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 .
Response to Amendment
In response to amendments, filed December 8, 2025, claims 1-12 and 14-19 have been amended. Claim 13 has been cancelled. No claims have been added. Claims 1-12 and 14-19 are pending.
Response to Arguments
Applicant’s arguments, see Remarks, filed December 8, 2025, with respect to interpretation under 35 USC 112(f), rejections under 35 USC 112(b), and rejections under 35 USC 101 have been fully considered and are persuasive in view of the amendments. The 35 USC 112(f) interpretation, 35 USC 112(b) rejections, and 35 USC 101 rejections have been withdrawn.
Applicant’s arguments with respect to the prior art rejection have been considered but are moot because the new ground of rejection does not rely on the same reference combination applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new ground(s) of rejection is made in view of the combinations of Barton-Sweeney (US 20160331244 A1), Caplin (US 20210353155 A1), Singh (US 20210345884 A1), and Levinson (US 20220330833 A1).
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) 1-6, 8-12, 14-15, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barton-Sweeney (US 20160331244 A1) and in view of Caplin (US 20210353155 A1).
Regarding claim 1, Barton-Sweeney teaches a prediction system ([0021] “A method to determine a physiological function result;” [0022] “A system for determining a health state;” Fig. 1) comprising: first circuitry ([0022] “processing unit”) configured to:
acquire body temperature information indicating a body temperature of a first user and first context information indicating a first context which is a context when the body temperature of the first user is detected ([0022] “a processing unit configured to receive and process signals produced by the temperature monitors and optional ambient environmental monitor, to determine time-dependent parameters of temperature change, and to calculate a core body temperature”);
predict information regarding a body temperature change of a second user in the future, on the basis of (a) a learned model that has learned a relation between the body temperature and the first context ([0028] “accurate physiological function result, such as an estimate of body temperature.” [0055] “The described method uses a predetermined prediction equation to obtain a physiological function result…. The predetermined prediction equation can be modified, as known in the art. The predetermined prediction equation can compare changes in amplitude of the temperature values over time, for example… The predetermined prediction equation can further comprise a personal parameter, wherein the personal parameter comprises age, gender, height, weight, fat percentage, body mass index, lean body mass, body size proportions, menstrual cycle day, chronotype, drug use (e.g. hormonal contraceptive), time zone, or a combination comprising one or more of the foregoing, in the determination of a physiological function result.” [0056] “The physiological function result can be a metabolic function parameter, such as an infection, or the presence of a fever.” [0072] “As used herein the term “population data model” is a mathematical representation of collected data for a large group of individuals. In one embodiment, the population data model may be trained over time as additional subject data is collected and stored. In one embodiment, the data model may be trained using other data such as from either other demographic groups or a historical data that is directed at different demographic groups to prepopulate the data model to provide the desired accuracy level prior to the availability of large amounts of subject data. In one embodiment, the population data model may represent an average value for the group measured. In one embodiment, the population data model may represent a target demographic of individuals, where the collected data is segregated by age.”) and (b) second context information indicating a second context that is a future context of the second user ([0055] “The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.” [0072] “the population data model may be trained over time as additional subject data is collected and stored.”);
acquire prediction information regarding a body temperature change predicted for the second user ([0073] “The comparison of temperature values to data models may include comparing absolute temperature values or a relative change in the temperature values, or other aspects of the temperature values, as described herein. This can include comparisons in relation to their expected values and data models, as well as each other's values and data models.”).
However, Barton-Sweeney fails to disclose measuring body temperature at a facility entrance to determine access based on an adjustable threshold. Caplin teaches a portable or semi-portable kiosk configured as a human body temperature scanning device, connected to a network that is also linked to a health history database and configured to allow or deny access to a building to a user.
