DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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) 67-70, 72, 83, 91, 103-104, 153, and 157-159 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pathak (US 20170213145 A1 – previously cited) in view of Sivertsen (US 20200289033 A1 - previously cited) in view of Huang (US 20180279915 A1 – previously cited) in view of Bunn (US 20100049095 A1 - previously cited) in view of Orellano (US 20180333083 A1 – previously cited), in view of Burwinkel (US 20180233018 A1 – previously cited).
In regards to claim 67 Pathak teaches a method for assessing fall risk, comprising:
receiving sensor data associated with an environment in which a resident is located ([0018-0019]);
analyzing, via a trained machine learning model, the sensor data to ([0018-0019] [0023]);
and generate a gait score based on the identified gait information ([0025]).
generating a fall inference associated with the resident based on the analyzed sensor data, wherein the fall inference is indicative of an occurrence of a fall event or a likelihood that the fall event will occur ([0047]);
and transmitting a signal in response to generating the fall inference, wherein the signal, when received, causes generation of an alert on a display device, wherein the alert identifies that the resident has fallen or that the resident has a high likelihood of falling ([0048]).
Pathak fails to teach a method wherein the sensor data includes electromagnetic energy transmission information and electromagnetic energy receipt information; determining, based on differences between the electromagnetic energy transmission information and the electromagnetic energy receipt information, movement information associated with the resident; and identifying gait information associated with the resident, the gait information including pathway information of the resident; and identifying, for the resident, an expected pathway of the resident determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident and generate, based on the deviation from the expected pathway of the resident, a gait score.
Pathak teaches a depth sensor ([0019] Pathak). Sivertsen teaches a depth sensor wherein the sensor data includes electromagnetic energy transmission information and electromagnetic energy receipt information; determining, based on differences between the electromagnetic energy transmission information and the electromagnetic energy receipt information movement information ([0119-0120] UWB sensors are a type of depth sensor). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to make the depth sensor of Pathak a UWB sensor. Doing so would merely be a simple substitution of one depth sensor for another to obtain predictable results
Pathak in view of Sivertsen fails to teach identifying gait information associated with the resident, the gait information including pathway information of the resident; and identifying, for the resident, an expected pathway of the resident determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident and generate, based on the deviation from the expected pathway of the resident, a gait score.
Huang teaches using sensors to determine gait information including pathway variation indicative of unstable gait ([0029]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Pathak in view of Sivertsen to determine pathway variation as a form of gait information like the processor of Huang and use that gait information to determine a gait score. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of having a gait score that is indicative of an amount of deviation from an expected pathway present in the pathway information.
Pathak in view of Sivertsen in view of Huang fails to teach how the pathway variation is determined. Pathak in view of Sivertsen in view of Huang also fails to teach identifying, via the trained machine learning algorithm, based on accumulated historical pathway data of the resident, an expected pathway of the resident determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident. Bunn teaches identifying, for the resident, based on accumulated historical pathway data of the resident, an expected pathway of the resident to determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident ([0035] “FIG. 4 illustrates examples of two movements of a subject which would normally be detected by a stagger algorithm to deviate from expected "Personal" or "Standard" movement. "Personal" means movement that has been previously observed and recorded in databases for this subject and is accepted as a base level of mobility for this subject.”, [0037 and 0046]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Pathak in view of Sivertsen in view of Huang to determine pathway variation using the method of Bunn. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of determining pathway variation of a resident in order to more comprehensively determine if the user is about to fall.
Pathak in view of Sivertsen in view of Huang in view of Bunn fails to teach identifying, via the trained machine learning algorithm based on the movement information associated with the resident, portions of the movement data associated with the resident that do not affect the fall risk identify, via the trained machine learning algorithm based on the movement information associated with the resident and not on the portions of the movement data associated with the resident that do not affect the fall risk, gait information associated with the resident, the gait information including pathway information of the resident. Orellano teaches canceling extraneous extremity motion from data used for fall detection and fall prediction ([0024] “techniques known to those of ordinary skill in the fields of signal processing, system identification, state estimation, spectral analysis, and filtering, such as Kalman or Complementary filtering, may be used to mitigate the accuracy degradation in fall detection and fall prediction by actively filtering or canceling extraneous extremity motion from the kinematic sensor 101 data used for fall detection and fall prediction”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of modified Pathak to filter out extraneous data not associated with fall detection/prediction like the method of Orellano. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of only determining fall using data relevant to fall risk in order to make the fall determination/prediction more accurate.
