 for presenting in the display 44175, the generating based at least in part on the determined at least one augmentation parameter 44156.[P-379]
Regarding claim 33, Jones modified via Cella teaches sensors detect the position of the vehicle on the ground as they sense the position of the rider and orientation of the vehicle(Paragraphs 160, 375, 377, 379, Cella)
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
An aspect provided herein includes a motorcycle helmet augmented reality system comprising: a display 44175 disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet; a circuit 4488 for registering at least one of location and orientation of a motorcycle that the rider is riding; a machine learning circuit 44179 that determines at least one augmentation parameter 44156 by processing at least one input relating to at least one of the rider 44163 and the motorcycle 44180; and a reality augmentation circuit 4488 that, responsive to the registered at least one of a location and orientation of the motorcycle generates an augmentation element 44177 for presenting in the display 44175, the generating based at least in part on the determined at least one augmentation parameter 44156.[P-379]
Here, we see Cella teach sensors detect the position of rider and the vehicle relative to is environment including wheel profile and suspension, indicative of its position of the ground as they sense the position of the rider and orientation of the vehicle
Claims 6 are rejected on the ground of non-statutory double patenting as being unpatentable over claim 1-34 of U.S. Patent No. 12361811 (hereinafter referred to as Jones) in view of Cella (US 20210272394 A1) as applied above in claim 5, in further view of Scripa et al. (US 20170172243 A1)
Regarding claim 6, Jones teaches one or more indicators communicatively coupled to the electronic device, the one or more indicators configured to receive the third set of signals from the electronic device and further configured to generate a corresponding indication associated with each of the one or more conditions of any one of the rider and the vehicle. (Claim 6)
The system of claim 5, further comprising one or more indicators communicatively coupled to the electronic device, the one or more indicators configured to receive the third set of signals from the electronic device and further configured to generate a corresponding indication associated with each of the one or more conditions of any one of the rider and the vehicle, the one or more indicators including at least one indicator placed on at least one of the elbow protective gear, the knee protective gear, the chest protective gear, and the boot protective gear.[P-6]
Jones fails to teach at least one of the plurality of sensors is secured to a chinstrap.
Scripa on the other hand teaches at least one of the plurality of sensors is secured to a chinstrap (Paragraph 33)
The illustrated chinstrap 80 further includes flexible central straps 88 with which the chinstrap 80 can be fastened below the jaw. A buckle 90 is provided to attach the central straps 88 together and includes a sensor 92 that can detect when the chinstrap 80 is fastened or not. Also attached to one of the central straps 88 is a microphone 94 that can be used by the wearer to communicate with search and rescue personnel. In alternative embodiments, the microphone 94 can be mounted to the helmet shell 12. In some embodiments, the chinstrap 80 or helmet shell 12 can also be provided with a speaker (not shown) to enable two-way communications. In further embodiments, one or more of the body parameters described above as being captured by the body parameter sensor 20 can be captured by one or more sensors incorporated into the chinstrap 80.[P-33]
It would have been obvious during the time of the filing date of the said invention to combine Scripa, teaching with Jones modified’s teaching in order to enable safer protocols for protective accessories from hazardous situations
Claims 28, 29 are rejected on the ground of non-statutory double patenting as being unpatentable over claim 1-34 of U.S. Patent No. 12361811 (hereinafter referred to as Jones) in view of Cella (US 20210272394 A1) as applied above in claim 27, in further view of De Bruyne et al. (US 20170143069 A1)
Regarding claim 28, Jones modified fails to teach the rider and vehicle positions are correlated to a ground position.
De Bruyne on the other hand teaches the rider and vehicle positions are correlated to a ground position (Paragraph 19, 45).
The helmet preferably is a cycling helmet; skiing helmet or snow-board helmet, or equestrian or motorcycle helmet and in particular a time trial cycling helmet, road cycling helmet or triathlon helmet[P-19]
In yet another alternative embodiment the system comprises at least one sensor unit (an inclination sensor) integrated in or provided on the helmet and two sensor units separate from the helmet, one to be provided in a predetermined position on the riders body and one to be provided on a predetermined position on the bike, whereby the two separate sensor units allow indicating the position of the rider on his bike together with his head position. Such embodiment provides more information on the overall position of the rider and allows more detailed and extensive feedback for optimizing the rider's position during a race through the output unit.[P-45]
Here, the vehicle positions are correlated to a ground position, i.e. ground position is a position on a motorcycle course or track.
Therefore, it would have been obvious during the time of the filing date of the invention to combine DeBruyne’s teaching with Jones modified in order to more effectively track the vehicle’s position on the ground
Regarding claim 29, Jones modified via De Bruyne teaches the ground position is a position on a motorcycle course or track. (Paragraph 19, 45).
The helmet preferably is a cycling helmet; skiing helmet or snow-board helmet, or equestrian or motorcycle helmet and in particular a time trial cycling helmet, road cycling helmet or triathlon helmet[P-19]
In yet another alternative embodiment the system comprises at least one sensor unit (an inclination sensor) integrated in or provided on the helmet and two sensor units separate from the helmet, one to be provided in a predetermined position on the riders body and one to be provided on a predetermined position on the bike, whereby the two separate sensor units allow indicating the position of the rider on his bike together with his head position. Such embodiment provides more information on the overall position of the rider and allows more detailed and extensive feedback for optimizing the rider's position during a race through the output unit.[P-45]
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 27, 30-32 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella (US 20210272394 A1).
In regards to claim 27, Cella teaches a system for monitoring riders relative to vehicles ridden, the system comprising: a plurality of rider position sensors secured to rider gear and configured to detect rider body orientation; at least one vehicle position sensor secured to the vehicle and configured to detect vehicle orientation and position relative to rider body position. (Paragraph 11, 331, 375-377)
In embodiments, the system includes a first neural network to detect a detected satisfaction state of a rider user occupying the vehicle through analysis of data gathered from sensors deployed in the vehicle for gathering physiological conditions of the rider user; and a second neural network to optimize, for achieving a favorable satisfaction state of the rider user, an operational parameter of the vehicle in response to the detected satisfaction state of the rider user.[P-11]
In embodiments, the operational parameter 36124 that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the artificial intelligence system 3636 interacts with a vehicle control system to optimize the operational parameter. In embodiments, the artificial intelligence system 3636 further comprises a neural net 3622 that includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the set of wearable sensors 36157 comprises at least two of a watch, a ring, a wrist band, an arm band, an ankle band, a torso band, a skin patch, a head-worn device, eye glasses, foot wear, a glove, an in-ear device, clothing, headphones, a belt, a finger ring, a thumb ring, a toe ring, and a necklace. In embodiments, the artificial intelligence system 3636 uses deep learning for determining patterns of wearable sensor-generated emotional state indicative data that indicate an emotional state of the rider as at least one of a favorable emotional state and an unfavorable emotional state. In embodiments, the artificial intelligence system 3636 is responsive to a rider indicated emotional state by at least optimizing the operation parameter to at least one of achieve and maintain the rider indicated emotional state.[P-331]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a system for transportation 4411, comprising: a motorcycle helmet 44170 to provide an augmented reality experience based on registration of a location and orientation of a wearer 44172 of the helmet 44170 in an environment 44171.[P-376]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
In regards to claim 30, Cella teaches the plurality of rider position sensors includes sensors on at least one of a chest protector, an elbow guard, a knee guard, a protective boot, and a helmet(Paragraph 331)
In embodiments, the operational parameter 36124 that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the artificial intelligence system 3636 interacts with a vehicle control system to optimize the operational parameter. In embodiments, the artificial intelligence system 3636 further comprises a neural net 3622 that includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the set of wearable sensors 36157 comprises at least two of a watch, a ring, a wrist band, an arm band, an ankle band, a torso band, a skin patch, a head-worn device, eye glasses, foot wear, a glove, an in-ear device, clothing, headphones, a belt, a finger ring, a thumb ring, a toe ring, and a necklace. In embodiments, the artificial intelligence system 3636 uses deep learning for determining patterns of wearable sensor-generated emotional state indicative data that indicate an emotional state of the rider as at least one of a favorable emotional state and an unfavorable emotional state. In embodiments, the artificial intelligence system 3636 is responsive to a rider indicated emotional state by at least optimizing the operation parameter to at least one of achieve and maintain the rider indicated emotional state.[P-331]
Here, Cella’s teaching integrates position sensors within footwear, in which case a protective boot would be included
In regards to claim 31, Cella teaches the at least one vehicle position sensor comprises at least one of a gyroscope, an inclinometer, and an accelerometer (Paragraph 169)
