DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Application filed on 10/16/2023. Claims 1-20 are pending in the case. Claims 1 and 19-20 are independent claims.
Claim Interpretation
The indentation of claim 11 suggests that only the “generating” step is performed repeatedly.
Claim Objections
Claims 8 and 15 are objected to because they recite an extra period where a comma was apparently intended. Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Chalas et al. (US 2018/0144100 A1, hereinafter Chalas) in view of Hu et al. (US 2017/0339031 A1, hereinafter Hu) and Jain et al. (US 11,056,242 B1, hereinafter Jain).
As to independent claim 1, Chalas teaches a method performed by one or more computers, the method comprising:
determining, by the one or more computers, first types of data to be collected in a first monitoring program from remote devices over a communication network (“Sensor device 112 may also be a data processing system of relatively limited but dedicated capability, configured for a specific task of sensing one or more parameters (such as heart rate, body temperature, etc.) and providing the values to a remote device, such as a wearable device (e.g. 110) or other mobile device (e.g. 102),” paragraph 0017 lines 1-6);
obtaining, by the one or more computers, data describing data collection results [] (“the process continues with the acceptance of the provided health event data as updates (222) to be analyzed,” paragraph 0045 lines 1-2), wherein the obtained data includes data [] that indicates (i) types of data acquired in the monitoring program (“The obtained self-perceived health status input, supplemental sensor data, and optionally primary sensor data, may be correlated as health event data of a health event saved to the event log,” paragraph 0044 lines 1-4), and (ii) data collection parameter values used for the monitoring programs for the respective types of data acquired (“upon detecting a predefined or configurable set of parameters (referred to herein as a trigger condition), a device such as a user’s mobile device, smartphone, tablet, or wearable device with a user interface can prompt the user for input. The parameters of a trigger condition can relate to primary sensor data being monitored. There are many potential types of sensors, and therefore sensor data, from which to key-off the parameter detection. In some embodiments, a doctor designs a particular set of parameters for health events or conditions that the doctor would like to focus on for the patient from a diagnosis and/or treatment perspective. A set of parameters may specify ‘concurrently elevated heartrate and body temperature’, for instance. Some parameters may be based on biometric data or other information controlled or dependent on the user, while other parameters may not. For instance, current time, location, or weather may be parameters used in recognizing a health event and meeting a trigger condition for supplemental data gathering,” paragraph 0027 lines 1-19);
selecting, by the one or more computers, one or more data collection parameter values for collecting one or more of the first types of data in the first monitoring program based on the performance measures [] and the corresponding data collection parameters used [] (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” paragraph 0045 lines 3-9); and
adjusting, by the one or more computers, the first monitoring program to apply the selected one or more data collection parameter values, such that the one or more of the first types of data in the first monitoring program are collected from the remote devices according to the selected one or more data collection parameter values (“by tuning the trigger condition, for instance adding, deleting, or modifying a parameter of the trigger condition, this changes how the trigger condition is satisfied and therefore the scope (timing, breadth, etc.) of supplemental sensor data capture,” paragraph 0045 lines 19-23).
Chalas does not appear to expressly teach a method wherein the data describing data collection results is data describing data collection results of multiple other monitoring programs.
Hu teaches a method wherein the data describing data collection results is data describing data collection results of multiple other monitoring programs (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” paragraph 0028 lines 1-4).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the data collection results of Chalas to comprise the multiple other monitoring programs of Hu. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely leveraging the data collection results of multiple other monitoring programs (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” Hu paragraph 0028 lines 1-4). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Chalas/Hu does not appear to expressly teach a method wherein the obtained data includes (iii) performance measures indicating monitoring quality achieved for the monitoring program for the respective types of data acquired.
Jain teaches a method wherein the obtained data includes (iii) performance measures indicating monitoring quality achieved for the monitoring program for the respective types of data acquired (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” column 32 lines 60-63).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the obtained data of Chalas/Hu to comprise the monitoring quality of Jain. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely ensuring adequate monitoring quality (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 2, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein the performance measures specify a level of at least one of accuracy of collected data, precision of collected data, completeness of collected data, participant retention, or participant compliance (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63).