Modified Pathak fails to teach a method, further comprising causing an operation of one or more electronic devices to be modified in response to generating the fall inference, wherein modification of the operation of the one or more electronic devices is selected to decrease the likelihood that the fall event will occur. Burwinkle teaches a method comprising causing an operation of one or more electronic devices to be modified in response to generating the fall inference, wherein modification of the operation of the one or more electronic devices is selected to decrease the likelihood that the fall event will occur ([0045] teaches a speaker to provide auditory guidance, [0087] teaches an illumination device). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pathak to trigger a light to illuminate a dark path or speaker to sound in response to a predicted fall in order to prevent the fall. Doing so would merely be combining prior art elements according to known methods to yield the predicable result of aiding a resident in order to prevent a fall.
In regards to claim 68, modified Pathak teaches the method of claim 67. Modified Pathak fails to teach a method wherein the fall inference further comprises a location where the fall is likely to occur. Burwinkel teaches a fall risk assessment device that includes sensors (beacons) in each room and wherein a fall inference that is determined further comprises a location where the fall is likely to occur by identifying locations of hazards ([0048]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date to modify the method of modified Pathak to include data collected from beacons like Burwinkle to determine a location of a hazard that can cause a fall. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of taking into account hazards around the location of the user when determining fall risk.
In regards to claim 69 modified Pathak teaches the method of claim 67, wherein the fall inference is indicative that a fall event will occur, and wherein the fall inference further comprises additional information selected from the group consisting of a time window when the fall is likely to occur, a time of day when the fall is likely to occur, and an activity associated with when the fall is likely to occur (Pathak [0025]).
In regards to claim 70 modified Pathak teaches the method of claim 67, further comprising calibrating the sensor data based on the analyzed sensor data, wherein calibrating the sensor data comprises receiving sensor data associated with the resident and other individuals in the environment (Pathak [0004] and [0021]).
In regards to claim 72 modified Pathak teaches the method of claim 67, further comprising receiving physiological data associated with the resident, wherein analyzing the sensor data comprises analyzing the sensor data and the physiological data, and wherein generating the fall inference is based on the analyzed sensor data and the analyzed physiological data (Pathak [0033]).
In regards to claim 83 modified Pathak teaches the method of claim 67, wherein the fall inference comprises a fall inference score, the method further comprising determining a risk stratification level associated with the fall inference score, wherein the alert on the display device comprises the risk stratification level (Pathak [0047-0048]).
In regards to claim 91 modified Pathak teaches the method of claim 67, further comprising accessing health data associated with the resident, wherein the health data comprises a diagnosis associated with the resident, and wherein generating the fall inference comprises adjusting an interpretation of sensor data based on the diagnosis (Pathak [0022] and [0047-0048]).
In regards to claim 103 modified Pathak teaches the method of claim 67, wherein the sensor data includes data associated with the environment itself, wherein analyzing the sensor data includes determining environmental information associated with the environment, and wherein generating the fall inference based on the analyzed sensor data is based on the environmental information (Pathak [0019]).
In regards to claim 104 modified Pathak teaches the method of claim 103, wherein the data associated with the environment itself includes an ambient temperature of the environment, and a light level of the environment (Pathak [0019]).
In regards to claim 153, modified Pathak teaches the method of claim 67. Modified Pathak fails to teach a method wherein analyzing the sensor data includes identifying information associated with at least one static object in the environment and determining an expected pathway of the resident within the environment, wherein generating the fall inference is based at least in part on the information associated with the at least one static object and the expected pathway of the resident. Burwinkel teaches a method of, wherein analyzing sensor data includes identifying information associated with at least one static object in the environment and determining an expected pathway of the resident within the environment, wherein generating a fall inference is based at least in part on the information associated with the at least one static object and the expected pathway of the resident ([0042] and [0046-0048]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date to modify the method of modified Pathak to include the static object determination of Burwinkel and determine a fall inference based on if the user will come into contact with the object and fall. Doing so would allow the user to avoid a hazard (Burwinkel [0046]).
