DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to the amendment filed on 2/19/26. Claims 1, 3-4, 6-10, 12-13, 15-18, and 21-26 are pending. Claims 2, 5, 11, 14, and 19-20 have been canceled. Claims 21-26 are new.
The IDSs filed on 3/3/26 has been considered by the Examiner.
Claim Rejections - 35 USC § 101
Patent eligibility
Claims 1, 3-4, 6-10, 12-13, 15-18, and 21-22. recite systems in which the function or configuration of the system sensors are changed based upon of the system findings. Therefore, the claims are found to recite significantly more than an abstract idea.
Similarly, claims 21-26 recite a method wherein responsive to determining a wellness or care event for the person under care may have occurred, changing a configuration of at least one of the plurality of environmental sensors that is already active to another active configuration(i.e. system sensors are changed based upon of the system findings.) Therefore, the claims are found to recite significantly more than an abstract idea.
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.
Claim(s) 1, 3-4, 6-10, 12-13, 15-18, and 22-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al ( US 2012/0289791) in view of Kristal et al ( WO 2021/046477).
Claim 1 Jain discloses a system (sensor network 100) to monitor a person under care by a stakeholder (Fig. 1; [0147], 'As an example and not by way of limitation, analysis system 180 could continuously monitor a subject's blood pressure over time to determine whether the subject's hypertension is improving. Such monitoring may be used to identify trends and to generate alerts or predictions regarding possible health states'; [0158], 'Analysis system 180 may also model the stress level with respect to time and identify any trends in the stress level of the user. Based on these changes and trends in stress level, various alerts or warnings may be provided to the user or to a third-party (e.g., the user's physician).'; a third-party is the stakeholder), comprising:
a plurality of environmental sensors (sensors 112, 212 of sensor array 110, 210) configured to monitor the person under care, and to provide a detected data set representing behaviors of the person under care in an environment (Fig. 1, 2A & 2B; [0025]. 'Sensor network 100 enables the collecting, processing, analyzing, sharing, visualizing, displaying, archiving, and searching of sensor data. The data collected by sensors 112 in sensor array 110 may be processed, analyzed, and stored using the computational and data storage resources of sensor network 100'; [00271, 'As used herein, a sensor 112 in a sensor array 110 is described with respect to a subject. Therefore, a sensor 112 may be personal or remote with respect to the subject. Personal sensors receive stimuli that are from or related to the subject. Personal sensors may include, for example, sensors that are affixed to or carried by the subject (e.g., a heart-rate monitor, an input by the subject into a smart phone), sensors that are proximate to the subject (e.g., a thermometer in the room where the subject is located)...Remote sensors may include, for example, environmental sensors (e.g., weather balloons, stock market ticker), network data feeds (e.g., news feeds), or sensors that are otherwise related to external information'; [0043], 'one or more sensors 212 may measure a variety of things, including physiological, psychological, behavioral, and environmental stimulus. Physiological stimulus may include, for example, physical aspects of a person (e.g., stretch, motion of the person, and position of appendages); metabolic aspects of a person (e.g., glucose level, oxygen level, osmolality), biochemical aspects of a person (e.g., enzymes, hormones, neurotransmitters, cytokines), and other aspects of a person related to physical health, disease, and homeostasis. Psychological stimulus may include, for example, emotion, mood, feeling, anxiety, stress, depression, and other psychological or mental states of a person. Behavioral stimulus may include, for example, behavior related a person (e.g., working, socializing, arguing, drinking, resting, driving), behavior related to a group (e.g., marches, protests, mob behavior), and other aspects related to behavior. Environmental stimulus may include, for example, physical aspects of the environment (e.g., light, motion, temperature, magnetic fields, gravity, humidity, vibration, pressure, electrical fields, sound, GPS location), environmental molecules (e.g., toxins, nutrients, pheromones), environmental conditions (e.g., pollen count, weather), other external condition (e.g., traffic conditions, stock market information, news feeds), and other aspects of the environment.');
each of the behaviors is represented by a multi-dimensional feature set (from sensor data stored in a data store 1740) forming part of a health care profile for the person under care (Fig. 2A, 2B, 10 & 17; [0043], 'one or more sensors 212 may measure a variety of things, including physiological, psychological, behavioral, and environmental stimulus'; [0082], 'As an example and not by way of limitation,
a sensor value may record a measurement taken by a sensor 1.12. A test parameter may correspond to a factor that describes a temporal, spatial, and/or environmental feature of a measurement process, and a test value may record the value of the feature when the measurements are taken'; [0165], 'a stress index may be a multidimensional index'; [0221], 'FIG. 10 illustrates an example method 1000 for monitoring stress using a stress profile created by renal Doppler sonography. The method begins at step 1010, where analysis system 180 may access one or more data streams from a plurality of sensors 112. The sensors 112 may comprise one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer'; [0418], 'A data store 1740 may store any suitable information, and the contents of a data store 1740 may be organized in any suitable manner. As an example and not by way or limitation, the contents of a data store 1740 may be stored as a dimensional, flat, hierarchical, network, object-oriented, relational, XML, or other suitable database or a combination or two or more of these'; a stress profile created from behaviors using the multi-dimensional sensor data is one example of a health profile);
