Detailed Notice
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/02/2026 has been entered.
Status of Claims
Claims 1 and 5 are currently pending.
Claims 1 and 5 are amended.
Claims 2-4 were canceled.
Claims 1 and 5 are rejected.
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 and 5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claim 1 is directed towards a system (i.e., machine) and claim 5 is directed toward a method (i.e. a process). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea
Step 2A—Prong 1:
Independent claims 1 and 5 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A system for providing a health promotion program for a user, comprising: a first sensor installed on a structure in a first facility that continuously collects biometric data of the user, the first facility having a first environment; a second sensor, different from the first sensor, installed on a structure in a second facility, different from the first facility, that continuously collects biometric data which is the same kind of data as the biometric data collected from the first sensor from the user, the second facility having a second environment different from the first environment; an analysis device determining a health condition of the user based on the different first and second environments using a comparison of the biometric data collected by the first sensor and the second sensor relative to the different first and second environments; and a program generation device generating the health promotion program based on the determination result of the health condition, wherein the biometric data is at least one kind of data which show facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, wherein the analysis device uses human biometric data which includes at least one kind of data, which data shows the human facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, and a human health condition including energy consumption and exercise effect of a person as teaching data for machine learning to generate a predictive model, with an input to the predictive model that is generated being the human biometric data and an output from the predictive model that is generated being the health condition of the person, and the health condition of the user is output from the collected biometric data by using the predictive model, wherein the biological characteristics of the user are extracted by comparing the collected biometric data with a predetermined evaluation index, and wherein the program generation device uses the predetermined evaluation index and evaluation values indicating the effectiveness of the health promotion program as teaching data for machine learning to generate an evaluation model, with an input to the evaluation model that is generated being the biometric data and an output from the evaluation model that is generated being the evaluation to the health promotion program, and the health promotion program presented to the user is output from the collected biometric data by using the evaluation model together with biological characteristics of the user”.
The limitations of continuously collects biometric data of the user, the first facility having a first environment; continuously collects biometric data which is the same kind of data as the biometric data collected from the first sensor from the user, the second facility having a second environment different from the first environment; determining a health condition of the user based on the different first and second environments using a comparison of the biometric data… relative to the different first and second environments; and generating the health promotion program based on the determination result of the health condition, wherein the biometric data is at least one kind of data which show facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, wherein the analysis device uses human biometric data which includes at least one kind of data, which data shows the human facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, and a human health condition including energy consumption and exercise effect of a person… to generate a predictive model, with an input… that is generated being the human biometric data and an output… that is generated being the health condition of the person, and the health condition of the user is output from the collected biometric data, wherein the biological characteristics of the user are extracted by comparing the collected biometric data with a predetermined evaluation index, and wherein the program generation device uses the predetermined evaluation index and evaluation values indicating the effectiveness of the health promotion program as teaching data, with an input… that is generated being the biometric data and an output… that is generated being the evaluation to the health promotion program, and the health promotion program presented to the user is output from the collected biometric data… together with biological characteristics of the user, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of collects, determining, generating, generate, comparing, and presented, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claim 5 is identical as the abstract idea of claim 1. The only difference between claim 5 and claim 1 is that claim 5 recites the limitation a “method” (not a “system”). This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea
Step 2A—Prong 2:
Claims 1 and 5 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “first sensor”, “second sensor”, “analysis device”, “program generation device”, “machine learning”, “predictive model”, “evaluation model”, “system”, and “computer”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, [0014]-[0015], and [0023], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “a first sensor installed on a structure in a first facility that”, “a second sensor, different from the first sensor, installed on a structure in a second facility, different from the first facility, that”, “an analysis device”, “collected by the first sensor and the second sensor”, “a program generation device”, “as teaching data for machine learning”, “to the predictive model”, “from the predictive model”, “by using the predictive model”, “for machine learning to generate an evaluation model”, “to the evaluation model”, “from the evaluation model”, and “by using the evaluation model”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “collects biometric data of the user”, “collects biometric data which is the same kind of data as the biometric data collected from the first sensor from the user”, “continuously collecting biometric data of the user”, and “continuously collecting the biometric data which is the same kind of data as the first sensor from the user”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Step 2B:
The claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “collects biometric data of the user”, “collects biometric data which is the same kind of data as the biometric data collected from the first sensor from the user”, “continuously collecting biometric data of the user”, and “continuously collecting the biometric data which is the same kind of data as the first sensor from the user”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
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 functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1 and 5 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Horseman (US 20140163336 A1), and Shelton et al. (US 20210345954 A1), hereinafter Shelton.
