DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-10, 12-16, and 18 are rejected under 35 U.S.C. 101 because the claimed invention
is directed to abstract idea without significantly more.
Step 1
Claims 1-4, 6-10, 12-16, and 18 are within the four statutory categories. However, as will be
shown below, claims 1-4, 6-10, 12-16, and 18 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 7, and 13 are representative of the inventive concept and recite:
Claim 1
A computer-implemented method, comprising:
monitoring, by one or more processors, sensor data associated with a home environment, wherein the sensor data is captured by one or more interior or exterior sensors, including one or more of: cameras, motion detectors, microphones, accelerometers, gyroscopes, humidity sensors, air flow sensors, water sensors, water flow sensors, lightning detectors, infrared sensors, room occupancy sensors, door sensors, window sensors, sensors associated with home appliances, or sensors associated with plumbing fixtures;
analyzing, by the one or more processors, the sensor data associated with the home environment over a first period of time in order to identify a health condition associated with a resident of the home environment, wherein analyzing the sensor data associated with the home environment over the first period of time in order to identify the health condition associated with the resident of the home environment includes applying a trained machine learning model to the sensor data associated with the home environment over the first period of time in order to identify the health condition associated with the resident of the home environment;
identifying, by the one or more processors, one or more mitigation techniques for alleviating the health condition associated with the resident of the home environment, wherein the one or more mitigation techniques include one or more of: adjusting one or more settings of the home environment, clearing an area of the home environment, or making one or more repairs in the home environment;
providing, by the one or more processors, an indication of the one or more mitigation techniques for alleviating the health condition associated with the resident of the home environment, via a user interface;
and analyzing, by the one or more processors, the sensor data associated with the home environment over a second period of time, subsequent to providing the indication of the one or more mitigation techniques for alleviating the health condition associated with the resident of the home environment via the user interface, in order to determine whether any of the one or more mitigation techniques for alleviating the health condition associated with the resident of the home environment were performed over the second period of time.
*Claims 7 and 13 recite similar limitations as claim 1 but for a computer system and non-transitory computer readable medium, respectively.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the highlighted components can practically be performed by the human mind (in this case, the process of identifying, analyzing, providing, determining ) or using pen and paper. Other than reciting generic computer components/functions such as “computer”, “processor”, “non-transitory computer-readable instructions”, “user interface”, and “sensor”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the “computer”, “processor”, “user interface”, and “sensor” language, the claim encompasses the user, several times, manually collecting data, determining the health status based on the data, and providing mitigation techniques. If a claim limitation, under its broadest reasonable interpretation, cover performance of the
limitation in the mind but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. The recitation of generic computer components/functions of providing also covers behavioral or interactions between people (i.e. the computer and user interface), and/or
managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions — in this case a person is able to physically follow the steps to gather and process data to provide actionable health status information), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 2-4, 6, 8-10, 12, 14-16, and 18 recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 3, reciting particular aspects on adding additional types of data used for data analysis (in the mind), can be found, but for recitation of generic computer components/functions).
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional limitations:
Claim 1 recites computer, processor , user interface, and “monitoring, by one or more processors, sensor data associated with a home environment, wherein the sensor data is captured by one or more interior or exterior sensors, including one or more of: cameras, motion detectors, microphones, accelerometers, gyroscopes, humidity sensors, air flow sensors, water sensors, water flow sensors, lightning detectors, infrared sensors, room occupancy sensors, door sensors, window sensors, sensors associated with home appliances, or sensors associated with plumbing fixtures”, “trained machine learning model”
In particular, the additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of obtaining and determining data are recited as being performed by a computer, processor, user interface, sensor (general computer), and a trained machine learning model. A computer system (or computer) is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The machine learning model is used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of “monitoring, by one or more processors, sensor data associated with a home environment, wherein the sensor data is captured by one or more interior or exterior sensors, including one or more of: cameras, motion detectors, microphones, accelerometers, gyroscopes, humidity sensors, air flow sensors, water sensors, water flow sensors, lightning detectors, infrared sensors, room occupancy sensors, door sensors, window sensors, sensors associated with home appliances, or sensors associated with plumbing fixtures”.