Caplin discloses in response to the body temperature of the second user being measured by second circuitry installed at a facility entrance, the second circuitry controlling access to the facility according to whether or not the body temperature exceeds a predetermined threshold ([0016] “ take said individual's wrist temperature; compare said wrist temperature with said individual's medical records, analyze taking into account the current environmental conditions, determine the likelihood of present infectious state of said individual, and access a perimeter barrier mechanism to allow or deny access through said barrier”); and adjust the predetermined threshold on the basis of the prediction information, and control the second circuitry to grant or deny access to the facility for the second user based on a comparison of the measured body temperature to the adjusted threshold ([0050] “the software and connectivity of the kiosk will, with permissions and positive identification, access medical records of a user to create a more accurate baseline for that individual and the software will be configured to adjust the threshold temperature for admittance or denial of admittance based on the surrounding temperature, the individual's baseline temperature, the individual's most recent temperatures, the time of day, and other known factors affecting body temperature.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Barton-Sweeney to include measuring body temperature at a facility entrance to determine access based on an adjustable threshold as disclosed in Caplin to facilitate an efficient, safe, and secure method of admitting healthy persons and denying admittance of sick persons to a particular area where persons will congregate to prevent spread of sickness (Caplin [0022]).
Regarding claim 2, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 1, wherein the first circuitry is further configured to acquire, as the first context information, the first context information indicating a position of the first user when the body temperature is detected, an action state of the first user when the body temperature is detected, a surrounding environment of the first user when the body temperature is detected, a date and time when the body temperature is detected, or an attribute of the first user (Barton-Sweeney: [0055] “ The predetermined prediction equation can further comprise a personal parameter, wherein the personal parameter comprises age, gender, height, weight, fat percentage, body mass index, lean body mass, body size proportions, menstrual cycle day, chronotype, drug use (e.g. hormonal contraceptive), time zone, or a combination comprising one or more of the foregoing, in the determination of a physiological function result. The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.” [0028] “accurate physiological function result, such as an estimate of body temperature.”).
Regarding claim 3, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 1, wherein the first circuitry is further configured to predict information regarding the body temperature change when a scheduled action is performed in the future, on the basis of second context information indicating an action schedule of the second user as the second context information (Caplin: [0016] “have a user place their wrist in a specific area within said slot area wherein an at least one temperature sensor is configured to take a reading of said user's wrist when placed in said specific area;” Barton-Sweeney: [0055] “The predetermined prediction equation can further comprise a personal parameter, wherein the personal parameter comprises age, gender, height, weight, fat percentage, body mass index, lean body mass, body size proportions, menstrual cycle day, chronotype, drug use (e.g. hormonal contraceptive), time zone, or a combination comprising one or more of the foregoing, in the determination of a physiological function result. The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.”).
Regarding claim 4, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 1, wherein the first circuitry is further configured to generate the learned model based on a relation between the body temperature and the first context (Barton-Sweeney: [0055] “The predetermined prediction equation uses as inputs the temperature values and determines a physiological function result ... The predetermined prediction equation is located in a memory, in an embodiment. The predetermined prediction equation can be modified, as known in the art. The predetermined prediction equation can compare changes in amplitude of the temperature values over time, for example. The predetermined prediction equation can further comprise a cyclic rhythmic parameter, such as a circadian rhythm, sleep-wake cycles, activity rhythms, circannual rhythm, circa mensal rhythm, or a combination comprising one or more of the foregoing, in the determination of a physiological function result. The predetermined prediction equation can further comprise masking parameters, entraining agent parameters, or both, in the determination of a physiological function result. Masking parameters or entraining agent parameters can include external factors, environmental, diet, lifestyle, clothing, drug consumption such as contraceptives, estrogen replacement therapy, liquid consumption such as alcohol consumption, meal timing, caloric restriction, sleep habits, light exposure, waking time, climate, time zone, schedule shifts, and temperature findings in diseased states.”).
Regarding claim 5, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 4, wherein the first circuitry is further configured to generate the learned model based on a relation between a body temperature change and a context as to what kind of body temperature change tends to occur in the first user according to the first context (Barton-Sweeney: [0073] “The comparison of temperature values to data models may include comparing absolute temperature values or a relative change in the temperature values, or other aspects of the temperature values, as described herein. This can include comparisons in relation to their expected values and data models, as well as each other's values and data models. In one embodiment, the comparison of temperature values to the data models includes comparing a profile trend of temperature values measured over a plurality of time periods, such as over the course of several days for example, with the data model. The comparison of the profile trend may include profiles such as a rapid increase in BBT followed by a rapid decline in measured values for example, or may include a rapid increase in skin thickness, or heart rate, for example.”).