In embodiments, the platform described herein may include, integrate with, or connect with a system for robotic process automation (RPA), whereby an artificial intelligence/machine learning system may be trained on a training set of data that consists of tracking and recording sets of interactions of humans as the humans interact with a set of interfaces, such as graphical user interfaces (e.g., via interactions with mouse, trackpad, keyboard, touch screen, joystick, remote control devices); audio system interfaces (such as by microphones, smart speakers, voice response interfaces, intelligent agent interfaces (e.g., Siri and Alexa) and the like); human-machine interfaces (such as involving robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wrist bands, glasses, arm bands, torso bands, belts, rings, necklaces and other accessories); physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers, handles, steering systems, wheels, and many others); optical interfaces (including ones triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, and the like); sensor-enabled interfaces (such as ones involving cameras, EEG or other electrical signal sensing (such as for brain-computer interfaces), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR and other sensor sets that are capable of recognizing thoughts, gestures (facial, hand, posture, or other), utterances, and the like, and others. In addition to tracking and recording human interactions, the RPA system may also track and record a set of states, actions, events and results that occur by, within, from or about the systems and processes with which the humans are engaging. For example, the RPA system may record mouse clicks on a frame of video that appears within a process by which a human review the video, such as where the human highlights points of interest within the video, tags objects in the video, captures parameters (such as sizes, dimensions, or the like), or otherwise operates on the video within a graphical user interface. The RPA system may also record system or process states and events, such as recording what elements were the subject of interaction, what the state of a system was before, during and after interaction, and what outputs were provided by the system or what results were achieved. Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision, such as by having a human correct the RPA system as it attempts in a set of trials to undertake the action that the human would have undertaken (e.g., tagging the right object, labeling an item correctly, selecting the correct button to trigger a next step in a process, or the like), such that over a set of trials the RPA system becomes increasingly effective at replicating the action the human would have taken. Learning may include deep learning, such as by reinforcing learning based on outcomes, such as successful outcomes (such as based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, an RPA system may be seeded during a learning phase with a set of expert human interactions, such that the RPA system begins to be able to replicate expert interaction with a system. For example, an expert driver's interactions with a robotic system, such as a remote-controlled vehicle or a UAV, may be recorded along with information about the vehicles state (e.g., the surrounding environment, navigation parameters, and purpose), such that the RPA system may learn to drive the vehicle in a way that reflects the same choices as an expert driver. After being taught to replicate the skills or expertise of an expert human, the RPA system may be transitioned to a deep learning mode, where the system further improves based on a set of outcomes, such as by being configured to attempt some level of variation in approach (e.g., trying different navigation paths to optimize time of arrival, or trying different approaches to deceleration and acceleration in curves) and tracking outcomes (with feedback), such that the RPA system can learn, by variation/experimentation (which may be randomized, rule-based, or the like, such as using genetic programming techniques, random-walk techniques, random forest techniques, and others) and selection, to exceed the expertise of the human expert. Thus, the RPA system learns from a human expert, acquires expertise in interacting with a system or process, facilitates automation of the process (such as by taking over some of the more repetitive tasks, including ones that require consistent execution of acquired skills), and provides a very effective seed for artificial intelligence, such as by providing a seed model or system that can be improved by machine learning with feedback on outcomes of a system or process.[P-169]
In regards to claim 32, Cella teaches the sensors detect the position of the rider's limbs and body relative to the position and orientation of the vehicle as the vehicle is being ridden (Paragraphs 160, 375, 377, 379)
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
An aspect provided herein includes a motorcycle helmet augmented reality system comprising: a display 44175 disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet; a circuit 4488 for registering at least one of location and orientation of a motorcycle that the rider is riding; a machine learning circuit 44179 that determines at least one augmentation parameter 44156 by processing at least one input relating to at least one of the rider 44163 and the motorcycle 44180; and a reality augmentation circuit 4488 that, responsive to the registered at least one of a location and orientation of the motorcycle generates an augmentation element 44177 for presenting in the display 44175, the generating based at least in part on the determined at least one augmentation parameter 44156.[P-379]
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 7, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella (US 20210272394 A1) in view of Flitsch et al. (CN 106361273 A)
In regards to claim 1, Cella teaches a system for monitoring vehicle riders, the system comprising: a plurality of sensors configured to detect a plurality of parameters associated with a rider (Paragraph 11)
In embodiments, the system includes a first neural network to detect a detected satisfaction state of a rider user occupying the vehicle through analysis of data gathered from sensors deployed in the vehicle for gathering physiological conditions of the rider user; and a second neural network to optimize, for achieving a favorable satisfaction state of the rider user, an operational parameter of the vehicle in response to the detected satisfaction state of the rider user.[P-11]
Cella teaches the plurality of sensors is placed on at least one of elbow protective gear, knee protective gear, chest protective gear, and boot protective gear that engage with torso or limbs of the rider, wherein the elbow, knee, chest, and boot protective gear refers to specific impact protective members that are utilized by the rider to get protection from different external effects at the specific location of the respective protective gear (Paragraphs 331, 338)
In embodiments, the operational parameter 36124 that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the artificial intelligence system 3636 interacts with a vehicle control system to optimize the operational parameter. In embodiments, the artificial intelligence system 3636 further comprises a neural net 3622 that includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the set of wearable sensors 36157 comprises at least two of a watch, a ring, a wrist band, an arm band, an ankle band, a torso band, a skin patch, a head-worn device, eye glasses, foot wear, a glove, an in-ear device, clothing, headphones, a belt, a finger ring, a thumb ring, a toe ring, and a necklace. In embodiments, the artificial intelligence system 3636 uses deep learning for determining patterns of wearable sensor-generated emotional state indicative data that indicate an emotional state of the rider as at least one of a favorable emotional state and an unfavorable emotional state. In embodiments, the artificial intelligence system 3636 is responsive to a rider indicated emotional state by at least optimizing the operation parameter to at least one of achieve and maintain the rider indicated emotional state.[P-331]
In embodiments, the set of wearable sensors comprises at least two of a watch, a ring, a wrist band, an arm band, an ankle band, a torso band, a skin patch, a head-worn device, eye glasses, foot wear, a glove, an in-ear device, clothing, headphones, a belt, a finger ring, a thumb ring, a toe ring, and a necklace. In embodiments, the artificial intelligence system 3636 uses deep learning for determining patterns of wearable sensor-generated emotional state indicative data that indicate the change in the emotional state of the rider. In embodiments, the artificial intelligence system 3636 further determines the change in emotional state of the rider based on context gathered from a plurality of sources including data indicating a purpose of the rider riding in the self-driving vehicle, a time of day, traffic conditions, weather conditions and optimizes the operating parameter 36124 to at least one of achieve and maintain the adapted favorable emotional state. In embodiments, the artificial intelligence system 3636 adjusts the operational parameter in real time responsive to the detecting of a change in rider emotional state.[P-338]
Cella teaches the system configured to generate a first set of signals indicative of the plurality of parameters, wherein the plurality of parameters represents at least one condition or performance of the rider (Paragraphs 34, 570)
In embodiments, the detected satisfaction state of the user is a detected emotional state of the user. In embodiments, the favorable satisfaction state of the user is a favorable emotional state of the user. In embodiments, the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. In embodiments, at least one of the neural networks is a hybrid neural network and includes a convolutional neural network. In embodiments, the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a satisfaction state of the user. In embodiments, the second neural network optimizes the operational parameter in real time responsive to the detecting of the detected satisfaction state of the user by the first neural network. In embodiments, the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.[P-34]
In some embodiments, the machine learning model 65102 may be and/or include an artificial neural network, e.g. a connectionist system configured to “learn” to perform tasks by considering examples and without being explicitly programmed with task-specific rules. The machine learning model 65102 may be based on a collection of connected units and/or nodes that may act like artificial neurons that may in some ways emulate neurons in a biological brain. The units and/or nodes may each have one or more connections to other units and/or nodes. The units and/or nodes may be configured to transmit information, e.g. one or more signals, to other units and/or nodes, process signals received from other units and/or nodes, and forward processed signals to other units and/or nodes. One or more of the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned. The assigned weights may be configured to facilitate learning, i.e. training, of the machine learning model 65102. The weights assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights. The one or more thresholds may be configured such that a signal is only sent between one or more units and/or nodes, if a signal and/or aggregate signal crosses the threshold. In some embodiments, the units and/or nodes may be assigned to a plurality of layers, each of the layers having one or both of inputs and outputs. A first layer may be configured to receive training data, transform at least a portion of the training data, and transmit signals related to the training data and transformation thereof to a second layer. A final layer may be configured to output an estimate, conclusion, product, or other consequence of processing of one or more inputs by the machine learning model 65102. Each of the layers may perform one or more types of transformations, and one or more signals may pass through one or more of the layers one or more times. In some embodiments, the machine learning model 65102 may employ deep learning and being at least partially modeled and/or configured as a deep neural network, a deep belief network, a recurrent neural network, and/or a convolutional neural network, such as by being configured to include one or more hidden layers.[P-570]
Here, we see Cella teach the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route; the units and/or nodes may be configured to transmit information, e.g. one or more signals, to other units and/or nodes, process signals received from other units and/or nodes, and forward processed signals to other units and/or nodes.