Dependent claims 6, 12, and 18 recite machine learning model. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). Determining data and generating outputs “applying” a machine learning model , but providing nothing more than mere instructions to implement an abstract idea on a generic computer. The machine learning model is used to generally apply the abstract idea without limiting how it functions.
Dependent claims 2-4, 8-10, and 14-16 do not include any additional elements beyond those already recited in dependent claims 1, 7, and 13, and dependent claims 6, 12, and 18, hence also do no integrate the aforementioned abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or machine learning model or improves any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 7, and 13 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: A computer in claim 1; amount to no more than mere instructions to apply and exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which amount to elements that have been recognized as well- understood, routine, and conventional activity in particular fields as demonstrated by the recitation of :
Camera(claims 1, 7, and 13), which is a device that captures still or moving images by recording light, either on film or digitally (Para 0052, Keane(US 20190188337 A1) discloses: “The one or more image capturing devices 46 may be capable and/or configured to provide oblique and/or vertical images, and may include, but are not limited to, conventional cameras, digital cameras, digital sensors, charge-coupled devices, thermographic cameras and/or the like.”) in a manner that would be well-understood, routine, and conventional.
Microphone(claims 1, 7, and 13), which is a device that converts sound waves into electrical signals (Para 0125, Brannmark(US 20180317037 A1) discloses: “In order to enable measurements of sound produced by the audio equipment under consideration, any conventional microphone unit(s) or similar audio recording equipment may be connected to the computer system.”) in a manner that would be well-understood, routine, and conventional.
Accelerometer(claims 1, 7, and 13), which is a device that measure acceleration (Para 0022, Saley(US 20190249746 A1) discloses: “Additionally, conventional accelerometers, such as those found in cellular phones, can also measure the angle at which the accelerometer is moving.”) in a manner that would be well-understood, routine, and conventional.
Gyroscope(claims 1, 7, and 13), which is a device that maintains its orientation and can measure orientation or velocity(Para 0050, Rahman(US 20170172493 A1) discloses: “The set of mobile sensors may also include a conventional gyroscope that outputs a data stream which includes the current 3D angular velocity of the user whose body is gyroscope is attached to, or who is carrying the gyroscope.”) in a manner that would be well-understood, routine, and conventional.
Dependent claims 2-4, 8-10, and 14-16 do not include any additional elements beyond those already recited in dependent claims 1, 7, and 13, and dependent claims 6, 12, and 18. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 7, and 13 hence do not amount to “significantly more” than the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation.
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.
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, 4, 6, 7-8, 10, 12-14, 16, and 18 are rejected under 35 U.S.C. 103 is being unpatentable over Anthapur (US20220230746A1) in view of Harris(US20240156401A1), Dettinger(US11551579B2), and Stefanski (US20190122522A1).
Claim 1
Anthapur discloses:
A computer-implemented method, comprising:
monitoring (Para 0009, Anthapur discloses: “system is provided to monitor a resident of a dwelling…” [MONITORING]) by one or more processors, sensor data (Figure 1A, 1200-1, Para 0041, Anthapur discloses: “sensor system 1200-1 of FIG. 1A, The example sensor system 1200-1 and the example sensor data processing and communication system 1200-2 are portions of an example monitoring system…” [EXAMPLE SENSOR DATA TYPES IN 1200-1]) associated with a home environment (Para 0010, Anthapur discloses: “…a system to monitor a resident of a dwelling [DWELLING IS HOME ENVIRONMENT].”), wherein the sensor data is captured by one or more interior or exterior sensors, including one or more of: cameras(Para 0122, Anthapur discloses camera), motion detectors(Para 0042, Anthapur discloses motion sensors in bathroom and living room), microphones(Para 0122, Anthapur discloses microphone), accelerometers(Para 0122, Anthapur discloses accelerometer), gyroscopes(Para 0122, Anthapur discloses gyroscope), humidity sensors, air flow sensors, water sensors, water flow sensors, lightning detectors, infrared sensors(Para 0098, Anthapur discloses temperature or heat activated sensors), room occupancy sensors(Para 0042, Anthapur discloses: “vital signs sensor 112 is located on a bed to sense occupancy “[OCCUPANCY