DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This is a continuation in part of application no. 18/968,599 filed 12/04/2024, which claims the benefit of US patent application no. 63/606,050 filed 12/04/2023.
Status of the Claims
Claims 1-21 are pending in the instant patent application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Regarding Claims 1-13, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 1-13 are directed to the abstract idea of calculating a user satisfaction index.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites to measure physiological and behavioral parameters comprising at least body motion, breathing rate, heart rate, heart rate variability, and micro-movements; filter noise from, normalize, and synchronize data; analyze user data to continuously monitor user movements, positions, and other metrics that form the basis for behavioral and emotional analysis; extract biometric and behavioral features from the filtered, normalized and synchronized data; and compute, a USI indicative of a user's real-time satisfaction state based on biometric and behavioral features; generate, based on the computed USI, signals to display the USI; and enable integration of the computed USI for further analysis or for issuing recommendations to improve the user's satisfaction state.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment). In addition, Certain Methods of Organizing Human Activity due to the managing of personal behavior. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 2-4, 7-9, 11 and 13 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of multisensory input sensors, a non-contact radar sensor, at least one processor, a tracking module, a behavioral and emotional analysis unit, at least one machine learning algorithm, an interface circuit, a visualization dashboard, a REST API and external systems or software. The multisensory input sensors, a non-contact radar sensor, at least one processor, a tracking module, a behavioral and emotional analysis unit, at least one machine learning algorithm, an interface circuit, a visualization dashboard, a REST API and external systems or software are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1, 5-6, 10 and 12 includes various elements that are not directed to the abstract idea under 2A. These elements include multisensory input sensors, a non-contact radar sensor, at least one processor, a tracking module, a behavioral and emotional analysis unit, at least one machine learning algorithm, an interface circuit, a visualization dashboard, a REST API, external systems or software and the generic computing elements described in the Applicant's specification in at least Pgs 15-19 “Example of Distributed Sensors System Deployment”. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 1, 5-6, 10 and 12, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 14-21, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 14-21 are directed to the abstract idea of calculating a user satisfaction index.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 14, claim 14 recites Collecting multisensory data including collecting radar data; Preprocessing the collected radar data to filter noise, standardized the data, and synchronize the data; Extracting emotional and behavioral features from the filtered, standardized and synchronized data; Based at least in part on the extracted emotional and behavioral features, Calculating a satisfaction index (USI); and Displaying the calculated satisfaction index, generating recommendations, and/or providing data access.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment). In addition, Certain Methods of Organizing Human Activity due to the managing of personal behavior. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 15-16, 18-19 and 21 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of radar sensors, at least one processor, analysis algorithms, a machine learning algorithm, a visualization dashboard and a REST API. The radar sensors, at least one processor, analysis algorithms, a machine learning algorithm, a visualization dashboard and a REST API. are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 14, 17 and 20 includes various elements that are not directed to the abstract idea under 2A. These elements include radar sensors, at least one processor, analysis algorithms, a machine learning algorithm, a visualization dashboard, a REST API and the generic computing elements described in the Applicant's specification in at least Pgs 15-19 “Example of Distributed Sensors System Deployment”. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 14, 17 and 20, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 6-7 and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2009/0203972 A1) in view of Sobhany (US 2020/0239002 A1) further in view of Krupat et al. (US 2018/0303397 A1).
Regarding Claim 1, Heneghan teaches the limitations of Claim 1 which state
multisensory input sensors including: a non-contact radar sensor configured to measure physiological and behavioral parameters comprising at least body motion, breathing rate, heart rate, heart rate variability, and micro-movements (Heneghan: Para 0050, 0079 via a system includes a sensor unit, which can be placed relatively close to where the subject is sleeping (e.g., on a bedside table) and a monitoring and display unit through which results can be analyzed, visualized and communicated to the user. The sensor unit and the display/monitoring unit may be incorporated into a single stand-alone unit, if required. The unit may contain one or more of the following features: a non-contact motion sensor for detection of general bodily movement, respiration, and heart rate; a processing capability to derive parameters such as sleep state, breathing rate, heart rate, and movement; a display capability to provide visual feedback; an auditory capability to provide acoustic feedback, e.g., a tone whose frequency varies with breathing, or an alarm which sounds when no motion is detected… Data from the respiration, cardiac and motion channels is segmented into epochs of time. For example, an epoch might consist of readings over 5 seconds or over 5 minutes, depending on the desired configuration. For each epoch, a set of features are calculated, which may include one or more of the following conventionally known and determined features: The count of activities; the mean amplitude of activity counts; the variance of activity counts; the dominant respiratory frequency; the respiratory power (e.g., the integral of the PSD in a region about the dominant respiratory frequency); the heart rate; the variability of the heart rate; the spectrum of the respiration signal; and the spectrum of the raw signal).
However, Heneghan does not explicitly disclose the limitations of Claim 1 which state at least one processor connected to the radar sensor and configured to perform operations comprising: filter noise from, normalize, and synchronize data derived from the radar sensor; analyze user data using a tracking module to continuously monitor user movements, positions, and other metrics that form the basis for behavioral and emotional analysis; extract biometric and behavioral features from the filtered, normalized and synchronized data using a behavioral and emotional analysis unit.