Regarding claim 6, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 5, wherein the first circuitry is further configured to:
use a set of information indicating the body temperature change and the first context information as learning data and generate, as the learned model, a first prediction model using context information as input and the information indicating the body temperature change as output (Barton-Sweeney: [0055] “The predetermined prediction equation can further comprise a personal parameter, wherein the personal parameter comprises age, gender, height, weight, fat percentage, body mass index, lean body mass, body size proportions, menstrual cycle day, chronotype, drug use (e.g. hormonal contraceptive), time zone, or a combination comprising one or more of the foregoing, in the determination of a physiological function result.” [0028] “accurate physiological function result, such as an estimate of body temperature.”), and
predicts a body temperature change occurring in the second user according to the second context, using the first prediction model and the second context information (Barton-Sweeney: [0055] “The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.” [0028] “accurate physiological function result, such as an estimate of body temperature.”).
Regarding claim 8, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 5, wherein the first circuitry is further configured to:
manage information indicating the relation between the body temperature change and the context learned by the learned model and context information indicating a future context estimated from the relation in association with each other (Barton-Sweeney: [0055] “ The predetermined prediction equation can further comprise a personal parameter, wherein the personal parameter comprises age, gender, height, weight, fat percentage, body mass index, lean body mass, body size proportions, menstrual cycle day, chronotype, drug use (e.g. hormonal contraceptive), time zone, or a combination comprising one or more of the foregoing, in the determination of a physiological function result.” [0028] “accurate physiological function result, such as an estimate of body temperature.”), and
predict information regarding a body temperature change of the second user, on the basis of a relation associated with context information corresponding to the second context information among context information managed by the first circuitry (Barton-Sweeney: [0055] “The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.” [0028] “accurate physiological function result, such as an estimate of body temperature.”).
Regarding claim 9, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 1, wherein in a case where the input of the second context information is received from the second user, the first circuitry predicts information regarding a body temperature change of the second user by using the received second context information and the learned model (Barton-Sweeney: [0055] “ The predetermined prediction equation can further comprise a personal parameter, wherein the personal parameter comprises age, gender, height, weight, fat percentage, body mass index, lean body mass, body size proportions, menstrual cycle day, chronotype, drug use (e.g. hormonal contraceptive), time zone, or a combination comprising one or more of the foregoing, in the determination of a physiological function result… The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.” [0028] “accurate physiological function result, such as an estimate of body temperature.”).
Regarding claim 10, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 1, wherein the first circuitry is further configured to provide predetermined information to the second user, on the basis of whether or not a prediction result predicted by the first circuitry satisfies a predetermined condition (Barton-Sweeney: [0093] “the changes in the health status or temperature status as a result of the values sensed can trigger a notification or change of an indicator on the sensing device or on an external device.” Caplin: [0050] “the software and connectivity of the kiosk will, with permissions and positive identification, access medical records of a user to create a more accurate baseline for that individual and the software will be configured to adjust the threshold temperature for admittance or denial of admittance based on the surrounding temperature, the individual's baseline temperature, the individual's most recent temperatures, the time of day, and other known factors affecting body temperature.”).
Regarding claim 11, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 10, wherein in a case where a physical risk is predicted in response to the prediction result predicted by the first circuitry satisfying the predetermined condition, the first circuitry provides, to the second user, proposal information in which a measure against the predicted risk is proposed (Barton-Sweeney: [0051] “determine the individual's health, predict disease or other conditions and their stage;” [0093] “an indicator on the device shows the current health state (e.g. condition status, fertility level, temperature in degrees, warning indicators). In one embodiment, the changes in the health status or temperature status as a result of the values sensed can trigger a notification or change of an indicator on the sensing device or on an external device.” Caplin: [0022] “deny admittance of sick persons to a particular area where persons will congregate to prevent spread of sickness… [or] can direct a person to use a mask before entry will be allowed, even if the individual is asymptomatic.” [0049] “software will be configured to enable the automatic lifting of an entryway gate, undo a door lock, and the like, to provide entrance to a visitor at the threshold upon the finding of a non-elevated temperature, and in the alternative, upon the finding of an elevated temperature, lock a door, secure a gate, and record the identity of the elevated temperature person and make a report to building security or human resources.”).