As to dependent claim 3, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein the method includes:
providing, for display in a user interface, an indication of the selected one or more data collection parameters (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9, emphasis added); and
receiving data indicating user input that confirms the selected one or more data collection parameters for the first monitoring program (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9, emphasis added);
wherein the first monitoring program is adjusted based (“by tuning the trigger condition, for instance adding, deleting, or modifying a parameter of the trigger condition, this changes how the trigger condition is satisfied and therefore the scope (timing, breadth, etc.) of supplemental sensor data capture,” Chalas paragraph 0045 lines 19-23) on receiving the data indicating the user input that confirms the selected one or more data collection parameters (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9).
As to dependent claim 4, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein adjusting the first monitoring program is performed automatically without requiring user input to confirm the adjustment (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9, emphasis added).
As to dependent claim 5, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein selecting the one or more data collection parameter values includes:
identifying a current (“the process continues with the acceptance of the provided health event data as updates (222) to be analyzed,” Chalas paragraph 0045 lines 1-2) parameter value that is currently used to collect a particular type of data in the first monitoring program (“upon detecting a predefined or configurable set of parameters (referred to herein as a trigger condition), a device such as a user’s mobile device, smartphone, tablet, or wearable device with a user interface can prompt the user for input. The parameters of a trigger condition can relate to primary sensor data being monitored. There are many potential types of sensors, and therefore sensor data, from which to key-off the parameter detection. In some embodiments, a doctor designs a particular set of parameters for health events or conditions that the doctor would like to focus on for the patient from a diagnosis and/or treatment perspective. A set of parameters may specify ‘concurrently elevated heartrate and body temperature’, for instance. Some parameters may be based on biometric data or other information controlled or dependent on the user, while other parameters may not. For instance, current time, location, or weather may be parameters used in recognizing a health event and meeting a trigger condition for supplemental data gathering,” Chalas paragraph 0027 lines 1-19);
identifying a target parameter value that is predicted, based on the data collection results of the multiple other monitoring programs, to improve monitoring performance for collecting the particular type of data in the first monitoring program (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63); and
selecting an intermediate parameter value that is between the current parameter value and the target parameter value (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9, emphasis added – a human is free to cautiously choose a gradual adjustment);
wherein adjusting the first monitoring program to apply the selected one or more data collection parameter values includes applying the selected intermediate parameter value for collection of the particular type of data in the first monitoring program (“by tuning the trigger condition, for instance adding, deleting, or modifying a parameter of the trigger condition, this changes how the trigger condition is satisfied and therefore the scope (timing, breadth, etc.) of supplemental sensor data capture,” Chalas paragraph 0045 lines 19-23).
As to dependent claim 6, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein selecting the one or more data collection parameter values includes identifying a target parameter value that is predicted, based on the data collection results of the multiple other monitoring programs, to improve monitoring performance for collecting a particular type of data in the first monitoring program (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63); and
adjusting the first monitoring program to apply the selected one or more data collection parameter values includes:
applying a series (“The process then cycles back to 206,” Chalas paragraph 0046 line 1) of adjustments to a data collection parameter used to collect the particular type of data in the first monitoring program, wherein the adjustments progressively move the value of the data collection parameter toward the target parameter value (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9, emphasis added – a human is free to cautiously choose gradual adjustments).
As to dependent claim 7, the rejection of claim 6 is incorporated. Chalas/Hu/Jain further teaches a method comprising, after each of the adjustments in the series of adjustments to the data collection parameter:
collecting data for the particular type of data from the remote devices (“The obtained self-perceived health status input, supplemental sensor data, and optionally primary sensor data, may be correlated as health event data of a health event saved to the event log,” Chalas paragraph 0044 lines 1-4) for a period of time using the adjusted value of the data collection parameter (“The process then cycles back to 206,” Chalas paragraph 0046 line 1; “This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9); and
evaluating monitoring performance achieved (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63) using the adjusted value of the data collection parameter (“The process then cycles back to 206,” Chalas paragraph 0046 line 1; “This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9);
wherein one or more of the adjustments to the data collection parameter are made (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9) based on the evaluation of monitoring performance achieved (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63) using the previous adjusted value of the data collection parameter (“The process then cycles back to 206,” Chalas paragraph 0046 line 1; “This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” Chalas paragraph 0045 lines 3-9).