Sobhany though, with the teachings of Heneghan, teaches of
at least one processor connected to the radar sensor and configured to perform operations comprising: filter noise from, normalize, and synchronize data derived from the radar sensor (Sobhany: Para 0065, 0068 via The sensor fusion module 326 receives normalized sensor inputs from the sensor abstraction component 312 and performs pre-processing on the normalized data. This pre-processing can include, for example, performing data alignment or filtering the sensor data. Depending on the type of data, the pre-processing can include more sophisticated processing and analysis of the data… The base layer of the vehicle abstraction includes vehicle sensors 410, which are hardware components that output analog or digital signals representing parameters of the vehicle 110, a context of the vehicle 110, or a state of a passenger in the vehicle. The sensor data layer 420 represents initial processing of the signals into data types or signals that are usable by various processing components of the vehicle 110 or remote server 120. For example, the sensor data layer 420 represents normalized or filtered data corresponding to the raw signals received from the sensors 215. The sensor fusion layer 430 represents a fusion of the sensor data 420, or an aggregation of the sensor data associated with multiple sensors 215 and collective analysis of this aggregated sensor data to generate parameters for personalization or control of the vehicle 110);
analyze user data using a tracking module to continuously monitor user movements, positions, and other metrics that form the basis for behavioral and emotional analysis (Sobhany: Para 0043, 0064-0067, 0077 via The processing engine 330 processes sensor data and determines a state of the vehicle. The vehicle state can include any information about the vehicle itself, the driver, or a passenger in the vehicle. For example, the state can include an emotion of the driver, an emotion of the passenger, or a safety concern (e.g., due to road or traffic conditions, the driver's attentiveness or emotion, or other factors). As shown in FIG. 1, the processing engine can include a sensor fusion module, a personalized data processing module, and a machine learning adaptation module…One or more of the sensors 215 can additionally or alternatively be used as an input device 220. For example, one of the sensors 215 can be a camera configured to capture image data of a passenger that can be input to a gaze tracking tool. The passenger's gaze, as tracked by the gaze tracking tool, can be used as an input signal…The machine learning adaptation module 328 continuously learns about the user of the vehicle as more data is ingested over time. The machine learning adaptation module may receive feedback indicating the user's response to the vehicle experience system 310 outputs and use the feedback to continuously improve the models applied by the personalized data processing module… generating a primitive emotional indication based on data from multiple sensors, a primitive emotional indication determined at step 604 may be a body position of the driver. The body position can be determined based on data received from a camera and one or more weight sensors in the driver's seat. For example, the driver can be determined to be sitting up straight if the camera data indicates that the driver's head is at a certain vertical position and the weight sensor data indicates that the driver's weight is approximately centered and evenly distributed on the seat. The driver can instead be determined to be slouching based on the same weight sensor data, but with camera data indicating that the driver's head is at a lower vertical position).
extract biometric and behavioral features from the filtered, normalized and synchronized data using a behavioral and emotional analysis unit (Sobhany: Para 0148-0152 via the use of heartbeat and other biometric features to determine the emotional state of a user);
compute, via at least one machine learning algorithm, a user’s real-time satisfaction state based on biometric and behavioral features (Sobhany: Para 0067 via The machine learning adaptation module 328 continuously learns about the user of the vehicle as more data is ingested over time. The machine learning adaptation module may receive feedback indicating the user's response to the vehicle experience system 310 outputs and use the feedback to continuously improve the models applied by the personalized data processing module).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan with the teachings of Sobhany in order to have at least one processor connected to the radar sensor and configured to perform operations comprising: filter noise from, normalize, and synchronize data derived from the radar sensor; analyze user data using a tracking module to continuously monitor user movements, positions, and other metrics that form the basis for behavioral and emotional analysis; extract biometric and behavioral features from the filtered, normalized and synchronized data using a behavioral and emotional analysis unit; compute, via at least one machine learning algorithm, a user’s real-time satisfaction state based on biometric and behavioral features. The motivations behind this being to incorporate the teachings of utilizing a plurality of sensors to generate data corresponding to different parameter of a passenger as taught by Sobhany. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Heneghany does not explicitly disclose the limitation of Claim 1 which states compute, a USI indicative of a user's real-time satisfaction state based on biometric and behavioral features; an interface circuit connected to the at least one processor and configured to perform operations comprising: generate, based on the computed USI, signals for a visualization dashboard and/or a REST API to display the USI; enable integration of the computed USI with external systems or software for further analysis or for issuing recommendations to improve the user's satisfaction state.
Krupat though, with the teachings of Heneghan/Sobhany, teaches of
compute, a USI indicative of a user's real-time satisfaction state based on biometric and behavioral features (Krupat: Para 0010, 0052-0059 via determining emotional content, emotional-intensity metrics, mood scores, moment by moment satisfaction metrics).