Regarding claim 1 Horseman teaches a system for providing a health promotion program for a user, comprising (Horseman, [0076]: “the health data, characteristics, conditions and/or risks are used to generate health plans for the employee. In certain embodiments, the health plans include preventative health plans that provide guidance to reduce health risks and/or promote a healthy lifestyle. In some embodiments, the health plans provide a suggested nutrition plan and/or a suggested exercise regime. In certain embodiments, the employee health monitoring system provides coaching (e.g., suggestions) to help the employee follow through with the health plan. In some embodiments, the health data, characteristics, conditions and/or plans may be logged over time to generate a health profile for the employee”): a first sensor installed on a structure in a first facility (Horseman, [0016]: “the employee workstation includes a floor, and the plurality of biometric sensors include a temperature sensor, a body fat sensor and a position sensor disposed in a floor mat positioned on the floor of the employee workstation”) that continuously collects biometric data of the user, the first facility having a first environment (Horseman, FIG. 4, [0005]: “various embodiments of the present invention advantageously provide systems, machines, non-transitory computer medium having computer program instructions stored thereon, and computer-implemented methods for monitoring the health of employees in their work environment using various sensors disposed about their work environment, for determining employee health profiles (e.g., including existing or predicted health conditions/risks and health plans to guide the employee with regard to a healthy lifestyle) based on the health data, and for providing feedback to communicate the determined health profile and associated information”, [0006]: “The system including a set of biometric health sensors located at the workstation for detecting biometric characteristics of the employee's health. The set of biometric health sensors being configured to collect health data via a plurality of points of contact with the employee while the employee is located in the employee workstation… collecting, via the communications network, the biometric sensor data output by the set of biometric sensors, determining an updated health profile for the employee using the biometric sensor data, serving the updated health profile for the employee for display to the employee via the computer workstation, and updating the health information stored in the database to reflect the updated health profile for the employee. The step of collecting the biometric sensor data output by the set of biometric sensors including the steps of activating the set of biometric sensors to conduct a health test of the employee, and monitoring the set of biometric sensors to collect the biometric sensor data sensor data. The step of determining an updated health profile for the employee using the biometric sensor data collected including the steps of determining one or more of body temperature, body weight, body fat, heart rate, blood pressure, respiration rate, and blood oxygenation for the employee using the biometric sensor data collected, and determining a health plan for the employee based on one or more of the body temperature, the body weight, the body fat, the heart rate, the blood pressure, the respiration rate, and the blood oxygenation determined for the employee, [0008], and [0018]); a second sensor, different from the first sensor, installed on a structure in a second facility (Horseman, [0016]: “the employee workstation includes a floor, and the plurality of biometric sensors include a temperature sensor, a body fat sensor and a position sensor disposed in a floor mat positioned on the floor of the employee workstation”, different from the first facility, that continuously collects biometric data which is the same kind of data as the biometric data collected from the first sensor from the user, the second facility having a second environment different from the first environment (Horseman, FIG. 4, [0005]: “various embodiments of the present invention advantageously provide systems, machines, non-transitory computer medium having computer program instructions stored thereon, and computer-implemented methods for monitoring the health of employees in their work environment using various sensors disposed about their work environment, for determining employee health profiles (e.g., including existing or predicted health conditions/risks and health plans to guide the employee with regard to a healthy lifestyle) based on the health data, and for providing feedback to communicate the determined health profile and associated information”, [0006]: “The system including a set of biometric health sensors located at the workstation for detecting biometric characteristics of the employee's health. The set of biometric health sensors being configured to collect health data via a plurality of points of contact with the employee while the employee is located in the employee workstation… collecting, via the communications network, the biometric sensor data output by the set of biometric sensors, determining an updated health profile for the employee using the biometric sensor data, serving the updated health profile for the employee for display to the employee via the computer workstation, and updating the health information stored in the database to reflect the updated health profile for the employee. The step of collecting the biometric sensor data output by the set of biometric sensors including the steps of activating the set of biometric sensors to conduct a health test of the employee, and monitoring the set of biometric sensors to collect the biometric sensor data sensor data. The step of determining an updated health profile for the employee using the biometric sensor data collected including the steps of determining one or more of body temperature, body weight, body fat, heart rate, blood pressure, respiration rate, and blood oxygenation for the employee using the biometric sensor data collected, and determining a health plan for the employee based on one or more of the body temperature, the body weight, the body fat, the heart rate, the blood pressure, the respiration rate, and the blood oxygenation determined for the employee, [0008], and [0018]); an analysis device determining a health condition of the user based on the different first and second environments using a comparison of the biometric data collected by the first sensor and the second sensor relative to the different first and second environments (Horseman, FIG. 4, [0005]: “various embodiments of the present invention advantageously provide systems, machines, non-transitory computer medium having computer program instructions stored thereon, and computer-implemented methods for monitoring the health of employees in their work environment using various sensors disposed about their work environment, for determining employee health profiles (e.g., including existing or predicted health conditions/risks and health plans to guide the employee with regard to a healthy lifestyle) based on the health data, and for providing feedback to communicate the determined health profile and associated information”, [0006]: “The system including a set of biometric health sensors located at the workstation for detecting biometric characteristics of the employee's health. The set