a care processing system (analysis system 180; Fig. 1; [0029], 'Analysis system 180 may monitor, store, and analyze one or more data streams from sensor array 110') comprising:
a transceiver (communication interface 1610) configured to receive the detected data set (Fig. 1 shows the analysis system 180 having connections to send and receive data between the local analysis system 120 and remote analysis system 150; Fig. 16 shows the components, including the communication interface 1610; [0033], 'Connections 116 may connect sensor array 110, sensors 112, node 114, analysis system 180, local analysis system 120, remote analysis system 150, display system 190. local display system 130, and remote display system 140 to network 160 or to each other...This disclosure contemplates any suitable connections 116. In particular embodiments, one or more connections 116 include one or more wireline (such as, for example, Digital Subscriber Line (DSL) or Data Over Cable Service Interface Specification (DOCSIS)), wireless (such as, for example, Wi-Fi or Worldwide Interoperability for Microwave Access (WiMAX)) or optical (such as, for example, Synchronous Optical Network (SONET) or Synchronous Digital Hierarchy (SDH))
connections. In particular embodiments, one or more connections 116 each include an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portion of the PSTN, a cellular telephone network, another connection 116, or a combination of two or more such connections 116'; [0121], 'Analysis system 180 may be any suitable computing device, such as, for example, computer system 1600.'; [0411], 'communication interface 1610 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system 1600 and one or more other computer systems 1600 or one or more networks'; a transceiver is a device that can both send and receive data),
a non-transitory computer-readable storage medium (storage 1606) configured to store a quiescent data set representing previous quiescent behaviors of the person under care in the environment (Fig. 16 shows storage 1606 for storing sensor data, such as data related to sleeping or exercising [quiescent data]; [0227], 'The data sets may include one or more data sets from one or more sensors 112 in sensor array 110. As an example and not by way of limitation, a first data set may be collected from the person when the person is in a first mood (e.g., stressed), and a second data set may be collect from the person when the person is in a second mood (e.g., unstressed). As another example and not by way of limitation, a first data set may be collected from the person when the person is engaged in a first activity (e.g., sleeping), and a second data set may be collected from the person when the person is engaged in a second activity (e.g., exercising)'; [0409], 'storage 1606 includes mass storage for data'; [0413], 'Herein, reference to a computer-readable storage medium encompasses one or more non-transitory, tangible computer-readable storage media possessing structure'; [0414], 'This disclosure contemplates one or more computer-readable storage media implementing any suitable storage. In particular embodiments, a computer-readable storage medium implements one or more portions of processor 1602 (such as, for example, one or more internal registers or caches), one or more portions of memory 1604, one or more portions of storage 1606, or a combination of these, where appropriate'), and
at least one hardware processing unit (processor 1602) to determine a wellness or care event for the person under care by comparing the detected data set and the quiescent data set (Fig. 1 & 16; [0084], 'As yet another example and not by way of limitation, sensor data may relate to a sensor subject because it may help detect or predict the occurrence of one or more problems or events concerning
the sensor subject'; [0089], 'As an example and not by way of limitation, a data stream could be recorded as one or more data sets based on the specific subject, sensor, time period, event, or other criteria.'; [0121], 'Analysis system 180 may be any suitable computing device, such as, for example, computer system 1600.'; [0157], 'sensor network 100 may analyze physiological, psychological, behavioral, or environmental data streams to diagnose and monitor stress in a user. Sensor array 110 may intermittently or continuously transmit physiological, psychological, behavioral, or environmental data streams to analysis system 180. Analysis system 180 may analyze one or more of these data streams to determine the stress index of the user. Any combination of two or more sensors may be used to generate a stress index value. These stress index values may be improved and calibrated by monitoring a person over time to determine when a change in physiological state is due to stress or due to a non-stress related event (such as, for example, exercise, dehydration).'; [0158], 'analysis system 180 may access physiological, psychological, behavioral, or environmental data previously generated to compare it to current physiological, psychological, behavioral, or environmental data'; [0407], 'processor 1602 includes hardware for executing instructions, such as those making up a computer program').