an interface circuit connected to the at least one processor and configured to perform operations comprising: generate, based on the computed USI, signals for a visualization dashboard and/or a REST API to display the USI (Krupat: Para 0056-0063 via mood scores on a dashboard, moment by moment metrics, recommendations and physiological information); and
enable integration of the computed USI with external systems or software for further analysis or for issuing recommendations to improve the user's satisfaction state (Krupat: Para 0061-0063 via suggestions, actions and recommendations utilizing a dashboard).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany with the teachings of Krupat in order to have ccompute, a USI indicative of a user's real-time satisfaction state based on biometric and behavioral features; an interface circuit connected to the at least one processor and configured to perform operations comprising: generate, based on the computed USI, signals for a visualization dashboard and/or a REST API to display the USI; enable integration of the computed USI with external systems or software for further analysis or for issuing recommendations to improve the user's satisfaction state. The motivations behind this being to incorporate the teachings of emotional metric evaluation as taught by Krupat. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 6, Heneghan/Sobhany/Krupat teaches the limitations of Claim 6 which state
further including acoustic sensors configured to analyze vocal and sound patterns to determine emotional states based on acoustic features and background noise (Heneghan: Para 0062 via The additional optional sensors can be incorporated as follows. The optional acoustic sensor in the monitoring is a microphone responsive to sound energy in the range 20-10 KHz (for example), and can be used to determine background noises, and noises associated with sleeping (e.g. snoring). Background noise cancellation techniques can be used to emphasise the person's breathing noise, if necessary. The subject's surface temperature can be measured using an infrared device. Other environmental parameters can be collected such as temperature, humidity and light level using known sensor technology. In particular, motion activity can also be collected from an under-mattress piezoelectric sensor, and this motion signal can then be used as a substitute or to complement the motion signal obtained from the radio-frequency sensor).
Regarding Claim 7, Heneghan/Sobhany/Krupat teaches the limitations of Claim 7 which state
further comprising a hardware-software complex for predictive analytics, capable of forecasting satisfaction level changes and generating recommendations for improving USI (Krupat: Para 0065, 0099, 0132 via The mood dashboard can be based on image analysis and representation for emotional metric threshold evaluation. Image data, including facial images, is collected at a client device from a user interacting with a media presentation. Processors are used to analyze the image data to extract emotional content. Emotional intensity metrics are determined and stored in a digital storage component. The emotional intensity metrics are coalesced into a summary intensity metric, and the summary intensity metric is represented. The representing can include displaying on a screen. A calendar displaying mood 700 can include displaying a mood dashboard 710 to the individual based on the analyzing. The mood dashboard 710 can include controls 712 that can be used to select display options, to monitor activities, to take steps to improve a mood, to receive suggestions for improving a mood, and so on… Facial analysis can be used to determine, predict, estimate, etc. cognitive states, emotions, and so on, of a person from whom facial data can be collected).
Regarding Claim 14, Heneghan teaches the limitations of Claim 14 which state
collecting multisensory data including collecting radar data from radar sensors (Heneghan: Para 0050, 0079 via a system includes a sensor unit, which can be placed relatively close to where the subject is sleeping (e.g., on a bedside table) and a monitoring and display unit through which results can be analyzed, visualized and communicated to the user. The sensor unit and the display/monitoring unit may be incorporated into a single stand-alone unit, if required. The unit may contain one or more of the following features: a non-contact motion sensor for detection of general bodily movement, respiration, and heart rate; a processing capability to derive parameters such as sleep state, breathing rate, heart rate, and movement; a display capability to provide visual feedback; an auditory capability to provide acoustic feedback, e.g., a tone whose frequency varies with breathing, or an alarm which sounds when no motion is detected… Data from the respiration, cardiac and motion channels is segmented into epochs of time. For example, an epoch might consist of readings over 5 seconds or over 5 minutes, depending on the desired configuration. For each epoch, a set of features are calculated, which may include one or more of the following conventionally known and determined features: The count of activities; the mean amplitude of activity counts; the variance of activity counts; the dominant respiratory frequency; the respiratory power (e.g., the integral of the PSD in a region about the dominant respiratory frequency); the heart rate; the variability of the heart rate; the spectrum of the respiration signal; and the spectrum of the raw signal).
However, Heneghan does not explicitly disclose the limitations of Claim 14 which state preprocessing the collected radar data to filter noise, standardized the data, and synchronize the data; with at least one processor, extracting emotional and behavioral features from the filtered, standardized and synchronized data using analysis algorithms; a machine learning algorithm.
Sobhany though, with the teachings of Heneghan, teaches of
preprocessing the collected radar data to filter noise, standardized the data, and synchronize the data (Sobhany: Para 0065, 0068 via The sensor fusion module 326 receives normalized sensor inputs from the sensor abstraction component 312 and performs pre-processing on the normalized data. This pre-processing can include, for example, performing data alignment or filtering the sensor data. Depending on the type of data, the pre-processing can include more sophisticated processing and analysis of the data… The base layer of the vehicle abstraction includes vehicle sensors 410, which are hardware components that output analog or digital signals representing parameters of the vehicle 110, a context of the vehicle 110, or a state of a passenger in the vehicle. The sensor data layer 420 represents initial processing of the signals into data types or signals that are usable by various processing components of the vehicle 110 or remote server 120. For example, the sensor data layer 420 represents normalized or filtered data corresponding to the raw signals received from the sensors 215. The sensor fusion layer 430 represents a fusion of the sensor data 420, or an aggregation of the sensor data associated with multiple sensors 215 and collective analysis of this aggregated sensor data to generate parameters for personalization or control of the vehicle 110);
with at least one processor, extracting emotional and behavioral features from the filtered, standardized and synchronized data using analysis algorithms (Sobhany: Para 0148-0152 via the use of heartbeat and other biometric features to determine the emotional state of a user);
a machine learning algorithm (Sobhany: Para 0067 via The machine learning adaptation module 328 continuously learns about the user of the vehicle as more data is ingested over time. The machine learning adaptation module may receive feedback indicating the user's response to the vehicle experience system 310 outputs and use the feedback to continuously improve the models applied by the personalized data processing module).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan with the teachings of Sobhany in order to have preprocessing the collected radar data to filter noise, standardized the data, and synchronize the data; with at least one processor, extracting emotional and behavioral features from the filtered, standardized and synchronized data using analysis algorithms; a machine learning algorithm. The motivations behind this being to incorporate the teachings of utilizing a plurality of sensors to generate data corresponding to different parameter of a passenger as taught by Sobhany. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Heneghany does not explicitly disclose the limitation of Claim 1 which states based on at least in part on the extracted emotional and behavioral features, calculating a satisfaction index (USI); displaying the calculated satisfaction index on a visualization dashboard, generating recommendations, and/or providing data access through a REST API.
