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 action is in reply to the application filed on 09/18/2024.
Claims 1-20 are currently pending and have been examined.
Priority
Applicant’s claim to the benefit of and priority to US Provisional Application 63/538,899 is acknowledged. Accordingly, a priority date of 09/18/2023 has been given to this application.
Drawing Objections
The drawings are objected to because Fig. 6 has typographical error of “Perosnal” meters at the top of the figure rather than “Personal” meters. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Notice to Applicant
The claims appear to be a translation from a foreign language and are replete with grammatical and punctuation errors, run-on and incomplete sentences, indefinite/unclear language, and extensive use of “and/or” and “or” in the claims. Examiner has included her interpretation of representative instances in the Claim Objections, Claim Interpretations, and 112(b) Rejections sections, as well as with the relevant claim mappings in 103 section, and has examined the application as best understood. Examiner strongly recommends amending the claim language to clarify the scope of the invention and what is actually being claimed.
Claim Interpretations
Claims 1-3, 6, 9, 10, 12-14 and 20 contain recitation of a “challenged regimen”. As the specification does not appear to define what constitutes a “challenged” regimen, Examiner is interpreting this to be any regimen being used to treat an individual.
The claims appear to use both terms “regimen” (e.g., Claims 1 and 13) and “regimes” (e.g., Claim 14). Examiner is interpreting these to refer to the same thing, but recommends streamlining terminology across all claims for improved coherence and clarity.
Claim 13 recites “a system…comprising one or more processing units configured for…”. Per paras. [0067], [0069], this “system” is being interpreted as a “computerized system” such as a smartphone, smartwatch, or tablet; per [0101] the computerized system may comprise a processor to implement the method; Examiner interprets “processor” and “processing units” to be the same thing. Under 112(f), the processor of a smartwatch, smartphone or tablet is considered to be a hardware structural component. Subsequently, 112(f) is not invoked.
Claim Objections
Claim 1 recites “utilizing a subject-tailored, continuously or semi-continuously developing a randomization-based or non-randomization-based algorithm capable of mixing two or more work tasks, whether relevant to the target for improving function” which appears to contain a typographical error and/or accidental omittance of one or more words. For purposes of examination, Examiner is interpreting the inclusion of “a” to be in error and is interpreting the limitation as “utilizing a subject-tailored, continuously or semi-continuously developing randomization-based or non-randomization-based algorithm capable of mixing two or more work tasks, whether relevant to the target for improving function” which aligns with the language in specification para. [0015] (“subject-tailored…continuously developing randomization-based algorithm”). Please correct and/or explain on the record.
Claim 12 appears to contain an unnecessary comma after “regimens” in line 1.
Claim 13 recites “using a subject/team/company-tailored continuously or semi-continuously developing a randomization-based algorithm that mixes two or more tasks, whether relevant to the task”, which appears to contain a typographical error and/or accidental omittance of one or more words. For purposes of examination, Examiner is interpreting the inclusion of “a” to be in error and is interpreting the limitation as “using a subject/team/company-tailored continuously or semi-continuously developing randomization-based algorithm that mixes two or more tasks, whether relevant to the task” which aligns with the language in specification para. [0015] (“subject- and/or team and/or company-tailored…continuously developing randomization-based algorithm”). Please correct and/or explain on the record.
Claim 13 has a typographical error of “team.” at line 20 and “company.” at line 32. Per MPEP 608.01(m), “Each claim begins with a capital letter and ends with a period. Periods may not be used elsewhere in the claims except for abbreviations.” Appropriate correction is required.
Dependent claims 2-12 and 14-20 are objected to as they inherit the deficiencies of their respective parent claims.
Claim Rejections - 35 USC § 112
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “utilizing a subject, team and/or company-tailored, continuously or semi-continuously randomization-based or non-randomization-based algorithm, for continually improving performance” which renders the metes and bounds of the claims indefinite. Specifically, it is unclear which performance is being continually improved; whether it is performance of the algorithm that is being used, or whether the subject, team and/or company-tailored algorithm is used to continuously improve performance of the subject or subject’s regimen. For purposes of examination, it is being interpreted as the latter.
