Response to Amendment
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6, 10-11, and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Coke et al. (US 20190108740 A1).
In regard to claim 1, Coke teaches a method, comprising: receiving sensor data indicating presence of a first individual and a second individual in a physical space (Coke, Para. 23, A tracking device placed in a room is primarily designated for permanent monitoring of a single occupant of the room (user), tracking user's state and performance of ADLs, learning user's habits, adjusting a tracking model (for example, modeling user gait) and customary routines, measuring and gathering statistics on vital signs of a user, determining significant deviations from a regular state, detecting dangerous situations (such as falls or unmotivated wondering around a room), communicating with the user via voice to confirm a dangerous state and offer an immediate advice, and generating warnings or alarms communicated to caretakers); identifying a defined set of regions of the physical space (Coke, Para. 24, Tracking devices must not be limited to a single room; for example, several devices may be installed in an adjacent room, a bathroom, etc., jointly monitoring a broader set of user ADLs, routes and routines); identifying, based on the sensor data, a first region, of the defined set of regions, where the first individual is located (Coke, Para. 49, At an early state of functioning of a newly installed tracking device, the system may build a portrait of the room, detecting various objects, such as bed, a table, chairs, a bookshelf, a door, a window by monitoring absolute coordinates of a bounding box of a user point cloud in various user states. For example, room areas that are customarily crossed by a walking user, may be subtracted from the space occupied by objects; bounding boxes for adjacent states of standing, sitting and laying down (at a certain height above the floor, corresponding to the height of the bed) may show an approximate position of a bed or a couch); in response to determining that the first individual is located in the first region, labeling the sensor data with a first user identifier, of a plurality of user identifiers associated with the physical space (Coke, Para. 79, system functioning in connection with identifying and categorizing user states and routines. Processing begins at a step 910, where one or more tracking devices are installed on long-term care premises, such as a user room. After the step 910, processing proceeds to a step 915, where training data in the form of point clouds captured by the radar(s) included with tracking device(s) are collected. After the step 915, processing proceeds to a step 920, where an initial round of training is conducted. Where a geometric signature of a master user is built for all user states for the purpose of user identification in situations when there are multiple users in the room or when a master user may be temporarily absent from the room); identifying, based on the sensor data, a second region, of the defined set of regions, where the second individual is located (Coke, Para. 15, system for permanent tracking of elderly individuals (users) within one or multiple rooms, including detection of a current state of a user, such as walking, standing, sitting, laying down, falling, leaving a room, etc.; Para. 49, At an early state of functioning of a newly installed tracking device, the system may build a portrait of the room, detecting various objects, such as bed, a table, chairs, a bookshelf, a door, a window by monitoring absolute coordinates of a bounding box of a user point cloud in various user states. For example, room areas that are customarily crossed by a walking user, may be subtracted from the space occupied by objects; bounding boxes for adjacent states of standing, sitting and laying down (at a certain height above the floor, corresponding to the height of the bed) may show an approximate position of a bed or a couch); in response to determining that the second individual is located in the second region, labeling the sensor data with a second user identifier, of the plurality of user identifiers (Coke, Para. 79, system functioning in connection with identifying and categorizing user states and routines. Processing begins at a step 910, where one or more tracking devices are installed on long-term care premises, such as a user room. After the step 910, processing proceeds to a step 915, where training data in the form of point clouds captured by the radar(s) included with tracking device(s) are collected. After the step 915, processing proceeds to a step 920, where an initial round of training is conducted. Where a geometric signature of a master user (second master user of elderly individuals (users) within one or multiple rooms as mentioned in Para. 15) is built for all user states for the purpose of user identification in situations when there are multiple users in the room or when a master user may be temporarily absent from the room); and providing one or more personal services to the first individual based on the first user identifier (Coke, Para. 37, In a guest mode, the system may suspend monitoring user states and, accordingly, measuring vital signs (which require determination of a static state, as explained elsewhere herein) until the master user appears alone in the room. A more advanced option may identify the master user based on unique parameters of the point cloud and dynamics of the point cloud (size, gait parameters, etc.) and continue monitoring the master user, ignoring other individuals present in the room).
In regard to claim 2, Coke teaches the method of claim 1, wherein the sensor data comprises radar data collected using one or more radar sensors in the physical space (Coke, Fig. 1, a tracking device 120; Para. 65, A chipset 210 enables data collection, processing and data exchange with the cloud. An ultra-wideband radar 220 has a coverage area close to 180 degrees and tracks moving objects in the room).