In regards to claim 157 modified Pathak teaches the method of claim 154, wherein the one or more electronic devices include at least one selected from the group consisting of: an illumination device configured to be actuated to aid in reducing the likelihood that the fall event will occur (Burwinkel [0087]) or a speaker configured to provide auditory guidance to aid in reducing the likelihood that the fall event will occur (Burwinkel [0045]).
In regards to claim 158 modified Pathak teaches the method of claim 67, further comprising: receiving, second sensor data associated with activity of the resident (Pathak [0019] depth sensor senses daily activity types by the user and daily activity duration);
generating, by aggregating via the trained machine learning algorithm, the sensor data associated with the environment in which the resident is located and the second sensor data associated with the activity of the resident, the movement information (Pathak [0025] “the machine learning module 200 may generate a model based on optimization of different types of fall risk scoring models, including but not limited to algorithms that analyze processed and/or raw data from each wearable sensor separately, or in any combination with processed data from the depth sensor, data from user inputs, or data from structured tests”, Model analyzes sensor data in combination with other sensor data). Modified Pathak fails to teach second sensor data associated with activity of the resident coming from a wearable sensor. Orellano teaches a wearable kinematic sensor that collects data representative of Activities of Daily Living ([0054] “In a first stage, at step 401, patient-wearable sensor apparatus 100 is configured to continuously capture, sample, filter, and analyze baseline kinematic fall detection data representative of: patient Activities of Daily Living”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the wearable sensor of modified Pathak to also detect activities of daily living like the wearable sensor of Orellano. Doing so would merely be choosing from a finite number of identified, predictable solutions (measuring activities with a depth sensor, measuring activities with a wearable sensor, etc.), with a reasonable expectation of success.
In regards to claim 159 modified Pathak teaches the method of claim 67, further comprising:
receiving, from a medical records system, electronic medical record information associated with the resident (Pathak [0022] “The external source 160 may also include historical health data of a user (e.g., a user's electronic medical records, or EMRs) from various health record sources (e.g., hospital records, records at the user's family doctors, or manually inputted data related to the user's health by the user's caretakers)”);
analyzing, via the trained machine learning model, the electronic medical record information (Pathak 0023] “The fall risk module 110 processes user inputs, data from the wearable sensor 140, data from the depth sensor 150, data from the external source 160, and/or data from a local database, and trains a machine learning model to determine a fall risk score for the user”, the model takes historical data as an input);
identifying, via the trained machine learning model based on the analysis of the electronic medical record information, data contained in the electronic medical record information that may impact the fall risk (Pathak [0022] “The historical health data could also include information about any conditions the user has that could affect the risk of falls for that individual, such as Parkinson's disease, factors in the user's medical history that might affect the user's balance (e.g., ear infections or other ear conditions, posture or back issues such as scoliosis, problems with legs, feet, or knees such as a prior knee injury, among other data”).
and wherein the analyzing the movement information associated with the resident includes analyzing the data contained in the electronic medical record information (Pathak [0021] “[0023] The fall risk module 110 processes user inputs, data from the wearable sensor 140, data from the depth sensor 150, data from the external source 160, and/or data from a local database, and trains a machine learning model to determine a fall risk score for the user”, the model takes historical data as an input)
Claim(s) 67, 71, and 156 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pathak (US 20170213145 A1 – previously cited) in view of Sivertsen (US 20200289033 A1 - previously cited) in view of Huang (US 20180279915 A1 – previously cited) in view of Bunn (US 20100049095 A1 - previously cited) in view of Orellano (US 20180333083 A1) as applied to claim 67, in view of Wolf (US 20200390368 A1 – previously cited).