As to dependent claim 8, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method comprising identifying a monitoring performance goal for the first monitoring program. The one or more data collection parameter values are selected based on the monitoring performance goal (“The adaptive dispatcher 102 can receive the pre-defined data collection goal, and determine the tasks required to accomplish the goal,” Hu paragraph 0020 lines 17-19).
As to dependent claim 9, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein the method includes identifying multiple monitoring performance goals for different aspects of monitoring performance for collecting a particular type of data in the first monitoring program (“by tuning the trigger condition, for instance adding, deleting, or modifying a parameter of the trigger condition, this changes how the trigger condition is satisfied and therefore the scope (timing, breadth, etc.) of supplemental sensor data capture,” Chalas paragraph 0045 lines 19-23, emphasis added); and
selecting the one or more data collection parameter values includes determining one or more data collection parameter values for collecting the particular type of data that are predicted to result in monitoring performance that satisfies each of the multiple monitoring performance goals (“by tuning the trigger condition, for instance adding, deleting, or modifying a parameter of the trigger condition, this changes how the trigger condition is satisfied and therefore the scope (timing, breadth, etc.) of supplemental sensor data capture,” Chalas paragraph 0045 lines 19-23).
As to dependent claim 10, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method comprising:
determining a performance measure for data collection of a particular type of data in the first monitoring program (“Sensor device 112 may also be a data processing system of relatively limited but dedicated capability, configured for a specific task of sensing one or more parameters (such as heart rate, body temperature, etc.) and providing the values to a remote device, such as a wearable device (e.g. 110) or other mobile device (e.g. 102),” Chalas paragraph 0017 lines 1-6) using a first set of data collection parameter settings (“upon detecting a predefined or configurable set of parameters (referred to herein as a trigger condition), a device such as a user’s mobile device, smartphone, tablet, or wearable device with a user interface can prompt the user for input. The parameters of a trigger condition can relate to primary sensor data being monitored. There are many potential types of sensors, and therefore sensor data, from which to key-off the parameter detection. In some embodiments, a doctor designs a particular set of parameters for health events or conditions that the doctor would like to focus on for the patient from a diagnosis and/or treatment perspective. A set of parameters may specify ‘concurrently elevated heartrate and body temperature’, for instance. Some parameters may be based on biometric data or other information controlled or dependent on the user, while other parameters may not. For instance, current time, location, or weather may be parameters used in recognizing a health event and meeting a trigger condition for supplemental data gathering,” Chalas paragraph 0027 lines 1-19);
determining a reference performance measure for data collection of the particular type of data based on the data collection results of the multiple other monitoring programs (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” Hu paragraph 0028 lines 1-4); and
determining that the performance measure indicates lower performance than the reference performance measure (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63);
wherein (i) selecting the one or more data collection parameter values and (ii) adjusting the first monitoring program to apply the selected one or more data collection parameter values are performed based on determining that the performance measure indicates lower performance than the reference performance measure (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63).
As to dependent claim 11, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method comprising:
repeatedly performing a cycle (“The process then cycles back to 206,” Chalas paragraph 0046 line 1) that includes:
generating performance measures that quantify monitoring performance of the first monitoring program (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63) since a most recent adjustment to data collection parameter values for the first monitoring program (“by tuning the trigger condition, for instance adding, deleting, or modifying a parameter of the trigger condition, this changes how the trigger condition is satisfied and therefore the scope (timing, breadth, etc.) of supplemental sensor data capture,” Chalas paragraph 0045 lines 19-23);
comparing the performance measures for the first monitoring program with reference performance measures determined based on the data collection results (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63) of the multiple other monitoring programs (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” Hu paragraph 0028 lines 1-4); and
selectively adjusting the data collection parameter values for the first monitoring program based on the comparison (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63).