Regarding claim 12, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 10, wherein in a case where a physical risk is predicted in response to the prediction result predicted by the first circuitry satisfying the predetermined condition, the first circuitry outputs an alert for warning occurrence of the risk to the second user (Barton-Sweeney: [0051] “determine the individual's health, predict disease or other conditions and their stage;” [0093] “an indicator on the device shows the current health state (e.g. condition status, fertility level, temperature in degrees, warning indicators). In one embodiment, the changes in the health status or temperature status as a result of the values sensed can trigger a notification or change of an indicator on the sensing device or on an external device.” Caplin: [0022] “deny admittance of sick persons to a particular area where persons will congregate to prevent spread of sickness;” [0049] “software will be configured to enable the automatic lifting of an entryway gate, undo a door lock, and the like, to provide entrance to a visitor at the threshold upon the finding of a non-elevated temperature, and in the alternative, upon the finding of an elevated temperature, lock a door, secure a gate, and record the identity of the elevated temperature person and make a report to building security or human resources.”).
Regarding claim 14, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 1, wherein the first circuitry is further configured to adjust the predetermined threshold on the basis of whether or not a body temperature change indicated by the prediction information satisfies a predetermined condition (Caplin: [0050] “the software and connectivity of the kiosk will, with permissions and positive identification, access medical records of a user to create a more accurate baseline for that individual and the software will be configured to adjust the threshold temperature for admittance or denial of admittance based on the surrounding temperature, the individual's baseline temperature, the individual's most recent temperatures, the time of day, and other known factors affecting body temperature.”).
Regarding claim 15, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 14, wherein the first circuitry is further configured to determine whether or not a cause satisfying the predetermined condition is a valid cause on the basis of context information in which the body temperature change is predicted in a case where the body temperature change satisfies the predetermined condition, and adjusts the predetermined threshold in a case where it is determined that the cause is the valid cause (Caplin: [0050] “the software and connectivity of the kiosk will, with permissions and positive identification, access medical records of a user to create a more accurate baseline for that individual and the software will be configured to adjust the threshold temperature for admittance or denial of admittance based on the surrounding temperature, the individual's baseline temperature, the individual's most recent temperatures, the time of day, and other known factors affecting body temperature.”).
Regarding claim 17, Barton-Sweeney teaches a prediction device ([0021] “A method to determine a physiological function result;” [0022] “A system for determining a health state;” Fig. 1) comprising: first circuitry ([0022] “processing unit”) configured to:
acquire body temperature information indicating a body temperature of a first user and first context information indicating a first context which is a context when the body temperature of the first user is detected ([0022] “a processing unit configured to receive and process signals produced by the temperature monitors and optional ambient environmental monitor, to determine time-dependent parameters of temperature change, and to calculate a core body temperature”);
predict information regarding a body temperature change of a second user in the future, on the basis of (a) a learned model that has learned a relation between the body temperature and the first context ([0028] “accurate physiological function result, such as an estimate of body temperature.” [0055] “The described method uses a predetermined prediction equation to obtain a physiological function result…. The predetermined prediction equation can be modified, as known in the art. The predetermined prediction equation can compare changes in amplitude of the temperature values over time, for example… The predetermined prediction equation can further comprise a personal parameter, wherein the personal parameter comprises age, gender, height, weight, fat percentage, body mass index, lean body mass, body size proportions, menstrual cycle day, chronotype, drug use (e.g. hormonal contraceptive), time zone, or a combination comprising one or more of the foregoing, in the determination of a physiological function result.” [0056] “The physiological function result can be a metabolic function parameter, such as an infection, or the presence of a fever.” [0072] “As used herein the term “population data model” is a mathematical representation of collected data for a large group of individuals. In one embodiment, the population data model may be trained over time as additional subject data is collected and stored. In one embodiment, the data model may be trained using other data such as from either other demographic groups or a historical data that is directed at different demographic groups to prepopulate the data model to provide the desired accuracy level prior to the availability of large amounts of subject data. In one embodiment, the population data model may represent an average value for the group measured. In one embodiment, the population data model may represent a target demographic of individuals, where the collected data is segregated by age.”) and (b) second context information indicating a second context that is a future context of the second user ([0055] “The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.” [0072] “the population data model may be trained over time as additional subject data is collected and stored.”);
acquire prediction information regarding a body temperature change predicted for the second user ([0073] “The comparison of temperature values to data models may include comparing absolute temperature values or a relative change in the temperature values, or other aspects of the temperature values, as described herein. This can include comparisons in relation to their expected values and data models, as well as each other's values and data models.”).