In regards to claim 67 Pathak teaches a method for assessing fall risk, comprising:
receiving sensor data associated with an environment in which a resident is located ([0018-0019]);
analyzing, via a trained machine learning model, the sensor data to ([0018-0019] [0023]);
and generate a gait score based on the identified gait information ([0025]).
generating a fall inference associated with the resident based on the analyzed sensor data, wherein the fall inference is indicative of an occurrence of a fall event or a likelihood that the fall event will occur ([0047]);
and transmitting a signal in response to generating the fall inference, wherein the signal, when received, causes generation of an alert on a display device, wherein the alert identifies that the resident has fallen or that the resident has a high likelihood of falling ([0048]).
Pathak fails to teach a method wherein the sensor data includes electromagnetic energy transmission information and electromagnetic energy receipt information; determining, based on differences between the electromagnetic energy transmission information and the electromagnetic energy receipt information, movement information associated with the resident; and identifying gait information associated with the resident, the gait information including pathway information of the resident; and identifying, for the resident, an expected pathway of the resident determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident and generate, based on the deviation from the expected pathway of the resident, a gait score.
Pathak teaches a depth sensor ([0019] Pathak). Sivertsen teaches a depth sensor wherein the sensor data includes electromagnetic energy transmission information and electromagnetic energy receipt information; determining, based on differences between the electromagnetic energy transmission information and the electromagnetic energy receipt information movement information ([0119-0120] UWB sensors are a type of depth sensor). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to make the depth sensor of Pathak a UWB sensor. Doing so would merely be a simple substitution of one depth sensor for another to obtain predictable results
Pathak in view of Sivertsen fails to teach identifying gait information associated with the resident, the gait information including pathway information of the resident; and identifying, for the resident, an expected pathway of the resident determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident and generate, based on the deviation from the expected pathway of the resident, a gait score.
Huang teaches using sensors to determine gait information including pathway variation indicative of unstable gait ([0029]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Pathak in view of Sivertsen to determine pathway variation as a form of gait information like the processor of Huang and use that gait information to determine a gait score. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of having a gait score that is indicative of an amount of deviation from an expected pathway present in the pathway information.
Pathak in view of Sivertsen in view of Huang fails to teach how the pathway variation is determined. Pathak in view of Sivertsen in view of Huang also fails to teach identifying, via the trained machine learning algorithm, based on accumulated historical pathway data of the resident, an expected pathway of the resident determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident. Bunn teaches identifying, for the resident, based on accumulated historical pathway data of the resident, an expected pathway of the resident to determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident ([0035] “FIG. 4 illustrates examples of two movements of a subject which would normally be detected by a stagger algorithm to deviate from expected "Personal" or "Standard" movement. "Personal" means movement that has been previously observed and recorded in databases for this subject and is accepted as a base level of mobility for this subject.”, [0037 and 0046]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Pathak in view of Sivertsen in view of Huang to determine pathway variation using the method of Bunn. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of determining pathway variation of a resident in order to more comprehensively determine if the user is about to fall.
Pathak in view of Sivertsen in view of Huang in view of Bunn fails to teach identifying, via the trained machine learning algorithm based on the movement information associated with the resident, portions of the movement data associated with the resident that do not affect the fall risk identify, via the trained machine learning algorithm based on the movement information associated with the resident and not on the portions of the movement data associated with the resident that do not affect the fall risk, gait information associated with the resident, the gait information including pathway information of the resident. Orellano teaches canceling extraneous extremity motion from data used for fall detection and fall prediction ([0024] “techniques known to those of ordinary skill in the fields of signal processing, system identification, state estimation, spectral analysis, and filtering, such as Kalman or Complementary filtering, may be used to mitigate the accuracy degradation in fall detection and fall prediction by actively filtering or canceling extraneous extremity motion from the kinematic sensor 101 data used for fall detection and fall prediction”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of modified Pathak to filter out extraneous data not associated with fall detection/prediction like the method of Orellano. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of only determining fall using data relevant to fall risk in order to make the fall determination/prediction more accurate.