of biometric health sensors being configured to collect health data via a plurality of points of contact with the employee while the employee is located in the employee workstation… collecting, via the communications network, the biometric sensor data output by the set of biometric sensors, determining an updated health profile for the employee using the biometric sensor data, serving the updated health profile for the employee for display to the employee via the computer workstation, and updating the health information stored in the database to reflect the updated health profile for the employee. The step of collecting the biometric sensor data output by the set of biometric sensors including the steps of activating the set of biometric sensors to conduct a health test of the employee, and monitoring the set of biometric sensors to collect the biometric sensor data sensor data. The step of determining an updated health profile for the employee using the biometric sensor data collected including the steps of determining one or more of body temperature, body weight, body fat, heart rate, blood pressure, respiration rate, and blood oxygenation for the employee using the biometric sensor data collected, and determining a health plan for the employee based on one or more of the body temperature, the body weight, the body fat, the heart rate, the blood pressure, the respiration rate, and the blood oxygenation determined for the employee, [0008], and [0018]); and a program generation device generating the health promotion program based on the determination result of the health condition (Horseman, [0076]: “the health data, characteristics, conditions and/or risks are used to generate health plans for the employee. In certain embodiments, the health plans include preventative health plans that provide guidance to reduce health risks and/or promote a healthy lifestyle. In some embodiments, the health plans provide a suggested nutrition plan and/or a suggested exercise regime. In certain embodiments, the employee health monitoring system provides coaching (e.g., suggestions) to help the employee follow through with the health plan. In some embodiments, the health data, characteristics, conditions and/or plans may be logged over time to generate a health profile for the employee” and [0289]: “a health plan 1308 may be generated based on the health characteristics 1302, the health conditions 1304 and/or the health risks 1306. Accordingly, the health plan 1308 may be based on biometric and/or biomechanical health information for the employee. The health plan 1308 may provide a listing of health goals (e.g., lose ten pounds, reduce calorie intake to two-thousand calories per day, etc.), suggested actions for the employee to take to reach the health goals (e.g., an exercise plan, a diet regime, regular breaks from using the computer, etc.) and/or the like. In some embodiments, the health plan 1308 includes a preventative health plan to help maintain and improve the employee's health over time. In some embodiments, the health plan 1308 may include an interactive health plan that can be modified by the employee and/or the employer and/or used to track the employee's progress relative to the plan goals, and/or the like”), with an input to the predictive model that is generated being the human biometric data and an output from the predictive model that is generated being the health condition of the person, and the health condition of the user is output from the collected biometric data by using the predictive model (Horseman, [0006]: “The step of determining an updated health profile for the employee using the biometric sensor data collected including the steps of determining one or more of body temperature, body weight, body fat, heart rate, blood pressure, respiration rate, and blood oxygenation for the employee using the biometric sensor data collected, and determining a health plan for the employee based on one or more of the body temperature, the body weight, the body fat, the heart rate, the blood pressure, the respiration rate, and the blood oxygenation determined for the employee”, [0075]: “In certain embodiments, health risks are determined via predictive analytics that use employee's current and/or historical health characteristics/conditions” and [0280]: “the server 104 may process the health conditions 1304, the health characteristics 1302 and/or the collected health data 200 using predictive analytics to extrapolate various biometric health risks 1306 a and/or biomechanical health risks 1306 b for the employee (i.e., risks for developing the associated health condition). Risk 1306 may include a prediction of a health condition that may occur”), wherein the biological characteristics of the user are extracted by comparing the collected biometric data with a predetermined evaluation index (Horseman, Claim 4: “comparing one or more of one or more of the health characteristics, health conditions and health risks determined for the employee to a corresponding predetermined threshold range for the one or more health characteristics, health conditions and health risks”, [0009]: “the employee to a corresponding predetermined threshold range for the one or more health characteristics, health conditions and health risks, determining, based on the comparison, that at least one of the one or more of the health characteristics, health conditions and health risks determined for the employee are outside of the corresponding predetermined threshold range for the one or more of health characteristics, health conditions and health risks, and in response to determining that at least one of the one or more of the health characteristics, health conditions and health risks determined for the employee are outside of the corresponding predetermined threshold range for the one or more of health characteristics, health conditions and health risks, alerting emergency response personnel regarding the at least one of the one or more health characteristics, health conditions and health risks determined to be outside of the corresponding predetermined threshold range for the one or more of health characteristics, health conditions and health risks” and [0334]: “the determination of whether an employee is experiencing an alert condition may be based on comparison of the health data 200 and/or the health profile 1300 to predetermined threshold limits”).
Horseman does not teach wherein the biometric data is at least one kind of data which show facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, wherein the analysis device uses human biometric data which includes at least one kind of data, which data shows the human facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, and a human health condition including energy consumption and exercise effect of a person as teaching data for machine learning to generate a predictive model, and wherein the program generation device uses the predetermined evaluation index and evaluation values indicating the effectiveness of the health promotion program as teaching data for machine learning to generate an evaluation model, with an input to the evaluation model that is generated being the biometric data and an output from the evaluation model that is generated being the evaluation to the health promotion program, and the health promotion program presented to the user is output from the collected biometric data by using the evaluation model together with biological characteristics of the user.
