DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This reply in response to the application filed on 24 August 2022.
Claims 1-17 are currently pending and have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 08/24/2022. The
submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the
information disclosure statement has been considered by the examiner.
Claim Interpretation
The following is a quotation of the first paragraph of 35 U.S.C. 112(f):
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.
The claims in this application are given their 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) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is 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 term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) 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 word “means” (or “step”) 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 word “means” (or “step”) 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 word “means” (or “step”) 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 10 recites the following:
“an activity complexity dataset determination module…. obtain…determine”
“a user complexity model determination module …. determine…”
“a distance vector determination module …. determine…”
“a user condition determination module …. determine…”
Claim 14 recites the following:
“a user condition determination module …. determine…”
Claim 15 recites the following:
“a user condition determination module …. determine…”
Claim 16 recites the following:
“a user condition tracking module …. track... determine…”
Claim 17 recites the following:
“a reference user complexity model determination module… predetermine”
which are limitations that invoke 35 U.S.C. § 112(f) or 35 U.S.C. § 112 (pre-AIA ),
sixth paragraph. The limitations create a rebuttable presumption that the claim elements are to be treated under § 112(f) based on the use of the word “means” or generic place holder (underlined) with functional language (in italics). The presumption is not rebutted because the limitations do not recite sufficient structure in the claim to perform the functions. When § 112(f) is invoked the broadest reasonable interpretation of the limitations is restricted to the structure in the disclosure and its equivalents.
the following functional claim limitations:
Of claim 10 recites the following:
“an activity complexity dataset determination module…. obtain”
recite non-specialized computer functions that can be accomplished by any general
purpose computer (e.g., any general purpose computer can receive, convert, and/or
display data, etc.), and as such an algorithm is not required to be described in the
specification to support an adequate disclosure of the limitations.
However, the following functional claim limitations:
Of claim 10 recites the following:
“an activity complexity dataset determination module…determine”
“a user complexity model determination module …. determine…”
“a distance vector determination module …. determine…”
“a user condition determination module …. determine…”
Of claim 14 recites the following:
“a user condition determination module …. determine…”
Of claim 15 recites the following:
“a user condition determination module …. determine…””
Of claim 16 recites the following:
“a user condition tracking module …. track... determine…”
Of claim 17 recites the following:
“a reference user complexity model determination module… predetermine”
recite specialized computer functions. A function performed by a programmed
computer requires both the computer and the algorithm that causes the computer to
perform the function. As such, a disclosure of an algorithm to perform these functions and to transform a general purpose computer into a programmed computer is required. Examiner notes that [0038] provides disclosure (i.e., to cure lack of written support and indefiniteness) by stating that any of the disclosed modules or units can be at least partially implemented by a computer processor.
If applicant wishes to provide further explanation or dispute the examiner’s
interpretation of the corresponding structure/algorithm, applicant must identify the
corresponding structure/algorithm with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35
U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the
claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination
Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of
Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claim 1-9) and machine (claim 10-17).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
monitoring one or more activities performed by a user during a predetermined monitoring time period;
determining one or more activity complexity datasets each for one of the one or more monitored activities;
determining a user complexity model based on at least one of the one or more activity complexity datasets;
determining a distance vector between the user complexity model and a reference user complexity model; and
determining a condition of the user based on the distance vector and a reference distance-condition model.
Claim 1 recites steps of
monitoring one or more activities performed by a user during a predetermined monitoring time period;
determining one or more activity complexity datasets each for one of the one or more monitored activities;
determining a user complexity model based on at least one of the one or more activity complexity datasets;
determining a distance vector between the user complexity model and a reference user complexity model; and
determining a condition of the user based on the distance vector and a reference distance-condition model.
Claim 10 recites steps of
an activity complexity dataset determination module configured to:
obtain one or more time series of data points for each of one or more monitored activities during a predetermined monitoring time period, and
determine the one or more activity complexity datasets, each for one of the one or more monitored activities, based on at least one of the one or more time series obtained for the respective monitored activity;
a user complexity model determination module configured to determine a user complexity model based on at least one of the one or more activity complexity datasets;
a distance vector determination module configured to determine a distance vector between the user complexity model and a reference user complexity model; and
a user condition determination module configured to determine a condition of the user based on the distance vector and a reference distance-condition model.