In regard to claim 3, Coke teaches the method of claim 1, wherein the first region is labeled with the first user identifier to indicate that a first user corresponding to the first user identifier is associated with the first region in the physical space (Coke, Para. 79, where one or more tracking devices are installed on long-term care premises, such as a user room. After the step 910, processing proceeds to a step 915, where training data in the form of point clouds captured by the radar(s) included with tracking device(s) are collected. After the step 915, processing proceeds to a step 920, where an initial round of training is conducted, as explained elsewhere herein (see, for example, FIG. 3 and the accompanying text). After the step 920, processing proceeds to a step 922, where a geometric signature of a master user is built for all user states for the purpose of user identification in situations when there are multiple users in the room or when a master user may be temporarily absent from the room).
In regard to claim 4, Coke teaches the method of claim 1, wherein identifying the defined set of regions comprises: identifying one or more manually defined regions in the physical space; and identifying, for each respective region of the one or more manually defined regions, a respective manually defined user identifier (Coke, Fig. 8, user Mary Rose; Para. 23, A tracking device placed in a room is primarily designated for permanent monitoring of a single occupant of the room (user), tracking user's state and performance of ADLs, learning user's habits, adjusting a tracking model (for example, modeling user gait) and customary routines, measuring and gathering statistics on vital signs of a user, determining significant deviations from a regular state, detecting dangerous situations (such as falls or unmotivated wondering around a room), communicating with the user via voice to confirm a dangerous state and offer an immediate advice, and generating warnings or alarms communicated to caretakers).
In regard to claim 5, Coke teaches the method of claim 1, wherein identifying the defined set of regions comprises, for at least one region of the defined set of regions, learning at least one user identifier associated with the at least one region based on historical sensor data for the physical space (Coke, Para. 79, processing proceeds to a step 922, where a geometric signature of a master user is built for all user states for the purpose of user identification in situations when there are multiple users in the room or when a master user may be temporarily absent from the room. After the step 922, processing proceeds to a step 925, where a state classifier is initially built, as explained, for example, in FIG. 3 and the accompanying text. After the step 925, processing proceeds to a step 930, where the system detects static objects in the room, such as furniture, based on a complementary location of the furniture with respect to user trajectories and user states. After the step 930, processing proceeds to a step 932, where the system monitors sequences of detected user states, builds initial routines, routine clusters and stats (see, in particular, FIG. 6 and the accompanying text)).
In regard to claim 6, Coke teaches the method of claim 1, further comprising assigning a completed action to a first user corresponding to the first user identifier based on the first region (Coke, Para. 42, As the installed device monitors user everyday behavior and habits, the system may accumulate a significant number of different customary routines, which may be mapped geometrically as clusters of points in a multi-dimensional space of objects, time intervals and other parameters and may represent complex user behaviors. Subsequently, new routines may be compared with the accumulated clusters and if the new routines stand significantly apart from each of the existing clusters, a signal may be sent to caretakers who may categorize a new routine as a first case of an emerging healthy habit or a deviation from healthy behavior that may require an action on the part of caretakers).