SENSOR]), door sensors(Figure 3, #216, Anthapur discloses a doorway sensor), window sensors, sensors associated with home appliances, or sensors associated with plumbing fixtures; analyzing, by one or more processors, the sensor data associated with the home environment over a first period of time(Para 0066, Anthapur discloses: “Operation 592 stores sensor data over a time interval [FIRST PERIOD OF TIME] that is long enough to identify changes…”) in order to identify a health condition associated with a resident of the home environment, wherein analyzing the sensor data associated with the home environment(Para 0043, Anthapur discloses: “sensor data processing and communication system 1200-2 includes one or more computing machines that use machine learning techniques to evaluate[ANALYZE] information received from one or more of the example sensors 110-124[SENSOR DATA] to determine the safety status[CAN BE HEALTH CONDITION] of a resident 98 within the dwelling 95[RESIDENT OF HOME ENVIRONMENT].”) over the first period of time in order to identify the health condition associated with the resident of the home environment includes applying a trained machine learning model to the sensor data associated with the home environment over the first period of time in order to identify the health condition associated with the resident of the home environment(Figure 5A, 540, Para 0059, Anthapur discloses: “The trained model 540 [TRAINED MACHINE LEARNING MODEL] uses as input sensor measurement values [SENSOR DATA] S202-S224 representing measurements of a resident's activity detected at one or more of sensors 202-224 to infer resident activity at one or more of ATPs 302-310 indicated using output values ATP302-ATP310. FIG. 5B and FIG. 5C, illustrate example processes used to send alerts and adjust parameters for determining alerts that are triggered using the trained ML model 540, based resident activity detecting using the sensors. FIG. 5D illustrates example process used to send periodic updates of resident's health status [IDENTIFIED HEALTH CONDITIONS] based resident activity detecting using the sensors.”);
identifying, by the one or more processors, wherein the one or more mitigation techniques include one or more of: ;
providing, by the one or more processors,
and analyzing, by the one or more processors, the sensor data associated with the home environment(Para 0043, Anthapur discloses: “sensor data processing and communication system 1200-2 includes one or more computing machines that use machine learning techniques to evaluate[ANALYZE] information received from one or more of the example sensors 110-124[SENSOR DATA] to determine the safety status[CAN BE HEALTH CONDITION] of a resident 98 within the dwelling 95[RESIDENT OF HOME ENVIRONMENT].”) over a second period of time(Para 0066, Anthapur discloses: “Operation 592 stores sensor data over a time interval [SECOND PERIOD OF TIME] that is long enough to identify changes…”, subsequent to providing the indication of the environment(Para 0010, Anthapur discloses: “…a system to monitor a resident of a dwelling [RESIDENT IN HOME ENVIRONMENT].”)
Anthapur does not explicitly disclose:
One or more mitigation techniques for alleviating the health condition
Indication
User interface
Second period of time
adjusting one or more settings of the home environment
Harris discloses:
one or more mitigation techniques for alleviating the health condition(Figure 9, Para 0067 Harris discloses: “the actions[MITIGATION TECHNIQUES] may be tailored to let patients, who are known to be sensitive, recommend that they stay indoors when there is poor air quality[ALLEVIATING HEALTH CONDITION]. The messaging can be adjusted to be even stronger when the air quality if poor and there is deviation from baseline of the patient specific sensor parameters.…”)
indication(Figure 9, Harris discloses messages texts to patient with indicator)
user interface(Figure 25-34, Harris discloses various version of user interfaces used to communicate with a home resident/patient)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the resident monitoring system of Anthapur to add one or more mitigation techniques for alleviating the health condition, indication, and user interface, as taught by Harris. One of ordinary skill would have been so motivated to provide a means to provide mitigation techniques for alleviating a health condition based on home sensor information and providing an indication/alert via user interface to communicate this to a home resident, but in this case, for a monitoring system for assessing control of a disease state (Para 0011, Harris discloses: “a substantial gap remains in the ability to reliably measure and monitor asthmatic status outside of healthcare facilities. It would thus be desirable to have a system and method for monitoring asthma status outside the hospital, which would empower families and healthcare providers to more effectively manage the disease.”).
