DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-15 are pending and presented for examination.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The representative claim 15 recites:
A system for determining a target value to be reached for a parameter over an upcoming time window, the system comprising:
a communication module arranged to receive a set of data relating to a plurality of preceding time windows;
a storage unit arranged to store the received set of data, the data relating to a preceding time window including the value reached by said parameter over said preceding time window and the associated target value; and
a processing unit configured to:
select an appropriate time scale among at least two predetermined time scales,
retrieve, from the storage unit, data relating to one or more preceding time windows based on said time scale,
calculate a success factor based on the comparison between the value reached by said parameter over one or more preceding time windows and the associated target value, and
determine the target value for the upcoming time window based on said success factor and the value reached by said parameter over each preceding time window of the selected data.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category (process).
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and/or mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because the additional limitations in the claim are only: a communication module arranged to receive a set of data relating to a plurality of preceding time windows; a storage unit arranged to store the received set of data, the data relating to a preceding time window including the value reached by said parameter over said preceding time window and the associated target value; and a processing unit configured to:...retrieve, from the storage unit, data relating to one or more preceding time windows based on said time scale. The limitations “a communication module arranged to receive a set of data relating to a plurality of preceding time windows; and retrieve, from the storage unit, data relating to one or more preceding time windows based on said time scale” are recited at a high level of generality (i.e., gathering or collecting data using a generic computer components) such that they amount no more than mere instructions to apply the exception using a generic computer components.
Further, the claim limitations “a communication module…a storage unit arranged to store the received set of data, the data relating to a preceding time window including the value reached by said parameter over said preceding time window and the associated target value; and a processing unit”, are recited at a high level of generality (i.e., as a generic computer structures performing a generic computer function of receiving, processing, and storing information) such that they amount no more than mere instructions to apply the exception using a generic computer components.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
Claim 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the additional limitations recited at a high level of generality (i.e., as a generic computer structures performing a generic computer function of receiving, retrieving, processing, and storing information). Further, the additional elements are conventional in the art, as evidenced by the art of record (see, Keen et al. US 2016/0058331 (hereinafter, Keen), ([0105], Fig. 1), and Ohnemus et al. US 2014/0135592 (hereinafter, Ohnemus), ([0068], Figs. 1-2). Therefore, claim 15 is directed to an abstract idea without significantly more.
The claim is not patent eligible.
Independent claim 1, the claim is rejected with the same rationale as in claim 15.
Dependent claim 2, recites addition element of “wherein the value of said parameter is measured by a portable device adapted to be worn by an individual”. However, this limitation is recited at a high level of generality (i.e., gathering data using a portable device) such that it amounts no more than mere instructions to apply the exception using a generic device. Further, the additional element is conventional in the art, as evidenced by the art of record (see, Keen, ([0025]), and Ohnemus ([0059], Fig. 1). Therefore, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible.
Dependent claim 3, the claim is rejected with the same rationale as in claim 2.
Dependent claims 4-13, add further details of the identified abstract idea. The claims are not patent eligible.
Dependent claim 14, the claim is rejected with the same rationale as in claim 15.
Claim Rejections - 35 USC § 112
5. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
6. Claims 1-15 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
7. Claim 1 and 15 recite the limitation “selecting an appropriate time scale among at least two predetermined time scales.” However, the claim language “appropriate” is a relative term which renders the claim indefinite. Examiner is unable to construe the extent or boundaries of “appropriate” in light of the Specification. This term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction/clarification is required.
Dependent claims 5, 6, and 8, the claims are rejected with the same rationale as in claims 1 and 15.
8. Claim 3 recites the limitation “wherein the parameter quantifies the intensity or quality of an individual's physical activity, such as a number of steps, a distance traveled by an individual, a number of calories burned or the duration of said physical activity.” However, the claim language “such as” is unclear. The features introduced by “such as” are not limiting the scope of the claim and which renders the claim indefinite. Appropriate correction/clarification is required.