In regard to claim 10, Coke teaches a method, comprising: receiving historical sensor data indicating, for one or more prior times, presence of an individual in a first region of a physical space ((Coke, Para. 23, A tracking device placed in a room is primarily designated for permanent monitoring of a single occupant of the room (user), tracking user's state and performance of ADLs, learning user's habits, adjusting a tracking model (for example, modeling user gait) and customary routines, measuring and gathering statistics on vital signs of a user, determining significant deviations from a regular state, detecting dangerous situations (such as falls or unmotivated wondering around a room), communicating with the user via voice to confirm a dangerous state and offer an immediate advice, and generating warnings or alarms communicated to caretakers); learning a first label for the first region based on the historical sensor data, comprising labeling the first region using a first user identifier corresponding to a first user (Coke, Para. 79, processing proceeds to a step 922, where a geometric signature of a master user is built for all user states for the purpose of user identification in situations when there are multiple users in the room or when a master user may be temporarily absent from the room. After the step 922, processing proceeds to a step 925, where a state classifier is initially built, as explained, for example, in FIG. 3 and the accompanying text. After the step 925, processing proceeds to a step 930, where the system detects static objects in the room, such as furniture, based on a complementary location of the furniture with respect to user trajectories and user states. After the step 930, processing proceeds to a step 932, where the system monitors sequences of detected user states, builds initial routines, routine clusters and stats (see, in particular, FIG. 6 and the accompanying text)); learning a second label for a second region based on the historical sensor data, comprising labeling the second region using a second user identifier corresponding to a second user; receiving current sensor data indicating presence of a first individual and a second individual in the physical space (Coke, Para. 15, system for permanent tracking of elderly individuals (users) within one or multiple rooms, including detection of a current state of a user, such as walking, standing, sitting, laying down, falling, leaving a room, etc.; Para. 79, processing proceeds to a step 922, where a geometric signature of a master user is built for all user states for the purpose of user identification in situations when there are multiple users in the room or when a master user may be temporarily absent from the room. After the step 922, processing proceeds to a step 925, where a state classifier is initially built, as explained, for example, in FIG. 3 and the accompanying text. After the step 925, processing proceeds to a step 930, where the system detects static objects in the room, such as furniture, based on a complementary location of the furniture with respect to user trajectories and user states. After the step 930, processing proceeds to a step 932, where the system monitors sequences of detected user states, builds initial routines, routine clusters and stats (see, in particular, FIG. 6 and the accompanying text)); in response to determining, based on the current sensor data, that the first individual is in the first region, labeling the current sensor data with at least one of (i) the first user identifier or (ii) an action identifier corresponding to the first region ((Coke, Para. 79, system functioning in connection with identifying and categorizing user states and routines. Processing begins at a step 910, where one or more tracking devices are installed on long-term care premises, such as a user room. After the step 910, processing proceeds to a step 915, where training data in the form of point clouds captured by the radar(s) included with tracking device(s) are collected. After the step 915, processing proceeds to a step 920, where an initial round of training is conducted. Where a geometric signature of a master user is built for all user states for the purpose of user identification in situations when there are multiple users in the room or when a master user may be temporarily absent from the room); and in response to determining, based on the current sensor data, that the second individual is in the second region, labeling the current sensor data with the second user identifier (Coke, Para. 15, system for permanent tracking of elderly individuals (users) within one or multiple rooms, including detection of a current state of a user, such as walking, standing, sitting, laying down, falling, leaving a room, etc.; Para. 79, system functioning in connection with identifying and categorizing user states and routines. Processing begins at a step 910, where one or more tracking devices are installed on long-term care premises, such as a user room. After the step 910, processing proceeds to a step 915, where training data in the form of point clouds captured by the radar(s) included with tracking device(s) are collected. After the step 915, processing proceeds to a step 920, where an initial round of training is conducted. Where a geometric signature of a master user (second master user of elderly individuals (users) within one or multiple rooms as mentioned in Para. 15) is built for all user states for the purpose of user identification in situations when there are multiple users in the room or when a master user may be temporarily absent from the room).
In regard to claim 11, Coke teaches the method of claim 10, wherein the historical sensor data and current sensor data comprise radar data collected using one or more radar sensors in the physical space (Coke, Fig. 1, a tracking device 120; Para. 65, A chipset 210 enables data collection, processing and data exchange with the cloud. An ultra-wideband radar 220 has a coverage area close to 180 degrees and tracks moving objects in the room).
In regard to claim 17, Combination of Coke and Nagpal teaches the method of claim 10, further comprising providing one or more personal services to the first user based on the first user identifier (Coke, Para. 37, In a guest mode, the system may suspend monitoring user states and, accordingly, measuring vital signs (which require determination of a static state, as explained elsewhere herein) until the master user appears alone in the room. A more advanced option may identify the master user based on unique parameters of the point cloud and dynamics of the point cloud (size, gait parameters, etc.) and continue monitoring the master user, ignoring other individuals present in the room).
In regard to claim 18, the claim is interpreted and rejected for the same reasons as stated in the rejection of claim 1 as stated above.
In regard to claim 19, the claim is interpreted and rejected for the same reasons as stated in the rejection of claim 5 as stated above.
In regard to claim 20, the claim is interpreted and rejected for the same reasons as stated in the rejection of claim 6 as stated above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 7-9 and 12-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Coke (US 20190108740 A1) in view of Nagpal et al. (US 20220268916 A1).
In regard to claim 7, Coke does not specifically teach the method of claim 6, wherein assigning the completed action to the first user comprises identifying one or more action identifiers having a manually defined association with the first region.