Claim 1 has been amended to recite:
wherein,when the wellness or care event is determined to have occurred has occurred, the care processing system is configured to change a configuration of at least one of state of the plurality of environmental sensors that is already active to a different active configuration
Jain does not expressly disclose, but Kristal teaches wherein, when the wellness or care event is determined to have occurred has occurred, the care processing system is configured to change a configuration of at least one of state of the plurality of environmental sensors that is already active to a different active configuration. (e.g. a changing the state of a plurality of environmental sensors alters a monitoring focus of the environmental sensors (Fig. 1 shows data streams from sensors; [0069], 'For example, with the another computing device having a plurality of sensors each of which can be utilized to determine a particular characteristic (e.g., if a human is present), can be caused (by the suitable computing device) to prevent either or both of data acquisition from a particular sensor (e.g., within the plurality of sensors), or data calculations (or manipulations) for data acquired from the particular sensor (based on the recommendation). In some specific cases, the computing device can be (or form part of) a patient monitoring system'; [00126], 'This would include both user-enabled systems such as a mobile phone, but would also include user-independent systems such as embedded/implanted and stand-alone sensors that need to act (e.g., send a signal) on an event occurring, and would need to decide which of multiple models to base this decision'; [00128], 'Sensors can range for items such as detectors (e.g., motion, fire, smoke), but also include devices such as hearing aids that clarify signals for direct consumption, or implantable medical monitors'; [00129], 'some non-limiting examples of the disclosure can improve the management of (meta) sensor arrays (e.g., by automating the management of meta-sensor arrays). For example, some non-limiting examples can determine whether the potential addition/activation of different sensors can add information or conversely, where deletion or inactivation of a sensor either reduces resource usage at minimal/acceptable cost or improves signal fidelity by reducing, for example, false negatives. Some non-limiting examples enable this type of adaption to occur either statically (e.g., the system adds/subtracts a sensor and produces a different series of models), or adaptively (e.g., in the presence of signal A, adapts and uses configuration of sensors A', but in the presence of signal B, adapts and uses confirmation of sensors B'; adding or inactivating sensors as needed).'; if an event occurs, the sensors can adapt by changing their configuration, thereby increasing the signal fidelity). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Jain with the changing of the sensors' state of Kristal for the purpose of altering a monitoring focus, thereby improving signal fidelity by reducing false negatives (Kristal; [00129]).
Claim 3 Jain discloses the system of claim 1, wherein the detected data set is from a temperature sensor, acoustic sensor or motion detector ([0043], 'one or more sensors 212 may measure a variety of things, including physiological, psychological, behavioral, and environmental stimulus. Physiological stimulus may include, for example, physical aspects of a person (e.g., stretch, motion of the person, and position of appendages): metabolic aspects of a person (e.g., glucose level, oxygen level, osmolality), biochemical aspects of a person (e.g., enzymes, hormones, neurotransmitters, cytokines), and other aspects of a person related to physical health, disease, and homeostasis. Psychological stimulus may include, for example, emotion, mood, feeling, anxiety, stress, depression, and other psychological or mental states of a person. Behavioral stimulus may include, for example, behavior related a person (e.g., working, socializing, arguing, drinking, resting, driving), behavior related to a group (e.g., marches, protests, mob behavior), and other aspects related to behavior. Environmental stimulus may include, for example, physical aspects of the environment (e.g., light, motion, temperature, magnetic fields, gravity, humidity, vibration, pressure, electrical fields, sound, GPS location), environmental molecules (e.g., toxins, nutrients, pheromones), environmental conditions (e.g., pollen count, weather), other external condition (e.g., traffic conditions, stock market information, news feeds), and other aspects of the environment.').
Claim 4 Jain discloses the system of claim 1, wherein the detected data set represents an aggregate data from the plurality of sensors (112, 212; Fig. 1, 2A & 28; [0078], 'a sensor 112 may be a data feed. A data feed may be a computing system that receives and aggregates physiological, psychological, behavioral, or environmental data from one or more sources and transmits one or more data streams based on the aggregated data'; [0092], 'the components of sensor network 100 may utilize a data aggregation system to process one or more data streams for use by analysis system 180 or display system 190').
Claim 6, Jain discloses the system of claim 1, wherein the detected data set is from a breathing sensor, or heart-rate sensor ([0027], 'Personal sensors may include, for example, sensors that are affixed to or carried by the subject (e.g., a heart-rate monitor').
Claim 7 Jain discloses the system of claim 6, wherein the quiescent data set represents: sleeping, eating, bathroom use, or exercise (Fig. 16 shows storage 1606 for storing sensor data, such as data related to sleeping or exercising [quiescent data]; [0227), 'The data sets may include one or more data sets from one or more sensors 112 in sensor array 110. As an example and not by way of limitation, a first data set may be collected from the person when the person is in a first mood (e.g., stressed), and a second data set may be collect from the person when the person is in a second mood (e.g., unstressed). As another example and not by way of limitation, a first data set may be collected from the person when the person is engaged in a first activity (e.g., sleeping), and a second data set may be collected from the person when the person is engaged in a second activity (e.g., exercising)').