Claim Objection
9. Claim 13 is objected to because of the following informalities: Claim 13 recites “a predictive model is generated by learning for each group…” should read “[[a]] the predictive model is generated by learning for each group…” Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
10. Claim 15 in this application is given its broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) Claim 15 limitation use the terms “a communication module, a storage unit, and a processing unit” that are generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the terms “a communication module, a storage unit, and a processing unit” or the generic placeholder are modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the terms “a communication module, a storage unit, and a processing unit” or the generic placeholder are not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the terms “a communication module, a storage unit, and a processing unit” in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the terms “a communication module, a storage unit, and a processing unit” in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the terms ““a communication module, a storage unit, and a processing unit” are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the terms “a communication module, a storage unit, and a processing unit” are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Rejections - 35 USC § 103
11. In the event the determination of the status of the application as subject to AlA 35 U.S.C. 102 and 103 (or as subject to pre-AlA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
12. Claims 1-10, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Keen et al. US 2016/0058331 (hereinafter, Keen).
13. Regarding claim 1, Keen discloses a computer-implemented method for determining a target value to be reached for a parameter over an upcoming time window, the method comprising:
providing a set of data relating to preceding time windows and including, for each preceding time window, the value reached by said parameter over said preceding time window and the target value associated with said preceding time window([0025], [0029], [0046]: receive historical activity data of a user corresponding to a particular time of day. For example, the service provider may receive information indicating that the user burned 50 calories by 11 am on the previous day, or by 11 am on a previous Tuesday….Additionally, the historical activity data may comprise calories burned by the user, heart beats of the user, steps walked by the user, distance traveled by the user, or minutes exercised by the user ([0007])),
an appropriate time scale among at least two predetermined time scales ([0029]);
retrieving data relating to one or more preceding time windows based on said time scale ([0029]: receive information indicating that the user burned 50 calories by 11 am on the previous day, or by 11 am on a previous Tuesday);
calculating a success factor based on the comparison between the value reached by said parameter over one or more preceding time windows and the associated target value ([0029]: receive historical activity data of a user corresponding to a particular time of day. For example, the service provider may receive information indicating that the user burned 50 calories by 11 am on the previous day, or by 11 am on a previous Tuesday... The service provider may then generate a user interface that displays an activity goal of the user (e.g., 1000 calories burned) along with a first indicator that displays that by 11 am of the current day (e.g., today), the user has burned 100 calories or 10% of the goal); and
determining the target value for the upcoming time window based on said success factor and the value reached by said parameter over each preceding time window of the retrieved data ([0029], [0034]: a service provider may be configured to provide a user interface with an indicator that displays cumulative daily progress (e.g., calories burned or the like) towards a goal…Additionally, the user interface may also include another indicator that displays a predicted cumulative daily progress for the same time of day, for at least one day occurring before the current day... The user interface may also display another indicator that displays that by 11 am of the current day, the users predicted accumulated activity level would be 50 calories based at least in part on the historical data that indicated that the user had burned 50 calories by 11 am previously).
Keen does not disclose:
selecting an appropriate time scale among at least two predetermined time scales.
However, Keen discloses:
“ In some implementations, a temporal forecast can be generated for an attribute or attribute value. The temporal forecast can indicate, for example, what values for the attributes are likely to occur at what times of the day and/or at what time of day an event associated with the attribute or attribute value is likely to occur. For example, a client of the sampling daemon can request a temporal forecast for the attribute (e.g., calories burned) over the last week (e.g., last 7 days). To generate the forecast, a 24-hour day can be divided into 96 15-minute timeslots. For a particular timeslot (e.g., 1:00-1:15 pm) on each of the last seven days, the sampling daemon can determine how many calories were burned during each time slot and generate a score for each timeslot that indicates the maximal expected calorie burn... It should be noted that while FIG. 3 illustrates Day 1 and Day 1, Interval 1 and Interval 2, etc., any number of previous days or time periods (e.g., weeks, months, etc.) may be used for calculating and/or collecting historical activity data…Further, in some examples, any time prior to the current time period (e.g., current time interval) may be considered a first time period, and the current day may be considered a second time period. As such, at a high level, predicted activity data for any interval of the current day (or the second time period) may be based at least in part on historical activity data from intervals of the first time period (see, [0029], [0034], [0046]).
Therefore, 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 system of Keen to use selecting an appropriate time scale among at least two predetermined time scales based on the teaching of Keen as disclosed above. The motivation for doing so would have been in order to indicate the relative likelihood of the target value occurring during a predetermined time period of interest (Keen, [0034]-[0035]).
14. Regarding claims 14 and 15, the claims are rejected with the same rationale as in claim 1.
15. Regarding claim 2, Keen discloses the method of claim 1, as disclosed above.
Keen further discloses wherein the value of said parameter is measured by a portable device adapted to be worn by an individual ([0025], [0070]).
16. Regarding claim 3, Keen discloses the method of claim 2, as disclosed above.
Keen further discloses the portable device being a pedometer-type device sensitive to motion, wherein the parameter quantifies the intensity or quality of an individual's physical activity, such as a number of steps, a distance traveled by an individual, a number of calories burned or the duration of said physical activity ([0007], [0025]).