However, Nagpal teaches wherein assigning the completed action to the first user comprises identifying one or more action identifiers having a manually defined association with the first region (Nagpal, Para. 111, toiling will occur in the bathroom with high probability and cooking will likely occur in the kitchen, or eating may happen in the kitchen or dining room, but is less likely to occur in a bedroom. Therefore, the processor may be programmed to identify functional space within the area 100. Room identification can proceed similarly as setting up room geometry or defining the area 100 (e.g., geofencing). The user would then explicitly label each space identified (e.g., via the user interface unit or the mobile app communicating with the communications unit of the device 102). The labeling could be done through an app via the mobile phone or tablet operated by the user, by the user saying the name of the room and microphone unit recording that statement, or the user looking at a map (e.g., via the user interface unit or the mobile app communicating with the communications unit of the device 102) generated by the processor to draw and define space).
Coke and Nagpal are analogous art because they both pertain to monitoring and tracking activities of daily living of an individual.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to manually label each space identified using user interface unit (as taught by Nagpal) resulting in predictable result of simplifying recognizing daily activities as the system knows how the space was designed (Para. 111).
In regard to claim 8, Combination of Coke and Nagpal teach the method of claim 6, wherein assigning the completed action to the first user comprises learning one or more action identifiers associated with the first region based on historical sensor data (Nagpal, Para. 111, The use of a space can be inferred by the processor from multiple probabilistic priors (i.e. statistics learned from the general population). For example, a person that stays relatively motionless in a horizontal pose for several hours in the evening is likely sleeping, as inferred by the processor. The area around them is likely a bedroom, as inferred by the processor. As a more complex example, a certain type of radar return corresponds to a person sitting, as inferred by the processor. A space where a person regularly sits could be a toilet, a favorite chair, or a dining table, as inferred by the processor. If the person only sits in that location for a short period, as determined by the processor, then more likely they are toileting, as inferred by the processor and the region immediately around that activity is a bathroom, as inferred by the processor).
In regard to claim 9, Coke teaches the method of claim 1, wherein the one or more personal services comprise one or more healthcare-related services selected to allow the first individual to reside in the physical space (Coke, 23, A tracking device placed in a room is primarily designated for permanent monitoring of a single occupant of the room (user), tracking user's state and performance of ADLs, learning user's habits, adjusting a tracking model (for example, modeling user gait) and customary routines, measuring and gathering statistics on vital signs of a user, determining significant deviations from a regular state, detecting dangerous situations (such as falls or unmotivated wondering around a room), communicating with the user via voice to confirm a dangerous state and offer an immediate advice, and generating warnings or alarms communicated to caretakers. Other tasks include generating user status reports and sharing the user status reports with a user and caretakers).
In regard to claim 12, Coke does not specifically teach the method of claim 10, wherein learning the first label for the first region comprises labeling the first region using the first user identifier in response to determining, based on the historical sensor data, that the first user has a stronger association with the first region, as compared to at least the second user.
However, Nagpal teaches the method of claim 10, wherein learning the label for the first region comprises labeling the first region using the first user identifier in response to determining, based on the historical sensor data, that the first user has a stronger association with the first region, as compared to at least a second user (Nagpal, Para. 73, the processor may track the object 104 and another object 104 (e.g., a visitor, a pet) positioned or living within the area 100 based on the set of data and distinguish the object 104 living in the area 100 from the another object 104 positioned or living within the area 100 based on the set of data (e.g., by learning habits and signatures of the object 104 over time) before determining whether the object 104 is experiencing the event involving the object 104 within the area 100 based on the set of data).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to learn habits and signatures of master user compared to a guest (as taught by Nagpal) resulting in predictable result of distinguishing the master user living in the area.
In regard to claim 13, Combination of Coke and Nagpal teach the method of claim 12, wherein determining that the first user has the stronger association comprises: determining, based on the historical sensor data, that an individual left a third region of the physical space at a first point in time, wherein the third region is labeled using the first user identifier; and determining, based on the historical sensor data, that an individual entered the first region of the physical space at a second point in time subsequent to the first point in time (Coke, Para. 38-39, Not all transitions are feasible: for example, a user cannot start walking immediately after laying down on a bed and cannot be standing near a window right after entering the room following a departure. All possible transitions between user states define elementary user routes, such as entering the room and walking to a chair, standing near a chair, sitting on a chair, walking to the bed, standing near the bed, sitting on the bed, laying down on the bed. Most transitions between states may have specific transition procedures assigned to the transitions; such transition procedures may be verified by the system to improve detection accuracy for user states).