However, Barton-Sweeney fails to disclose measuring body temperature at a facility entrance to determine access based on an adjustable threshold.
Caplin discloses in response to the body temperature of the second user being measured by second circuitry installed at a facility entrance, the second circuitry controlling access to the facility according to whether or not the body temperature exceeds a predetermined threshold ([0016] “ take said individual's wrist temperature; compare said wrist temperature with said individual's medical records, analyze taking into account the current environmental conditions, determine the likelihood of present infectious state of said individual, and access a perimeter barrier mechanism to allow or deny access through said barrier”); and adjust the predetermined threshold on the basis of the prediction information, and control the second circuitry to grant or deny access to the facility for the second user based on a comparison of the measured body temperature to the adjusted threshold ([0050] “the software and connectivity of the kiosk will, with permissions and positive identification, access medical records of a user to create a more accurate baseline for that individual and the software will be configured to adjust the threshold temperature for admittance or denial of admittance based on the surrounding temperature, the individual's baseline temperature, the individual's most recent temperatures, the time of day, and other known factors affecting body temperature.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Barton-Sweeney to include measuring body temperature at a facility entrance to determine access based on an adjustable threshold as disclosed in Caplin to facilitate an efficient, safe, and secure method of admitting healthy persons and denying admittance of sick persons to a particular area where persons will congregate to prevent spread of sickness (Caplin [0022]).
Regarding claim 18, Barton-Sweeney teaches a prediction method executed by a prediction device ([0021] “A method to determine a physiological function result;” [0022] “A system for determining a health state;” Fig. 1), comprising:
acquiring body temperature information indicating a body temperature of a first user and first context information indicating a first context which is a context when the body temperature of the first user is detected ([0022] “a processing unit configured to receive and process signals produced by the temperature monitors and optional ambient environmental monitor, to determine time-dependent parameters of temperature change, and to calculate a core body temperature”);
predicting information regarding a body temperature change of a second user in the future, on the basis of (a) a learned model that has learned a relation between the body temperature and the first context ([0028] “accurate physiological function result, such as an estimate of body temperature.” [0055] “The described method uses a predetermined prediction equation to obtain a physiological function result…. The predetermined prediction equation can be modified, as known in the art. The predetermined prediction equation can compare changes in amplitude of the temperature values over time, for example… The predetermined prediction equation can further comprise a personal parameter, wherein the personal parameter comprises age, gender, height, weight, fat percentage, body mass index, lean body mass, body size proportions, menstrual cycle day, chronotype, drug use (e.g. hormonal contraceptive), time zone, or a combination comprising one or more of the foregoing, in the determination of a physiological function result.” [0056] “The physiological function result can be a metabolic function parameter, such as an infection, or the presence of a fever.” [0072] “As used herein the term “population data model” is a mathematical representation of collected data for a large group of individuals. In one embodiment, the population data model may be trained over time as additional subject data is collected and stored. In one embodiment, the data model may be trained using other data such as from either other demographic groups or a historical data that is directed at different demographic groups to prepopulate the data model to provide the desired accuracy level prior to the availability of large amounts of subject data. In one embodiment, the population data model may represent an average value for the group measured. In one embodiment, the population data model may represent a target demographic of individuals, where the collected data is segregated by age.”) and (b) second context information indicating a second context that is a future context of the second user ([0055] “The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.” [0072] “the population data model may be trained over time as additional subject data is collected and stored.”);
acquiring prediction information regarding a body temperature change predicted for the second user ([0073] “The comparison of temperature values to data models may include comparing absolute temperature values or a relative change in the temperature values, or other aspects of the temperature values, as described herein. This can include comparisons in relation to their expected values and data models, as well as each other's values and data models.”).
However, Barton-Sweeney fails to disclose measuring body temperature at a facility entrance to determine access based on an adjustable threshold.