17. Regarding claim 4, Keen discloses the method of claim 3, as disclosed above.
Keen further discloses wherein the success factor is calculated based on a profile of said individual depending on physiological characteristics or data relating to a regular physical activity or health of said individual ([0029]: receive historical activity data of a user corresponding to a particular time of day. For example, the service provider may receive information indicating that the user burned 50 calories by 11 am on the previous day, or by 11 am on a previous Tuesday... The service provider may then generate a user interface that displays an activity goal of the user (e.g., 1000 calories burned) along with a first indicator that displays that by 11 am of the current day (e.g., today), the user has burned 100 calories or 10% of the goal).
18. Regarding claim 5, Keen discloses the method of claim 1, as disclosed above.
Keen further discloses wherein the selection of the appropriate time scale depends on the variations in the parameter value over one or more preceding time windows ([0029], [0045]-[0046]).
19. Regarding claim 6, Keen discloses the method of claim 5, as disclosed above.
Keen further discloses wherein each time scale of the at least two predetermined time scales relates to a period of time, and wherein selecting the appropriate time scale comprises: for each time scale of the at least two predetermined time scales: selecting data relating to preceding time windows spaced from the upcoming time window by a multiple of the period of time to which said time scale relates ([0029]: the service provider may receive historical activity data of a user corresponding to a particular time of day. For example, the service provider may receive information indicating that the user burned 50 calories by 11 am on the previous day, or by 11 am on a previous Tuesday. The service provider may then generate a user interface that displays an activity goal of the user (e.g., 1000 calories burned) along with a first indicator that displays that by 11 am of the current day (e.g., today), the user has burned 100 calories or 10% of the goal. The user interface may also display another indicator that displays that by 11 am of the current day, the users predicted accumulated activity level would be 50 calories based at least in part on the historical data that indicated that the user had burned 50 calories by 11 am previously….[Further], [0034], [0044]: For example, a client of the sampling daemon can request a temporal forecast for the attribute (e.g., calories burned) over the last week (e.g., last 7 days). To generate the forecast, a 24-hour day can be divided into 96 15-minute timeslots. For a particular timeslot (e.g., 1:00-1:15 pm) on each of the last seven days, the sampling daemon can determine how many calories were burned during each time slot and generate a score for each timeslot that indicates the maximal expected calorie burn),
calculating, for each pair of two consecutive preceding time windows of the selected data, a difference between the values reached by the parameter respectively over each preceding time window of said pair of two consecutives preceding time windows ([0029], [0045]-[0046]), and
assigning to said time scale a stability score characterizing the one or more calculated differences; and selecting the appropriate time scale based on the respective stability scores of the time scales of the at least two predetermined time scales ([0029], [0034], [0045]-[0046]).
20. Regarding claim 7, Keen discloses the method of claim 6, as disclosed above.
Keen further discloses wherein the stability score assigned to a time scale depends on a ratio of pair of two consecutive preceding time windows of the selected data for which the calculated difference is lower than or equal to a predetermined threshold ([0005], [0029]: In some examples, the activity may be capable of being tracked cumulatively. The predicted amount of activity may be based at least in part on a probability that the user will meet a threshold activity level during the at least one interval. The at least one interval may comprise the current interval and/or the predicted amount of activity may be based at least in part on historical activity data of the user…the service provider may receive historical activity data of a user corresponding to a particular time of day. For example, the service provider may receive information indicating that the user burned 50 calories by 11 am on the previous day, or by 11 am on a previous Tuesday. The service provider may then generate a user interface that displays an activity goal of the user (e.g., 1000 calories burned) along with a first indicator that displays that by 11 am of the current day (e.g., today), the user has burned 100 calories or 10% of the goal...[Further], [0034], [0045]: To generate the forecast, a 24-hour day can be divided into 96 15-minute timeslots. For a particular timeslot (e.g., 1:00-1:15 pm) on each of the last seven days, the sampling daemon can determine how many calories were burned during each time slot and generate a score for each timeslot that indicates the maximal expected calorie burn. In some implementations, the temporal forecast can be generated based on an event history window specification. For example, if the client provides a window specification that specifies a 4-hour time period of interest, the temporal forecast will only generate likelihood scores for the 15-minute timeslots that are in the 4-hour time period of interest. For example, if the time period of interest corresponds to 12:00-4:00 pm for each of the last 3 days, then 16 timeslots will be generated during the 4 hour period of interest and a score will be generated for each of the 16 15 minute timeslots. Scores will not be generated for timeslots outside the specified 4 hour time period of interest).