As to dependent claim 12, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method comprising:
evaluating monitoring performance for data collection in the first monitoring program (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63); and
based on the evaluation, identifying an opportunity to improve monitoring performance for data collection in the first monitoring program (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63), where identifying the opportunity is based on at least one of:
detecting a decline in monitoring performance for the first monitoring program;
detecting that a measure of monitoring performance is below a predetermined threshold (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63) or goal for the first monitoring program (“The adaptive dispatcher 102 can receive the pre-defined data collection goal, and determine the tasks required to accomplish the goal,” Hu paragraph 0020 lines 17-19);
predicting a future decline in monitoring performance for the first monitoring program based on a progression of monitoring performance in one or more other monitoring programs;
predicting, based on data collection results for the other monitoring programs, that an increased level of monitoring performance is available for the first monitoring program (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63); or
determining that a measure of monitoring performance for the first monitoring program indicates lower performance than a measure of peer monitoring performance, wherein the measure of peer monitoring performance is an aggregate measure based on data collection results of each of a proper subset of the other monitoring programs, wherein the proper subset is selected based on similarity with the first monitoring program;
wherein selecting the one or more data collection parameter values and adjusting the first monitoring program to apply the selected one or more data collection parameter values are triggered based on identifying the opportunity (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63).
As to dependent claim 13, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein selecting the one or more data collection parameters includes selecting the one or more data collection parameters based on output of a machine learning model (“The data used to drive the selection and adjustment of monitoring actions (or treatment actions as discussed below) can be stored in any appropriate data structure or format. For example, the data can be provided as a decision tree, a table, a set of rules, a machine learning model configured to classify or predict which actions are appropriate given input about a user and the user’s community, and so on,” Jain column 42 lines 23-30).
As to dependent claim 14, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein the method includes training a machine learning model based on the obtained data (“The data used to drive the selection and adjustment of monitoring actions (or treatment actions as discussed below) can be stored in any appropriate data structure or format. For example, the data can be provided as a decision tree, a table, a set of rules, a machine learning model configured to classify or predict which actions are appropriate given input about a user and the user’s community, and so on,” Jain column 42 lines 23-30) for the other monitoring programs (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” Hu paragraph 0028 lines 1-4), the machine learning model being trained to predict data collection parameters for a monitoring program based on input of characteristics of a the monitoring program (“The data used to drive the selection and adjustment of monitoring actions (or treatment actions as discussed below) can be stored in any appropriate data structure or format. For example, the data can be provided as a decision tree, a table, a set of rules, a machine learning model configured to classify or predict which actions are appropriate given input about a user and the user’s community, and so on,” Jain column 42 lines 23-30).
As to dependent claim 15, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein the first monitoring program is a new monitoring program. Selecting the one or more data collection parameter values for collecting one or more of the first types of data in the first monitoring program includes selecting initial data collection parameter values to apply in the first monitoring program (“upon detecting a predefined or configurable set of parameters (referred to herein as a trigger condition), a device such as a user’s mobile device, smartphone, tablet, or wearable device with a user interface can prompt the user for input. The parameters of a trigger condition can relate to primary sensor data being monitored. There are many potential types of sensors, and therefore sensor data, from which to key-off the parameter detection. In some embodiments, a doctor designs a particular set of parameters for health events or conditions that the doctor would like to focus on for the patient from a diagnosis and/or treatment perspective. A set of parameters may specify ‘concurrently elevated heartrate and body temperature’, for instance. Some parameters may be based on biometric data or other information controlled or dependent on the user, while other parameters may not. For instance, current time, location, or weather may be parameters used in recognizing a health event and meeting a trigger condition for supplemental data gathering,” Chalas paragraph 0027 lines 1-19).