These steps for determining a condition of a user, as drafted, under the broadest reasonable interpretation, includes performance of the limitations in the mind. That is, nothing in the claim element precludes the italicized portions from practically being performed in the mind through the evaluation and determination of a condition of a user based on monitored daily activity of the user. This could be analogized to collecting information, analyzing it, and displaying certain results of the collection and analysis (MPEP 2106). In addition, the italicized portion containing the recitation of determining a distance vector also recites mathematical calculations which falls within the abstract idea of mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations in the mind and mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Process” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements, non-italicized portions identified above for claim 10, do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of an activity complexity dataset determination module; a user complexity model determination module; a distance vector determination module; and, a user condition determination module amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of obtain one or more time series of data points amounts to mere data gathering since it does not add meaningful limitations to the obtaining action performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity and add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as an activity complexity dataset determination module; a user complexity model determination module; a distance vector determination module; and, a user condition determination module, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, see Intellectual Ventures I LLC v. Capital One Bank, MPEP 2106.05(f)
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of obtain one or more time series of data points; e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Therefore, in consideration of all the facts, the present invention is clearly not a patent-eligible invention under USC 101. Additionally, it is evident that the present claims monopolizes the judicial exception since it covers any computer-implemented monitoring system that uses a model to determine user complexity, restricting further innovation in this area without offering a specific, technical improvement to how the computer actually operates; “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)).
Claim Rejections - 35 USC § 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 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, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5 and 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Bowers et al. (US20160140986A1) in view of Widanapathirana et al. (US20190108471A1)
Regarding claim 1, Bowers discloses monitoring one or more activities performed by a user during a predetermined monitoring time period ([0113] “In some aspects, various device functions (e.g., acquisition of activity data, performance of activity analysis, or transmission of activity data signal 134 to the monitoring location) are initiated in response to detection of the presence of patient 102. […] For example, data may be collected at certain times of day, contingent upon the presence of patient 102.” [0118] “a particular time of day, such as morning, afternoon, evening, or night”)
determining one or more activity complexity datasets each for one of the one or more monitored activities ([0119] “compare non-speech activity pattern 120 with a plurality of characteristic activity patterns 256, 258, and 260 (three characteristic activity patterns are provided as an example but the comparison is not limited to any specific number of characteristic activity patterns).” [0122] “comparison performed by comparator 254 is a determination […] match one or more characteristic activity data sets 256, 258, 260, patterns 262, 264, 266, or parameters 268, 270, 272.”)
determining a distance vector between the user complexity model and a reference user complexity model ([0255] “Determination of compliance may be accomplished by a […] distance computation of one or multiple parameters relative to characteristic threshold […] Comparator 5129 may utilize various types of distance computations to determine whether patient parameter values are within a threshold distance or distance range from characteristic values.”)
and determining a condition of the user based on the distance vector and a reference distance-condition model ([0255] “determination of whether the speech corresponds to at least one of a plurality of characteristic speech patterns. […] identifying which characteristic speech pattern the patient speech pattern matches or is closest to”)
Bowers does not explicitly disclose however Widanapathirana teaches determining a user complexity model based on at least one of the one or more activity complexity datasets ([0113] “Recall that our data consists of vector-valued time-series that are non-periodic, and do not have external observable predictors that we can use to build a model.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers determining a user complexity model based on at least one of the one or more activity complexity datasets as taught by Widanapathirana since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 2, Bowers discloses monitoring the one or more activities by obtaining one or more time series of data points for each of the one or more monitored activities during the predetermined monitoring time period from one or more sensors wearable by the user ([0117] “In an aspect, unobtrusive activity-detection system 108 includes one or more physiological sensors 332. In some aspects, physiological sensor 332 provides physiological activity signal 380 to activity detection circuitry 122. […] In some aspects, physiological activity data signal 382, including physiological activity data based on information from physiological activity signal 380 is transmitted to a monitoring system for further analysis.” [0128] “Activity sensor 116 includes one or more devices of one or more types capable of sensing activity of the patient.” [0113] “For example, data may be collected at certain times of day, contingent upon the presence of patient 102.”)
and determining the one or more activity complexity datasets, each for one of the one or more monitored activities, based on at least one of the one or more time series obtained for the respective monitored activity ([0118] “In an aspect, an activity pattern characterizes one or both of coarse and fine temporal patterns of activity (e.g., whether an activity occurs at a particular time of day, such as morning, afternoon, evening, or night; frequency of occurrence of the activity during a particular time window).” [0119] “compare non-speech activity pattern 120 with a plurality of characteristic activity patterns 256, 258, and 260 (three characteristic activity patterns are provided as an example but the comparison is not limited to any specific number of characteristic activity patterns).” [0122] “comparison performed by comparator 254 is a determination […] match one or more characteristic activity data sets 256, 258, 260, patterns 262, 264, 266, or parameters 268, 270, 272.”)
Regarding claim 3, Bowers discloses determining at least one of: whether the user has an anomaly and a probability that the user has the anomaly, based on the distance vector; and determining the condition of the user when it has been determined that the user has the anomaly or when the probability thereof is above a predetermined probability threshold ([0255] “Determination of compliance may be accomplished by a thresholding, windowing, or distance computation of one or multiple parameters relative to characteristic threshold or range values for the parameter […] a patient parameter value outside the range of characteristic values indicates non-compliance.”)