Claim 8, Jain discloses the system of claim 1 as explained.
Jain fails to explicitly disclose wherein the changing the configuration of at least one of the plurality of environmental sensors alters a monitoring focus of the environmental sensors. Kristal is in the field of data models for patient monitoring (Title and Abstract) and teaches wherein a changing the state of a plurality of environmental sensors alters a monitoring focus of the environmental sensors (Fig. 1 shows data streams from sensors; [0069], 'For example, with the another computing device having a plurality of sensors each of which can be utilized to determine a particular characteristic (e.g., if a human is present), can be caused (by the suitable computing device) to prevent either or both of data acquisition from a particular sensor (e.g., within the plurality of sensors), or data calculations (or manipulations) for data acquired from the particular sensor (based on the recommendation). In some specific cases, the computing device can be (or form part of) a patient monitoring system'; [00126], 'This would include both user-enabled systems such as a mobile phone, but would also include user-independent systems such as embedded/implanted and stand-alone sensors that need to act (e.g., send a signal) on an event occurring, and would need to decide which of multiple models to base this decision'; [00128], 'Sensors can range for items such as detectors (e.g., motion, fire, smoke), but also include devices such as hearing aids that clarify signals for direct consumption, or implantable medical monitors'; [00129], 'some non-limiting examples of the disclosure can improve the management of (meta) sensor arrays (e.g., by automating the management of meta-sensor arrays). For example, some non-limiting examples can determine whether the potential addition/activation of different sensors can add information or conversely, where deletion or inactivation of a sensor either reduces resource usage at minimal/acceptable cost or improves signal fidelity by reducing, for example, false negatives. Some non-limiting examples enable this type of adaption to occur either statically (e.g., the system adds/subtracts a sensor and produces a different series of models), or adaptively (e.g., in the presence of signal A, adapts and uses configuration of sensors A', but in the presence of signal B, adapts and uses confirmation of sensors B'; adding or inactivating sensors as needed).'; if an event occurs, the sensors can adapt by changing their configuration, thereby increasing the signal fidelity). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Jain with the changing of the sensors' state of Kristal for the purpose of altering a monitoring focus, thereby improving signal fidelity by reducing false negatives (Kristal; [00129]).
Claim 9, claim 22 Jain and Kristal in combination disclose the system of claim 1. Jain fails to explicitly disclose wherein the changing the configuration of at least one of the plurality of environmental sensors increases the fidelity or granularity of the environmental sensors. Kristal is in the field of data models for patient monitoring (Title and Abstract) and teaches wherein changing configuration increases the fidelity or granularity of the environmental sensors (Fig. 1 shows data streams from sensors; [0069], 'For example, with the another computing device having a plurality of sensors each of which can be utilized to determine a particular characteristic (e.g., if a human is present), can be caused (by the suitable computing device) to prevent either or both of data acquisition from a particular sensor (e.g., within the plurality of sensors), or data calculations (or manipulations) for data acquired from the particular sensor (based on the recommendation). In some specific cases, the computing device can be (or form part of) a patient monitoring system'; [00126], 'This would include both user-enabled systems such as a mobile phone, but would also include user-independent systems such as embedded/implanted and stand-alone sensors that need to act (e.g., send a signal) on an event occurring, and would need to decide which of multiple models to base this decision'; [00128], 'Sensors can range for items such as detectors (e.g., motion, fire, smoke), but also include devices such as hearing aids that clarify signals for direct consumption, or implantable medical monitors'; [00129], 'some non-limiting examples of the disclosure can improve the management of (meta) sensor arrays (e.g., by automating the management of meta-sensor arrays). For example, some non-limiting examples can determine whether the potential addition/activation of different sensors can add information or conversely, where deletion or inactivation of a sensor either reduces resource usage at minimal/acceptable cost or improves signal fidelity by reducing, for example, false negatives. Some non-limiting examples enable this type of adaption to occur either statically (e.g., the system adds/subtracts a sensor and produces a different series of models), or adaptively (e.g., in the presence of signal A, adapts and uses configuration of sensors A', but in the presence of signal B, adapts and uses confirmation of sensors B'; adding or inactivating sensors as needed).'; if an event occurs, the sensors can adapt by changing their configuration, thereby increasing the signal fidelity). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Jain with the changing of the sensors' state of Kristal for the purpose of altering a monitoring focus with regards to signal fidelity, thereby improving signal fidelity by reducing false negatives (Kristal: [00129]).