Claim 1 recites “utilizing a subject-tailored, continuously or semi-continuously developing randomization-based or non-randomization-based algorithm capable of mixing two or more work tasks, whether relevant to the target for improving function” which renders the metes and bounds of the claims indefinite for the following reasons: Specifically, it is unclear for what purpose the algorithm is actually being used, or if the algorithm is actually being used at all. The Examiner notes that it has been held that the recitation that an element is "adapted to" perform or "is capable" of performing a function is not a positive limitation, but only requires the ability to so perform. As such, the limitation reciting “algorithm capable of mixing two or more work tasks” does not constitute a limitation in any patentable sense. In re Hutchison, 69 USPQ 138 (CCPA 1946). Furthermore, recitation of “whether relevant to the target for improving function” is unclear; specifically, it is unclear what is “relevant to the target for improving function” (the algorithm, the two or more work tasks, or something else), and, if use of the term “whether” constitutes a conditional situation. For purposes of examination, Examiner is interpreting the claim limitation as “utilizing…an algorithm to mix two or more work tasks that are relevant to the target for improving function”.
Claim 2 recites “further comprising updating output parameters comprising, challenged-regimens-related parameters; and/or device-generated maneuver or stimulation parameters comprising amplitude, frequency, interval, and/or duration; or any combinations thereof, comprising updating changes and alterations in each of the parameters, which are of relevance to target performance” which renders the metes and bounds of the claim indefinite. Specifically, given claim construction with multiple uses of “and/or” and “or”, it is unclear if “comprising updating changes and alterations in each of the parameters, which are of relevance to target performance” applies only to the last limitation (“or any combinations thereof, comprising updating changes and alterations in each of the parameters, which are of relevance to target performance”), or if it applies to any of the preceding limitations (e.g., “updating output parameters comprising, challenged-regimens-related parameters comprising updating changes and alterations in each of the parameters, which are of relevance to target performance” and/or device-generated maneuver or stimulation parameters comprising amplitude, frequency, interval, and/or duration, comprising updating changes and alterations in each of the parameters, which are of relevance to target performance”. For purposes of examination, it is being interpreted as the former.
Claim 5 recites “wherein the machine learning algorithm further considers personal and/or group and/or company data selected from sources comprising subject, team and/or company performance, function-related scores, parameters relevant to performance, age, weight, tasks, gender, ethnicity, geography, pathological history and/or state, temperature, metabolic rate, brain function, health status, heart, lung muscle function, blood tests, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or the task and/or the subject, team and/or company”, which renders the metes and bounds of the claim indefinite for the following reasons:
Specifically, by using the language “personal and/or group and/or company data selected from sources comprising subject, team and/or company performance, function-related scores, parameters relevant to performance, age…blood tests, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or the task and/or the subject, team and/or company”, it is unclear whether the claim requires data from every single category source listed, or if only one piece of data selected from any of the recited categories is required. For purposes of examination, Examiner is interpreting this claim as requiring only one category of data.
Specifically, regarding the emphasized portion of the limitation reading, “personal and/or group and/or company data selected from sources comprising subject, team and/or company performance, function-related scores, parameters relevant to performance, age…blood tests, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or the task and/or the subject, team and/or company”, it is unclear if “directly or indirectly associated with the physiological target or the task and/or the subject, team and/or company” applies only to “physiological or pathological biomarkers or parameters that can be measured” or if this applies to every single category recited by the claim. For purposes of examination, it is being in interpreted as the former.