Cella then teaches an electronic device communicatively coupled to the plurality of sensors, the electronic device comprising a processing unit and a transceiver, the electronic device configured to receive the first set of signals from the plurality of sensors, and further configured to generate a second set of signals (Paragraphs 9,18, 33, 62, 151, 154)
In embodiments, the system includes an identity management system to manage a set of identities and roles of a user of the vehicle. In embodiments, the identity management system includes capabilities to view, modify and configure the digital twin system is based on an identity from the set of identities of the user of the vehicle. In embodiments, the digital twin system is populated via an API from an edge intelligence system of the vehicle that provides 5G connectivity to a system external to the vehicle. In embodiments, the digital twin system is populated via an API from an edge intelligence system of the vehicle that provides internal 5G connectivity to a set of sensors and data sources of the vehicle. In embodiments, the digital twin system is populated via an API from an edge intelligence system of the vehicle that provides 5G connectivity to an onboard artificial intelligence system.[P-9]
In embodiments, the method includes detecting a detected satisfaction state of a rider user occupying the vehicle through analysis, using a first neural network, of data gathered from sensors deployed in the vehicle for gathering physiological conditions of the rider user; and optimizing to achieve a favorable satisfaction state of the rider user an operational parameter of the vehicle in response to the detected satisfaction state of the rider user using a second neural network.[P-18]
In embodiments, the system includes a first neural network to detect a detected satisfaction state of a user occupying the vehicle through analysis of data gathered from sensors deployed in the vehicle for gathering physiological conditions of the user; and a second neural network to optimize, for achieving a favorable satisfaction state of the user, an operational parameter of the vehicle in response to the detected satisfaction state of the user.[P-33]
In embodiments, the detected satisfaction state of the user is a detected emotional state of the user. In embodiments, the favorable satisfaction state of the user is a favorable emotional state of the user. In embodiments, the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. In embodiments, at least one of the neural networks is a hybrid neural network and includes a convolutional neural network. In embodiments, the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a satisfaction state of the user. In embodiments, the second neural network optimizes the operational parameter in real time responsive to the detecting of the detected satisfaction state of the user by the first neural network. In embodiments, the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.[P-45]
In embodiments, the detected satisfaction state of the rider user is a detected emotional state of the rider user. In embodiments, the favorable satisfaction state of the rider user is a favorable emotional state of the rider user. In embodiments, the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. In embodiments, at least one of the neural networks is a hybrid neural network and includes a convolutional neural network. In embodiments, the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider satisfaction state of the rider user. In embodiments, the second neural network optimizes the operational parameter in real time responsive to the detecting of the detected satisfaction state of the rider user by the first neural network. In embodiments, the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.[P-62]
Referring to FIG. 2, provided herein are transportation systems having a hybrid neural network 247 for optimizing a powertrain 213 of a vehicle, wherein at least two parts of the hybrid neural network 247 optimize distinct parts of the powertrain 213. An artificial intelligence system may control a powertrain component 215 based on an operational model (such as a physics model, an electrodynamic model, a hydrodynamic model, a chemical model, or the like for energy conversion, as well as a mechanical model for operation of various dynamically interacting system components). For example, the AI system may control a powertrain component 215 by manipulating a powertrain operating parameter 260 to achieve a powertrain state 261. The AI system may be trained to operate a powertrain component 215, such as by training on a data set of outcomes (e.g., fuel efficiency, safety, rider satisfaction, or the like) and/or by training on a data set of operator actions (e.g., driver actions sensed by a sensor set, camera or the like or by a vehicle information system). In embodiments, a hybrid approach may be used, where one neural network optimizes one part of a powertrain (e.g., for gear shifting operations), while another neural network optimizes another part (e.g., braking, clutch engagement, or energy discharge and recharging, among others). Any of the powertrain components described throughout this disclosure may be controlled by a set of control instructions that consist of output from at least one component of a hybrid neural network 247[P-151]
FIG. 5 illustrates a set of vehicle user interfaces 523. Vehicle user interfaces 523 may include electromechanical interfaces 568, such as steering interfaces, braking interfaces, interfaces for seats, windows, moonroof, glove box and the like. Interfaces 523 may include various software interfaces (which may have touch screen, dials, knobs, buttons, icons or other features), such as a game interface 569, a navigation interface 570, an entertainment interface 571, a vehicle settings interface 572, a search interface 573, an ecommerce interface 574, and many others. Vehicle interfaces may be used to provide inputs to, and may be governed by, one or more AI systems/expert systems such as described in embodiments throughout this disclosure.[P-154]
Here, Cella describes a vehicular onboard unit with user interface that interacts with the driver and several data sources including sensors via an artificial intelligence processing system, capable of receiving a first signal readings (by 5G connectivity) from the vehicular related sensors (such as physiological conditions of the driver), and thereby configured to generate a second set of signals (to optimize for achieving a favorable satisfaction state of the rider user, if the read sensor readings a deemed unsatisfactory).
Cella teaches a network for real-time communication of from at least one of the sensors transmitted to other vehicles within the network. (Paragraph 383)
In embodiments, the transportation system may be a vehicle transportation system. Such a vehicle transportation system may include a network-enabled vehicle information ingestion port 4532 that may provide a network (e.g., Internet and the like) interface through which inputs, such as inputs comprising operational state and energy consumption information from at least one of a plurality of network-enabled vehicles 4510 may be gathered. In embodiments, such inputs may be gathered in real time as the plurality of network-enabled vehicles 4510 connect to and deliver vehicle operational state, energy consumption and other related information. In embodiments, the inputs may relate to vehicle energy consumption and may be determined from a battery charge state of a portion of the plurality of vehicles. The inputs may include a route plan for the vehicle, an indicator of the value of charging of the vehicle, and the like. The inputs may include predicted traffic conditions for the plurality of vehicles. The transportation system may also include vehicle charging or refueling infrastructure that may include one or more vehicle charging infrastructure control system(s) 4534. These control system(s) 4534 may receive the operational state and energy consumption information for the plurality of network-enabled vehicles 4510 via the ingestion port 4532 or directly through a common or set of connected networks, such as the Internet and the like. Such a transportation system may further include an artificial intelligence system 4536 that may be functionally connected with the vehicle charging infrastructure control system(s) 4534 that, for example, responsive to the receiving of the operational state and energy consumption information, may determine, provide, adjust or create at least one charging plan parameter 4514 upon which a charging plan 4512 for at least a portion of the plurality of network-enabled vehicles 4510 is dependent. This dependency may yield changes in the application of the charging plan 4512 by the control system(s) 4534, such as when a processor of the control system(s) 4534 executes a program derived from or based on the charging plan 4512. The charging infrastructure control system(s) 4534 may include a cloud-based computing system remote from charging infrastructure systems (e.g., remote from an electric vehicle charging kiosk and the like); it may also include a local charging infrastructure system 4538 that may be disposed with and/or integrated with an infrastructure element, such as a fuel station, a charging kiosk and the like. In embodiments, the artificial intelligence system 4536 may interface and coordinate with the cloud-based system 4534, the local charging infrastructure system 4538 or both. In embodiments, coordination of the cloud-based system may take on a different form of interfacing, such as providing parameters that affect more than one charging kiosk and the like than may coordination with the local charging infrastructure system 4538, which may provide information that the local system could use to adapt charging system control commands and the like that may be provided from, for example, a cloud-based control system 4534. In an example, a cloud-based control system (that may control only a portion, such as a localized set, of available charging/refueling infrastructure devices) may respond to the charging plan parameter 4514 of the artificial intelligence system 4536 by setting a charging rate that facilitates highly parallel vehicle charging. However, the local charging infrastructure system 4538 may adapt this control plan, such as based on a control plan parameter provided to it by the artificial intelligence system 4536, to permit a different charging rate (e.g., a faster charging rate), such as for a brief period to accommodate an accumulation of vehicles queued up or estimated to use a local charging kiosk in the period. In this way, an adjustment to the at least one parameter 4514 that when made to the charge infrastructure operation plan 4512 ensures that the at least one of the plurality of vehicles 4510 has access to energy renewal in a target energy renewal geographic region 4516.[P-383]
In embodiments, a transportation system may be distributed and may include an artificial intelligence system 4536 for taking inputs relating to a plurality of vehicles 4510 and determining at least one parameter 4514 of a re-charging and refueling plan 4512 for at least one of the plurality of vehicles based on the inputs. In embodiments, such inputs may be gathered in real time as plurality of vehicles 4510 connect to and deliver vehicle operational state, energy consumption and other related information. In embodiments, the inputs may relate to vehicle energy consumption and may be determined from a battery charge state of a portion of the plurality of vehicles. The inputs may include a route plan for the vehicle, an indicator of the value of charging of the vehicle, and the like. The inputs may include predicted traffic conditions for the plurality of vehicles. The distributed transportation system may also include cloud-based and vehicle-based systems that exchange information about the vehicle, such as energy consumption and operational information and information about the transportation system, such as recharging or refueling infrastructure. The artificial intelligence system may respond to transportation system and vehicle information shared by the cloud and vehicle-based system with control parameters that facilitate executing a cognitive charging plan for at least a portion of charging or refueling infrastructure of the transportation system. The artificial intelligence system 4536 may determine, provide, adjust or create at least one charging plan parameter 4514 upon which a charging plan 4512 for at least a portion of the plurality of vehicles 4510 is dependent. This dependency may yield changes in the execution of the charging plan 4512 by at least one the cloud-based and vehicle-based systems, such as when a processor executes a program derived from or based on the charging plan 4512.[P-391]
Cella fails to teach the communication of rider biometric data from at least one of the sensors transmitted to another person via at least one user device.