Claim 10 Jain discloses a system (sensor network 100) to deploy a pattern representing a health state of a person under care by a stakeholder (Fig. 1; (0147), 'As an example and not by way of limitation, analysis system 180 could continuously monitor a subject's blood pressure over time to determine whether the subject's hypertension is improving. Such monitoring may be used to identify trends and to generate alerts or predictions regarding possible health states'; (0157), 'sensor network 100 may analyze physiological, psychological, behavioral, or environmental data streams to diagnose and monitor stress in a user. Sensor array 110 may intermittently or continuously transmit physiological, psychological, behavioral, or environmental data streams to analysis system 180. Analysis system 180 may analyze one or more of these data streams to determine the stress index of the user. Any combination of two or
more sensors may be used to generate a stress index value. These stress index values may be improved and calibrated by monitoring a person over time to determine when a change in physiological state is due to stress or due to a non-stress related event (such as, for example, exercise, dehydration).'; [0158), 'Analysis system 180 may also model the stress level with respect to time and identify any trends in the stress level of the user. Based on these changes and trends in stress level, various alerts or warnings may be provided to the user or to a third-party (e.g., the user's physician).'; a third-party is the stakeholder), comprising:
a plurality of environmental sensors (sensors 112,212 of sensor array 110,210) configured to monitor the person under care, and to provide a detected data set representing behaviors of the person under care in an environment (Fig. 1, 2A & 2B; [0025], 'Sensor network 100 enables the collecting, processing, analyzing, sharing, visualizing, displaying, archiving, and searching of sensor data. The data collected by sensors 112 in sensor array 110 may be processed, analyzed, and stored using the computational and data storage resources of sensor network 100'; [0027), 'As used herein, a sensor 112 in a sensor array 110 is described with respect to a subject. Therefore, a sensor 112 may be personal or remote with respect to the subject. Personal sensors receive stimuli that are from or related to the subject. Personal sensors may include, for example, sensors that are affixed to or carried by the subject e.g., a heart-rate monitor, an input by the subject into a smart phone), sensors that are proximate to the subject (e.g., a thermometer in the room where the subject Is located)...Remote sensors may include, for example, environmental sensors (e.g., weather balloons, stock market ticker), network data feeds (e.g., news feeds), or sensors that are otherwise related to external information'; [0043], 'one or more sensors 212 may measure a variety of things, including physiological, psychological, behavioral, and environmental stimulus. Physiological stimulus may include, for example, physical aspects of a person (e.g., stretch, motion of the person, and position of appendages); metabolic aspects of a person (e.g., glucose level, oxygen level, osmolality), biochemical aspects of a person (e.g., enzymes, hormones, neurotransmitters, cytokines), and other aspects of a person related to physical health, disease, and homeostasis. Psychological stimulus may include, for example, emotion, mood, feeling, anxiety, stress, depression, and other psychological or mental states of a person. Behavioral stimulus may include, for example, behavior related a person (e.g., working, socializing, arguing, drinking, resting, driving), behavior related to a group (e.g., marches, protests, mob behavior), and other aspects related to behavior. Environmental stimulus may include, for example, physical aspects of the environment {e.g., light, motion, temperature, magnetic fields, gravity, humidity, vibration, pressure, electrical fields, sound, GPS location), environmental molecules (e.g., toxins, nutrients, pheromones), environmental conditions (e.g., pollen count, weather), other external condition (e.g., traffic conditions, stock market information, news feeds), and other aspects of the environment.');
each of the behaviors represented by a multi-dimensional feature set (from sensor data stored in a data store 1740) forming part of a health care profile for the person under care (Fig. 2A, 2B, 10 & 17; [0043), 'one or more sensors 212 may measure a variety of things, including physiological, psychological, behavioral, and environmental stimulus'; [0082), 'As an example and not by way of limitation,
a sensor value may record a measurement taken by a sensor 112. A test parameter may correspond to a factor that describes a temporal, spatial, and/or environmental feature of a measurement process, and a test value may record the value of the feature when the measurements are taken'; [0165], 'a stress index may be a multidimensional index'; [0221], 'FIG. 10 illustrates an example method 1000 for monitoring stress using a stress profile created by renal Doppler sonography. The method begins at step 1010, where analysis system 180 may access one or more data streams from a plurality of sensors 112. The sensors 112 may comprise one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer'; [0418], 'A data store 1740 may store any suitable information, and the contents of a data store 1740 may be organized in any suitable manner. As an example and not by way or limitation, the contents of a data store 1740 may be stored as a dimensional, flat, hierarchical, network, object-oriented, relational, XML, or other suitable database or a combination or two or more of these'; a stress profile created from behaviors using the multi-dimensional sensor data is one example of a health profile);
a care processing system (analysis system 180; Fig. 1; [0029], 'Analysis system 180 may monitor, store, and analyze one or more data streams from sensor array 110') comprising:
a transceiver (communication interface 1610) configured to receive the detected data set (Fig. 1 shows the analysis system 180 having connections to send and receive data between the local analysis system 120 and remote analysis system 150; Fig. 16 shows the components, including the communication interface 1610; (0033), 'Connections 116 may connect sensor array 110, sensors 112, node 114, analysis system 180, local analysis system 120, remote analysis system 150, display system 190, local display system 130, and remote display system 140 to network 160 or to each other...This disclosure contemplates any suitable connections 116. In particular embodiments, one or more connections 116 include one or more wireline (such as, for example, Digital Subscriber Line (DSL) or Data Over Cable Service Interface Specification (DOCSIS)), wireless (such as, for example, Wi-Fi or Worldwide Interoperability for Microwave Access (WiMAX)) or optical (such as, for example, Synchronous Optical Network (SONET) or Synchronous Digital Hierarchy (SDH)) connections. In particular embodiments, one or more connections 116 each include an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portion of the PSTN, a cellular telephone network, another connection 116, or a combination of two or more such connections 116'; [0121), 'Analysis system 180 may be any suitable computing device, such as, for example, computer system 1600.'; [0411], 'communication interface 1610 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system 1600 and one or more other computer systems 1600 or one or more networks'; a transceiver is a device that can both send and receive data),
at least one hardware processing unit (processor 1602) to determine a variation in the detected data set Indicating a transition state between a first pattern and a second pattern within the health state representing a wellness and care state of the person under care (Fig. 1 & 16; [0084], 'As yet another example and not by way of limitation, sensor data may relate to a sensor subject because ii may help detect or predict the occurrence of one or more problems or events concerning the sensor subject'; [0089], 'As an example and not by way of limitation, a data stream could be recorded as one or more data sets based on the specific subject, sensor, time period, event, or other criteria.'; [0121], 'Analysis system 180 may be any suitable computing device, such as, for example, computer system 1600.'; [0157], 'sensor network 100 may analyze physiological, psychological, behavioral, or environmental data streams to diagnose and monitor stress in a user. Sensor array 110 may intermittently or continuously transmit physiological, psychological, behavioral, or environmental data
streams to analysis system 180. Analysis system 180 may analyze one or more of these data streams to determine the stress index of the user. Any combination of two or more sensors may be used to generate a stress index value. These stress index values may be improved and calibrated by monitoring a person over time to determine when a change in physiological state is due to stress or due to a non-stress related event (such as, for example, exercise, dehydration).'; [0158], 'analysis system 180 may access physiological, psychological, behavioral, or environmental data previously generated to compare it to current physiological, psychological, behavioral, or environmental data'; [0407], 'processor 1602 includes hardware for executing instructions, such as those making up a computer program'; an event may include a transition between a first state pattern [such as non-stress] to a second state pattern [such as stress]).
Jain fails to explicitly disclose the care processing system is configured to, responsive to determining the variation in the detected data set indicates the transition state, change a sensor configuration of the plurality of environmental sensors that is already active to a different active configuration to adjust for the transition state.
Kristal is in the field of data models for patient monitoring and teaches a care processing system (one of computing devices 102, 104) is configured to change a sensor configuration of a plurality of environmental sensors to adjust for a transition state (Fig. 1 shows data streams from sensors; [0068], 'the computing devices 102, 104 can include a processor device, memory, communication systems, a display, inputs (e.g., a mouse, a keyboard, touch screen or the like, to provide a user input, other sensors, such as physiological sensors, anatomical sensors, etc., communication systems, power sources'; [0069], 'For example, with the another computing device having a plurality of sensors each of which can be utilized to determine a particular characteristic (e.g., if a human is present), can be caused (by the suitable computing device) to prevent either or both of data acquisition from a particular sensor (e.g., within the plurality of sensors), or data calculations (or manipulations) for data acquired from the particular sensor (based on the recommendation). In some specific cases, the computing device can be (or form part of) a patient monitoring system'; [00126], 'This would include both user-enabled systems such as a mobile phone, but would also include user-independent systems such as embedded/implanted and stand-alone sensors that need to act (e.g., send a signal) on an event occurring, and would need to decide which of multiple models to base this decision'; [00128], 'Sensors can range for items such as detectors (e.g., motion, fire, smoke), but also include devices such as hearing aids that clarify signals for direct consumption, or implantable medical monitors'; [00129], 'some non-limiting examples of the disclosure can improve the management of (meta) sensor arrays (e.g., by automating the management of meta-sensor arrays). For example, some non-limiting examples can determine whether the potential addition/activation of different sensors can add information or conversely, where deletion or inactivation of a sensor either reduces resource usage at minimal/acceptable cost or improves signal fidelity by reducing, for example, false negatives. Some non-limiting examples enable this type of adaption to occur either statically (e.g., the system adds/subtracts a sensor and produces a different series of models), or adaptively (e.g., in the presence of signal A, adapts and uses configuration of sensors A', but in the presence of signal B, adapts and uses confirmation of sensors B'; adding or inactivating sensors as needed).'; if an event [transition state] occurs, the sensors can adapt by changing their configuration, thereby increasing the signal fidelity). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Jain with the changing of a sensor configuration of Kristal for the purpose of altering a monitoring focus with regards to signal fidelity, thereby improving signal fidelity by reducing false negatives (Kristal: [00129]).