Regarding claim 4, Bowers discloses further comprising determining whether the user has the anomaly based on at least one of: a norm of the distance vector and a predetermined norm threshold; one or more distance values in one or more dimensions of the distance vector and one or more predetermined distance thresholds; and a pre-trained machine learning model ([0255] “patient parameter value equal to or lower than the threshold value may indicate non-compliance […] Comparator 5129 may utilize various types of distance computations to determine whether patient parameter values are within a threshold distance or distance range from characteristic values. Distance computations based on one or more parameters or data values are known (including, but not limited to, least-squares calculations).”)
Regarding claim 5, Bowers does not explicitly disclose however Widanapathirana teaches further comprising determining the probability that the user has the anomaly based on at least one of: a reference anomaly probability logistic model; and a pre-trained machine learning model ([0060] “detects unusual statistical patterns in activity data using a custom machine learning algorithm.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers a pre-trained machine learning model as taught by Widanapathirana since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 10, Bowers discloses an activity complexity dataset determination module ([0155] “A number of program modules may be stored in the ROM 524 or RAM 525”)
obtain one or more time series of data points for each of one or more monitored activities during a predetermined monitoring time period ([0117] “In some aspects, physiological activity data signal 382, including physiological activity data based on information from physiological activity signal 380 is transmitted to a monitoring system for further analysis.” [0113] “For example, data may be collected at certain times of day, contingent upon the presence of patient 102.”)
and determine the one or more activity complexity datasets, each for one of the one or more monitored activities, based on at least one of the one or more time series obtained for the respective monitored activity ([0118] “In an aspect, an activity pattern characterizes one or both of coarse and fine temporal patterns of activity (e.g., whether an activity occurs at a particular time of day, such as morning, afternoon, evening, or night; frequency of occurrence of the activity during a particular time window).” [0119] “compare non-speech activity pattern 120 with a plurality of characteristic activity patterns 256, 258, and 260 (three characteristic activity patterns are provided as an example but the comparison is not limited to any specific number of characteristic activity patterns).” [0122] “comparison performed by comparator 254 is a determination […] match one or more characteristic activity data sets 256, 258, 260, patterns 262, 264, 266, or parameters 268, 270, 272.”)
a distance vector determination module configured to determine a distance vector between the user complexity model and a reference user complexity model ([0155] “A number of program modules may be stored in the ROM 524 or RAM 525” [0255] “Determination of compliance may be accomplished by a […] distance computation of one or multiple parameters relative to characteristic threshold […] Comparator 5129 may utilize various types of distance computations to determine whether patient parameter values are within a threshold distance or distance range from characteristic values.”)
and a user condition determination module configured to determine a condition of the user based on the distance vector and a reference distance-condition model ([0155] “A number of program modules may be stored in the ROM 524 or RAM 525” [0255] “determination of whether the speech corresponds to at least one of a plurality of characteristic speech patterns. […] identifying which characteristic speech pattern the patient speech pattern matches or is closest to”)
Bowers does not explicitly disclose however Widanapathirana teaches a user complexity model determination module configured to determine a user complexity model based on at least one of the one or more activity complexity datasets ([0006] “FIG. 2 is a schematic depicting various modules of the system in one embodiment.” [0113] “Recall that our data consists of vector-valued time-series that are non-periodic, and do not have external observable predictors that we can use to build a model.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers determining a user complexity model based on at least one of the one or more activity complexity datasets as taught by Widanapathirana since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 11, Bowers discloses at least one of: whether the user has an anomaly and a probability that the user has the anomaly, based on the distance vector; and wherein the user condition determination model is configured to determine the condition of the user when it has been determined that the user has the anomaly or when the probability thereof is above a predetermined probability threshold ([0255] “Determination of compliance may be accomplished by a thresholding, windowing, or distance computation of one or multiple parameters relative to characteristic threshold or range values for the parameter […] a patient parameter value outside the range of characteristic values indicates non-compliance.”)
Bowers does not explicitly disclose however Widanapathirana teaches an anomaly determination model that is configured to determine ([0017] “novel anomaly detection analyses to construct a model that calculates”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers an anomaly determination model that is configured to determine as taught by Widanapathirana since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 12, the limitations are rejected for the same reasons as stated above for claim 4.
Regarding claim 13, the limitations are rejected for the same reasons as stated above for claim 5.