Flitsch on the other hand teaches the communication of rider biometric data from at least one of the sensors transmitted to another person via at least one user device.(Abstract; Page 27,; Claim 19)
The invention is based on bio-medical device for information communication of biometric. The invention claims a method for forming information communication system based on biometric method and device. In some examples, the information communication system based on biometric includes a biomedical device having a sensing component, wherein the sensing component to generate biometric result. In some examples, the information communication system based on biometric may include user device such as a smart phone, the user device to pair communication with the bio-medical device. biometric measurement result can be triggered based on communication of biometric information communication message.[Abstr]
The device can be worn by user of the drive a motor vehicle (such as a car, truck or motorcycle and other examples). the biomedical device with a user of a GPS enabled smart phone pairing, both can be connected to the vehicle and can transmit information to the user either by screen presenting to the user directly, or sounding the vehicle speaker system. communication with the user may potentially be realized by the screen and speaker of the intelligent telephone, but since when operating a vehicle using a smart phone is dangerous, for the sake of safety, may desire can conveniently for the communication system of the vehicle. The biomedical device can be used for collecting biometric data from a user, as a non-limiting example, the device may be used as a glucose monitor for collecting blood glucose level of the user related data. The biomedical device can detect the user in the vehicle when low blood sugar, available communication capability, the vehicle transmits the information to the user. Therefore, using the location-based tracking system can be recommended to a user area of the food item, the food item can be used for their increased glycemic level. In some examples, it can use biometric data value to start communication to a content, a storage and processing system, and may be based on the algorithm of the user preference analysis, customized information can be transmitted to the user. In some examples, such a preference may be based on past experience the user has some option in the region. In another example, the content system can combine each aspect of the user associated with the biometric data, then provides information to the user, the information can be related to glucose level, exercise program, improving the control of professional medical provider and other such examples.[Pg 27. P-4]
Some biometric sensor can be wearing sensor 1850. the wearable sensor 1850 can indirectly measure various biometric. In some examples, the sensing element can be independent of any body tissue or body fluid of the user. This sensing element capable of monitoring the whole machine with the user related biometric, such as motion of the user. other wearable sensor capable of directly or indirectly sensing or detecting user cell tissue layer so as to permit measurement of temperature, oxygenation and jersey liquid chemical analysis (as a non-limiting example). In some examples, form the wearable sensor 1850 may take a clothing or jewelry or incorporated into clothing or jewelry. In other examples, the wearable sensor 1850 can be attached to clothing or jewelry.[Pg 30, P-7]
. A method for transmitting information, the method comprising: providing to the user performs biometric measurement of a biomedical device. wherein the medical device directly sensed or indirectly sensing the user of cell tissue layer or layer of user contact with the cell tissue fluid, using the bio-medical device performs a biometric measurement of the user, transmitted by the biometric data obtained by the biometric measurement result, at the content server receives the biometric data result; the transmitting and receiving message on the basis of the biometric data obtained by the biometric measurement result, and feedback device for transmitting the message to the at least one of the user and a third party.[Cl-19]
Here, Flitsch teaches a motorcycle rider wearing clothing with sensors capable of measuring biometric data of the rider and transmit via a user device biometric data to another user or third party.
Therefore, it was obvious during the time of the filing date of the invention to combine Flitsch’s teaching with Cella’s teaching in order to effective communicate a drivers behavior to e third party of concern.
In regards to claim 2, Cella modified teaches the plurality of sensors is configured to detect a plurality of parameters associated with a corresponding vehicle of the rider, the plurality of parameters associated with the corresponding vehicle represent at least one condition or performance of the vehicle, including the orientation of the vehicle (Paragraph 12, 160, 375, 377, Cella)
In embodiments, the detected satisfaction state of the rider user is a detected emotional state of the rider user. In embodiments, the favorable satisfaction state of the rider user is a favorable emotional state of the rider user. In embodiments, the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. In embodiments, at least one of the neural networks is a hybrid neural network and includes a convolutional neural network. In embodiments, the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider satisfaction state of the rider user. In embodiments, the second neural network optimizes the operational parameter in real time responsive to the detecting of the detected satisfaction state of the rider user by the first neural network. In embodiments, the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.[P-12]
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
Here, Cella teaches the parameters associated with the corresponding vehicle of the driver may include at least one condition or performance of the vehicle such as a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.
Cella teaches the performance of the vehicle including the orientation of the vehicle (Paragraphs 160, 375, 377, Cella)
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
In regards to claim 3, Cella modified teaches one or more user devices communicatively coupled to the electronic device using a communication network, wherein the one or more user devices are configured to receive the second set of signals, and further configured to analyze the received second set of signals to determine the at least one condition or the performance of the rider.(Paragraphs 11-12, 287, 301, Cella)
In embodiments, the system includes a first neural network to detect a detected satisfaction state of a rider user occupying the vehicle through analysis of data gathered from sensors deployed in the vehicle for gathering physiological conditions of the rider user; and a second neural network to optimize, for achieving a favorable satisfaction state of the rider user, an operational parameter of the vehicle in response to the detected satisfaction state of the rider user.[P-11]
In embodiments, the detected satisfaction state of the rider user is a detected emotional state of the rider user. In embodiments, the favorable satisfaction state of the rider user is a favorable emotional state of the rider user. In embodiments, the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. In embodiments, at least one of the neural networks is a hybrid neural network and includes a convolutional neural network. In embodiments, the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider satisfaction state of the rider user. In embodiments, the second neural network optimizes the operational parameter in real time responsive to the detecting of the detected satisfaction state of the rider user by the first neural network. In embodiments, the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.[P-12]
Referring to FIG. 30, in embodiments provided herein are transportation systems 3011 having an artificial intelligence system 3036 for processing feature vectors of an image of a face of a rider in a vehicle to determine an emotional state and optimizing at least one operating parameter of the vehicle to improve the rider's emotional state. A face may be classified based on images from in-vehicle cameras, available cellphone or other mobile device cameras, or other sources. An expert system, optionally trained based on a training set of data provided by humans or trained by deep learning, may learn to adjust vehicle parameters (such as any described herein) to provide improved emotional states. For example, if a rider's face indicates stress, the vehicle may select a less stressful route, play relaxing music, play humorous content, or the like.[P-287]
In embodiments, the vehicle control system adjusts the at least one of the plurality of vehicle operational parameters 30124 that are indicative of a favorable rider emotional state. In embodiments, the vehicle control system 30134 selects an adjustment of the at least one of the plurality of vehicle operational parameters 30124 that is indicative of producing a favorable rider emotional state. In embodiments, the recurrent neural network further learns to classify the patterns of feature vectors and associate them to emotional states and changes thereto from a training data set 30131 sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the vehicle control system 30134 adjusts the at least one of the plurality of vehicle operation parameters 30124 in real time. In embodiments, the recurrent neural network detects a pattern of the feature vectors that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the vehicle operation control system adjusts an operational parameter of the vehicle in response to the indicated change in emotional state. In embodiments, the recurrent neural network comprises a plurality of connected nodes that form a directed cycle, the recurrent neural network further facilitating bi-directional flow of data among the connected nodes.[P-301]
Here, Cella describes a plurality of devices such as vehicular cameras, telemetry systems, mobile device cameras, or wearable sensor data to which after their received read data by the AI processing unit is processed and analyzed, adjustment commands/signals from the AI learning unit may then be sent to the sensors such that parameters may be adjusted to meet rider/user satisfaction such as adjustment in music to satisfy the user's/driver's emotional state.