Claim 12 Jain and Kristal in combination disclose the system of claim 10, wherein the detected data set is from a temperature sensor, acoustic sensor or motion detector ([0043], 'one or more sensors 212 may measure a variety of things, including physiological, psychological, behavioral, and environmental stimulus. Physiological stimulus may include, for example, .physical aspects of a person (e.g.,
stretch, motion of the person, and position of appendages); metabolic aspects of a person (e.g., glucose level, oxygen level, osmolality), biochemical aspects of a person (e.g., enzymes, hormones, neurotransmitters, cytokines), and other aspects of a person related to physical health, disease, and homeostasis. Psychological stimulus may include, for example, emotion, mood, feeling, anxiety, stress, depression, and other psychological or mental states of a person. Behavioral stimulus may include, for example, behavior related a person (e.g., working, socializing, arguing, drinking, resting, driving), behavior related to a group (e.g., marches, protests, mob behavior), and other aspects related to behavior. Environmental stimulus may include, for example, physical aspects of the environment (e.g., light, motion, temperature, magnetic fields, gravity, humidity, vibration, pressure, electrical fields, sound, GPS location), environmental molecules (e.g., toxins, nutrients, pheromones), environmental conditions (e.g., pollen count, weather), other external condition (e.g., traffic conditions, stock market information, news feeds), and other aspects of the environment.').
Claim 13 Jain and Kristal in combination disclose the system of claim 10, wherein the detected data set represents an aggregate data from the plurality of sensors (112, 212; Fig. 1, 2A & 2B; [0078], 'a sensor 112 may be a data feed. A data feed may be a computing system that receives and aggregates physiological, psychological, behavioral, or environmental data from one or more sources and transmits one or more data streams based on the aggregated data'; [0092], 'the components of sensor network 100 may utilize a data aggregation system to process one or more data streams for use by analysis system 180 or display system 190').
Claim 15 Jain and Kristal in combination disclose the system of claim 10, wherein the detected data set is from a breathing sensor, or heart-rate sensor ([0027], 'Personal sensors may include, for example, sensors that are affixed to or carried by the subject (e.g., a heart-rate monitor').
Claim 16 Jain discloses the system of claim 10, wherein the quiescent data set represents: sleeping, eating, bathroom use, or exercise (Fig. 16 shows storage 1606 for storing sensor data, such as data related to sleeping or exercising [quiescent
data]; [0227], 'The data sets may include one or more data sets from one or more sensors 112 in sensor array 110. As an example and not by way of limitation, a first data set may be collected from the person when the person is In a first mood (e.g., stressed), and a second data set maybe collect from the person when the person is in a second mood (e.g., unstressed). As another example and not by way of limitation, a first data set may be collected from the person when the person is engaged in a first activity (e.g., sleeping), and a second data set may be collected from the person when the person is engaged in a second activity (e.g., exercising)').
Claim 17 Jain and Kristal in combination disclose the system of claim 110.
Jain fails to explicitly disclose wherein the changing the state of the plurality of environmental sensors alters a monitoring focus of the environmental sensors.