Krupat though, with the teachings of Heneghan/Sobhany, teaches of
based on at least in part on the extracted emotional and behavioral features, calculating a satisfaction index (USI) (Krupat: Para 0010, 0052-0059 via determining emotional content, emotional-intensity metrics, mood scores, moment by moment satisfaction metrics);
displaying the calculated satisfaction index on a visualization dashboard, generating recommendations, and/or providing data access through a REST API (Krupat: Para 0056-0063 via mood scores on a dashboard, moment by moment metrics, recommendations and physiological information).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany with the teachings of Krupat in order to have based on at least in part on the extracted emotional and behavioral features, calculating a satisfaction index (USI); displaying the calculated satisfaction index on a visualization dashboard, generating recommendations, and/or providing data access through a REST API. The motivations behind this being to incorporate the teachings of emotional metric evaluation as taught by Krupat. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 15, Heneghan/Sobhany/Krupat teaches the limitations of Claim 15 which state
wherein the method accounts for individual user parameters and environmental context factors (Sobhany: Para 0080 via Based on the primitive emotional indications (and optionally also based on the sensor data, the environmental data, or historical data associated with the user), the vehicle experience system 310 generates, at step 606, contextualized emotional indications. Each contextualized emotional indication can be generated based on multiple types of data, such as one or more primitive emotional indications, one or more types of raw sensor or environmental data, or one or more pieces of historical data. By basing the contextualized emotional indications on multiple types of data, the vehicle experience system 310 can more accurately identify the driver's emotional state and, in some cases, the reason for the emotional state).
Regarding Claim 16, Heneghan/Sobhany/Krupat teaches the limitations of Claim 16 which state
further comprising adaptively learning including retraining a machine learning model on new data to improve satisfaction level prediction accuracy (Sobhany: Para 0067, 0088 via The machine learning adaptation module 328 continuously learns about the user of the vehicle as more data is ingested over time. The machine learning adaptation module may receive feedback indicating the user's response to the vehicle experience system 310 outputs and use the feedback to continuously improve the models applied by the personalized data processing module… the contextualized emotional indications can be determined by applying a trained model, such as a neural network or classifier, to multiple types of data. For example, primitive emotional indication 1 shown in FIG. 6A may be a determination that the driver is happy. The vehicle experience system 310 can generate contextualized emotional indication 1—a determination that the driver is happy because the weather is good and traffic is light—by applying primitive emotional indication 1 and environmental data (such as weather and traffic data) to a classifier. The classifier can be trained based on historical data, indicating for example that the driver tends to be happy when the weather is good and traffic is light, versus being angry, frustrated, or sad when it is raining or traffic is heavy. In some cases, the model is trained using explicit feedback provided by the passenger).
Regarding Claim 17, Heneghan/Sobhany/Krupat teaches the limitations of Claim 17 which state
wherein the visualization dashboard and REST API interface include automatic notifications and recommendations for adjusting environmental factors, preferably temperature, lighting, and noise, that impact satisfaction (Sobhany: Para 0090-0091, 0154 via the vehicle experience system 310 can use the contextualized emotional indications to generate or recommend one or more emotional assessment and response plans. The emotional assessment and response plans may be designed to enhance the driver's current emotional state (as indicated by one or more contextualized emotional indications), mitigate the emotional state, or change the emotional state. For example, if the contextualized emotional indication indicates that the driver is happy because she enjoys the music that is playing in the vehicle, the vehicle experience system 310 can select additional songs similar to the song that the driver enjoyed to ensure that the driver remains happy. As another example, if the driver is currently frustrated due to heavy traffic but the vehicle experience system 310 has determined (based on historical data) that the driver will become happier if certain music is played, the vehicle experience system 310 can play this music to change the driver's emotional state from frustration to happiness. Below are example scenarios and corresponding corrective responses that can be generated by the vehicle experience system 310:… input information 1104 and a detected emotion 1106 can be obtained. The obtained information can be processed using the user profile 1102 to determine a user-specific output action 1108. Example output actions can relate to media settings 1108a, seat settings 1108b, display settings 1108c, vehicle environment settings 1108d, etc).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2009/0203972 A1) in view of Sobhany (US 2020/0239002 A1) in view of Krupat et al. (US 2018/0303397 A1) further in view of Nagpal (US 2022/0268916 A1).
Regarding Claim 2, while the combination of Heneghan/Sobhany/Krupat teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 2 which state wherein the non-contact radar sensor is a primary data source to ensure privacy, with optical and acoustic sensors activated upon user consent for deeper analytics.