Cella further teaches the performance of the rider, including the orientation of at least a portion of the rider relative to the orientation of the vehicle. (Paragraphs 160, 375, 377, 379, Cella)
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
An aspect provided herein includes a motorcycle helmet augmented reality system comprising: a display 44175 disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet; a circuit 4488 for registering at least one of location and orientation of a motorcycle that the rider is riding; a machine learning circuit 44179 that determines at least one augmentation parameter 44156 by processing at least one input relating to at least one of the rider 44163 and the motorcycle 44180; and a reality augmentation circuit 4488 that, responsive to the registered at least one of a location and orientation of the motorcycle generates an augmentation element 44177 for presenting in the display 44175, the generating based at least in part on the determined at least one augmentation parameter 44156.[P-379]
In regards to claim 4, Cella modified teaches the plurality of sensors is placed at least on one or more accessories of the rider and on the vehicle to detect the plurality of parameters (Paragraphs 290, 294, Cella)
In embodiments, the first neural network 3022 is a recurrent neural network and the second neural network 3020 is a radial basis function neural network. In embodiments, the second neural network 3020 optimizes the operational parameter 30124 based on a correlation between the vehicle operating state 3045 and the emotional state 3066 of the rider. In embodiments, the second neural network 3020 is to determine an optimum value for the operational parameter of the vehicle, and the transportation system 3011 is to adjust the operational parameter 30124 of the vehicle to the optimum value to induce the favorable emotional state of the rider. In embodiments, the first neural network 3022 further learns to classify the patterns in the feature vectors and associate the patterns with a set of emotional states and changes thereto by processing a training data set 30131. In embodiments, the training data set 30131 is sourced from at least one of a stream of data from an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, a rider headgear, and a rider voice recognition system.[P-290]
In embodiments, the radial basis function neural network is to optimize the operational parameter based on a correlation between a vehicle operating state and a rider emotional state. In embodiments, the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state. In embodiments, the recurrent neural network further learns to classify the patterns of the feature vectors and associate the patterns of the feature vectors to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the radial basis function neural network is to optimize the operational parameter in real time responsive to the detecting of the change in the emotional state of the rider by the recurrent neural network. In embodiments, the recurrent neural network detects a pattern of the feature vectors that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the radial basis function neural network is to optimize the operational parameter of the vehicle in response to the indicated change in emotional state.[P-294]
Cella describes wearable devices such as a rider's helmet may contain plurality of sensors may detect a plurality of parameters such as optimizing the operational parameter of the vehicle in response to the indicated change in emotional state detected by the sensor within the driver's helmet. The in-vehicle sensors may also be configured to detect the plurality of parameters.
Cella further teaches the plurality of sensors on the vehicle includes one or more of accelerometers, gyroscopes, and inclinometers. is placed at least on one or more accessories of the rider and on the vehicle (Paragraph 169, Cella)
In embodiments, the platform described herein may include, integrate with, or connect with a system for robotic process automation (RPA), whereby an artificial intelligence/machine learning system may be trained on a training set of data that consists of tracking and recording sets of interactions of humans as the humans interact with a set of interfaces, such as graphical user interfaces (e.g., via interactions with mouse, trackpad, keyboard, touch screen, joystick, remote control devices); audio system interfaces (such as by microphones, smart speakers, voice response interfaces, intelligent agent interfaces (e.g., Siri and Alexa) and the like); human-machine interfaces (such as involving robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wrist bands, glasses, arm bands, torso bands, belts, rings, necklaces and other accessories); physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers, handles, steering systems, wheels, and many others); optical interfaces (including ones triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, and the like); sensor-enabled interfaces (such as ones involving cameras, EEG or other electrical signal sensing (such as for brain-computer interfaces), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR and other sensor sets that are capable of recognizing thoughts, gestures (facial, hand, posture, or other), utterances, and the like, and others. In addition to tracking and recording human interactions, the RPA system may also track and record a set of states, actions, events and results that occur by, within, from or about the systems and processes with which the humans are engaging. For example, the RPA system may record mouse clicks on a frame of video that appears within a process by which a human review the video, such as where the human highlights points of interest within the video, tags objects in the video, captures parameters (such as sizes, dimensions, or the like), or otherwise operates on the video within a graphical user interface. The RPA system may also record system or process states and events, such as recording what elements were the subject of interaction, what the state of a system was before, during and after interaction, and what outputs were provided by the system or what results were achieved. Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision, such as by having a human correct the RPA system as it attempts in a set of trials to undertake the action that the human would have undertaken (e.g., tagging the right object, labeling an item correctly, selecting the correct button to trigger a next step in a process, or the like), such that over a set of trials the RPA system becomes increasingly effective at replicating the action the human would have taken. Learning may include deep learning, such as by reinforcing learning based on outcomes, such as successful outcomes (such as based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, an RPA system may be seeded during a learning phase with a set of expert human interactions, such that the RPA system begins to be able to replicate expert interaction with a system. For example, an expert driver's interactions with a robotic system, such as a remote-controlled vehicle or a UAV, may be recorded along with information about the vehicles state (e.g., the surrounding environment, navigation parameters, and purpose), such that the RPA system may learn to drive the vehicle in a way that reflects the same choices as an expert driver. After being taught to replicate the skills or expertise of an expert human, the RPA system may be transitioned to a deep learning mode, where the system further improves based on a set of outcomes, such as by being configured to attempt some level of variation in approach (e.g., trying different navigation paths to optimize time of arrival, or trying different approaches to deceleration and acceleration in curves) and tracking outcomes (with feedback), such that the RPA system can learn, by variation/experimentation (which may be randomized, rule-based, or the like, such as using genetic programming techniques, random-walk techniques, random forest techniques, and others) and selection, to exceed the expertise of the human expert. Thus, the RPA system learns from a human expert, acquires expertise in interacting with a system or process, facilitates automation of the process (such as by taking over some of the more repetitive tasks, including ones that require consistent execution of acquired skills), and provides a very effective seed for artificial intelligence, such as by providing a seed model or system that can be improved by machine learning with feedback on outcomes of a system or process.[P-169]
In regards to claim 5, Cella modified teaches the plurality of sensors are placed adjacent a head and torso of a rider, and the plurality of sensors are placed adjacent at least one of the arms or legs of the rider.(Paragraphs 22, 169, 170, 377, 459, Cella)
In embodiments, a computer-implemented method for generating a digital twin of a vehicle includes receiving, through an interface, a request from a user of the vehicle to display state information of the vehicle; generating, using one or more processors, a digital twin representation of the vehicle based on one or more user inputs based on the state information of the vehicle; displaying, using the interface, the state information of the vehicle using the digital twin representation of the vehicle. In embodiments, the state information of the vehicle includes one or more of a vehicle maintenance state, a vehicle energy utilization state, a vehicle navigation state, a vehicle component state, or a vehicle driver state. In embodiments, the user inputs for the digital twin representation include one or more of an on-board diagnostic system, a telemetry system, a vehicle-located sensor, or a system external to the vehicle.[P-22]
In embodiments, the platform described herein may include, integrate with, or connect with a system for robotic process automation (RPA), whereby an artificial intelligence/machine learning system may be trained on a training set of data that consists of tracking and recording sets of interactions of humans as the humans interact with a set of interfaces, such as graphical user interfaces (e.g., via interactions with mouse, trackpad, keyboard, touch screen, joystick, remote control devices); audio system interfaces (such as by microphones, smart speakers, voice response interfaces, intelligent agent interfaces (e.g., Siri and Alexa) and the like); human-machine interfaces (such as involving robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wrist bands, glasses, arm bands, torso bands, belts, rings, necklaces and other accessories); physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers, handles, steering systems, wheels, and many others); optical interfaces (including ones triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, and the like); sensor-enabled interfaces (such as ones involving cameras, EEG or other electrical signal sensing (such as for brain-computer interfaces), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR and other sensor sets that are capable of recognizing thoughts, gestures (facial, hand, posture, or other), utterances, and the like, and others. In addition to tracking and recording human interactions, the RPA system may also track and record a set of states, actions, events and results that occur by, within, from or about the systems and processes with which the humans are engaging. For example, the RPA system may record mouse clicks on a frame of video that appears within a process by which a human review the video, such as where the human highlights points of interest within the video, tags objects in the video, captures parameters (such as sizes, dimensions, or the like), or otherwise operates on the video within a graphical user interface. The RPA system may also record system or process states and events, such as recording what elements were the subject of interaction, what the state of a system was before, during and after interaction, and what outputs were provided by the system or what results were achieved. Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision, such as by having a human correct the RPA system as it attempts in a set of trials to undertake the action that the human would have undertaken (e.g., tagging the right object, labeling an item correctly, selecting the correct button to trigger a next step in a process, or the like), such that over a set of trials the RPA system becomes increasingly effective at replicating the action the human would have taken. Learning may include deep learning, such as by reinforcing learning based on outcomes, such as successful outcomes (such as based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, an RPA system may be seeded during a learning phase with a set of expert human interactions, such that the RPA system begins to be able to replicate expert interaction with a system. For example, an expert driver's interactions with a robotic system, such as a remote-controlled vehicle or a UAV, may be recorded along with information about the vehicles state (e.g., the surrounding environment, navigation parameters, and purpose), such that the RPA system may learn to drive the vehicle in a way that reflects the same choices as an expert driver. After being taught to replicate the skills or expertise of an expert human, the RPA system may be transitioned to a deep learning mode, where the system further improves based on a set of outcomes, such as by being configured to attempt some level of variation in approach (e.g., trying different navigation paths to optimize time of arrival, or trying different approaches to deceleration and acceleration in curves) and tracking outcomes (with feedback), such that the RPA system can learn, by variation/experimentation (which may be randomized, rule-based, or the like, such as using genetic programming techniques, random-walk techniques, random forest techniques, and others) and selection, to exceed the expertise of the human expert. Thus, the RPA system learns from a human expert, acquires expertise in interacting with a system or process, facilitates automation of the process (such as by taking over some of the more repetitive tasks, including ones that require consistent execution of acquired skills), and provides a very effective seed for artificial intelligence, such as by providing a seed model or system that can be improved by machine learning with feedback on outcomes of a system or process.[P-169]