As to dependent claim 16, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein the first monitoring program is an existing monitoring program that has collected data over a period of time (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” Hu paragraph 0028 lines 1-4) using a first set of data collection parameter values to collect one or more of the first types of data (“upon detecting a predefined or configurable set of parameters (referred to herein as a trigger condition), a device such as a user’s mobile device, smartphone, tablet, or wearable device with a user interface can prompt the user for input. The parameters of a trigger condition can relate to primary sensor data being monitored. There are many potential types of sensors, and therefore sensor data, from which to key-off the parameter detection. In some embodiments, a doctor designs a particular set of parameters for health events or conditions that the doctor would like to focus on for the patient from a diagnosis and/or treatment perspective. A set of parameters may specify ‘concurrently elevated heartrate and body temperature’, for instance. Some parameters may be based on biometric data or other information controlled or dependent on the user, while other parameters may not. For instance, current time, location, or weather may be parameters used in recognizing a health event and meeting a trigger condition for supplemental data gathering,” Chalas paragraph 0027 lines 1-19); and
selecting the one or more data collection parameter values for collecting one or more of the first types of data in the first monitoring program includes selecting a second set of data collection parameter values for collecting one or more of the first types of data, wherein the second set of data collection parameter values is selected based on a level of monitoring performance achieved over the period of time (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63).
As to dependent claim 17, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein the selected one or more data collection parameter values include values for data collection parameters including at least one of frequency of data collection, time of day for data collection, location of data collection, sensor settings, survey content provided, measurement precision, sampling rate, window size, or sensor operation settings (“upon detecting a predefined or configurable set of parameters (referred to herein as a trigger condition), a device such as a user’s mobile device, smartphone, tablet, or wearable device with a user interface can prompt the user for input. The parameters of a trigger condition can relate to primary sensor data being monitored. There are many potential types of sensors, and therefore sensor data, from which to key-off the parameter detection. In some embodiments, a doctor designs a particular set of parameters for health events or conditions that the doctor would like to focus on for the patient from a diagnosis and/or treatment perspective. A set of parameters may specify ‘concurrently elevated heartrate and body temperature’, for instance. Some parameters may be based on biometric data or other information controlled or dependent on the user, while other parameters may not. For instance, current time, location, or weather may be parameters used in recognizing a health event and meeting a trigger condition for supplemental data gathering,” Chalas paragraph 0027 lines 1-19; “measuring different physiological parameters, different behavioral parameters, different mental health or cognitive parameters, and so on. Similarly, different monitoring levels may involve different frequency, intensity, or precision of data collection,” Jain column 42 lines 44-48).
As to dependent claim 18, the rejection of claim 1 is incorporated. Chalas/Hu/Jain further teaches a method wherein the first types of data include types of data in one or more categories from among at least behavioral data, physiological data, mental health data, and environmental data (“upon detecting a predefined or configurable set of parameters (referred to herein as a trigger condition), a device such as a user’s mobile device, smartphone, tablet, or wearable device with a user interface can prompt the user for input. The parameters of a trigger condition can relate to primary sensor data being monitored. There are many potential types of sensors, and therefore sensor data, from which to key-off the parameter detection. In some embodiments, a doctor designs a particular set of parameters for health events or conditions that the doctor would like to focus on for the patient from a diagnosis and/or treatment perspective. A set of parameters may specify ‘concurrently elevated heartrate and body temperature’, for instance. Some parameters may be based on biometric data or other information controlled or dependent on the user, while other parameters may not. For instance, current time, location, or weather may be parameters used in recognizing a health event and meeting a trigger condition for supplemental data gathering,” Chalas paragraph 0027 lines 1-19; “measuring different physiological parameters, different behavioral parameters, different mental health or cognitive parameters, and so on. Similarly, different monitoring levels may involve different frequency, intensity, or precision of data collection,” Jain column 42 lines 44-48).