Harris does not explicitly disclose:
second period of time
adjusting one or more settings of the home environment
Dettinger discloses:
second period of time(Col. 5, Line 8, Dettinger discloses: “… the mobile device may receive information from various sensors that monitor patient activity (e.g., heart rate, weight, blood pressure). The application can record the information to the care plan and relay information to the care platform. As a result, the physician can monitor the patient's adherence to the care plan.” [HEALTH CONDITION OF PATIENT BEING CONITUOUSLY MONITERED/ANALYZED FOR ADHERENCE TO CARE PLAN CAN BE SIMILAR TO A RESIDENT OF A HOME BEING MONITORED FOR ADHERENCE TO A CARE PLAN]) .
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the resident monitoring system of Anthapur to add analysis over a second period of time, as taught by Dettinger. One of ordinary skill would have been so motivated to provide a means to determine adherence to the provided care plan after it was provided to a patient or resident of a home, but in this case, for administering a care plan for a patient and collecting feedback (Col. 1, Line 56, Dettinger discloses: “…providers may employ call centers to contact the patient periodically and determine whether the patient is following the care plan. However, such an approach is costly and further exposes a patient's information to more individuals than necessary”).
Dettinger does not explicitly disclose:
adjusting one or more settings of the home environment
Stefanski discloses:
adjusting one or more settings of the home environment(Para 0044, Stefanski discloses: “the resident can view a current setpoint temperature for a device and adjust it, using a computer…”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the resident monitoring system of Anthapur to add adjusting one or more settings of the home environment, as taught by Stefanski. One of ordinary skill would have been so motivated to provide a means to provide mitigation techniques which include adjusting settings for the home environment to prevent or minimize health conditions, but in this case, for thoughtful monitoring of the elderly in a home environment(Para 0001, Stefanski discloses: “Such an arrangement can tend to lead to check-ins with the parent only when convenient to the adult child, check-ins occurring at random times, and/or significant time gaps occurring between check-ins due to when the adult child remembers to check in. Further, such an arrangement may not allow the adult child to truly understand if the parent is in need of help. For instance, the parent may be well enough to conduct a phone call, but his or her behavior off the phone may be erratic, possibly indicative of a physical or mental condition.”).
Claim 2
Anthapur discloses:
The computer-implemented method of claim 1, further comprising: analyzing, by the one or more processors, the sensor data associated with the home environment over a third period of time(Para 0066, Anthapur discloses: “Operation 592 stores sensor data over a time interval [THIRD PERIOD OF TIME] that is long enough to identify changes…”), subsequent to determining that at least one of the one or more mitigation techniques for alleviating the health condition associated with the resident of the home environment(Para 0010, Anthapur discloses: “…a system to monitor a resident of a dwelling [RESIDENT IN HOME ENVIRONMENT].”) were performed, in order to identify a change (Para 0066, Anthapur discloses: “Operation 592 stores sensor data over a time interval [FIRST, SECOND, THIRD PERIOD OF TIME] that is long enough to identify changes [CHANGE IDENTIFICATION] in path traversal activity that may be indicative of decline in health or wellbeing, such as over a period of 90 days”) in the health condition associated with the resident of the home environment.
Anthapur does not explicitly disclose:
One or more mitigation techniques for alleviating the health condition
Harris discloses:
one or more mitigation techniques for alleviating the health condition(Figure 9, Para 0067 Harris discloses: “the actions[MITIGATION TECHNIQUES] may be tailored to let patients, who are known to be sensitive, recommend that they stay indoors when there is poor air quality[ALLEVIATING HEALTH CONDITION]. The messaging can be adjusted to be even stronger when the air quality if poor and there is deviation from baseline of the patient specific sensor parameters.…”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the resident monitoring system of Anthapur to add one or more mitigation techniques for alleviating the health condition, as taught by Harris. One of ordinary skill would have been so motivated to provide a means to provide mitigation techniques for alleviating a health condition based on home sensor information, but in this case, for a monitoring system for assessing control of a disease state (Para 0011, Harris discloses: “a substantial gap remains in the ability to reliably measure and monitor asthmatic status outside of healthcare facilities. It would thus be desirable to have a system and method for monitoring asthma status outside the hospital, which would empower families and healthcare providers to more effectively manage the disease.”).