Claim 7 recites “wherein the subject/team/company challenged- regimen, is based on a deep machine learning closed loop-irregularity, regularity, randomization, or non-randomization” which renders the metes and bounds of the claim indefinite. It is unclear what “a deep machine learning closed loop-irregularity” is, as “closed loop-irregularity” does not appear to be an established term. This term is not defined by the specification; the specification only reiterates the claim language at para. [0127]. It is unclear whether the regimen is based on a deep machine learning closed-loop irregularity OR a regularity OR randomization OR a non-randomization; or, if the regimen is based on a deep learning closed loop, which may include irregularity, regularity, randomization, or non-randomization. For purposes of examination, it is being interpreted as the latter.
Claim 13 recites “using a subject/group of subjects/company-tailored continuously, semi-continuously, and non-continuous information for developing randomization-based or non-randomization-based algorithms for improving function following challenged regimens and/or any maneuver that can improve the function for continuously improving the performance related to the function of the said subject/group/company”, which renders the metes and bounds of the claim indefinite for the following reasons: with respect to “continuously, semi-continuously, or non-continuous”, it is unclear if these describe how subject/group/company-tailored information is used for developing algorithms, or if the information used to develop algorithms is utilized is continuous, semi-continuous, and non-continuous information, or something else. For purposes of examination, it is being interpreted as the former.
Claim 13 recites “…a randomization-based algorithm that mixes two or more tasks, whether relevant to the task” which renders the metes and bounds of the claim indefinite. Specifically, “whether relevant to the task” is unclear; it is unclear what is “relevant to the task” (the algorithm, the two or more work tasks, or something else), and, if use of the term “whether” constitutes a conditional situation. It is also unclear what it means to “mix two or more tasks, whether relevant to the task”. It is unclear if “the task” is one of the “two or more tasks” or if this is intended to mean “target” to align with Claim 1. For purposes of examination, Examiner is interpreting the claim limitation as “utilizing…an algorithm to mix two or more work tasks that are relevant to the task” where the “task” is synonymous with a target/goal.
Claim 15 recites “wherein the machine learning algorithm further considers subject/team/company data selected from the data comprising subject /team/company performance, task-related scores, parameters relevant to performance, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or with function and/or health status of the subject and/or subject's chronic condition that can be measured, directly or indirectly associated with the target to be achieved continuously” which renders the metes and bounds of the claim indefinite for the following reasons:
Specifically, by using the language “subject/team/company data selected from the data comprising subject /team/company performance, task-related scores, parameters relevant to performance, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or with function and/or health status of the subject and/or subject's chronic condition that can be measured, directly or indirectly associated with the target to be achieved continuously”, it is unclear whether the claim requires data from every single category source listed, or if only one piece of data selected from any of the recited categories is required. For purposes of examination, Examiner is interpreting this claim as requiring only one category of data.
Specifically, regarding the emphasized portion of the limitation reading, “personal and/or group and/or company data selected from sources comprising subject, team and/or company performance, function-related scores, parameters relevant to performance, age…blood tests, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or the task and/or the subject, team and/or company”, it is unclear if “directly or indirectly associated with the physiological target or the task and/or the subject, team and/or company” applies only to “physiological or pathological biomarkers or parameters that can be measured” or if this applies to every single category recited by the claim. For purposes of examination, it is being in interpreted as the former.
Dependent claims 2-12 inherit the deficiencies of parent claim 1, and dependent claims 14-20 inherit the deficiencies of parent claim 13, and are subsequently rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1
Claims 1-12 are drawn to a method, and Claims 13-20 are drawn to a system, both of which are within the four statutory categories. Claims 1-20 are further directed to an abstract idea on the grounds set out in detail below.
Step 2A Prong 1
Claim 1 recites implementing the steps of:
receiving a plurality of physiological or pathological parameters of the subject and/or information from the subject, a team and/or a company, and/or a device;
applying an algorithm on the plurality of physiological or pathological parameters;
determining output parameters relating to subject, team and/or company-specific challenged regimens, for facilitating a continuous improvement of the regimen or device-based maneuver or stimulation, wherein the output parameters comprise regimen or maneuver parameters, thereof,
utilizing a subject, team and/or company-tailored algorithm, for continually improving performance; and
utilizing a subject-tailored algorithm capable of mixing two or more work tasks, whether relevant to the target for improving function.