As to independent claim 19, Chalas teaches a system comprising:
one or more computers (“Environment 100 includes mobile device 102 that communicates with cloud server(s) 104 via network(s) 106,” paragraph 0014 lines 2-4); and
one or more computer-readable media storing instructions (“program modules may be located in both local and remote computer system storage media including memory storage devices,” paragraph 0074 lines 12-14) that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
determining, by the one or more computers, first types of data to be collected in a first monitoring program from remote devices over a communication network (“Sensor device 112 may also be a data processing system of relatively limited but dedicated capability, configured for a specific task of sensing one or more parameters (such as heart rate, body temperature, etc.) and providing the values to a remote device, such as a wearable device (e.g. 110) or other mobile device (e.g. 102),” paragraph 0017 lines 1-6);
obtaining, by the one or more computers, data describing data collection results [] (“the process continues with the acceptance of the provided health event data as updates (222) to be analyzed,” paragraph 0045 lines 1-2), wherein the obtained data includes data [] that indicates (i) types of data acquired in the monitoring program (“The obtained self-perceived health status input, supplemental sensor data, and optionally primary sensor data, may be correlated as health event data of a health event saved to the event log,” paragraph 0044 lines 1-4), and (ii) data collection parameter values used for the monitoring programs for the respective types of data acquired (“upon detecting a predefined or configurable set of parameters (referred to herein as a trigger condition), a device such as a user’s mobile device, smartphone, tablet, or wearable device with a user interface can prompt the user for input. The parameters of a trigger condition can relate to primary sensor data being monitored. There are many potential types of sensors, and therefore sensor data, from which to key-off the parameter detection. In some embodiments, a doctor designs a particular set of parameters for health events or conditions that the doctor would like to focus on for the patient from a diagnosis and/or treatment perspective. A set of parameters may specify ‘concurrently elevated heartrate and body temperature’, for instance. Some parameters may be based on biometric data or other information controlled or dependent on the user, while other parameters may not. For instance, current time, location, or weather may be parameters used in recognizing a health event and meeting a trigger condition for supplemental data gathering,” paragraph 0027 lines 1-19);
selecting, by the one or more computers, one or more data collection parameter values for collecting one or more of the first types of data in the first monitoring program based on the performance measures [] and the corresponding data collection parameters used [] (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” paragraph 0045 lines 3-9); and
adjusting, by the one or more computers, the first monitoring program to apply the selected one or more data collection parameter values, such that the one or more of the first types of data in the first monitoring program are collected from the remote devices according to the selected one or more data collection parameter values (“by tuning the trigger condition, for instance adding, deleting, or modifying a parameter of the trigger condition, this changes how the trigger condition is satisfied and therefore the scope (timing, breadth, etc.) of supplemental sensor data capture,” paragraph 0045 lines 19-23).
Chalas does not appear to expressly teach a system wherein the data describing data collection results is data describing data collection results of multiple other monitoring programs.
Hu teaches a system wherein the data describing data collection results is data describing data collection results of multiple other monitoring programs (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” paragraph 0028 lines 1-4).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the data collection results of Chalas to comprise the multiple other monitoring programs of Hu. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely leveraging the data collection results of multiple other monitoring programs (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” Hu paragraph 0028 lines 1-4). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Chalas/Hu does not appear to expressly teach a system wherein the obtained data includes (iii) performance measures indicating monitoring quality achieved for the monitoring program for the respective types of data acquired.