However, Shelton teaches wherein the biometric data is at least one kind of data which show facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components (Shelton, [0009]: “the condition can be at least one of blood sugar level, blood pressure, perspiration level, heart rate, core temperature, tremor detection, time of day, date, patient activity level, blood pressure, metabolic rate, altitude, temperature of the drug, viscosity of the drug, geographic location information, angular rate, current of a motor used in delivering the drug, blood oxygenation level, sun exposure, osmolality, and air quality”, [0082]-[0083], and [0128]), wherein the analysis device uses human biometric data which includes at least one kind of data, which data shows the human facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, and a human health condition including energy consumption and exercise effect of a person as teaching data for machine learning to generate a predictive model (Shelton, [0009]: “the condition can be at least one of blood sugar level, blood pressure, perspiration level, heart rate, core temperature, tremor detection, time of day, date, patient activity level, blood pressure, metabolic rate, altitude, temperature of the drug, viscosity of the drug, geographic location information, angular rate, current of a motor used in delivering the drug, blood oxygenation level, sun exposure, osmolality, and air quality”, [0082]-[0083], and [0128]), and wherein the program generation device uses the predetermined evaluation index and evaluation values indicating the effectiveness of the health promotion program as teaching data for machine learning to generate an evaluation model (Shelton, [0133]: “These relationships can be evaluated by the system 700 through multiple algorithms to provide more accurate trends and/or more accurate recommendations, e.g., recommendations of treatments for the patient and their symptoms to result in an optimized outcome, recommendations that result in cost saving, recommendations that result in fewer and/or less severe side effects, etc.”, [0139]:“ Evaluating compliance can thus allow monitoring and management of a patient's treatment, which can help the patient's doctor (and/or other medical professional) evaluate the patient's medical progress and/or can help determine whether and when modifications to the patient's treatment plan may be necessary, such as by adjusting the treatment plan (e.g., changing a dose size of the drug delivered from the drug administration device 500, changing a timing of doses delivered by the drug delivery device 500, changing dietary requirements, changing a frequency of doctor check-ups, etc.) or replacing the treatment plan (e.g., a treatment plan including use of the drug administration device 500 delivering a specific drug) with another treatment plan (e.g., a treatment that does not include any use of the drug administration device 500 and/or the specific drug)”, [0157]: “he receiving party may therefore aggregate data received from the transmitting party and related to a drug administration device, a drug deliverable/delivered from the drug administration device, and/or a patient for evaluation of the patient's condition and treatment and/or for evaluation of the efficacy of the drug administration device and/or the drug. The evaluation can be manually performed by a medical professional or can be automated by a computer system, as discussed above”, and [0159]: “Such analysis may be useful for any number of reasons, such as allowing for evaluation of treatments of different patients for optimal clinical outcomes, allowing for evaluation of patients' compliance with their individual medical treatment plans, and/or allowing an insurance company to more effectively correlate drug usage and drug cost. For another example, a computer system can be configured to establish a unique key with each of a plurality of other computer systems, thereby allowing for data analysis between different patients and/or data analysis between different devices/housings/packagings”), with an input to the evaluation model that is generated being the biometric data and an output from the evaluation model that is generated being the evaluation to the health promotion program (Shelton, [0133]: “These relationships can be evaluated by the system 700 through multiple algorithms to provide more accurate trends and/or more accurate recommendations, e.g., recommendations of treatments for the patient and their symptoms to result in an optimized outcome, recommendations that result in cost saving, recommendations that result in fewer and/or less severe side effects, etc.”, [0139]:“ Evaluating compliance can thus allow monitoring and management of a patient's treatment, which can help the patient's doctor (and/or other medical professional) evaluate the patient's medical progress and/or can help determine whether and when modifications to the patient's treatment plan may be necessary, such as by adjusting the treatment plan (e.g., changing a dose size of the drug delivered from the drug administration device 500, changing a timing of doses delivered by the drug delivery device 500, changing dietary requirements, changing a frequency of doctor check-ups, etc.) or replacing the treatment plan (e.g., a treatment plan including use of the drug administration device 500 delivering a specific drug) with another treatment plan (e.g., a treatment that does not include any use of the drug administration device 500 and/or the specific drug)”, [0157]: “he receiving party may therefore aggregate data received from the transmitting party and related to a drug administration device, a drug deliverable/delivered from the drug administration device, and/or a patient for evaluation of the patient's condition and treatment and/or for evaluation of the efficacy of the drug administration device and/or the drug. The evaluation can be manually performed by a medical professional or can be automated by a computer system, as discussed above”, and [0159]: “Such analysis may be useful for any number of reasons, such as allowing for evaluation of treatments of different patients for optimal clinical outcomes, allowing for evaluation of patients' compliance with their individual medical treatment plans, and/or allowing an insurance company to more effectively correlate drug usage and drug cost. For another example, a computer system can be configured to establish a unique key with each of a plurality of other computer systems, thereby allowing for data analysis between different patients and/or data analysis between different devices/housings/packagings”), and the health promotion program presented to the user is output from the collected biometric data by using the evaluation model together with biological characteristics of the user (Shelton, [0133]: “These relationships can be evaluated by the system 700 through multiple algorithms to provide more accurate trends and/or more accurate recommendations, e.g., recommendations of treatments for the patient and their symptoms to result in an optimized outcome, recommendations that result in cost saving, recommendations that result in fewer and/or less severe side effects, etc.”, [0139]:“ Evaluating compliance can thus allow monitoring and management of a patient's treatment, which can help the patient's doctor (and/or other medical professional) evaluate the patient's medical progress and/or can help determine whether and when modifications to the patient's treatment plan may be necessary, such as by adjusting the treatment plan (e.g., changing a dose size of the drug delivered from the drug administration device 500, changing a timing of doses delivered by the drug delivery device 500, changing dietary requirements, changing a frequency of doctor check-ups, etc.) or replacing the treatment plan (e.g., a treatment plan including use of the drug administration device 500 delivering a specific drug) with another treatment plan (e.g., a treatment that does not include any use of the drug administration device 500 and/or the specific drug)”, [0157]: “he receiving party may therefore aggregate data received from the transmitting party and related to a drug administration device, a drug deliverable/delivered from the drug administration device, and/or a patient for evaluation of the patient's condition and treatment and/or for evaluation of the efficacy of the drug administration device and/or the drug. The evaluation can be manually performed by a medical professional or can be automated by a computer system, as discussed above”, and [0159]: “Such analysis may be useful for any number of reasons, such as allowing for evaluation of treatments of different patients for optimal clinical outcomes, allowing for evaluation of patients' compliance with their individual medical treatment plans, and/or allowing an insurance company to more effectively correlate drug usage and drug cost. For another example, a computer system can be configured to establish a unique key with each of a plurality of other computer systems, thereby allowing for data analysis between different patients and/or data analysis between different devices/housings/packagings”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Horseman to incorporate the teachings of Shelton and account for system and method for monitoring and communicating information, such as biometrics, drug administration, and compliance for treating and evaluating patient conditions (Shelton, Abstract and [0003]-[0006]).