In regard to claim 14, Combination of Coke and Nagpal teach the method of claim 10, wherein learning the first label for the first region further comprises labeling the first region using the action identifier based on (i) a location of the first region in the physical space and (ii) an action performed by the individual, determined based on the historical sensor data (Nagpal, Para. 111, The use of a space can be inferred by the processor from multiple probabilistic priors (i.e. statistics learned from the general population). For example, a person that stays relatively motionless in a horizontal pose for several hours in the evening is likely sleeping, as inferred by the processor. The area around them is likely a bedroom, as inferred by the processor. As a more complex example, a certain type of radar return corresponds to a person sitting, as inferred by the processor. A space where a person regularly sits could be a toilet, a favorite chair, or a dining table, as inferred by the processor. If the person only sits in that location for a short period, as determined by the processor, then more likely they are toileting, as inferred by the processor and the region immediately around that activity is a bathroom, as inferred by the processor).
In regard to claim 15, Combination of Coke and Nagpal teach the method of claim 14, wherein the location of the first region corresponds to a defined area in a room, of a set of rooms in the physical space (Nagpal, Fig. 1; Para. 111, the user may be instructed or the processor may be programmed to assign an identifier (e.g., a kitchen, a bathroom) to a subarea within the area 100 such that the processor determines whether the object 104 is experiencing the event within the subarea based on the set of data and the identifier, and takes the action responsive to the event determined to be occurring within the subarea).
In regard to claim 16, Combination of Coke and Nagpal teach the method of claim 14, wherein the action performed by the individual comprises at least one of: (i) sitting, (ii) standing, (iii) laying down, or (iv) sleeping (Nagpal, Para. 73, the device 102 can detect (e.g., as tracked by the radar and detected by the processor) a human's engagement in several key activities of daily living, including sleeping, eating, drinking, toileting, socializing, or others. For example, based on the set of data received from the radar, the processor may track sleeping—detect sleep interruptions, schedule changes, time to restful sleep, or early awakening. For example, based on the set of data received from the radar, the processor may track eating or drinking—timing, frequency, prep duration, eating or drinking duration, or whether or not cooking. For example, based on the set of data received from the radar, the processor may track toileting—timing and frequency of toilet use).
Response to Arguments
Applicant's arguments filed on 12/29/2025 have been fully considered but they are not persuasive. In that remarks, applicant's argues in substance:
Applicant argues: " For example, as explained in the specification, "it is often difficult to know what services are needed or what actions patients are capable of performing without assistance." [0004]. In conventional approaches, "third parties (e.g., family members, friends, healthcare providers, and the like) often attempt to determine the patient's state and abilities based on general conversations and observations," but "these approaches frequently fail to accurately assess how the patient fares when alone, and whether they are performing the daily activities needed (such as eating) to ensure they remain healthy and safe at home."Id. However, "the presence of multiple users can pose a substantial challenge to collecting and processing sensor data to identify user actions and movement." [0030]. For example, "if a married couple lives together in the space, sensors (such as radar sensors) may be used to identify the presence and movement of individuals generically, but conventionally cannot be used to determine the specific identity of each detected individual."Id. That is, "though the sensors may indicate that an individual used the bathroom at a given time, they are generally not able to indicate which user (of a set of multiple users in the home) corresponds to the detected individual."Id. Stated differently, "conventional sensor-based approaches to monitor user actions are not able to individualize the data."Id.
To solve these and other problems, aspects of the present disclosure provide techniques to "enhance sensor data processing to enable individualization of the data." [0031]. For example, using the described and claimed techniques, "the system can detect actions or movement of an individual (e.g., a human in a space)" and "identify or infer the specific user (e.g., the name or other unique identifier) that performed the actions/corresponds to the detected individual" which "enables significantly improved sensor accuracy, enabling non-invasive and reliable monitoring even when multiple users reside in the environment and even with otherwise-limited sensor technologies."Id..”
Examiner's Response: Based on applicant’s argument, 101 rejections have been withdrawn.
Response to amended claims is considered above in claim Rejections.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARMIN AKHTER whose telephone number is (571)272-9365. The examiner can normally be reached on Monday - Thursday 8:00am-5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Davetta W Goins can be reached on (571) 272.2957. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHARMIN AKHTER/
Examiner, Art Unit 2689
/DAVETTA W GOINS/Supervisory Patent Examiner, Art Unit 2689