RPA systems may have particular value in situations where human expertise or knowledge is acquired with training and experience, as well as in situations where the human brain and sensory systems are particularly adapted and evolved to solve problems that are computationally difficult or highly complex. Thus, in embodiments, RPA systems may be used to learn to undertake, among other things: visual pattern recognition tasks with respect to the various systems, processes, workflows and environments described herein (such as recognizing the meaning of dynamic interactions of objects or entities within a video stream (e.g., to understand what is taking place as humans and objects interact in a video); recognition of the significance of visual patterns (e.g., recognizing objects, structures, defects and conditions in a photograph or radiography image); tagging of relevant objects within a visual pattern (e.g., tagging or labeling objects by type, category, or specific identity (such as person recognition); indication of metrics in a visual pattern (such as dimensions of objects indicated by clicking on dimensions in an x-ray or the like); labeling activities in a visual pattern by category (e.g., what work process is being done); recognizing a pattern that is displayed as a signal (e.g., a wave or similar pattern in a frequency domain, time domain, or other signal processing representation); anticipate a n future state based on a current state (e.g., anticipating motion of a flying or rolling object, anticipating a next action by a human in a process, anticipating a next step by a machine, anticipating a reaction by a person to an event, and many others); recognize and predicting emotional states and reactions (such as based on facial expression, posture, body language or the like); apply a heuristic to achieve a favorable state without deterministic calculation (e.g., selecting a favorable strategy in sport or game, selecting a business strategy, selecting a negotiating strategy, setting a price for a product, developing a message to promote a product or idea, generating creative content, recognizing a favorable style or fashion, and many others); any many others. In embodiments, an RPA system may automate workflows that involve visual inspection of people, systems, and objects (including internal components), workflows that involve performing software tasks, such as involving sequential interactions with a series of screens in a software interface, workflows that involve remote control of robots and other systems and devices, workflows that involve content creation (such as selecting, editing and sequencing content), workflows that involve financial decision-making and negotiation (such as setting prices and other terms and conditions of financial and other transactions), workflows that involve decision-making (such as selecting an optimal configuration for a system or sub-system, selecting an optimal path or sequence of actions in a workflow, process or other activity that involves dynamic decision-making), and many others.[P-170]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
In embodiments, the visual elements 56197 display a plurality of models that can be selected for use in the set of expert systems 5657. In embodiments, the visual elements 56197 display a plurality of neural network categories that can be selected for use in the set of expert systems 5657. In embodiments, at least one of the plurality of neural network categories includes a convolutional neural network. In embodiments, the visual elements 56197 include one or more indicators of suitability of items represented by the plurality of visual elements 56197 for a given purpose. In embodiments, configuring a plurality of expert systems 5657 comprises facilitating selection sources of inputs for use by at least a portion of the plurality of expert systems 5657. In embodiments, the interface 56133 facilitates selection, for at least a portion of the plurality of expert systems 5657, one or more output types, targets, durations, and purposes.[P-459]
The dealer 60702 of the vehicle 60104 interacts with the digital twin 60136 using a configurator view 60724 of the interface 60700 and requests for assistance in configuring a vehicle for a customer. The digital twin 60136 may display the GUI 60704 of the configurator view 60724 to the dealer 60702 showing all the different options available for one or more components. The dealer 60702 may then select one or more components using a drop-down menu or use drag and drop operations to add one or more components to configure the vehicle as per the preference of the customer. In the example embodiment, the GUI view 60704 of the digital twin displays options for vehicle grade 60804, engine 60808, seats 60812, color 60816 and wheels 60820.[P-516]
In regards to claim 7, Cella modified teaches data from a communication network having data regarding conditions in the area of the rider (Paragraphs 305, 320, Cella)
Referring to FIG. 31, in embodiments, provided herein are transportation systems having an artificial intelligence system for processing a voice of a rider in a vehicle to determine an emotional state and optimizing at least one operating parameter of the vehicle to improve the rider's emotional state. A voice-analysis module may take voice input and, using a training set of labeled data where individuals indicate emotional states while speaking and/or whether others tag the data to indicate perceived emotional states while individuals are talking, a machine learning system (such as any of the types described herein) may be trained (such as using supervised learning, deep learning, or the like) to classify the emotional state of the individual based on the voice. Machine learning may improve classification by using feedback from a large set of trials, where feedback in each instance indicates whether the system has correctly assessed the emotional state of the individual in the case of an instance of speaking. Once trained to classify the emotional state, an expert system (optionally using a different machine learning system or other artificial intelligence system) may, based on feedback of outcomes of the emotional states of a set of individuals, be trained to optimize various vehicle parameters noted throughout this disclosure to maintain or induce more favorable states. For example, among many other indicators, where a voice of an individual indicates happiness, the expert system may select or recommend upbeat music to maintain that state. Where a voice indicates stress, the system may recommend or provide a control signal to change a planned route to one that is less stressful (e.g., has less stop-and-go traffic, or that has a higher probability of an on-time arrival). In embodiments, the system may be configured to engage in a dialog (such as on on-screen dialog or an audio dialog), such as using an intelligent agent module of the system, that is configured to use a series of questions to help obtain feedback from a user about the user's emotional state, such as asking the rider about whether the rider is experiencing stress, what the source of the stress may be (e.g., traffic conditions, potential for late arrival, behavior of other drivers, or other sources unrelated to the nature of the ride), what might mitigate the stress (route options, communication options (such as offering to send a note that arrival may be delayed), entertainment options, ride configuration options, and the like), and the like. Driver responses may be fed as inputs to the expert system as indicators of emotional state, as well as to constrain efforts to optimize one or more vehicle parameters, such as by eliminating options for configuration that are not related to a driver's source of stress from a set of available configurations.[P-305]
Referring to FIG. 34 and FIG. 35, in embodiments, the vehicle 3410 comprises a system for automating at least one control parameter 34153 of the vehicle 3410. In embodiments, the vehicle 3410 is at least a semi-autonomous vehicle. In embodiments, the vehicle 3410 is automatically routed. In embodiments, the vehicle 3410 is a self-driving vehicle. In embodiments, the at least one Internet-of-things device 34150 is disposed in an operating environment 34154 of the vehicle. In embodiments, the at least one Internet-of-things device 34150 that captures the data about the vehicle 3410 is disposed external to the vehicle 3410. In embodiments, the at least one Internet-of-things device is a dashboard camera. In embodiments, the at least one Internet-of-things device is a mirror camera. In embodiments, the at least one Internet-of-things device is a motion sensor. In embodiments, the at least one Internet-of-things device is a seat-based sensor system. In embodiments, the at least one Internet-of-things device is an IoT enabled lighting system. In embodiments, the lighting system is a vehicle interior lighting system. In embodiments, the lighting system is a headlight lighting system. In embodiments, the at least one Internet-of-things device is a traffic light camera or sensor. In embodiments, the at least one Internet-of-things device is a roadway camera. In embodiments, the roadway camera is disposed on at least one of a telephone phone and a light pole. In embodiments, the at least one Internet-of-things device is an in-road sensor. In embodiments, the at least one Internet-of-things device is an in-vehicle thermostat. In embodiments, the at least one Internet-of-things device is a toll booth. In embodiments, the at least one Internet-of-things device is a street sign. In embodiments, the at least one Internet-of-things device is a traffic control light. In embodiments, the at least one Internet-of-things device is a vehicle mounted sensor. In embodiments, the at least one Internet-of-things device is a refueling system. In embodiments, the at least one Internet-of-things device is a recharging system. In embodiments, the at least one Internet-of-things device is a wireless charging station [P-320]
In regards to claim 8, Cella modified teaches conditions in the area of the rider include other riders and objects in the path of the rider. (Paragraphs 12, 21, Cella)
In embodiments, the detected satisfaction state of the rider user is a detected emotional state of the rider user. In embodiments, the favorable satisfaction state of the rider user is a favorable emotional state of the rider user. In embodiments, the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. In embodiments, at least one of the neural networks is a hybrid neural network and includes a convolutional neural network. In embodiments, the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider satisfaction state of the rider user. In embodiments, the second neural network optimizes the operational parameter in real time responsive to the detecting of the detected satisfaction state of the rider user by the first neural network. In embodiments, the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.[P-12]