Claims 6-9 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bowers et al. (US20160140986A1) in view of Widanapathirana et al. (US20190108471A1) and further in view of Staib et al. (US20170277865A1)
Regarding claim 6, Bowers in view of Widanapathirana does not explicitly disclose however Staib teaches further comprising determining a condition of the user based on at least one of: a norm of the distance vector; and one or more distance values in one or more dimensions of the distance vector ([0008] “the vector space comprises multiple norm trajectories, each of which specify different disease progressions from a healthy normal patient via a pre-diabetic condition to an insulin-dependent diabetic disease.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers and Widanapathirana a norm of the distance vector as taught by Staib since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 7, Bowers in view of Widanapathirana does not explicitly disclose however Staib teaches wherein determining the condition of the user comprises determining one of: a specific condition of the user; or a group of conditions from which the user may suffer ([0052] “a different disease progression from a healthy normal patient via a pre-diabetic condition to an insulin-dependent diabetic disease.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers and Widanapathirana a specific condition of the user as taught by Staib since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 8, Bowers discloses and determining a trend of the condition of the user based on the tracking thereof ([0065] “A vector LM=(1, 2, 3, . . . , M)/∥LM∥, i.e. a linearly increasing sample profile. For the example of FIGS. 2 to 4, L=(1, 2, 3, 4, 5)/√55.”)
Bowers in view of Widanapathirana does not explicitly disclose however Staib teaches tracking the condition of the user as the time progresses ([0052] “It is significant in this context that multiple norm trajectories may exist in the considered vector space, whereby each specifies a different disease progression from a healthy normal patient via a pre-diabetic condition to an insulin-dependent diabetic disease.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers and Widanapathirana a specific condition of the user as taught by Staib since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 9, Bowers in view of Widanapathirana does not explicitly disclose however Staib teaches further comprising predetermining the reference user complexity model by monitoring the one or more activities during a predetermined reference time period prior to the predetermined monitoring time period ([0033] “For example, the expected curve of the glucose concentration profile in a glucose tolerance test of a patient with a certain state of health can be used as glucose reference profile.” [0034] “Exemplary glucose reference profiles may also be functions that are linear over time, or at least over sections thereof. Even simple functions of this type alone allow sections of the time profile of the glucose concentration to be characterized for a certain state of health.” [0037] “In like manner, a vector can also be formed from a reference profile in that the concentration value of the reference profile at the relevant time point t1, t2 to tn is used as vector component.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers and Widanapathirana predetermining the reference user complexity model by monitoring the one or more activities during a predetermined reference time period prior to the predetermined monitoring time period as taught by Staib since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 14, the limitations are rejected for the same reasons as stated above for claim 6.
Regarding claim 15, the limitations are rejected for the same reasons as stated above for claim 7.
Regarding claim 16, Bowers discloses a user condition tracking module ([0155] “A number of program modules may be stored in the ROM 524 or RAM 525”)
Bowers in view of Widanapathirana does not explicitly disclose however Staib teaches track the condition of the user as the time progresses ([0052] “It is significant in this context that multiple norm trajectories may exist in the considered vector space, whereby each specifies a different disease progression from a healthy normal patient via a pre-diabetic condition to an insulin-dependent diabetic disease.”)
and determine a trend of the condition of the user based on the tracking thereof ([0065] “A vector LM=(1, 2, 3, . . . , M)/∥LM∥, i.e. a linearly increasing sample profile. For the example of FIGS. 2 to 4, L=(1, 2, 3, 4, 5)/√55.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers and Widanapathirana track the condition of the user as the time progresses; and determine a trend of the condition of the user based on the tracking thereof as taught by Staib since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 17, Bowers discloses a reference user complexity model determination module ([0155] “A number of program modules may be stored in the ROM 524 or RAM 525”)
Bowers in view of Widanapathirana does not explicitly disclose however Staib teaches configured to predetermine the reference user complexity model by monitoring the one or more activities during a predetermined reference time period prior to the predetermined monitoring time period ([0033] “For example, the expected curve of the glucose concentration profile in a glucose tolerance test of a patient with a certain state of health can be used as glucose reference profile.” [0034] “Exemplary glucose reference profiles may also be functions that are linear over time, or at least over sections thereof. Even simple functions of this type alone allow sections of the time profile of the glucose concentration to be characterized for a certain state of health.” [0037] “In like manner, a vector can also be formed from a reference profile in that the concentration value of the reference profile at the relevant time point t1, t2 to tn is used as vector component.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Bowers and Widanapathirana to predetermine the reference user complexity model by monitoring the one or more activities during a predetermined reference time period prior to the predetermined monitoring time period as taught by Staib since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Prior Art Cited but Not Relied Upon
Pereira, J., & Silveira, M. (2019, February). Learning representations from healthcare time series data for unsupervised anomaly detection. In 2019 IEEE international conference on big data and smart computing (BigComp) (pp. 1-7). IEEE.
This reference is relevant because it conceptually discloses the applicant’s invention.
Conclusion
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/WINSTON R FURTADO/Examiner, Art Unit 3687