These steps amount to managing personal behavior or relationships or interactions
between people and therefore recite certain methods of organizing human activity including managing personal behaviors. Applying an algorithm to received physiological data, determining output parameters for a subject’s challenged regimen for facilitating continuous improvement of the regimen utilizing an algorithm, and utilizing a subject-tailored algorithm for mixing two or more work tasks relevant to the target for improving function are a personal behaviors that may be performed by healthcare providers.
Claim 13 recites implementing the steps of:
receiving, a plurality of physiological or pathological parameters of the subject, team and/or company, and/or information therefrom and/or device, or other sources;
applying an algorithm on the plurality of physiological or pathological parameters;
determining output parameters relating to subject, team, and/or company-specific challenged-regimens, and/or in combination with device-generated maneuvers/stimulation parameters, for facilitating improvement of work regimens or device-based maneuvers, wherein the output parameters comprise regimen administration parameters, maneuver/ stimulation parameters, or any combination thereof;
using a subject/group of subjects/company-tailored continuously, semi-continuously, and non-continuous information for developing algorithms for improving function following challenged regimens and/or any maneuver that can improve the function for continuously improving the performance related to the function of the said subject/group/company
using a subject/team/company-tailored algorithm that mixes two or more tasks, whether relevant to the task.
These steps amount to managing personal behavior or relationships or interactions
between people and therefore recite certain methods of organizing human activity including managing personal behaviors. Applying an algorithm to received physiological data, determining output parameters for a subject’s challenged regimen for facilitating continuous improvement of the regimen utilizing an algorithm, and utilizing a subject-tailored algorithm for mixing two or more work tasks relevant to the task are a personal behaviors that may be performed by healthcare providers.
The above claims are therefore directed to an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application because the additional
elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The independent claims additionally recite:
an open or a closed loop machine learning algorithm as the type of algorithm applied to the plurality of physiological or pathological parameters (Claim 1)
a closed loop machine learning algorithm as the type of algorithm applied to the plurality of physiological or pathological parameters (Claim 13)
continuously or semi-continuously randomization-based or non-randomization-based algorithm as the type of algorithm being used for continually improving performance (Claim 1, 13)
continuously or semi-continuously developing randomization-based or non-randomization-based algorithm (Claim 1, 13)
a system being continuous, semi-continuous, conditional or non-continuous closed loop, comprising one or more processing units as implementing the steps of the abstract idea (Claim 13)
The broad recitation of general purpose computing elements at a high level of generality only amounts to mere instructions to implement the abstract idea using computing components as tools. Recitation of the open or closed loop machine learning algorithm and continuously or semi-continuously (developing) randomization-based or non-randomization-based algorithm at a high level of generality only amounts to mere instructions to implement the abstract idea using computing components as tools, e.g., using an algorithm on a computer to produce a result or output. Regarding the open/closed loop machine learning algorithms and continuously or semi-continuously (developing) randomization-based or non-randomization-based algorithm, no particulars of the algorithms are provided. The claim does not disclose the particulars of the machine learning model (e.g., the specific type of machine learning model, the data used for training it). The broad recitation of a machine learning model, in this case to be used with a plurality of physiological or pathological parameters, only amounts to using the machine learning model as a tool to apply data to a model and generate a result (see MPEP 2106.05(f)(2)). Regarding the continuously or semi-continuously (developing) randomization-based or non-randomization-based algorithm, this is also understood to amount to applying data to an algorithm to generate an output (see MPEP 2106.05(f)(2)).
Regarding the “system being continuous, semi-continuous, conditional or non-continuous closed loop, comprising one or more processing units”, per paras. [0068]-[0069], the system is interpreted as being a computerized system comprising a smartphone, smartwatch, tablet, or electronic device functioning in its ordinary capacity. No other particulars of the system or processing unit/processor are provided. The processing unit is interpreted as being a computer processor functioning in its ordinary capacity.