Caplin discloses in response to the body temperature of the second user being measured by circuitry installed at a facility entrance, the circuitry controlling access to the facility according to whether or not the body temperature exceeds a predetermined threshold ([0016] “ take said individual's wrist temperature; compare said wrist temperature with said individual's medical records, analyze taking into account the current environmental conditions, determine the likelihood of present infectious state of said individual, and access a perimeter barrier mechanism to allow or deny access through said barrier”); and adjusting the predetermined threshold on the basis of the prediction information, and controlling the circuitry to grant or deny access to the facility for the second user based on a comparison of the measured body temperature to the adjusted threshold ([0050] “the software and connectivity of the kiosk will, with permissions and positive identification, access medical records of a user to create a more accurate baseline for that individual and the software will be configured to adjust the threshold temperature for admittance or denial of admittance based on the surrounding temperature, the individual's baseline temperature, the individual's most recent temperatures, the time of day, and other known factors affecting body temperature.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Barton-Sweeney to include measuring body temperature at a facility entrance to determine access based on an adjustable threshold as disclosed in Caplin to facilitate an efficient, safe, and secure method of admitting healthy persons and denying admittance of sick persons to a particular area where persons will congregate to prevent spread of sickness (Caplin [0022]).
Regarding claim 19, Barton-Sweeney teaches a non-transitory computer-readable storage medium having stored therein a prediction program for causing a prediction device ([0055] “The predetermined prediction equation is located in a memory;” [0021] “A method to determine a physiological function result;” [0022] “A system for determining a health state;”) to execute:
acquiring body temperature information indicating a body temperature of a first user and first context information indicating a first context which is a context when the body temperature of the first user is detected ([0022] “a processing unit configured to receive and process signals produced by the temperature monitors and optional ambient environmental monitor, to determine time-dependent parameters of temperature change, and to calculate a core body temperature”);
predicting information regarding a body temperature change of a second user in the future, on the basis of (a) a learned model that has learned a relation between the body temperature and the first context ([0028] “accurate physiological function result, such as an estimate of body temperature.” [0055] “The described method uses a predetermined prediction equation to obtain a physiological function result…. The predetermined prediction equation can be modified, as known in the art. The predetermined prediction equation can compare changes in amplitude of the temperature values over time, for example… The predetermined prediction equation can further comprise a personal parameter, wherein the personal parameter comprises age, gender, height, weight, fat percentage, body mass index, lean body mass, body size proportions, menstrual cycle day, chronotype, drug use (e.g. hormonal contraceptive), time zone, or a combination comprising one or more of the foregoing, in the determination of a physiological function result.” [0056] “The physiological function result can be a metabolic function parameter, such as an infection, or the presence of a fever.” [0072] “As used herein the term “population data model” is a mathematical representation of collected data for a large group of individuals. In one embodiment, the population data model may be trained over time as additional subject data is collected and stored. In one embodiment, the data model may be trained using other data such as from either other demographic groups or a historical data that is directed at different demographic groups to prepopulate the data model to provide the desired accuracy level prior to the availability of large amounts of subject data. In one embodiment, the population data model may represent an average value for the group measured. In one embodiment, the population data model may represent a target demographic of individuals, where the collected data is segregated by age.”) and (b) second context information indicating a second context that is a future context of the second user ([0055] “The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.” [0072] “the population data model may be trained over time as additional subject data is collected and stored.”);
acquiring prediction information regarding a body temperature change predicted for the second user ([0073] “The comparison of temperature values to data models may include comparing absolute temperature values or a relative change in the temperature values, or other aspects of the temperature values, as described herein. This can include comparisons in relation to their expected values and data models, as well as each other's values and data models.”).
However, Barton-Sweeney fails to disclose measuring body temperature at a facility entrance to determine access based on an adjustable threshold.