Claim 3:
Anthapur discloses:
The computer-implemented method of claim 1, wherein monitoring the sensor data associated with the home environment(Figure 1A, 1200-1, Para 0041, Anthapur discloses: “sensor system 1200-1 of FIG. 1A[IN HOME ENVIRONMENT], The example sensor system 1200-1 and the example sensor data processing and communication system 1200-2 are portions of an example monitoring system…” [SENSOR DATA MONITORING SYSTEM]) and analyzing the sensor data associated with the home environment(Para 0043, Anthapur discloses: “sensor data processing and communication system 1200-2 includes one or more computing machines that use machine learning techniques to evaluate[ANALYZE] information received from one or more of the example sensors 110-124[SENSOR DATA] to determine the safety status[CAN BE HEALTH CONDITION] of a resident 98 within the dwelling 95[RESIDENT OF HOME ENVIRONMENT].”) includes monitoring operational data associated with the home environment and analyzing the operational data associated with the home environment.
Anthapur, Harris, and Dettinger do not explicitly disclose:
monitoring operational data
analyzing operational data
Stefanski discloses:
monitoring(Figure 4, Stefanski discloses monitoring system) operational data associated with the home environment(Para 0056, Stefanski discloses: “collected home data [OPERATIONAL DATA AS DEFINED BY SPECIFICATIONS] 302 includes, for example, power consumption data, occupancy data, HVAC settings and usage data, carbon monoxide levels data, carbon dioxide levels data, volatile organic compounds levels data, sleeping schedule data, cooking schedule data, inside and outside temperature humidity data, television viewership data, inside and outside noise level data...”)
analyzing the operational data associated with the home environment(Para 0007, Stefanski discloses the collection and analysis of data collected from smart home devices (operational data))
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the resident monitoring system of Anthapur to add monitoring and analyzing operational data, as taught by Stefanski. One of ordinary skill would have been so motivated to provide a means to utilize operational data (in addition to other data types) to maximize safety of the resident, in this case, an elderly parent (Para 0001, Stefanski discloses: “Conventionally, this task is accomplished by frequent telephone calls and emails. Such an arrangement can tend to lead to check-ins with the parent only when convenient to the adult child, check-ins occurring at random times, and/or significant time gaps occurring between check-ins due to when the adult child remembers to check in. Further, such an arrangement may not allow the adult child to truly understand if the parent is in need of help. For instance, the parent may be well enough to conduct a phone call, but his or her behavior off the phone may be erratic, possibly indicative of a physical or mental condition.”).
Claim 4
Anthapur discloses:
The computer-implemented method of claim 1, further comprising: generating, by the one or more processors, an alert related to the health condition associated with the resident of the home environment; and sending, by the one or more processors, the alert related to the health condition(Para 0045, Anthapur discloses: “The reports of certain critical events that need attention, such as a fall incident [HEALTH CONDITION], are sent as alert notifications [ALERT] in real time.”) associated with the resident of the home environment to a medical provider or to a provider of emergency services (Para, 0085 Anthapur discloses: “Such diagnoses may be further forwarded to the responsible party, a member of a care circle, for clinical evaluation by medical professional [MEDICAL PROFESSIONAL IS A MEDICAL PROVIDER OR PROVIDER OF EMERGENCY SERVICES] or third-party Applications and third-party users, for example.”)