Regarding claim 5 Horseman teaches a method executed by a computer for providing a health promotion program for a user, the method comprising (Horseman, [0076]: “the health data, characteristics, conditions and/or risks are used to generate health plans for the employee. In certain embodiments, the health plans include preventative health plans that provide guidance to reduce health risks and/or promote a healthy lifestyle. In some embodiments, the health plans provide a suggested nutrition plan and/or a suggested exercise regime. In certain embodiments, the employee health monitoring system provides coaching (e.g., suggestions) to help the employee follow through with the health plan. In some embodiments, the health data, characteristics, conditions and/or plans may be logged over time to generate a health profile for the employee”): continuously collecting biometric data of the user using a first sensor installed on a structure in a first facility (Horseman, [0016]: “the employee workstation includes a floor, and the plurality of biometric sensors include a temperature sensor, a body fat sensor and a position sensor disposed in a floor mat positioned on the floor of the employee workstation”, the first facility having a first environment (Horseman, FIG. 4, [0005]: “various embodiments of the present invention advantageously provide systems, machines, non-transitory computer medium having computer program instructions stored thereon, and computer-implemented methods for monitoring the health of employees in their work environment using various sensors disposed about their work environment, for determining employee health profiles (e.g., including existing or predicted health conditions/risks and health plans to guide the employee with regard to a healthy lifestyle) based on the health data, and for providing feedback to communicate the determined health profile and associated information”, [0006]: “The system including a set of biometric health sensors located at the workstation for detecting biometric characteristics of the employee's health. The set of biometric health sensors being configured to collect health data via a plurality of points of contact with the employee while the employee is located in the employee workstation… collecting, via the communications network, the biometric sensor data output by the set of biometric sensors, determining an updated health profile for the employee using the biometric sensor data, serving the updated health profile for the employee for display to the employee via the computer workstation, and updating the health information stored in the database to reflect the updated health profile for the employee. The step of collecting the biometric sensor data output by the set of biometric sensors including the steps of activating the set of biometric sensors to conduct a health test of the employee, and monitoring the set of biometric sensors to collect the biometric sensor data sensor data. The step of determining an updated health profile for the employee using the biometric sensor data collected including the steps of determining one or more of body temperature, body weight, body fat, heart rate, blood pressure, respiration rate, and blood oxygenation for the employee using the biometric sensor data collected, and determining a health plan for the employee based on one or more of the body temperature, the body weight, the body fat, the heart rate, the blood pressure, the respiration rate, and the blood oxygenation determined for the employee, [0008], and [0018]); continuously collecting biometric data which is the same kind of data as the biometric data collected from the first sensor from the user using a second sensor, different from the first sensor, installed on a structure in a second facility (Horseman, [0016]: “the employee workstation includes a floor, and the plurality of biometric sensors include a temperature sensor, a body fat sensor and a position sensor disposed in a floor mat positioned on the floor of the employee workstation”, the second facility having a second environment different from the first environment (Horseman, FIG. 4, [0005]: “various embodiments of the present invention advantageously provide systems, machines, non-transitory computer medium having computer program instructions stored thereon, and computer-implemented methods for monitoring the health of employees in their work environment using various sensors disposed about their work environment, for determining employee health profiles (e.g., including existing or predicted health conditions/risks and health plans to guide the employee with regard to a healthy lifestyle) based on the health data, and for providing feedback to communicate the determined health profile and associated information”, [0006]: “The system including a set of biometric health sensors located at the workstation for detecting biometric characteristics of the employee's health. The set of biometric health sensors being configured to collect health data via a plurality of points of contact with the employee while the employee is located in the employee workstation… collecting, via the communications network, the biometric sensor data output by the set of biometric sensors, determining an updated health profile for the employee using the biometric sensor data, serving the updated health profile for the employee for display to the employee via the computer workstation, and updating the health information stored in the database to reflect the updated health profile for the employee. The step of collecting the biometric sensor data output by the set of biometric sensors including the steps of activating the set of biometric sensors to conduct a health test of the employee, and monitoring the set of biometric sensors to collect the biometric sensor data sensor data. The step of determining an updated health profile for the employee using the biometric sensor data collected including the steps of determining one or more of body temperature, body weight, body fat, heart rate, blood pressure, respiration rate, and blood oxygenation for the employee using the biometric sensor data collected, and determining a health plan for the employee based on one or more of the body temperature, the body weight, the body fat, the heart rate, the blood pressure, the respiration rate, and the blood oxygenation determined for the employee, [0008], and [0018]); performing analysis including comparison of the biometric data collected by the first sensor and the second sensor relative to the different first and second environments, and determining the health condition of the user based on the different first and second environments (Horseman, FIG. 4, [0005]: “various embodiments of the present invention advantageously provide systems, machines, non-transitory computer medium having computer program instructions stored thereon, and computer-implemented methods for monitoring the health of employees in