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 .
Information Disclosure Statement
The information disclosure statements submitted on 6/25/25 and 7/2/25 have been considered by the examiner and made of record in the application file.
Response to Arguments
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Examiner respectfully disagrees. In the previous rejection, while Wu is relied upon for certain aspects, the primary reference, Fadell, discloses motion-based information associated with time, including intra-environment occupancy information indicating which individuals are present in which locations at different times, including in real time (Par. 105: Lines 10-16). Such disclosures provide time-indicated events occurring during motion within the environment and correspond to the claimed temporal range of motion comprising a timestamp indicating events occurring during the motion event. Accordingly, when the applied references are considered together, the claimed limitation is taught or suggested by the cited art.
Further, Wu provides corroborative support that motion-related activity may be associated with time-based indicators, even where such activity is represented using a finite state machine. As described in Wu, motion is analyzed as a sequence of time-indexed events (Par. 263: Lines 4-7; The processing results in a time series of events, denoted as E=[e(t₁), e(t₂), …], where e(tᵢ) is the event occurs at time tᵢ; Motion-related events are explicitly associated with corresponding times). Wu further explains that these events drive transitions of a finite state machine (Par. 260: Lines 15-18; For each detected step, the algorithm outputs the timing information of the detected step: the time point of the corresponding S_ZC is the starting time, while the time it enters S_DT implies the ending time; Time indicators are used to characterize motion occurring over a temporal range). Thus, even though Wu employs a finite state machine representation, it nevertheless demonstrates the well-known concepts that timestamps may be used to indicate events occurring during a motion event. Wu is cited for this supporting context only and serves to reinforce the disclosure of Fadell, which provides the basis for the claimed temporal range of motion in the combination of references.
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.
Claims 1,2,5-6,7-10,13,14,17-18,19-22,25,26,29-33,34 are rejected under 35 U.S.C. 103 as being unpatentable over Fadell et al. (U.S. Pub. No. 2015/0154850 A1, hereinafter Fadell), in view of Piao et al. (U.S. Patent No. 10108903 B1, hereinafter Piao) in further view of Wu et al. (U.S. Pub. No. 2022/0026519 A1, hereinafter Wu).
Regarding claim 1, Fadell discloses a method comprising:
obtaining motion data at a wireless communication device in a motion detection system configured to detect motion in space (Par. 0105: Lines 10-16; “…the data collected and logged may include maps of homes, maps of users' in-home movements from room to room as determined by network-connected smart devices equipped with motion and/or identification technology, time spent in each room, intra-home occupancy maps that indicate which rooms are occupied and by whom at different times (including in real time)…”),
the motion detection system comprising a plurality of wireless communication devices (Par. 0068: Lines 1-5; “According to embodiments, the network-connected devices (a.k.a. the low- and high-power nodes) of the smart-home environment 100 are capable of enhancing home security. For example, as discussed, all or some of the network-connected smart devices are equipped with motion sensing…”),
the motion data derived from wireless signals communicated through the space by a plurality of wireless communication devices (Par. 0203: Lines 1-3; “ Also included are sensors 428 such as temperature, humidity, occupancy, ambient light, fire, smoke, carbon monoxide, active proximity, passive infrared motion, ultrasound…”; and Par. 0084: Lines 1-4; “The smart-home environment 100 may include a gated entry 116 that communicates information through the mesh network or directly to the central server or cloud-computing system 164 regarding detected movement”);
a motion event comprising a temporal range of motion and a spatial range of motion, the temporal range of motion comprising a time stamp indicating at least one of a start time, a stop time, or events occurring during the motion event space (Par. 0105: Lines 10-16; “…the data collected and logged may include maps of homes, maps of users' in-home movements from room to room as determined by network-connected smart devices equipped with motion and/or identification technology, time spent in each room, intra-home occupancy maps that indicate which rooms are occupied and by whom at different times (including in real time)…”; Occupancy states are determined based on motion and is tracked at different times).
and sending, from the wireless communication device to the cloud-based computer system, a message comprising an indication of that the motion event was detected (Par. 0084: Lines 1-4 in; “The smart-home environment 100 may include a gated entry 116 that communicates information through the mesh network or directly to the central server or cloud-computing system 164 regarding detected movement”).
Fadell fails to disclose providing, over a training period, the motion data to a cloud-based computer system; receiving, from the cloud-based computer system, a motion pattern detection function, the motion pattern detection function corresponding to a motion event that occurred in the space during the training period; after the training period, obtaining additional motion data at the wireless communication device; by operation of the wireless communication device, applying the motion pattern detection function to the additional motion data to detect an occurrence of the motion event in the space.