Nagpal though, with the teachings of Heneghan/Sobhany/Krupat teaches of
wherein the non-contact radar sensor is a primary data source to ensure privacy, with optical and acoustic sensors activated upon user consent for deeper analytics (Nagpal: Para 0004, 0089, 0093-0100 via privacy concerns…tracking and fall detection from radar data with cameras and microphones…line of sight sensors…use of a microphone to supplement the radar and provide contextual information with user consent…use of a optical/IR camera to supplement the radar with user consent).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Nagpal in order to have wherein the non-contact radar sensor is a primary data source to ensure privacy, with optical and acoustic sensors activated upon user consent for deeper analytics. The motivations behind this being to incorporate the teachings of object tracking as taught by Nagpal. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2009/0203972 A1) in view of Sobhany (US 2020/0239002 A1) in view of Krupat et al. (US 2018/0303397 A1) further in view of Song et al. (US 2022/0151549 A1).
Regarding Claim 3, while the combination of Heneghan/Sobhany/Krupat teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 3 which state wherein the non-contact radar sensor measures physiological parameters, including heart rate (HR) and respiratory rate, using phase and amplitude modulation of signals.
Song though, with the teachings of Heneghan/Sobhany/Krupat teaches of
wherein the non-contact radar sensor measures physiological parameters, including heart rate (HR) and respiratory rate, using phase and amplitude modulation of signals (Song: Para 0019 via updating the user interface to include at least one of: an updated heart rate, an updated respiratory rate, an updated movement detection, an updated alert or an updated posture. In various aspects, the method further comprises determining the heart rate based at least in part on a local maxima statistics method. In various aspects, the method further comprises determining the respiratory rate by estimating an amplitude, frequency and phase associated with the sensor data. In various aspects, the method further comprises detecting the posture of the subject according to an instantaneous amplitude of respiration extracted from sensor data. In various aspects, the method further comprises detecting an event based at least in part on at least one of the heart rate, the respiratory rate, the posture of a subject, or a movement of the subject. In various aspects, the method the event comprises at least one of a fall, the heart rate being outside a predefined range, the respiratory rate being outside a predefined range, or a change in the posture).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Song in order to have wherein the non-contact radar sensor measures physiological parameters, including heart rate (HR) and respiratory rate, using phase and amplitude modulation of signals. The motivation behind this being to incorporate the teachings of monitoring characteristics of a subject as taught by Song. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2009/0203972 A1) in view of Sobhany (US 2020/0239002 A1) in view of Krupat et al. (US 2018/0303397 A1) further in view of Santra et al. (US 2019/0227156 A1).
Regarding Claim 4, while the combination of Heneghan/Sobhany/Krupat teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 4 which states wherein the non-contact radar sensor is capable of detecting gestures, kinematic parameters of the human body, position, velocity in space, and spatial interactions with environment.
Santra though, with the teachings of Heneghan/Sobhany/Krupat teaches of
wherein the non-contact radar sensor is capable of detecting gestures, kinematic parameters of the human body, position, velocity in space, and spatial interactions with environment (Santra: Para 0030 via In embodiments that utilize a frequency modulated continuous wave (FMCW) radar sensor, the location of each object 112, 114, and 116-1 to 116-11 within the area 110 may be found by taking a range fast Fourier transform (FFT) of the baseband radar signal produced by the millimeter-wave radar sensor 102, and the motion of the various objects may be determined, for example, by taking a further FFTs to determine each object's velocity using Doppler analysis techniques known in the art. In embodiments in which the millimeter-wave radar sensor 102 includes a receive antenna array, further FFTs may also be used to determine the azimuth of each object 112, 114, and 116-1 to 116-11 with respect to the millimeter-wave radar sensor 102. In the example illustrated in FIG. 1A and with regards to macro-Doppler techniques, furniture 112 may be identified as being a static object, the fan 114 is identified as being a moving object, the static human being 116-2 is identified as being a static object, and the moving human being 116-1 is identified as being a moving object. With regards to micro-Doppler and vital-Doppler techniques, small detected motions are analyzed to determine whether these motions are indicative of small bodily movements or the heart rate and respiration of a human being. During micro-Doppler and vital-Doppler steps, the millimeter-wave radar sensor 102 makes a series of radar measures that are more specifically directed toward each object 112, 114, and 116-1 to 116-11. For example, in embodiments in which the millimeter-wave radar sensor 102 includes a transmit antenna array, these directed measurements are performed by steering the radar beam produced by the millimeter-wave radar sensor 102 using phase-array radar techniques. Based on these more directed radar measurements made during micro-Doppler and vital-Doppler steps, the processor 104 determines whether each object 112, 114, and 116-1 to 116-11 experiences small motions consistent with human vital signs such as heart rate and respiration).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Santra in order to have wherein the non-contact radar sensor is capable of detecting gestures, kinematic parameters of the human body, position, velocity in space, and spatial interactions with environment. The motivations behind this being to incorporate the teachings of human behavior modeling as taught by Santra. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2009/0203972 A1) in view of Sobhany (US 2020/0239002 A1) in view of Krupat et al. (US 2018/0303397 A1) further in view of Hsu et al. (US 2021/0392116 A1).
Regarding Claim 5, while Heneghan/Sobhany/Krupat teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 5 which states further including optical sensors configured to detect gestures, facial expressions, and spatial interactions, with pixelation and anonymization for enhanced privacy.