their work environment using various sensors disposed about their work environment, for determining employee health profiles (e.g., including existing or predicted health conditions/risks and health plans to guide the employee with regard to a healthy lifestyle) based on the health data, and for providing feedback to communicate the determined health profile and associated information”, [0006]: “The system including a set of biometric health sensors located at the workstation for detecting biometric characteristics of the employee's health. The set of biometric health sensors being configured to collect health data via a plurality of points of contact with the employee while the employee is located in the employee workstation… collecting, via the communications network, the biometric sensor data output by the set of biometric sensors, determining an updated health profile for the employee using the biometric sensor data, serving the updated health profile for the employee for display to the employee via the computer workstation, and updating the health information stored in the database to reflect the updated health profile for the employee. The step of collecting the biometric sensor data output by the set of biometric sensors including the steps of activating the set of biometric sensors to conduct a health test of the employee, and monitoring the set of biometric sensors to collect the biometric sensor data sensor data. The step of determining an updated health profile for the employee using the biometric sensor data collected including the steps of determining one or more of body temperature, body weight, body fat, heart rate, blood pressure, respiration rate, and blood oxygenation for the employee using the biometric sensor data collected, and determining a health plan for the employee based on one or more of the body temperature, the body weight, the body fat, the heart rate, the blood pressure, the respiration rate, and the blood oxygenation determined for the employee, [0008], and [0018]); and generating a health promotion program according to the user based on the results of the determination of the health condition (Horseman, [0076]: “the health data, characteristics, conditions and/or risks are used to generate health plans for the employee. In certain embodiments, the health plans include preventative health plans that provide guidance to reduce health risks and/or promote a healthy lifestyle. In some embodiments, the health plans provide a suggested nutrition plan and/or a suggested exercise regime. In certain embodiments, the employee health monitoring system provides coaching (e.g., suggestions) to help the employee follow through with the health plan. In some embodiments, the health data, characteristics, conditions and/or plans may be logged over time to generate a health profile for the employee” and [0289]: “a health plan 1308 may be generated based on the health characteristics 1302, the health conditions 1304 and/or the health risks 1306. Accordingly, the health plan 1308 may be based on biometric and/or biomechanical health information for the employee. The health plan 1308 may provide a listing of health goals (e.g., lose ten pounds, reduce calorie intake to two-thousand calories per day, etc.), suggested actions for the employee to take to reach the health goals (e.g., an exercise plan, a diet regime, regular breaks from using the computer, etc.) and/or the like. In some embodiments, the health plan 1308 includes a preventative health plan to help maintain and improve the employee's health over time. In some embodiments, the health plan 1308 may include an interactive health plan that can be modified by the employee and/or the employer and/or used to track the employee's progress relative to the plan goals, and/or the like”), with an input to the predictive model that is generated being the human biometric data and an output from the predictive model that is generated being the health condition of the person, and the health condition of the user is output from the collected biometric data by using the predictive model (Horseman, [0006]: “The step of determining an updated health profile for the employee using the biometric sensor data collected including the steps of determining one or more of body temperature, body weight, body fat, heart rate, blood pressure, respiration rate, and blood oxygenation for the employee using the biometric sensor data collected, and determining a health plan for the employee based on one or more of the body temperature, the body weight, the body fat, the heart rate, the blood pressure, the respiration rate, and the blood oxygenation determined for the employee”, [0075]: “In certain embodiments, health risks are determined via predictive analytics that use employee's current and/or historical health characteristics/conditions” and [0280]: “the server 104 may process the health conditions 1304, the health characteristics 1302 and/or the collected health data 200 using predictive analytics to extrapolate various biometric health risks 1306 a and/or biomechanical health risks 1306 b for the employee (i.e., risks for developing the associated health condition). Risk 1306 may include a prediction of a health condition that may occur”), and wherein the biological characteristics of the user are extracted by comparing the collected biometric data with a predetermined evaluation index (Horseman, Claim 4: “comparing one or more of one or more of the health characteristics, health conditions and health risks determined for the employee to a corresponding predetermined threshold range for the one or more health characteristics, health conditions and health risks”, [0009]: “the employee to a corresponding predetermined threshold range for the one or more health characteristics, health conditions and health risks, determining, based on the comparison, that at least one of the one or more of the health characteristics, health conditions and health risks determined for the employee are outside of the corresponding predetermined threshold range for the one or more of health characteristics, health conditions and health risks, and in response to determining that at least one of the one or more of the health characteristics, health conditions and health risks determined for the employee are outside of the corresponding predetermined threshold range for the one or more of health characteristics, health conditions and health risks, alerting emergency response personnel regarding the at least one of the one or more health characteristics, health conditions and health risks determined to be outside of the corresponding predetermined threshold range for the one or more of health characteristics, health conditions and health risks” and [0334]: “the determination of whether an employee is experiencing an alert condition may be based on comparison of the health data 200 and/or the health profile 1300 to predetermined threshold limits”),.