However, Piao discloses providing, over a training period, the motion data to a cloud-based computing system (Col. 15: Lines 34-39; “the underlying data of the plots, or data associated with the underlying data of the plots (e.g., histogram data) may be input to a neural network training system to train a neural network to detect whether a certain category of motion (e.g., motion by a human) has occurred in the space.”); a motion pattern detection function, the motion pattern detection function corresponding to a motion event that occurred in the space during the training period (Col. 1: Lines 59-66; “Once trained, the neural network system may be used to detect whether motion has occurred in a space based on untagged data. The untagged data may be formatted in the same manner as the tagged data, but without an indication of whether motion has occurred. The neural network system may process the untagged data using nodes that were programmed during the training process to provide an output that includes a motion indication.”); after the training period, obtaining additional motion data at the wireless communication device; by operation of the wireless communication device, applying the motion pattern detection function to the additional motion data to detect an occurrence of the motion event in the space (Col. 15: Lines 54-57; “After training the neural network system with the tagged data, newly collected data may be input to the neural network system to detect whether motion (or a distinct category of motion) has occurred in the space…”). While Piao does not explicitly disclose that the motion pattern detection function is received from a cloud-based computing system, Fadell disclose such a cloud-based infrastructure for the communication of wireless devices.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the applicant’s claimed invention to have incorporated the teachings of Piao into the invention of Fadell in order to enable adaptive and intelligent motion detection. Training a model and then applying the model to future data is a well-established known method known to reduce false positives and increase robustness in dynamic environments.
Furthermore, the combined teaching of Fadell and Piao do not explicitly teach the use of time stamps to indicate a stop time, start time, or events occurring during the motion event space, Wu demonstrates the well-known concepts that timestamps may be used to indicate events occurring during a motion event (Par. 263: Lines 4-7; The processing results in a time series of events, denoted as E=[e(t₁), e(t₂), …], where e(tᵢ) is the event occurs at time tᵢ; Motion-related events are explicitly associated with corresponding times; Par. 260: Lines 15-18; For each detected step, the algorithm outputs the timing information of the detected step: the time point of the corresponding S_ZC is the starting time, while the time it enters S_DT implies the ending time; Time indicators are used to characterize motion occurring over a temporal range). Wu serves to reinforce the disclosure of Fadell in view of Piao, which provides the basis for the claimed temporal range of motion.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate timestamping techniques, as evidenced by Wu, into the motion detection system of Fadell in view of Piao, because temporal information enables motion data to be ordered, segmented, and correlated over time, which is necessary for analyzing motion behavior and supporting reliable motion detection and interpretation. Applying timestamps is a predictable design choice that yields expected improvements in motion analysis.
Consider claim 2 and as applied to claim 1. Fadell further discloses wherein the motion event is a periodic update of historical motion information (Par. 0062: Lines 3-7; “ Individual low-power nodes in the smart-home environment regularly send out messages regarding what they are sensing, and the other low-powered nodes in the smart-home environment--in addition to sending out their own messages--repeat the messages…”; and Par. 0062: Lines 21-24; “…the mesh network enables the central server or cloud-computing system 164 to regularly receive data from all of the network-connected smart devices in the smart-home environment…).
Consider claim 5 and as applied to claim 3. Fadell and Piao disclose the claimed invention put fail to teach wherein the spatial range of motion comprises parameters describing a curve.
However, Wu discloses wherein the spatial range of motion comprises parameters describing a curve (Par. 0155: Lines 1-7; “The characteristics and/or STI (e.g. motion information) may comprise: location, location coordinate, change in location, position (e.g. initial position, new position), position on map, height, horizontal location, vertical location, distance, displacement, speed, acceleration, rotational speed, rotational acceleration, direction, angle of motion, azimuth, direction of motion, rotation, path…”). These terms describe a curve, for example a path or shape traced by an object’s movement over time. Parameters such as displacement, angle of motion, or direction are commonly used in tracking systems to represent a curve.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wu’s shape and path-based motion representation into the system of Fadell in view of Piao to enable more descriptive modeling of movement through space, leveraging known methods of capturing the structure of motion paths.
Consider claim 6 and as applied to claim 1. Fadell further discloses wherein the message comprises the indication and additional information describing the occurrence of a motion event (Par.219: Lines 18-20; “And the message could provide additional information. For example, the message could be "Intruder detected in den", "Fire detected in kitchen", etc.). This shows the message can include information beyond mere detection, such as the type and location of the detected event.