Modified Pathak fails to teach a method, further comprising causing an operation of one or more electronic devices to be modified in response to generating the fall inference, wherein modification of the operation of the one or more electronic devices is selected to decrease the likelihood that the fall event will occur. Wolf teaches a method comprising causing a smart sole in a shoe to configured to adjust a gait of the resident ([0046 and 0052]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Pathak to trigger the actuation of the smart sole of Wolf when a fall is predicted in order to improve the subject’s gait and help prevent a fall. Doing so would merely be combining prior art elements according to known methods to yield the predicable result of correcting a resident’s gait to prevent a fall. MPEP 2423.I
In regards to claim 71 modified Pathak teaches the method of claim 67, wherein transmitting a signal further comprises actuating an actuatable element of an assistance device associated with the resident, wherein actuation of the actuatable element of the assistance device is configured to affect a gait or position of the resident to reduce a likelihood of falling (Wolf [0046 and 0052]).
In regards to claim 156 modified Pathak in view of Wolf teaches the method of claim 67, wherein the one or more electronic devices include a smart sole in a shoe to configured to adjust a gait of the resident to aid in preventing the resident from falling ([0046 and 0052]).
Claim(s) 67 and 155 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pathak (US 20170213145 A1 – previously cited) in view of Sivertsen (US 20200289033 A1 - previously cited) in view of Huang (US 20180279915 A1 – previously cited) in view of Bunn (US 20100049095 A1 - previously cited) in view of Orellano (US 20180333083 A1 – previously cited) in view of Aguglia (US 20210378895 A1 – previously cited)
In regards to claim 67 Pathak teaches a method for assessing fall risk, comprising:
receiving sensor data associated with an environment in which a resident is located ([0018-0019]);
analyzing, via a trained machine learning model, the sensor data to ([0018-0019] [0023]);
and generate a gait score based on the identified gait information ([0025]).
generating a fall inference associated with the resident based on the analyzed sensor data, wherein the fall inference is indicative of an occurrence of a fall event or a likelihood that the fall event will occur ([0047]);
and transmitting a signal in response to generating the fall inference, wherein the signal, when received, causes generation of an alert on a display device, wherein the alert identifies that the resident has fallen or that the resident has a high likelihood of falling ([0048]).
Pathak fails to teach a method wherein the sensor data includes electromagnetic energy transmission information and electromagnetic energy receipt information; determining, based on differences between the electromagnetic energy transmission information and the electromagnetic energy receipt information, movement information associated with the resident; and identifying gait information associated with the resident, the gait information including pathway information of the resident; and identifying, for the resident, an expected pathway of the resident determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident and generate, based on the deviation from the expected pathway of the resident, a gait score.
Pathak teaches a depth sensor ([0019] Pathak). Sivertsen teaches a depth sensor wherein the sensor data includes electromagnetic energy transmission information and electromagnetic energy receipt information; determining, based on differences between the electromagnetic energy transmission information and the electromagnetic energy receipt information movement information ([0119-0120] UWB sensors are a type of depth sensor). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to make the depth sensor of Pathak a UWB sensor. Doing so would merely be a simple substitution of one depth sensor for another to obtain predictable results
Pathak in view of Sivertsen fails to teach identifying gait information associated with the resident, the gait information including pathway information of the resident; and identifying, for the resident, an expected pathway of the resident determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident and generate, based on the deviation from the expected pathway of the resident, a gait score.
Huang teaches using sensors to determine gait information including pathway variation indicative of unstable gait ([0029]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Pathak in view of Sivertsen to determine pathway variation as a form of gait information like the processor of Huang and use that gait information to determine a gait score. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of having a gait score that is indicative of an amount of deviation from an expected pathway present in the pathway information.
Pathak in view of Sivertsen in view of Huang fails to teach how the pathway variation is determined. Pathak in view of Sivertsen in view of Huang also fails to teach identifying, via the trained machine learning algorithm, based on accumulated historical pathway data of the resident, an expected pathway of the resident determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident. Bunn teaches identifying, for the resident, based on accumulated historical pathway data of the resident, an expected pathway of the resident to determine, based on the pathway information of the resident and the expected pathway of the resident, a deviation from the expected pathway of the resident ([0035] “FIG. 4 illustrates examples of two movements of a subject which would normally be detected by a stagger algorithm to deviate from expected "Personal" or "Standard" movement. "Personal" means movement that has been previously observed and recorded in databases for this subject and is accepted as a base level of mobility for this subject.”, [0037 and 0046]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Pathak in view of Sivertsen in view of Huang to determine pathway variation using the method of Bunn. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of determining pathway variation of a resident in order to more comprehensively determine if the user is about to fall.