In embodiments, the method includes forming, using the first neural network, one or more connected nodes that form a directed cycle. In embodiments, the first neural network further facilitates bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.[P-21]
In regards to claim 9, Cella modified teaches one or more indicators are configured to signal the rider of unsafe conditions in the area of the rider (Paragraphs 47, 183, 431, Cella)
In embodiments, the interface for the digital twin system provides a fleet monitoring view to the owner for tracking and monitoring movement/route/condition of one or more vehicles. In embodiments, the interface for the digital twin system provides a driver behavior monitoring view to the owner for allowing the owner to monitor instances of unsafe or dangerous driving by a driver. In embodiments, the interface for the digital twin system provides an insurance view to the owner for assisting the owner in determining an insurance policy quote of a vehicle based on a vehicle condition. In embodiments, the interface for the digital twin system provides a compliance view to the owner for showing compliance status with respect to emission/pollution and other regulatory norms based on a condition of the vehicle. In embodiments, the interface provides a performance tuning view to the owner for modifying or tuning characteristics of one or more components to personalize the performance of the vehicle based on a preference of the owner.[P-47]
Referring to FIG. 10, in embodiments provided herein are transportation systems 1011 having a hybrid neural network 1047 for optimizing the operating state of a continuously variable powertrain 1013 of a vehicle 1010. In embodiments, at least one part of the hybrid neural network 1047 operates to classify a state of the vehicle 1010 and another part of the hybrid neural network 1047 operates to optimize at least one operating parameter 1060 of the transmission 1019. In embodiments, the vehicle 1010 may be a self-driving vehicle. In an example, the first portion 1085 of the hybrid neural network may classify the vehicle 1010 as operating in a high-traffic state (such as by use of LIDAR, RADAR, or the like that indicates the presence of other vehicles, or by taking input from a traffic monitoring system, or by detecting the presence of a high density of mobile devices, or the like) and a bad weather state (such as by taking inputs indicating wet roads (such as using vision-based systems), precipitation (such as determined by radar), presence of ice (such as by temperature sensing, vision-based sensing, or the like), hail (such as by impact detection, sound-sensing, or the like), lightning (such as by vision-based systems, sound-based systems, or the like), or the like. Once classified, another neural network 1086 (optionally of another type) may optimize the vehicle operating parameter based on the classified state, such as by putting the vehicle 1010 into a safe-driving mode (e.g., by providing forward-sensing alerts at greater distances and/lower speeds than in good weather, by providing automated braking earlier and more aggressively than in good weather, and the like).[P-183]
An artificial intelligence-based control system 5136 may be trained on a set of outcomes (of various types described herein) to provide a level of variation of a user experience that achieves desired outcomes, including selection of the timing and extent of such variations. As another example, an audio system may be varied to preserve hearing (such as based on tracking accumulated sound pressure levels, accumulated dosage, or the like), to promote alertness (such as by varying the type of content), and/or to improve health (such as by providing a mix of stimulating and relaxing content). In embodiments, such an artificial intelligence system 5136 may be fed sensor data 51444, such as from a wearable device 51157 (including a sensor set) or a physiological sensing system 51190, which includes a set of systems and/or sensors capable of providing physiological monitoring within a vehicle 5110 (e.g., a vison-based system 51186 that observes a user, a sensor 5125 embedded in a seat, a steering wheel, or the like that can measure a physiological parameter, or the like). For example, a vehicle interface 51188 (such as a steering wheel or any other interface described herein) can measure a physiological parameter (e.g., galvanic skin response, such as to indicate a stress level, cortisol level, or the like of a driver or other user), which can be used to indicate a current state for purposes of control or can be used as part of a training data set to optimize one or more parameters that may benefit from control, including control of variation of user experience to achieve desired outcomes. In one such example, an artificial intelligence system 5136 may vary parameters, such as driving experience, music and the like, to account for changes in hormonal systems of the user (such as cortisol and other adrenal system hormones), such as to induce healthy changes in state (consistent with evidence that varying cortisol levels over the course of a day are typical in healthy individuals, but excessively high or low levels at certain times of day may be unhealthy or unsafe). Such a system may, for example, “amp up” the experience with more aggressive settings (e.g., more acceleration into curves, tighter suspension, and/or louder music) in the morning when rising cortisol levels are healthy and “mellow out” the experience (such as by softer suspension, relaxing music and/or gentle driving motion) in the afternoon when cortisol levels should be dropping to lower levels to promote health. Experiences may consider both health of the user and safety, such as by ensuring that levels vary over time, but are sufficiently high to assure alertness (and hence safety) in situations where high alertness is required. While cortisol (an important hormone) is provided as an example, user experience parameters may be controlled (optionally with random or configured variation) with respect to other hormonal or biological systems, including insulin-related systems, cardiovascular systems (e.g., relating to pulse and blood pressure), gastrointestinal systems, and many others.[P-431]
Here, Cella illustrates one or more indicators may be configured after the AI unit receives sensory data and further transmitting to indicator information of one or more unsafe surrounding conditions such as wet conditions by optimizing the driving mode as well as further providing forward alerts.
.Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella (US 20210272394 A1) in view of Flitsch et al. (CN 106361273 A) as applied to claim 5 above, and further in view of Scripa et al. (US 20170172243 A1).
In regards to claim 6, Cella modified fails to teach at least one of the plurality of sensors is secured to a chinstrap.
Scripa on the other hand teaches at least one of the plurality of sensors is secured to a chinstrap (Paragraph 33)
The illustrated chinstrap 80 further includes flexible central straps 88 with which the chinstrap 80 can be fastened below the jaw. A buckle 90 is provided to attach the central straps 88 together and includes a sensor 92 that can detect when the chinstrap 80 is fastened or not. Also attached to one of the central straps 88 is a microphone 94 that can be used by the wearer to communicate with search and rescue personnel. In alternative embodiments, the microphone 94 can be mounted to the helmet shell 12. In some embodiments, the chinstrap 80 or helmet shell 12 can also be provided with a speaker (not shown) to enable two-way communications. In further embodiments, one or more of the body parameters described above as being captured by the body parameter sensor 20 can be captured by one or more sensors incorporated into the chinstrap 80.[P-33]
It would have been obvious during the time of the filing date of the said invention to combine Scripa, teaching with Cella modified’s teaching in order to enable safer protocols for protective accessories from hazardous situations
.Claim(s) 13-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella (US 20210272394 A1) in view of Flitsch et al. (CN 106361273 A) as applied to claim 1 above, and further in view of Chen et al. (US 10736370 B2)
In regards to claim 13, Cella modified fails to teach a microphone and a speaker for facilitating a communication of the rider with a third person using the one or more user devices via the communication network.
Chen on the other hand teaches a microphone and a speaker for facilitating a communication of the rider with a third person using the one or more user devices via the communication network. (Column 5, lines 41-53)
The protective wearable system may further include a bicycle seat comprising a LED strip installed on the rear side of the bicycle seat. The bicycle seat LED strip may include a red LED strip positioned at a center part for indicating a brake signal, and two yellow LEDs positioned at the left and right side of the bicycle seat respectively for indicating left and right turn signals. The protective wearable system may further include a backpack with a 2D array of RGB LEDs for displaying graphical and/or textual signs.[Col ln 41-53]
Here, Chen discloses the helmet may further comprise a push-and-talk button installed on the helmet and configured to work with the microphone and the speaker for allowing the rider to have point-to-multipoint communication. When the group riding mode is selected, the control module is configured to control the LED display to display a logo to identify the rider from other members of a riding group; and the communication module is configured to establish a point-to-multipoint communication group such that the rider can transmit or receive real-time and/or just-in-time information to other members of the riding group.
It would have been obvious to a person of ordinary skill in the art before the effective filing of the invention to combine Chen's teaching with Cella modified's teaching in order enable communication between the rider and other riders connected via a network of modules without comprising the safe operation of the ridden vehicle.
In regards to claim 14, Cella modified fails to teach a haptic device having one or more haptic sensors for detecting one or more parameters and a haptic indicator, the haptic device being configured to be in communication with the electronic device to transmit the one or more detected parameters.
Chen on the other hand teaches a haptic device having one or more haptic sensors for detecting one or more parameters and a haptic indicator, the haptic device being configured to be in communication with the electronic device to transmit the one or more detected parameters. (Column 7, lines 20-35)
be able to communicate the fact that the rider is slowing down to other people around the rider via using a brake signal light feature; connect the helmet with the rider's phone or another electronic device, and interact with other application software, activity tracking software, or software to change and control the pattern of lights on the helmet; sense when the rider might have been in a crash, and automatically send a signal to a pre-selected emergency contact via a phone or other means through the Bluetooth connection; receive audio, visual or haptic feedback via one or more proximity sensors if an object approaches the rider; record photographs, video signals and/or audio signals of areas surrounding the rider via an integrated camera[Col7, ln 20-35]
Chen here, describes protective smart helmet system is configured such that a rider of a vehicle (especially a bicycle or a motorcycle) is enabled to: be more visible on the road; be able to communicate the rider's turning intentions to other people around the rider; control turn signals on the helmet wirelessly via a remote control; be able to communicate the fact that the rider is slowing down to other people around the rider via using a brake signal light feature; connect the helmet with the rider's phone or another electronic device, and interact with other application software, activity tracking software, or software to change and control the pattern of lights on the helmet; sense when the rider might have been in a crash, and automatically send a signal to a pre-selected emergency contact via a phone or other means through the Bluetooth connection; receive audio, visual or haptic feedback via one or more proximity sensors if an object approaches the rider.
It would have been obvious to a person of ordinary skill in the art before the effective filing of the invention to combine Chen's teaching with Cella modified's teaching in order to provide adequate indication and safety to a rider and their surroundings.