Consider claim 7 and as applied to claim 6. Fadell teaches the limitation of claim 6 but fails to teach wherein the additional information comprises a time that the motion event occurred.
However, Wu teaches obtaining a time that the motion event occurred (Lines 6-9 in First Col. Of Page 29; “For each detected step, the algorithm outputs the timing information of the detected step: The time point of the corresponding S_ZC is the starting time, while the time it enters S_DT implies the ending time.”).
While Wu does not explicitly state that this timing information is sent in a message, Fadell teaches transmitting messages with additional information to the cloud. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the applicant’s claimed invention to include Wu’s disclosed timing information in the message structure taught by Fadell in order to support time-based motion logging, synchronization, or cloud-based analysis of detected events. These are well known motivations in the field of motion detection.
Consider claim 8 and as applied to claim 6. Fadell teaches the limitations of claim 6 but fails to teach wherein the additional information comprises a motion path traversed during the occurrence of a motion event.
However, Wu teaches obtaining a motion path traversed during the occurrence of a motion event (Par. 0155: Lines 1-7; “The characteristics and/or STI (e.g. motion information) may comprise: location, location coordinate, change in location, position (e.g. initial position, new position), position on map, height, horizontal location, vertical location, distance, displacement, speed, acceleration, rotational speed, rotational acceleration, direction, angle of motion, azimuth, direction of motion, rotation, path…”).
While Wu does not explicitly state that these curved based parameters are sent in a message, Fadell teaches sending motion event messages to the cloud that may include additional information. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the applicant’s claimed invention to include Wu’s spatial curve parameters in the message structure taught by Fadell, in order to provide detailed spatial context useful for downstream processing.
Consider claim 9 and as applied to claim 8. Fadell teaches the limitations of claim 6 while Fadell and Wu teach the limitations of claim 8. Wu further teaches wherein the motion path comprises a location of motion (Par. 0045: Lines 2-4; “More specifically, the present teaching relates to methods and systems for tracking a location and trajectory of a moving object…).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the applicant’s claimed invention to incorporate the location-detection ability of Wu into the motion detection system of Fadell in order to provide spatial specificity to the motion event messaging, an expected enhancement in intelligent monitoring systems.
Consider claim 10 and as applied to claim 1. Fadell discloses the claimed invention but fails to teach wherein the motion pattern detection function comprises a neural network, and the occurrence of the motion event is detected based on an output of the neural network.
However, Piao discloses wherein the motion pattern detection function comprises a neural network, and the occurrence of the motion event is detected based on an output of the neural network (Col. 15: Lines 54-57; “After training the neural network system with the tagged data, newly collected data may be input to the neural network system to detect whether motion (or a distinct category of motion) has occurred in the space…”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the applicant’s claimed invention to have incorporated the teachings of Piao into the invention of Fadell in order to improve the accuracy, adaptability, and intelligence of motion event detection using a machine-learning approach that can generalize across varying signal environments.
Regarding claim 13, the rejection of Claim 1 addresses all the limitations presented in claim 13 so therefore the limitations are addressed. Furthermore, Fadell teaches the non-transitory computer-readable medium (Par. 0294: Lines 1-5; “RAM 1670 and non-volatile storage drive 1680 are examples of tangible computer-readable media configured to store data such as computer-program product embodiments of the present disclosure, including executable computer code…”).
Regarding claim 14, the rejection of claim 2 addresses all the limitations presented in claim 14 so therefore the limitations are addressed.
Regarding claim 17, the rejection of claim 5 addresses all the limitations presented in claim 17 so therefore the limitations are addressed.
Regarding claim 18, the rejection of claim 6 addresses all the limitations presented in claim 14 so therefore the limitations are addressed.
Regarding claim 19, the rejection of claim 7 addresses all the limitations presented in claim 19 so therefore the limitations are addressed.
Regarding claim 20, the rejection of claim 8 addresses all the limitations presented in claim 20 so therefore the limitations are addressed.
Regarding claim 21, the rejection of claim 9 addresses all the limitations presented in claim 21 so therefore the limitations are addressed.
Regarding claim 22, the rejection of claim 10 addresses all the limitations presented in claim 14 so therefore the limitations are addressed.