Kristal is in the field of data models for patient monitoring (Title and Abstract) and teaches wherein a changing the state of a plurality of environmental sensors alters a monitoring focus of the environmental sensors (Fig. 1 shows data streams from sensors; [0069], 'For example, with the another computing device having a plurality of sensors each of which can be utilized to determine a particular characteristic (e.g., if a human is present), can be caused (by the suitable computing device) to prevent either or both of data acquisition from a particular sensor (e.g., within the plurality of sensors), or data calculations (or manipulations) for data acquired from the particular sensor (based on the recommendation). In some specific cases, the computing device can be (or form part of) a patient monitoring system'; [00126], 'This would include both user-enabled systems such as a mobile phone, but would also include user-independent systems such as embedded/implanted and stand-alone sensors that need to act (e.g., send a signal) on an event occurring, and would need to decide which of multiple models to base this decision'; [00128], 'Sensors can range for items such as detectors (e.g., motion, fire, smoke), but also include devices such as hearing aids that clarify signals for direct consumption, or implantable medical monitors'; [00129], 'some non-limiting examples of the disclosure can improve the management of (meta) sensor arrays (e.g., by automating the management of meta-sensor arrays). For example, some non-limiting examples can determine whether the potential addition/activation of different sensors can add information or conversely, where deletion or inactivation of a sensor either reduces resource usage at minimal/acceptable cost or improves signal fidelity by reducing, for example, false negatives. Some non-limiting examples enable this type of adaption to occur either statically (e.g., the system adds/subtracts a sensor and produces a different series of models), or adaptively (e.g., in the presence of signal A, adapts and uses configuration of sensors A', but in the presence of signal B, adapts and uses confirmation of sensors B'; adding or inactivating sensors as needed).'; if an event occurs, the sensors can adapt by changing their configuration, thereby increasing the signal fidelity). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Jain with the changing of the sensors' state of Kristal for the purpose of altering a monitoring focus, thereby improving signal fidelity by reducing false negatives (Kristal ; [00129]).
Claim 18 Jain and Kristal in combination disclose the system of claim 17. Jain fails to explicitly disclose wherein the monitoring focus increases the fidelity or granularity of the environmental sensors. Kristal is in the field of data models for patient monitoring (Title and Abstract) and teaches wherein the monitoring focus increases the fidelity or granularity of the environmental sensors (Fig. 1 shows data streams from sensors; [0069], 'For example, with the another computing device having a plurality of sensors each of which can be utilized to determine a particular characteristic (e.g., if a human is present), can be caused (by the suitable computing device) to prevent either or both of data acquisition from a particular sensor (e.g., within the plurality of sensors), or data calculations (or manipulations) for data acquired from the particular sensor (based on the recommendation). In some specific cases, the computing device can be (or form part of) a patient monitoring system'; [00126], 'This would include both user-enabled systems such as a mobile phone, but would also include user-Independent systems such as embedded/implanted and stand-alone sensors that need to act (e.g., send a signal) on an event occurring, and would need to decide which of multiple models to base this decision'; [00128], 'Sensors can range for items such as detectors (e.g., motion, fire, smoke), but also include devices such as hearing aids that clarify signals for direct consumption, or implantable medical monitors'; [00129], 'some non-limiting examples of the disclosure can improve the management of (meta) sensor arrays (e.g., by automating the management of meta-sensor arrays). For example, some non-limiting examples can determine whether the potential addition/activation of different sensors can add information or conversely, where deletion or inactivation of a sensor either reduces resource usage at minimal/acceptable cost or improves signal fidelity by reducing, for example, false negatives. Some non-limiting examples enable this type of adaption to occur either statically (e.g., the system adds/subtracts a sensor and produces a different series of models), or adaptively (e.g., in the presence of signal A, adapts and uses configuration of sensors A', but in the presence of signal B, adapts and uses confirmation of sensors B'; adding or inactivating sensors as needed).'; if an event occurs, the sensors can adapt by changing their configuration, thereby increasing the signal fidelity). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Jain with the changing of the sensors' state of Kristal for the purpose of altering a monitoring focus with regards to signal fidelity, thereby improving signal fidelity by reducing false negatives (Kristal; [00129]).
Claims 23-26- The limitations of claims 23-26 are addressed by the rejections of claims 1, 3-4, 6-10, 12-13, 15-18, and are incorporate herein.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al ( US 2012/0289791) in view of Kristal et al ( WO 2021/046477), and in further view of Sivertsen et al ( US 20240099607 A1)
Claim 21 Jain and Kristal do not expressly disclose, but Sivertsen teaches a system wherein changing the state of the at least one of the environmental sensors changes a rate of sampling data from the at least one of the environmental sensors. (par. 121- Different strategies may be selected for the radar pulses both with respect to the shape of the signal (narrow base band pulses, frequency sweeps, frequency jumps) and with respect to how often pulses are emitted (sampling rate).) At the time of filing it would have been obvious to one of ordinary skill in the art to further modify the combination of Jain and Kristal with the teaching of Sivertsen with the motivation of improving the performance and quality of data collection for a desired parameter. (par. 121)
Response to Arguments
Applicant's arguments filed 2/19/26 have been fully considered but they are not persuasive.
(A) Applicant argues the newly added claim limitations and the new claims.
In response, the examiner has provided additional citations to address the newly added claim features.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SOORI-ARACHI et al ( US 20250364140 A1)
Unnikrishnan et (US 20220139556 A1)
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/Rachel L. Porter/Primary Examiner, Art Unit 3684
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