Pathak in view of Sivertsen in view of Huang in view of Bunn fails to teach identifying, via the trained machine learning algorithm based on the movement information associated with the resident, portions of the movement data associated with the resident that do not affect the fall risk identify, via the trained machine learning algorithm based on the movement information associated with the resident and not on the portions of the movement data associated with the resident that do not affect the fall risk, gait information associated with the resident, the gait information including pathway information of the resident. Orellano teaches canceling extraneous extremity motion from data used for fall detection and fall prediction ([0024] “techniques known to those of ordinary skill in the fields of signal processing, system identification, state estimation, spectral analysis, and filtering, such as Kalman or Complementary filtering, may be used to mitigate the accuracy degradation in fall detection and fall prediction by actively filtering or canceling extraneous extremity motion from the kinematic sensor 101 data used for fall detection and fall prediction”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of modified Pathak to filter out extraneous data not associated with fall detection/prediction like the method of Orellano. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of only determining fall using data relevant to fall risk in order to make the fall determination/prediction more accurate.
Modified Pathak fails to teach a method, further comprising causing an operation of one or more electronic devices to be modified in response to generating the fall inference, wherein modification of the operation of the one or more electronic devices is selected to decrease the likelihood that the fall event will occur. Aguglia teaches a method comprising causing an operation of a configurable bed apparatus, to be modified in response to generating the fall inference, wherein modification of the operation of the configurable bed apparatus is selected to decrease the likelihood that the fall event will occur in order to timely effect raising the bed rails to a height necessary to restrain the patient in bed ([0031]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of modified Pathak to trigger the actuation of the configurable bed apparatus of Aguglia when a fall is predicted in order to timely effect raising the rails to a height necessary to restrain the patient in bed. Doing so would merely be combining prior art elements according to known methods to yield the predicable result of preventing a fall when a user is in bed. MPEP 2423.I
In regards to claim 155 modified Pathak teaches the method of claim 64, wherein the one or more electronic devices include a configurable bed apparatus, the configurable bed apparatus including first and second moveable guard rails configured to aid in preventing the resident from falling out of the configurable bed apparatus (Aguglia [0031]) , wherein modification of the configurable bed apparatus includes moving the first movable guard rail into a position to prevent the resident from falling out of the configurable bed apparatus and moving the second movable guard rail to avoid entrapping the resident in the configurable bed apparatus ([0032]).
Response to Arguments
Applicant’s arguments, see remarks, filed 07/08/2026, with respect to the 35 U.S.C 101 rejection of claim 67 and its dependent claims have been fully considered and are persuasive. The 35 U.S.C 101 rejection of claim 67 and its dependent claims has been withdrawn.
Applicant’s arguments, see remarks, filed 07/08/2026, with respect to the 35 U.S.C 103 rejections of claim 67 and its dependent claims have been fully considered and are persuasive. The 35 U.S.C 103 rejections of claim 67 and its dependent claims has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Pathak (US 20170213145 A1 – previously cited) in view of Sivertsen (US 20200289033 A1 - previously cited) in view of Huang (US 20180279915 A1 – previously cited) in view of Bunn (US 20100049095 A1 - previously cited) in view of Orellano (US 20180333083 A1 – previously cited), in view of Burwinkel (US 20180233018 A1 – previously cited). The examiner contends that Bunn teaches the “accumulated historical pathway data of the resident” of claim 67 as the baseline pathway data of Bunn is “movement that has been previously observed and recorded in databases for this subject” (Bunn [0035]).
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 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 LUCY EPPERT whose telephone number is (571)270-0818. The examiner can normally be reached M-F 7:30-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson can be reached at (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUCY EPPERT/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791
Prosecution Insights