Claim 6
Anthapur discloses:
The computer-implemented method of claim 1, further comprising: obtaining, by the one or more processors, historical sensor data (Para 0062, Anthapur discloses: “ to determine, based upon historical sensor activity [HISTORICAL SENSOR DATA] information saved at operation 554, whether there exists a pattern of occurrences of non-completions of the one or more ATPs identified in operation 558 within corresponding arrival times identified in operation 560 that suggest the non-completions represent a possible change in resident behavior instead of emergency events…”) associated with historical home environments (Para 0062, Anthapur discloses: “ to determine, based upon historical sensor activity [HISTORICAL SENSOR DATA CAN INDICATE HISTORICAL HOME ENVIRONMENTS] information saved at operation 554, whether there exists a pattern of occurrences of non-completions of the one or more ATPs identified in operation 558 within corresponding arrival times identified in operation 560 that suggest the non-completions represent a possible change in resident behavior instead of emergency events…”) over historical periods of time, and historical health conditions (Para 0066, Anthapur discloses: “Operation 592 stores sensor data over a time interval that is long enough to identify changes in path traversal activity that may be indicative of decline in health or wellbeing [HISTORICAL HEALTH CONDITIONS], such as over a period of 90 days. More specifically, an example operation 594 stores sensor data over a time interval long enough to capture changes in a resident's PTF or in a resident's TAP, determined using trained ML model, that indicate possible decline in health or wellbeing) associated with residents of the historical home environments over the historical periods of time; and training, by the one or more processors, a machine learning model to identify new health conditions associated with residents of new home environments over new periods of time based upon new sensor data associated with the new home environments over the new periods of time, based upon the historical sensor data associated with historical home environments over historical periods of time, and historical health conditions associated with residents of the historical home environments over the historical periods of time, resulting in the trained machine learning model. (Para 0115, Anthapur discloses: “machine learning involved studying existing data, learning from existing data and making predictions about new data…produces a trained model 540 based upon a training data set 1906. A data sampler operation 1908 receives training data 1910, which includes principal user data (e.g., user height, weight, date of birth, user equipment such as wheelchair, dwelling data) 1912, rules data (e.g., go to bed, wake up schedule, dining, and medication schedule) 1914, historic data past activity data long term) 1916, and sensor data (e.g., present activity data) 1918—to produce one or more templates—for example daily living pattern of a resident). These templates are used. to process runtime data 1920 such as sensor measurements taken at a dwelling.”[TRAINED MACHINE LEARNING MODEL WHICH INCORPORATES HISTORICAL DATA TO BE ABLE TO PREDICT FUTURE OUTCOMES])
Claim 7:
See claim 1 analysis as it describes similar components
Claim 8:
See claim 2 analysis as it describes similar components
Claim 9
See claim 3 analyses as it describes similar components
Claim 10:
See claim 4 analysis as it describes similar components
Claim 12:
See claim 6 analysis as it describes similar components
Claim 13:
See claim 1 analysis as it describes similar components
Claim 14:
See claim 2 analysis as it describes similar components
Claim 15
See claim 3 analyses as it describes similar components
Claim 16:
See claim 4 analysis as it describes similar components
Claim 18:
See claim 6 analysis as it describes similar components
Response to Arguments
Regarding 35 U.S.C. 101
(Page 10) Regarding the assertion that the claims do not recite any abstract ideas.
Applicant's arguments filed have been fully considered but they are not persuasive. The claims as presented do not provide sufficient complexity of how the AI if performed in a way that would not be considered a mental process. Applying a machine learning model and training a machine learning model are described in the broadest sense, and can be considered a mental process since they can be done mentally or using pen and paper.
(Page 11) Regarding the assertion that the claims integrate any allegedly recited abstract ideas into a practical application
Applicant's arguments filed have been fully considered but they are not persuasive. The specification provides no description of the improvement required for eligibility under the grounds that the claim must reflect an improvement to the functioning of a computer or to another technology or technical field. Regarding the argument on the applications of “apply it”, the claim indeed fails to recite details on how the “AI” functions, and merely uses generic computer or computer functions to carry out abstract ideas. The machine learning model is used to generally apply the abstract idea without limiting how it functions.
Regarding 35 U.S.C. 103
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gil et al (US20230107712): A system which uses biometrics as feedback for home control monitoring to enhance well-being. Some disclosures made for this invention as similar to that of this application (Specifications, Para 0007).
Barrett et al (US11769598B2): An analytics system for treating, monitoring, and managing rescue events resulting from asthma or other respiratory diseases via input from sensors attached to a medicament device. Some disclosures made for this invention as similar to that of this application (Specifications, Para 0007).
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 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 SHERYL GOPAL PATEL whose telephone number is (703)756-1990. The examiner can normally be reached Monday - Friday 5:30am to 2:30pm PST.
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/S.G.P./Examiner, Art Unit 3685
/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685