Caplin discloses in response to the body temperature of the second user being measured by circuitry installed at a facility entrance, the circuitry controlling access to the facility according to whether or not the body temperature exceeds a predetermined threshold ([0016] “ take said individual's wrist temperature; compare said wrist temperature with said individual's medical records, analyze taking into account the current environmental conditions, determine the likelihood of present infectious state of said individual, and access a perimeter barrier mechanism to allow or deny access through said barrier”); and adjusting the predetermined threshold on the basis of the prediction information, and controlling the circuitry to grant or deny access to the facility for the second user based on a comparison of the measured body temperature to the adjusted threshold ([0050] “the software and connectivity of the kiosk will, with permissions and positive identification, access medical records of a user to create a more accurate baseline for that individual and the software will be configured to adjust the threshold temperature for admittance or denial of admittance based on the surrounding temperature, the individual's baseline temperature, the individual's most recent temperatures, the time of day, and other known factors affecting body temperature.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Barton-Sweeney to include measuring body temperature at a facility entrance to determine access based on an adjustable threshold as disclosed in Caplin to facilitate an efficient, safe, and secure method of admitting healthy persons and denying admittance of sick persons to a particular area where persons will congregate to prevent spread of sickness (Caplin [0022]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barton-Sweeney (US 20160331244 A1) in view of Caplin (US 20210353155 A1), and in further view of Singh (US 20210345884 A1).
Regarding claim 7, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 5. However, the combination of Barton-Sweeney/Caplin fails disclose to disclose probability information.
Singh teaches systems and methods for tracking temperature and assessing the probability that the user is experiences a fever. Singh discloses wherein the first circuitry is further configured to:
use a set of probability information indicating a probability that the body temperature change will occur according to the first context and the first context information as learning data and generate, as the learned model ([0037] “To estimate the probability of recording a fever during a given time period, a generalized linear model with a log-link and binomial distribution can be used, along with the same sinusoidal component as the mean temperature model, interacted with age and gender. Finally, both the mean temperature curve and the probability of recording a fever for a representative healthcare population can be estimated.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Barton-Sweeney to a set of probability information indicating a probability that the body temperature change will occur as disclosed in Singh because understanding how the diurnal temperature patterns and other temperature datasets relate to an individual measurement taken at a particular time can impact the ability to detect true fevers for clinical and public health purposes (Singh [0022]).
The combination of Barton-Sweeney/Caplin/Singh discloses a second prediction model using context information as input and information indicating what kind of body temperature change occurs with what probability as output, and predict a body temperature change occurring in the second user according to the second context and a probability that the body temperature change will occur, using the second prediction model and the second context information (Barton-Sweeney: [0055] “The predetermined prediction equation can further comprise one or more of the following variables can be included in the predetermined prediction equation: time of day, time of year, physical activity, sleep deprivation, circadian rhythms, dietary factors, alcohol consumption, lighting conditions (e.g., ultraviolet (UV) index value or brightness), or a combination comprising one or more of the foregoing in the determination of a physiological function result.” [0028] “accurate physiological function result, such as an estimate of body temperature.” Singh: [0037] “Finally, both the mean temperature curve and the probability of recording a fever for a representative healthcare population can be estimated… Parameter estimates can be weighted using the breakdown of age-specific employment percentages from the Bureau of Labor Statistics, for example, and use the delta method to estimate the confidence interval around these estimates.” Fig. 12).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barton-Sweeney (US 20160331244 A1) in view of Caplin (US 20210353155 A1), and in view of Levinson (US 20220330833 A1).
Regarding claim 16, the combination of Barton-Sweeney/Caplin discloses the prediction system according to claim 15, wherein in a case where it is determined that the cause satisfying the predetermined condition is the valid cause, the first circuitry adjusts the threshold (Caplin: [0050] “the software and connectivity of the kiosk will, with permissions and positive identification, access medical records of a user to create a more accurate baseline for that individual and the software will be configured to adjust the threshold temperature for admittance or denial of admittance based on the surrounding temperature, the individual's baseline temperature, the individual's most recent temperatures, the time of day, and other known factors affecting body temperature.”).
However, the combination of Barton-Sweeney/Caplin fails to explicitly disclose increasing the threshold. Levinson teaches a method of monitoring body temperature and activity to prevent injury. Levinson discloses to be increased by a value according to the predetermined condition (Levinson: [0133] “the predetermined amount of temperature increase may be increased in response to a determination that an average temperature of a geographical location where the user is present has increased, the predetermined amount of temperature increase may be decreased in response to a determination that an average temperature of a geographical location where the user is present has decreased, based on a weather forecast, etc.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Barton-Sweeney/Caplin to include increasing the threshold according to the predetermined condition as disclosed in Levinson to maximize productivity at a job site while minimize injury and illnesses and removing doubt regarding whether users are safe to partake in the activity (Levinson [0147]).
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.
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/M.H./Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791