Jain teaches a system wherein the obtained data includes (iii) performance measures indicating monitoring quality achieved for the monitoring program for the respective types of data acquired (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” column 32 lines 60-63).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the obtained data of Chalas/Hu to comprise the monitoring quality of Jain. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely ensuring adequate monitoring quality (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to independent claim 20, Chalas teaches one or more non-transitory computer-readable media storing instructions (“program modules may be located in both local and remote computer system storage media including memory storage devices,” paragraph 0074 lines 12-14) that are operable, when executed by one or more computers, to cause the one or more computers to perform operations comprising:
determining, by the one or more computers, first types of data to be collected in a first monitoring program from remote devices over a communication network (“Sensor device 112 may also be a data processing system of relatively limited but dedicated capability, configured for a specific task of sensing one or more parameters (such as heart rate, body temperature, etc.) and providing the values to a remote device, such as a wearable device (e.g. 110) or other mobile device (e.g. 102),” paragraph 0017 lines 1-6);
obtaining, by the one or more computers, data describing data collection results [] (“the process continues with the acceptance of the provided health event data as updates (222) to be analyzed,” paragraph 0045 lines 1-2), wherein the obtained data includes data [] that indicates (i) types of data acquired in the monitoring program (“The obtained self-perceived health status input, supplemental sensor data, and optionally primary sensor data, may be correlated as health event data of a health event saved to the event log,” paragraph 0044 lines 1-4), and (ii) data collection parameter values used for the monitoring programs for the respective types of data acquired (“upon detecting a predefined or configurable set of parameters (referred to herein as a trigger condition), a device such as a user’s mobile device, smartphone, tablet, or wearable device with a user interface can prompt the user for input. The parameters of a trigger condition can relate to primary sensor data being monitored. There are many potential types of sensors, and therefore sensor data, from which to key-off the parameter detection. In some embodiments, a doctor designs a particular set of parameters for health events or conditions that the doctor would like to focus on for the patient from a diagnosis and/or treatment perspective. A set of parameters may specify ‘concurrently elevated heartrate and body temperature’, for instance. Some parameters may be based on biometric data or other information controlled or dependent on the user, while other parameters may not. For instance, current time, location, or weather may be parameters used in recognizing a health event and meeting a trigger condition for supplemental data gathering,” paragraph 0027 lines 1-19);
selecting, by the one or more computers, one or more data collection parameter values for collecting one or more of the first types of data in the first monitoring program based on the performance measures [] and the corresponding data collection parameters used [] (“This can represent the point at which the doctor, other medical professional, or analytics system, as examples, has an opportunity to accept the data and optionally take any appropriate actions, such as making diagnoses, treatment plans, and/or changes to the configuration 224 in terms of what the trigger conditions 226 should be and the specification 228 of the user input prompts,” paragraph 0045 lines 3-9); and
adjusting, by the one or more computers, the first monitoring program to apply the selected one or more data collection parameter values, such that the one or more of the first types of data in the first monitoring program are collected from the remote devices according to the selected one or more data collection parameter values (“by tuning the trigger condition, for instance adding, deleting, or modifying a parameter of the trigger condition, this changes how the trigger condition is satisfied and therefore the scope (timing, breadth, etc.) of supplemental sensor data capture,” paragraph 0045 lines 19-23).
Chalas does not appear to expressly teach media wherein the data describing data collection results is data describing data collection results of multiple other monitoring programs.
Hu teaches media wherein the data describing data collection results is data describing data collection results of multiple other monitoring programs (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” paragraph 0028 lines 1-4).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the data collection results of Chalas to comprise the multiple other monitoring programs of Hu. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely leveraging the data collection results of multiple other monitoring programs (“the adaptive dispatcher 102 can receive historical data from the historical collection analyzer module 116 and/or real-time data from the real-time collection analyzer module 118,” Hu paragraph 0028 lines 1-4). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Chalas/Hu does not appear to expressly teach media wherein the obtained data includes (iii) performance measures indicating monitoring quality achieved for the monitoring program for the respective types of data acquired.
Jain teaches media wherein the obtained data includes (iii) performance measures indicating monitoring quality achieved for the monitoring program for the respective types of data acquired (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” column 32 lines 60-63).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the obtained data of Chalas/Hu to comprise the monitoring quality of Jain. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely ensuring adequate monitoring quality (“the computer system 110 evaluates the quality of data received and may initiate adjustments to the monitoring process to improve the completeness or quality of data collected for a user,” Jain column 32 lines 60-63). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Jain et al., US 11,127,506 B1 disclosing data collection systems
Jain et al., US 11,102,304 B1 disclosing data collection systems
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/Ryan Barrett/
Primary Examiner, Art Unit 2148
Prosecution Insights