Horseman does not teach wherein the biometric data is at least one kind of data which show facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, wherein the performing of analysis includes using human biometric data which includes at least one kind of data, which data shows the human facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, and a human health condition including energy consumption and exercise effect of a person as teaching data for machine learning to generate a predictive model, wherein the generating of the health promotion program includes using the predetermined evaluation index and evaluation values indicating the effectiveness of the health promotion program as teaching data for machine learning to generate an evaluation model, with an input to the evaluation model that is generated being the biometric data and an output from the evaluation model that is generated being the evaluation to the health promotion program, and the health promotion program presented to the user is output from the collected biometric data by using the evaluation model together with biological characteristics of the user.
However, Shelton teaches wherein the biometric data is at least one kind of data which show facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, wherein the performing of analysis includes using human biometric data which includes at least one kind of data, which data shows the human facial expressions, heart rate, oxygen saturation, carbon dioxide exhaled amount, surface body temperature, core body temperature, skin protein analysis, body composition, autonomic nerves, HbAlc, internal water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, amount of activity, myoelectricity, electrocardiography, brain waves, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleeping time, amount and time of excretion, blood components, urine components, saliva components, intraoral images, and fecal components, and a human health condition including energy consumption and exercise effect of a person as teaching data for machine learning to generate a predictive model (Shelton, [0009]: “the condition can be at least one of blood sugar level, blood pressure, perspiration level, heart rate, core temperature, tremor detection, time of day, date, patient activity level, blood pressure, metabolic rate, altitude, temperature of the drug, viscosity of the drug, geographic location information, angular rate, current of a motor used in delivering the drug, blood oxygenation level, sun exposure, osmolality, and air quality”, [0082]-[0083], and [0128]), and wherein the generating of the health promotion program includes using the predetermined evaluation index and evaluation values indicating the effectiveness of the health promotion program as teaching data for machine learning to generate an evaluation model (Shelton, [0133]: “These relationships can be evaluated by the system 700 through multiple algorithms to provide more accurate trends and/or more accurate recommendations, e.g., recommendations of treatments for the patient and their symptoms to result in an optimized outcome, recommendations that result in cost saving, recommendations that result in fewer and/or less severe side effects, etc.”, [0139]:“ Evaluating compliance can thus allow monitoring and management of a patient's treatment, which can help the patient's doctor (and/or other medical professional) evaluate the patient's medical progress and/or can help determine whether and when modifications to the patient's treatment plan may be necessary, such as by adjusting the treatment plan (e.g., changing a dose size of the drug delivered from the drug administration device 500, changing a timing of doses delivered by the drug delivery device 500, changing dietary requirements, changing a frequency of doctor check-ups, etc.) or replacing the treatment plan (e.g., a treatment plan including use of the drug administration device 500 delivering a specific drug) with another treatment plan (e.g., a treatment that does not include any use of the drug administration device 500 and/or the specific drug)”, [0157]: “he receiving party may therefore aggregate data received from the transmitting party and related to a drug administration device, a drug deliverable/delivered from the drug administration device, and/or a patient for evaluation of the patient's condition and treatment and/or for evaluation of the efficacy of the drug administration device and/or the drug. The evaluation can be manually performed by a medical professional or can be automated by a computer system, as discussed above”, and [0159]: “Such analysis may be useful for any number of reasons, such as allowing for evaluation of treatments of different patients for optimal clinical outcomes, allowing for evaluation of patients' compliance with their individual medical treatment plans, and/or allowing an insurance company to more effectively correlate drug usage and drug cost. For another example, a computer system can be configured to establish a unique key with each of a plurality of other computer systems, thereby allowing for data analysis between different patients and/or data analysis between different devices/housings/packagings”), with an input to the evaluation model that is generated being the biometric data and an output from the evaluation model that is generated being the evaluation to the health promotion program (Shelton, [0133]: “These relationships can be evaluated by the system 700 through multiple algorithms to provide more accurate trends and/or more accurate recommendations, e.g., recommendations of treatments for the patient and their symptoms to result in an optimized outcome, recommendations that result in cost saving, recommendations that result in fewer and/or less severe side effects, etc.”, [0139]:“ Evaluating compliance can thus allow monitoring and management of a patient's treatment, which can help the patient's doctor (and/or other medical professional) evaluate the patient's medical progress and/or can help determine whether and when modifications to the patient's treatment plan may be necessary, such as by adjusting the treatment plan (e.g., changing a dose size of the drug delivered from the drug administration device 500, changing a timing of doses delivered by the drug delivery device 500, changing dietary requirements, changing a frequency of doctor check-ups, etc.) or replacing the treatment plan (e.g., a treatment plan including use of the drug administration device 500 delivering a specific drug) with another treatment plan (e.g., a treatment that does not include any use of the drug administration device 500 and/or the specific drug)”, [0157]: “he receiving party may therefore aggregate data received from the transmitting party and related to a drug administration device, a drug deliverable/delivered from the drug administration device, and/or a patient for evaluation of the patient's condition and treatment and/or for evaluation of the efficacy of the drug administration device and/or the drug. The evaluation can be manually performed by a medical professional or can be automated by a computer system, as discussed above”, and [0159]: “Such analysis may be useful for any number of reasons, such as allowing for evaluation