Hsu though, with the teachings of Heneghan/Sobhany/Krupat teaches of
further including optical sensors configured to detect gestures, facial expressions, and spatial interactions, with pixelation and anonymization for enhanced privacy (Hsu: Para 0016-0017, 0028-0031 via A video camera may produce anonymized video feeds by detecting human faces and removing those faces from the video feed. The resulting anonymized video feed may show human faces as pixelized images, blank areas, or some other anonymized representation. The human face detection mechanism and anonymization mechanism may be embedded in the device such that during initial installation, raw or non-anonymized images may not be accessible. The video camera may be a single device with an image sensor and lens systems, an output mechanism, and a processor that may detect faces and obscure or anonymize the faces prior to transmitting a video feed on the output mechanism. Such an embodiment may be a privacy-enabled camera that operates in such a mode by default, thereby only generating anonymized video streams when installed… A camera may produce anonymized video feeds, and in some instances, non-anonymized video feeds. Anonymized images may be produced by detecting human faces in an image, then obscuring the faces beyond detection. The resulting image or video feeds of such images, may be obscured such that recreating the original image may be impossible. Several different types of non-anonymized video feeds may be produced. One non-anonymized video feed may be the raw video feed with no facial recognition or other processing. Another may be a feed that may include images of people recognized in the video feed. Such a feed may be used in parallel with the anonymized video feed for security and other uses. Still another feed may be a metadata feed that may include metadata describing faces recognized in the raw video feed. The facial detection technology may analyze images within a video stream to detect human faces. Such analyses may be performed in real time or near-real time on a video stream. In many cases, a facial detection algorithm may return a set of coordinates representing the location of the human face within the image. An obfuscation technique may be applied within the coordinates to obscure the face, thereby generating an anonymized face. A face may be obscured by several mechanisms. For example, a face may be obscured by pixelating or mosaicking the area of the face. Another method may be to replace the face with a solid color, such as grey or flesh tone. The resulting obscured image may not be able to be analyzed to reverse the obscured image to determine the original image).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Hsu in order to have further including optical sensors configured to detect gestures, facial expressions, and spatial interactions, with pixelation and anonymization for enhanced privacy. The motivations behind this being to incorporate the teachings of anonymizing video streams as taught by Hsu. Furthermore, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 8-9, 12-13 and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2009/0203972 A1) in view of Sobhany (US 2020/0239002 A1) in view of Krupat et al. (US 2018/0303397 A1) further in view of Doshi et al. (US 2020/0134207 A1).
Regarding Claim 8, while Heneghan/Sobhany/Krupat teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 8 which state wherein the system is designed for scalable deployment, allowing the installation of multiple sensors across large spaces such as retail stores, office environments, or public areas.
Doshi though, with the teachings of Heneghan/Sobhany/Krupat teaches of
wherein the system is designed for scalable deployment, allowing the installation of multiple sensors across large spaces such as retail stores, office environments, or public areas (Doshi: Para 0062, 0065-0069, 0083 via use of large numbers of distributed IoT sensors in commercial, industrial and private/public environments).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Doshi in order to have wherein the system is designed for scalable deployment, allowing the installation of multiple sensors across large spaces such as retail stores, office environments, or public areas. The motivations behind this being to incorporate the teachings of IoT architecture. Furthermore, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 9, the combination of Heneghan/Sobhany/Krupat/Doshi teaches the limitations of Claim 9 which state
wherein each sensor operates independently but synchronizes data through a central server or cloud infrastructure for integrated analysis (Doshi: Para 0065, 0068-0069, 0073-0074, 0077 via autonomous/local devices operate and process independently while clusters, gateways and cloud systems coordinate and aggregate data for processing).
Regarding Claim 12, while Heneghan/Sobhany/Krupat teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 12 which states wherein data processing is supported across multiple architectures, including: Cloud Computing comprising Centralized processing on cloud servers for scalability and large-scale data integration, Fog Computing comprising Local server-based processing for reduced latency and efficient resource use. Edge Computing comprising On-device processing for real-time analytics and enhanced privacy.