21. Regarding claim 8, Keen discloses the method of claim 5, as disclosed above.
Keen further discloses wherein each time scale of the at least two predetermined time scales relates to a period of time, and wherein selecting the appropriate time scale comprises: for each time scale of the at least two predetermined time scales: selecting data relating to one or more preceding time windows spaced from the upcoming time window by a multiple of the period of time to which said time scale relates ([0029]: the service provider may receive historical activity data of a user corresponding to a particular time of day. For example, the service provider may receive information indicating that the user burned 50 calories by 11 am on the previous day, or by 11 am on a previous Tuesday. The service provider may then generate a user interface that displays an activity goal of the user (e.g., 1000 calories burned) along with a first indicator that displays that by 11 am of the current day (e.g., today), the user has burned 100 calories or 10% of the goal. The user interface may also display another indicator that displays that by 11 am of the current day, the users predicted accumulated activity level would be 50 calories based at least in part on the historical data that indicated that the user had burned 50 calories by 11 am previously….[Further], [0034], [0044]: For example, a client of the sampling daemon can request a temporal forecast for the attribute (e.g., calories burned) over the last week (e.g., last 7 days). To generate the forecast, a 24-hour day can be divided into 96 15-minute timeslots. For a particular timeslot (e.g., 1:00-1:15 pm) on each of the last seven days, the sampling daemon can determine how many calories were burned during each time slot and generate a score for each timeslot that indicates the maximal expected calorie burn),
calculating, for each preceding time window of the selected data, a difference between the value reached by the parameter over said preceding time window and the target value associated with said preceding time window ([0029], [0045]-[0046]), and
assigning to said time scale a regularity score characterizing the one or more calculated differences; and selecting the appropriate time scale based on the respective regularity scores of the time scales of the at least two predetermined time scales ([0029], [0034], [0045]-[0046]).
22. Regarding claim 9, Keen discloses the method of claim 8, as disclosed above.
Keen further discloses wherein the regularity score assigned to a time scale depends on a ratio of preceding time windows of the selected data for which the calculated difference is lower than or equal to a predetermined threshold ([0005], [0029]: In some examples, the activity may be capable of being tracked cumulatively. The predicted amount of activity may be based at least in part on a probability that the user will meet a threshold activity level during the at least one interval. The at least one interval may comprise the current interval and/or the predicted amount of activity may be based at least in part on historical activity data of the user…the service provider may receive historical activity data of a user corresponding to a particular time of day. For example, the service provider may receive information indicating that the user burned 50 calories by 11 am on the previous day, or by 11 am on a previous Tuesday. The service provider may then generate a user interface that displays an activity goal of the user (e.g., 1000 calories burned) along with a first indicator that displays that by 11 am of the current day (e.g., today), the user has burned 100 calories or 10% of the goal...[Further], [0034], [0045]: To generate the forecast, a 24-hour day can be divided into 96 15-minute timeslots. For a particular timeslot (e.g., 1:00-1:15 pm) on each of the last seven days, the sampling daemon can determine how many calories were burned during each time slot and generate a score for each timeslot that indicates the maximal expected calorie burn. In some implementations, the temporal forecast can be generated based on an event history window specification. For example, if the client provides a window specification that specifies a 4-hour time period of interest, the temporal forecast will only generate likelihood scores for the 15-minute timeslots that are in the 4-hour time period of interest. For example, if the time period of interest corresponds to 12:00-4:00 pm for each of the last 3 days, then 16 timeslots will be generated during the 4 hour period of interest and a score will be generated for each of the 16 15 minute timeslots. Scores will not be generated for timeslots outside the specified 4 hour time period of interest).
23. Regarding claim 10, Keen discloses the method of claim 1, as disclosed above.
Keen further discloses wherein each time scale of the at least two predetermined time scales relates to a period of time, and wherein the retrieved data relate to one or more preceding time windows spaced from the upcoming time window by a multiple of the period of time to which the selected time scale relates ([0029], [0034], [0046]).
24. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Keen, in view of Ohnemus et al. US 2014/0135592 (hereinafter, Ohnemus).