of treatments of different patients for optimal clinical outcomes, allowing for evaluation of patients' compliance with their individual medical treatment plans, and/or allowing an insurance company to more effectively correlate drug usage and drug cost. For another example, a computer system can be configured to establish a unique key with each of a plurality of other computer systems, thereby allowing for data analysis between different patients and/or data analysis between different devices/housings/packagings”), and the health promotion program presented to the user is output from the collected biometric data by using the evaluation model together with biological characteristics of the user (Shelton, [0133]: “These relationships can be evaluated by the system 700 through multiple algorithms to provide more accurate trends and/or more accurate recommendations, e.g., recommendations of treatments for the patient and their symptoms to result in an optimized outcome, recommendations that result in cost saving, recommendations that result in fewer and/or less severe side effects, etc.”, [0139]:“ Evaluating compliance can thus allow monitoring and management of a patient's treatment, which can help the patient's doctor (and/or other medical professional) evaluate the patient's medical progress and/or can help determine whether and when modifications to the patient's treatment plan may be necessary, such as by adjusting the treatment plan (e.g., changing a dose size of the drug delivered from the drug administration device 500, changing a timing of doses delivered by the drug delivery device 500, changing dietary requirements, changing a frequency of doctor check-ups, etc.) or replacing the treatment plan (e.g., a treatment plan including use of the drug administration device 500 delivering a specific drug) with another treatment plan (e.g., a treatment that does not include any use of the drug administration device 500 and/or the specific drug)”, [0157]: “he receiving party may therefore aggregate data received from the transmitting party and related to a drug administration device, a drug deliverable/delivered from the drug administration device, and/or a patient for evaluation of the patient's condition and treatment and/or for evaluation of the efficacy of the drug administration device and/or the drug. The evaluation can be manually performed by a medical professional or can be automated by a computer system, as discussed above”, and [0159]: “Such analysis may be useful for any number of reasons, such as allowing for evaluation of treatments of different patients for optimal clinical outcomes, allowing for evaluation of patients' compliance with their individual medical treatment plans, and/or allowing an insurance company to more effectively correlate drug usage and drug cost. For another example, a computer system can be configured to establish a unique key with each of a plurality of other computer systems, thereby allowing for data analysis between different patients and/or data analysis between different devices/housings/packagings”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Horseman to incorporate the teachings of Shelton and account for system and method for monitoring and communicating information, such as biometrics, drug administration, and compliance for treating and evaluating patient conditions (Shelton, Abstract and [0003]-[0006]).
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 101 Rejection, Applicant argues there is no support that the additional elements are recited at a high level of generality to amount to generic computer tools and there is no support these features fall into an “apply it” situation. Applicant argues the analysis device is no “generic” and the “apply it” situation does not exist because of a specific “particular solution”. Examiner respectfully disagrees. The USPTO’s August 4th, 2025 Reminder are merely reminders and MPEP 2106.05(f) states “claim limitations that do not amount to more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection”, the prior and current Office Action clearly states how and why the limitations are evaluated at a high level and merely “apply it” citing to MPEP 2106.05(f) and MPEP 2106.05(h). MPEP 2106.05(f) states “implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer”. Additionally, the present claims are similar to Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016) in that the additional elements did limit the use of the abstract idea, but the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment (cellular telephones) and thus fails to add an inventive concept to the claims (see MPEP 2106.05(h)). It is further noted that employing generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient. Therefore, the additional elements are generic and the abstract idea is merely being applied to it. Applicant also argues the claims are similar to USPTO Example #39 in which there was no recitation of a judicial exception. Examiner respectfully disagrees. Example #39 is about a training a neural network for facial detection. More specifically the limitations of collecting digital facial images and applying one or more transformations make it so that the claims cannot be performed in the human mind (mental process), does not contain mathematical concepts, or recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people. The present claims are not similar because the biometric data is not the same digital facial images. Therefore, the claims are not similar to Example #39. Therefore, the 35 U.S.C 101 Rejection is maintained.
Regarding the 35 U.S.C. 103 Rejection, Applicant argues the Hong reference does not disclose two different sensors respectively installed on a structure in different first and second facilities. Examiner respectfully disagrees. Applicant’s arguments with respect to claims 1 and 5 regarding “two different sensors respectively installed on a structure in different first and second facilities”, 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.
Applicant also argues the prior art(s) do not address any “environment” associated with different first and second facilities. Examiner respectfully disagrees. Under broadest reasonable interpretation, Horseman teaches at [0005] “various embodiments of the present invention advantageously provide systems, machines, non-transitory computer medium having computer program instructions stored thereon, and computer-implemented methods for monitoring the health of employees in their work environment using various sensors disposed about their work environment”, [0016] “the employee workstation includes a floor, and the plurality of biometric sensors include a temperature sensor, a body fat sensor and a position sensor disposed in a floor mat positioned on the floor of the employee workstation”, and [0074] “various monitoring devices (e.g., health sensors) are placed in the employee's work environment to collect health data that can be used to assess various biometric and biomechanical characteristics (e.g., characteristics, conditions and risks) of the employee”. Therefore, the 35 U.S.C. 103 Rejection is maintained.
Conclusion
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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