Doshi though, with the teachings of Heneghan/Sobhany/Krupat teaches of
wherein data processing is supported across multiple architectures, including: Cloud Computing comprising Centralized processing on cloud servers for scalability and large-scale data integration, Fog Computing comprising Local server-based processing for reduced latency and efficient resource use. Edge Computing comprising On-device processing for real-time analytics and enhanced privacy (Doshi: Para 0042-0043, 0054 via Depending on the real-time requirements in a communications context, a hierarchical structure of data processing and storage nodes may be defined in an edge computing deployment. For example, such a deployment may include local ultra-low-latency processing, regional storage, and processing as well as remote cloud data-center based storage and processing. Key performance indicators (KPIs) may be used to identify where sensor data is best transferred and where it is processed or stored…FIG. 2 illustrates deployment and orchestration for virtual edge configurations across an edge-computing system operated among multiple edge nodes and multiple tenants. Specifically, FIG. 2 depicts coordination of a first edge node 222 and a second edge node 224 in an edge-computing system 200, to fulfill requests and responses for various client endpoints 210 from various virtual edge instances. The virtual edge instances provide edge compute capabilities and processing in an edge cloud, with access to a cloud/data center 240 for higher-latency requests for websites, applications, database servers, etc. Thus, the edge cloud enables coordination of processing among multiple edge nodes for multiple tenants or entities…Each of the edge gateway nodes 320 may communicate with one or more edge resource nodes 340, which are illustratively embodied as compute servers, appliances or components located at or in a communication base station 342 (e.g., a base station of a cellular network). As discussed above, each edge resource node 340 includes some processing and storage capabilities and, as such, some processing and/or storage of data for the client compute nodes 310 may be performed on the edge resource node 340. For example, the processing of data that is less urgent or important may be performed by the edge resource node 340, while the processing of data that is of a higher urgency or importance may be performed by edge gateway devices or the client nodes themselves (depending on, for example, the capabilities of each component). Further, various wired or wireless communication links (e.g., fiber optic wired backhaul, 5G wireless links) may exist among the edge nodes 320, edge resource node(s) 340, core data center 350, and network cloud 360).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Doshi in order to have wherein data processing is supported across multiple architectures, including: Cloud Computing comprising Centralized processing on cloud servers for scalability and large-scale data integration, Fog Computing comprising Local server-based processing for reduced latency and efficient resource use. Edge Computing comprising On-device processing for real-time analytics and enhanced privacy. The motivations behind this being to incorporate the teaching of architecture in edge computing and Internet of Things (IoT) device networks as taught by Doshi. Furthermore, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 13, Heneghan/Sobhany/Krupat/Doshi teaches the limitations of Claim 13 which state
wherein a processing architecture is dynamically selected based on network conditions, computational requirements, or user preferences (Doshi: Para 0039 via an edge cloud architecture that covers multiple potential deployments and addresses restrictions that some network operators or service providers may have in their own infrastructures. These include variation of configurations based on the edge location (because edges at a base station level, for instance, may have more constrained performance); configurations based on the type of compute, memory, storage, fabric, acceleration, or like resources available to edge locations, tiers of locations, or groups of locations; the service, security, and management and orchestration capabilities; and related objectives to achieve usability and performance of end services).
Regarding Claim 20, while Heneghan/Sobhany/Krupat teaches the limitations of Claim 14, it does not explicitly disclose the limitations of Claim 20 which states wherein processing the radar data comprises executing on: Cloud servers for aggregated large-scale analysis; Local fog servers to ensure low-latency and near-site computation; and Edge devices for immediate real-time processing and enhanced privacy.
Doshi though, with the teachings of Heneghan/Sobhany/Krupat teaches of
wherein processing the radar data comprises executing on: Cloud servers for aggregated large-scale analysis; Local fog servers to ensure low-latency and near-site computation; and Edge devices for immediate real-time processing and enhanced privacy (Doshi: Para 0042-0043, 0054 via Depending on the real-time requirements in a communications context, a hierarchical structure of data processing and storage nodes may be defined in an edge computing deployment. For example, such a deployment may include local ultra-low-latency processing, regional storage, and processing as well as remote cloud data-center based storage and processing. Key performance indicators (KPIs) may be used to identify where sensor data is best transferred and where it is processed or stored…FIG. 2 illustrates deployment and orchestration for virtual edge configurations across an edge-computing system operated among multiple edge nodes and multiple tenants. Specifically, FIG. 2 depicts coordination of a first edge node 222 and a second edge node 224 in an edge-computing system 200, to fulfill requests and responses for various client endpoints 210 from various virtual edge instances. The virtual edge instances provide edge compute capabilities and processing in an edge cloud, with access to a cloud/data center 240 for higher-latency requests for websites, applications, database servers, etc. Thus, the edge cloud enables coordination of processing among multiple edge nodes for multiple tenants or entities…Each of the edge gateway nodes 320 may communicate with one or more edge resource nodes 340, which are illustratively embodied as compute servers, appliances or components located at or in a communication base station 342 (e.g., a base station of a cellular network). As discussed above, each edge resource node 340 includes some processing and storage capabilities and, as such, some processing and/or storage of data for the client compute nodes 310 may be performed on the edge resource node 340. For example, the processing of data that is less urgent or important may be performed by the edge resource node 340, while the processing of data that is of a higher urgency or importance may be performed by edge gateway devices or the client nodes themselves (depending on, for example, the capabilities of each component). Further, various wired or wireless communication links (e.g., fiber optic wired backhaul, 5G wireless links) may exist among the edge nodes 320, edge resource node(s) 340, core data center 350, and network cloud 360).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Doshi in order to have wherein processing the radar data comprises executing on: Cloud servers for aggregated large-scale analysis; Local fog servers to ensure low-latency and near-site computation; and Edge devices for immediate real-time processing and enhanced privacy. The motivations behind this being to incorporate the teaching of architecture in edge computing and Internet of Things (IoT) device networks as taught by Doshi. Furthermore, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 21, while Heneghan/Sobhany/Krupat teaches the limitations of Claim 14, it does not explicitly disclose the limitations of Claim 21 which states further including dynamically allocating processing tasks between cloud, fog, and edge computing layers based on computational load, latency requirements, and privacy considerations.
Doshi though, with the teachings of Heneghan/Sobhany/Krupat, teaches of
further including dynamically allocating processing tasks between cloud, fog, and edge computing layers based on computational load, latency requirements, and privacy considerations (Doshi: Para 0029-0031, 0039, 0042, 0045, 0054-0055, 0066 via dynamic workload allocation/reassignment across endpoint, edge/fog and cloud tiers; including latency, security/privacy and resource KPIs).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Doshi in order to have further including dynamically allocating processing tasks between cloud, fog, and edge computing layers based on computational load, latency requirements, and privacy considerations. The motivations behind this being to incorporate the teaching of architecture in edge computing and Internet of Things (IoT) device networks as taught by Doshi. Furthermore, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2009/0203972 A1) in view of Sobhany (US 2020/0239002 A1) in view of Krupat et al. (US 2018/0303397 A1) further in view of Nam et al. (US 2012/0069190 A1).