Regarding claim 25, the rejection of Claim 1 addresses all the limitations presented in claim 25 so therefore the limitations are addressed. Furthermore, Fadell teaches the computer device comprising one or more processors (Par. 0289: Lines 5-9; “Each such computer-program product may comprise sets of instructions (codes) embodied on a computer-readable medium that directs the processor of a computer system to perform corresponding actions.”).
Regarding claim 26, the rejection of claim 2 addresses all the limitations presented in claim 26 so therefore the limitations are addressed.
Regarding claim 29, the rejection of claim 5 addresses all the limitations presented in claim 29 so therefore the limitations are addressed.
Regarding claim 30, the rejection of claim 6 addresses all the limitations presented in claim 30 so therefore the limitations are addressed.
Regarding claim 31, the rejection of claim 7 addresses all the limitations presented in claim 31 so therefore the limitations are addressed.
Regarding claim 32, the rejection of claim 8 addresses all the limitations presented in claim 32 so therefore the limitations are addressed.
Regarding claim 33, the rejection of claim 9 addresses all the limitations presented in claim 33 so therefore the limitations are addressed.
Regarding claim 34, the rejection of claim 10 addresses all the limitations presented in claim 34 so therefore the limitations are addressed.
Claims 11-12,23-24,35-36 are is rejected under 35 U.S.C. 103 as being unpatentable over Fadell et al. (U.S. Pub. No. 2015/0154850 A1, hereinafter Fadell), in view of Piao et al. (U.S. Patent No. 10108903 B1, hereinafter Piao), in further view of Wu et al. (U.S. Pub. No. 2022/0026519 A1, hereinafter Wu), in further view of Sugar et al. (U.S. Pub. No. 2022/0369068 A1, hereinafter Sugar).
Consider claim 11 as applied to claim 1, Fadell in view of Piao and Wu disclose the claimed invention but fail to teach wherein the motion pattern detection function comprises a curve fitting algorithm, and the occurrence of the motion event is detected based on an output of the curve fitting algorithm.
However, Sugar discloses wherein the motion pattern detection function comprises a curve fitting algorithm, and the occurrence of the motion event is detected based on an output of the curve fitting algorithm (Par. 0172: Lines 1-5; “…the techniques described herein relate to a room occupancy monitor, wherein the algorithm is a machine learning algorithm that uses any one of the following techniques: Decision Trees, Support Vector Machines, K Nearest Neighbors, Naïve Bayes Classifier, Logistic Regression…”). Logistic Regression is a well-known form of curve fitting.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the applicant’s claimed invention to have incorporated the curve fitting technique of Sugar, such as logistic regression, into the motion pattern detection function of Fadell in view of Piao in further view of Wu in order to leverage known data modeling methods for improving the accuracy of motion event classification based on signal-derived features.
Consider claim 12 as applied to claim 1, Fadell in view of Piao in further view of Wu disclose the claimed invention but fail to teach wherein the motion pattern detection function comprises a clustering algorithm, and the occurrence of the motion event is detected based on an output of the clustering algorithm.
However, Sugar discloses wherein the motion pattern detection function comprises a clustering algorithm, and the occurrence of the motion event is detected based on an output of the clustering algorithm (Par. 0172: Lines 1-5; “…the techniques described herein relate to a room occupancy monitor, wherein the algorithm is a machine learning algorithm that uses any one of the following techniques: Decision Trees, Support Vector Machines, K Nearest Neighbors, Naïve Bayes Classifier, Logistic Regression, K-means …”). K-means is a classic clustering algorithm.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the applicant’s claimed invention to have incorporated the clustering technique of Sugar, such as K-means, into the motion pattern detection function of Fadell in view of Piao in further view of Wu in order to leverage known data modeling methods for improving the accuracy of motion event classification based on signal-derived features.
Regarding claim 23, the rejection of claim 11 addresses all the limitations presented in claim 23 so therefore the limitations are addressed.
Regarding claim 24, the rejection of claim 12 addresses all the limitations presented in claim 24 so therefore the limitations are addressed.
Regarding claim 35, the rejection of claim 11 addresses all the limitations presented in claim 35 so therefore the limitations are addressed.
Regarding claim 36, the rejection of claim 12 addresses all the limitations presented in claim 36 so therefore the limitations are addressed.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FABIAN BOTELLO whose telephone number is (571)272-4439. The examiner can normally be reached Monday - Friday 8:30 am - 5:30 pm.
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/FABIAN BOTELLO/Examiner, Art Unit 2648
/WESLEY L KIM/Supervisory Patent Examiner, Art Unit 2648