In regards to claim 15, Cella modified via Chen teaches the electronic device is configured to detect a condition of any one of the rider and a corresponding vehicle, generate a haptic alert signal corresponding to the detected condition, and is further configured to transmit the generated haptic alert signal to the haptic indicator in real-time (Column 7, lines 23-33, Chen)
be able to communicate the fact that the rider is slowing down to other people around the rider via using a brake signal light feature; connect the helmet with the rider's phone or another electronic device, and interact with other application software, activity tracking software, or software to change and control the pattern of lights on the helmet; sense when the rider might have been in a crash, and automatically send a signal to a pre-selected emergency contact via a phone or other means through the Bluetooth connection; receive audio, visual or haptic feedback via one or more proximity sensors if an object approaches the rider; record photographs, video signals and/or audio signals of areas surrounding the rider via an integrated camera [Col 7, ln 22-33]
Chen discloses connecting the helmet with the rider's phone or another electronic device, and interact with other application software, activity tracking software, or software to change and control the pattern of lights on the helmet; sense when the rider might have been in a crash, and automatically send a signal to a pre-selected emergency contact via a phone or other means through the Bluetooth connection; receive audio, visual or haptic feedback via one or more proximity sensors if an object approaches the rider (detected condition).
In regards to claim 16, Cella modified via Chen teaches the haptic indicator is a vibrator secured to any of the protective gear or helmet of the rider, the haptic indicator being configured to indicate the generated haptic alert signal, and consequently the detected condition to the rider in real-time. (Column 7, lines 23-33; Column 8, lines 31-35; Column 9, lines 3-10, lines 18-24, Chen)
be able to communicate the fact that the rider is slowing down to other people around the rider via using a brake signal light feature; connect the helmet with the rider's phone or another electronic device, and interact with other application software, activity tracking software, or software to change and control the pattern of lights on the helmet; sense when the rider might have been in a crash, and automatically send a signal to a pre-selected emergency contact via a phone or other means through the Bluetooth connection; receive audio, visual or haptic feedback via one or more proximity sensors if an object approaches the rider; record photographs, video signals and/or audio signals of areas surrounding the rider via an integrated camera [Col 7, ln 22-33]
FIG. 3 depicts a perspective exploded view of the electronics box 70 of the helmet 20 according to one embodiment of the present invention. The electronics box 70 comprises a bottom plastic housing 75 and an upper plastic housing 76 both configured to form an enclosure when the bottom plastic housing 75 is fitted to the upper plastic housing 76. In one implementation, a PCB board 73 is also installed in the electronics box 70. The electronics box 70 may also include a magnetic charging port 71.[Col 8, ln 31-35]
The electronic box further comprises one or more of proximity sensors 77 for sensing surrounding objects, including vehicles, pedestrians, and other stationary obstacles; one or more speakers 78, one or more vibration motors 79 and one or more motion sensors 710 (e.g. inertial sensors) for sensing deceleration of the rider or detecting sharp movement of the rider caused by an accident such as a crash.[Col 9, ln 3-10]
When detecting an approaching vehicle via the one or more proximity sensors 77, the control module is configured, to control the center brim LED 673 to blink and increase the brightness of the LED strip 60, and to trigger the one or more vibration motors 79 to vibrate and the one or more speakers 78 to emit alarming sound thus to notify the rider.[Col 9, ln 18-24]
Chen teaches detecting an approaching vehicle via the one or more proximity sensors, the control module is configured, to control the center brim LED to blink and increase the brightness of the LED strip, and to trigger the one or more vibration motors to vibrate and the one or more speakers to emit alarming sound thus to notify the rider.
In regards to claim 17, Cella modified via Chen teaches the haptic device is placed on any one of one or more accessories of the rider and a portion of a corresponding vehicle (Column 8, lines 31-35, Chen)
FIG. 3 depicts a perspective exploded view of the electronics box 70 of the helmet 20 according to one embodiment of the present invention. The electronics box 70 comprises a bottom plastic housing 75 and an upper plastic housing 76 both configured to form an enclosure when the bottom plastic housing 75 is fitted to the upper plastic housing 76. In one implementation, a PCB board 73 is also installed in the electronics box 70. The electronics box 70 may also include a magnetic charging port 71.[Col 8, ln 31-35]
Chen’s teaching discloses the haptic device may be placed on the helmet.
Furthermore, Cella teaches vibration sensors integrated to vehicle (Paragraph 536, Cella)
FIG. 69 is an example embodiment depicting the deployment of the digital twin 60136 to perform predictive maintenance on the vehicle 60104. Digital twin 60136 receives data from the database 60118 on a real-time or near real-time basis. The database 60118 may store different types of data in different datastores. For example, the vehicle datastore 61102 may store data related to vehicle identification and attributes, vehicle state and event data, data from maintenance records, historical operating data, notes from vehicle service engineer, etc. The sensor datastore 61104 may store sensor data from operations including data from temperature, pressure, and vibration sensors that may be stored as signal or time-series data. The failure datastore 61106 may store failure data from the vehicle 60104 including failure data of components or similar vehicles at different times and under different operating conditions. The model datastore 61108 may store data related to different predictive models including fault detection and remaining life prediction models.[P-536]
Claim(s) 28, 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella (US 20210272394 A1) in view of De Bruyne et al. (US 20170143069 A1)
In regards to claim 28, Cella fails to teach the rider and vehicle positions are correlated to a ground position.
De Bruyne on the other hand teaches the rider and vehicle positions are correlated to a ground position (Paragraph 19, 45).
The helmet preferably is a cycling helmet; skiing helmet or snow-board helmet, or equestrian or motorcycle helmet and in particular a time trial cycling helmet, road cycling helmet or triathlon helmet[P-19]
In yet another alternative embodiment the system comprises at least one sensor unit (an inclination sensor) integrated in or provided on the helmet and two sensor units separate from the helmet, one to be provided in a predetermined position on the riders body and one to be provided on a predetermined position on the bike, whereby the two separate sensor units allow indicating the position of the rider on his bike together with his head position. Such embodiment provides more information on the overall position of the rider and allows more detailed and extensive feedback for optimizing the rider's position during a race through the output unit.[P-45]
Here, the vehicle positions are correlated to a ground position, i.e. ground position is a position on a motorcycle course or track.
Therefore, it would have been obvious during the time of the filing date of the invention to combine DeBruyne’s teaching with Cella in order to more effectively track the vehicle’s position on the ground
In regards to claim 29, Cella modified via De Bruyne teaches the ground position is a position on a motorcycle course or track. (Paragraph 19, 45, DeBruyne).
The helmet preferably is a cycling helmet; skiing helmet or snow-board helmet, or equestrian or motorcycle helmet and in particular a time trial cycling helmet, road cycling helmet or triathlon helmet[P-19]
In yet another alternative embodiment the system comprises at least one sensor unit (an inclination sensor) integrated in or provided on the helmet and two sensor units separate from the helmet, one to be provided in a predetermined position on the riders body and one to be provided on a predetermined position on the bike, whereby the two separate sensor units allow indicating the position of the rider on his bike together with his head position. Such embodiment provides more information on the overall position of the rider and allows more detailed and extensive feedback for optimizing the rider's position during a race through the output unit.[P-45]
Claim(s) 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella (US 20210272394 A1).
In regards to claim 33, Cella does not explicitly teach sensors detect the position of the vehicle on the ground
Cella teaches the sensors detecting the position of the rider and orientation of the vehicle (Paragraphs 160, 375, 377, 379)
Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed/acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.[P-160]
Referring to FIG. 44, in embodiments provided herein are transportation systems 4411 having a motorcycle helmet 44170 that is configured to provide an augmented reality experience based on registration of the location and orientation of the wearer 44172 in an environment 44171.[P-375]
An aspect provided herein includes a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the motorcycle 44169 and the helmet 44170 communicating location and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 with a display 44175 disposed to facilitate presenting an augmentation of content in an environment 44171 of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider 44172 and the motorcycle 44180.[P-377]
An aspect provided herein includes a motorcycle helmet augmented reality system comprising: a display 44175 disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet; a circuit 4488 for registering at least one of location and orientation of a motorcycle that the rider is riding; a machine learning circuit 44179 that determines at least one augmentation parameter 44156 by processing at least one input relating to at least one of the rider 44163 and the motorcycle 44180; and a reality augmentation circuit 4488 that, responsive to the registered at least one of a location and orientation of the motorcycle generates an augmentation element 44177 for presenting in the display 44175, the generating based at least in part on the determined at least one augmentation parameter 44156.[P-379]
Here, we see Cella teach sensors detect the position of rider and the vehicle’s orientation relative to is environment including wheel profile and suspension as the vehicle travels, which would obvious indicate to one of ordinary skill in the art the vehicle’s position to the ground (road environment of travel) as they sense the position of the rider and orientation of the vehicle, in order to more accurately monitor and track the rider’s relative position to the vehicle as well as more effectively the safe operation of the vehicle.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY D AFRIFA-KYEI whose telephone number is (571)270-7826. The examiner can normally be reached Monday-Friday 10am-7pm.
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/ANTHONY D AFRIFA-KYEI/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686
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