Regarding Claim 10, while the combination of Heneghan/Sobhany/Krupat/Doshi teaches the limitations of Claim 8, it does not explicitly disclose the limitations of Claim 10 which state further comprising a setup and configuration module that automates sensor placement calibration, ensuring optimal data capture and alignment within the monitored environment.
Nam though, with the teachings of Heneghan/Sobhany/Krupat/Doshi teaches of
further comprising a setup and configuration module that automates sensor placement calibration, ensuring optimal data capture and alignment within the monitored environment (Nam: Para 0089-0094, 0105-0107 via optimal coverage, field of view, location and viewing angle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat/Doshi with the teachings of Nam in order to have further comprising a setup and configuration module that automates sensor placement calibration, ensuring optimal data capture and alignment within the monitored environment. The motivations behind this being to incorporate the teachings of vision sensor placement as taught by Nam. Furthermore, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 18, while the combination of Heneghan/Sobhany/Krupat teaches the limitations of Claim 14, it does not explicitly disclose the limitations of Claim 18 which state further comprising placing and calibrating sensors, and networking the sensors based on predefined spatial analysis and environmental requirements.
Nam though, with the teachings of Heneghan/Sobhany/Krupat teaches of
further comprising placing and calibrating sensors, and networking the sensors based on predefined spatial analysis and environmental requirements (Nam: Para 0014-0015, 0030-0035, 0089-0094, 0105-0107, 0119-0122 via sensors based on modeled space, coverage, placement, field of view and resource/environment constraints).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Nam in order to have further comprising placing and calibrating sensors, and networking the sensors based on predefined spatial analysis and environmental requirements. The motivations behind this being to incorporate the teachings of vision sensor placement as taught by Nam. Furthermore, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2009/0203972 A1) in view of Sobhany (US 2020/0239002 A1) in view of Krupat et al. (US 2018/0303397 A1) in view of Doshi et al. (US 2020/0134207 A1) further in view of Bermudez Rodriguez (US 2018/0017997 A1).
Regarding Claim 11, while the combination of Heneghan/Sobhany/Krupat/Doshi teaches the limitations of Claim 8, it does not explicitly disclose the limitations of Claim 11 which state wherein sensor scalability supports dynamic network adjustments, allowing addition or removal of sensors without disrupting ongoing operations.
Bermudez Rodriguez though, with the teachings of Heneghan/Sobhany/Krupat/Doshi teaches of
wherein sensor scalability supports dynamic network adjustments, allowing addition or removal of sensors without disrupting ongoing operations (Bermudez Rodriguez: Para 0025 via The mote 104 includes one or more sensor modules 308, each configured to receive information from one or more sensors. Processor 302 assembles the information collected by sensor module 308 in memory 304. A wireless module 310 communicates with control module 106 to convey asset identification information and sensor information. The wireless module 310 may also communicate with the wireless modules 310 of other motes 104 in other racks 102. This allows the formation of a mesh network. The wireless module 310 may include a low-power wireless transmitter, for example using a wireless protocol such as WirelessHART®, ZigBee®, Bluetooth®, 6LoWPAN, or Wi-Fi®, that connects to the wireless modules 310 of neighboring motes 104. Said motes 104 in turn connect to other motes 104, creating a network of low-power wireless connections that allows information from any point in the data center to reach the control module 106. The mesh network may have self-healing and auto-discovery features, allowing motes 104 to be added and removed without disrupting communications or needing substantial operator oversight).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat/Doshi with the teachings of Bermudez Rodriguez in order to have wherein sensor scalability supports dynamic network adjustments, allowing addition or removal of sensors without disrupting ongoing operations. The motivations behind this being to incorporate the teachings of sensor organization as taught by Bermudez Rodriguez. Furthermore, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2009/0203972 A1) in view of Sobhany (US 2020/0239002 A1) in view of Krupat et al. (US 2018/0303397 A1) further in view of Cerqueira et al. (US 2017/0075033 A1).
Regarding Claim 19, while the combination of Heneghan/Sobhany/Krupat teaches the limitations of Claim 14, it does not explicitly disclose the limitations of Claim 19 which states further including achieving scalability through modular configurations, allowing real-time adjustments to the number and placement of sensors depending on the monitored area's size and density.
Cerqueira though, with the teachings of Heneghan/Sobhany/Krupat teaches of
further including achieving scalability through modular configurations, allowing real-time adjustments to the number and placement of sensors depending on the monitored area's size and density (Cerqueira: Para 0012-0019, 0021, 0024-0025, 0028-0031 via adaptively changing portable sensor placement and number considering geographic size and local conditions; generate deployment instructions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan/Sobhany/Krupat with the teachings of Cerqueira in order to have further including achieving scalability through modular configurations, allowing real-time adjustments to the number and placement of sensors depending on the monitored area's size and density. The motivations behind this being to incorporate the teachings of dynamic sensor arrangement due to various constraints as taught by Cerqueira. Furthermore, combining prior art elements according to known methods and simple substitution will yield predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Antar et al.(US 2020/0394886 A1)
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/T.E.S./Examiner, Art Unit 3625
/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625
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