25. Regarding claim 11, Keen discloses the method of claim 1, as disclosed above.
Keen further discloses wherein determining the target value for the upcoming time window comprises weighting of the value reached by the parameter over each preceding time window of the retrieved data ([0026], [0064]: the probability graph may illustrate what the user's collected activity data is expected to resemble and/or the maximal likelihood activity data collected for any given moment in time (e.g., the attribute value with the highest probability may be provided). For example, if a user typically exercises on Monday mornings, the probability graph may show a high probability that during the morning hours on Monday, the user may have accumulated a higher than average (or at least higher than during other parts of the day) value for the activity…In some cases, the sampling daemon can generate duration statistics. For example, the sampling daemon can determine a duration associated with an attribute value by comparing an attribute's start event with the attribute's stop event. The time difference between when the start event occurred and when the stop event occurred will be the duration of the event. In some implementations, a client can request and the sampling daemon can return the minimum, maximum, mean, mode, or standard deviation for all durations associated with the specified attribute or attribute value within the specified history window).
Keen does not disclose:
weighting of the value reached by the parameter over each preceding time window of the retrieved data so that the weighting coefficient of the value reached by the parameter over a given time window is greater than or equal to the weighting coefficient of the value reached by the parameter over a time window prior to said given time window.
However, Ohnemus discloses:
“ a weighting module recalls weighting factors from the memory. The weighting factors can be multiplication coefficients that are used to increase or decrease the relative value of each health parameters. A weighting factor is assigned to each health parameter as shown in the formulas herein. The weighting factors are used to control the relative values of the health parameters. Some health parameters are more important than others in the calculation of the users' health score. Accordingly, weighting factors are applied to the health parameters increase or decrease the relative affect each factor has in the calculation of the user's health score. For example, a user's current body weight can be more important than the amount of fitness activity the user engages in. In this example, the body weight parameter would be weighted more heavily by assigning a larger weighting factor to this parameter. At step 750, the weighting module applies the recalled weighting factors to the collected health parameter values to provide weighted health parameter values. The weighting factor can be zero in which case a particular parameter has no impact on the health score. The weighting factor can be a negative value for use in some algorithms” (see, [0068]-[0069])).
Therefore, 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 system of Keen to use weighting of the value reached by the parameter over each preceding time window of the retrieved data so that the weighting coefficient of the value reached by the parameter over a given time window is greater than or equal to the weighting coefficient of the value reached by the parameter over a time window prior to said given time window as taught by Ohnemus. The motivation for doing so would have been in order to quantifying and weighting the relative values of the parameters (Ohnemus, [0068]).
26. Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Keen, in view of Kerber US 10602964 (hereinafter, Kerber).
27. Regarding claim 12, Keen discloses the method of claim 1, as disclosed above.
Keen further discloses generating a time series including values of the parameter, said time series being updated with each new value reached by said parameter over a given time window ([0025], [0029], [0034]); and
generating at least one predictive model of the value of the parameter for a given time window as a function of a time component characterizing said time window, wherein the target value for the upcoming time window is determined using said at least one predictive model ([0025], [0028]-[0029], [0034]).
Keen does not disclose:
generating by learning at least one predictive model of the value of the parameter for a given time window as a function of a time component characterizing said time window.
However, Kerber discloses:
generating by learning at least one predictive model of the value of the parameter for a given time window as a function of a time component characterizing said time window (column 3, line 6 - column 4, line 7, and column 9, lines 56-67).
Therefore, 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 system of Keen to use generating by learning at least one predictive model of the value of the parameter for a given time window as a function of a time component characterizing said time window as taught by Kerber. The motivation for doing so would have been in order to enhance the accuracy and reliability of the target value (Kerber, column 9, lines 56-67).
28. Regarding claim 13, Keen discloses the method of claim 12, as disclosed above.
Keen further discloses wherein the time series is segmented into a plurality of groups of time windows according to a criterion related to the respective time components of said time windows ([0029], [0034], [0046]); and
a predictive model is generated for each group of time windows, and wherein determining the target value for the upcoming time window comprises: applying said criterion to the time component of the upcoming time window in order to determine the corresponding group of time windows ([0029], [0034], [0046]); and
using the predictive model associated with said group of time windows to determine the target value for the upcoming time window ([0025], [0029], [0034]).
Keen does not disclose:
a predictive model is generated by learning for each group of time windows.
However, Kerber discloses:
a predictive model is generated by learning for each group of time windows (column 3, line 6-column 4, line 7, and column 9, lines 56-67).
Therefore, 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 system of Keen to use a predictive model is generated by learning for each group of time windows as taught by Kerber. The motivation for doing so would have been in order to enhance the accuracy and reliability of the target value (Kerber, column 9, lines 56-67).
Conclusion
29. Examiner has cited particular columns and line numbers, and/or paragraphs, and/or pages in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
30. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EYOB HAGOS whose telephone number is (571)272-3508. The examiner can normally be reached on 8:30-5:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Eyob Hagos/
Primary Examiner, Art Unit 2857