These elements are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to
more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1 and 13 only recite the aforementioned computing elements as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f).
Thus, taken alone, the additional elements do not amount to significantly more than the
above-identified judicial exception. Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. Their
collective functions merely provide conventional computer implementation.
Depending Claims
Dependent claims 2-12, 14-20 recite additional subject matter which further narrows or defines the abstract idea embodied in the claims:
Claim 2 recites limitations pertaining to updating output parameters comprising, challenged-regimens-related parameters; and/or device-generated maneuver or stimulation parameters comprising amplitude, frequency, interval, and/or duration; or any combinations thereof, comprising updating changes and alterations in each of the parameters, which are of relevance to target performance, which are also certain methods of organizing human activity including managing personal behavior, as a healthcare provider could update output parameters comprising challenged-regimen-related parameters relevant to target performance. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 3 recites limitations pertaining to determining challenged regimens and/or maneuvers or stimulation parameters, which is also certain methods of organizing human activity including managing personal behavior, as a healthcare provider could determine challenged regimens, maneuvers or stimulation parameters. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 4 recites limitations pertaining to updating regimen parameters based on data being continuously or semi-continuously learned from user(s), which is also certain methods of organizing human activity including managing personal behavior, as a healthcare provider could update the parameters of a regimen using data continuously learned from a user. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 5 recites limitations pertaining to wherein the machine learning algorithm further considers personal and/or group and/or company data selected from sources comprising subject, team and/or company performance, function-related scores, parameters relevant to performance, age, weight, tasks, gender, ethnicity, geography, pathological history and/or state, temperature, metabolic rate, brain function, health status, heart, lung muscle function, blood tests, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or the task and/or the subject, team and/or company, which further narrows the scope of the abstract idea as set out above by specifying the types of data that may be considered by the algorithm. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 6 recites limitations pertaining to wherein at least one of the physiological or pathological parameters is obtained from a sensor, which further narrows the scope of the abstract idea. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 7 recites limitations pertaining to the subject/team/company challenged- regimen, is based on a deep machine learning closed loop-irregularity, regularity, randomization, or non-randomization, which only amounts to mere instructions to apply the abstract idea on a computer, e.g., using a general purpose computing device to apply data to an algorithm (see paras. [0068]-[0069], disclosing that the computerized system may comprise a mechanical and electrical device, smart phone, smartwatch, a tablet, or any combination thereof). MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 8 recites limitations pertaining to comprising notifying the subject /team/company in real-time, which is also certain methods of organizing human activity including managing personal behavior, as a healthcare provider could provide a notification to a subject/team/company in real-time. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 9 recites limitations pertaining to further comprising challenged- regimens and/or maneuvers or stimulating-generating devices to evoke a reaction by a form of external, wearable, swallowed and/or implanted device associated with improving function, which only amounts to mere instructions to apply the abstract idea. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 10 recites limitations pertaining to administering the challenged regimen to the subject/team/company which only amounts to applying the abstract idea in a generic way. No particulars of the challenged regimen are provided. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 11 recites limitations pertaining to improving function in healthy subjects who wish to improve performance, and/or for reaching a better target, for prevention or slowing down of aging processes, and /or for improving the effect of anti-aging drugs, maneuvers and/or techniques, which further narrows the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 12 recites limitations pertaining to where challenged regimens, are utilized in combination with device-generated maneuvers or stimulation parameters, or with regimens of conditions wherein enhanced functioning is required, or for improving performance, for prevention or overcoming of adaptation to chronic regimens, or for continuously overcoming partial/complete loss of an effect of these regimens, and/or for improving the beneficial effects of a regimen, which further narrows the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 14 recites limitations pertaining to wherein the machine learning algorithm is further configured to update output parameters comprising challenged-regimen and/or administration, and precisely parameters which are relevant to the regimens which are specific for the task and/or to stimulation signals; based on initial regimens parameters and/or initial stimulation parameters and/or on continuous or semi-continuous information obtained during or following the challenged- working session, and/or a maneuver which can improve the function by overcoming adaptation to maneuvers or regimes, which amounts to mere instructions to apply the abstract idea on a computer. MPEP 2106.05(f). As discussed above with respect to independent claims, the machine learning algorithm is recited at a high level of generality as implementing the steps of the abstract idea (updating output parameters). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 15 recites limitations pertaining to wherein the machine learning algorithm further considers subject/team/company data selected from the data comprising subject /team/company performance, task-related scores, parameters relevant to performance, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or with function and/or health status of the subject and/or subject's chronic condition that can be measured, directly or indirectly associated with the target to be achieved continuously, which further narrows the scope of the abstract idea as set out above by specifying the types of data that may be considered by the algorithm. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 16 recites limitations pertaining to wherein at least one of the physiological or pathological parameters is obtained from a sensor. which further narrows the scope of the abstract idea. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 17 recites limitations pertaining to wherein the subject regimen or any type of maneuver/regimen/regimens is irregular, which further narrows the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 18 recites limitations pertaining to wherein processor configured to notify the subject/team/company regarding regimen and/or device-generated maneuvers/stimulation regimens-relevant parameters, including relevant and irrelevant work-related parameters for administering these regimens, which amounts to mere instructions to apply the abstract idea on a computer. MPEP 2106.05(f). Per claim construction “and/or”, Examiner is interpreting this claim to recite “wherein processor configured to notify the subject/team/company regarding regimen-relevant parameters, including relevant and irrelevant work-related parameters for administering these regimens”, which only amounts to using a general purpose processor to apply the abstract idea, e.g., notifying a subject regarding regimen relevant parameters is also certain methods of organizing human activity including managing personal behaviors as it is a personal behavior that may be performed by a healthcare provider. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 19 recites limitations pertaining to further comprising a processor configured to use a work regimen and/or to improve function or manipulate/stimulate an organ of the subject/group to evoke a reaction by a form of external, wearable, swallowed and/or an implanted device, which amounts to mere instructions to apply the abstract idea on a computer. MPEP 2106.05(f). Per claim construction “and/or”, Examiner is interpreting this claim to recite “further comprising a processor configured to use a work regimen”, which only amounts to using a general purpose processor to apply the abstract idea. MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 20 recites limitations pertaining to wherein a closed algorithm receives input from a subject, groups of subjects, or companies, for determining a change of challenged regimen relevant to improving the target or non-target function by said regimens, which amounts to mere instructions to apply the abstract idea on a computer, e.g., receiving input from a subject for determining a change of challenged regimen relevant to improving a target or non-target by the regimen is a personal behavior that may be performed by a healthcare provider. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101 as they include all of the limitations of claim 1 or claim 13 respectively. The additional recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea. Beyond the limitations which recite the abstract idea, the claims recite additional elements consistent with those identified above with respect to the independent claims which encompass adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 2-12, 14-20 recite additional subject matter which amounts to additional elements consistent with those identified in the analysis of Claim 1 and 13 above. As discussed above with respect to Claim 1 and 13 and integration of the abstract idea into a practical application, recitation of these additional elements only amounts to invoking computers as a tool to perform the abstract idea. 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 conventional computer implementation.
Dependent claims 2-12, 14-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
For the reasons stated, Claims 1-20 fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
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 (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, 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 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ilan (US Publication 20200251201A1) in view of Sim et. al. (US Publication 20210049440A1).
Regarding Claim 1, Ilan discloses A method for improving function and performance and/or for preventing, mitigating, or overcoming partial or complete loss of effect of regimen, due to adaptation to the regimen; and/or partial or complete loss of effect of device-generated maneuvers or stimulations administered to or used by a subject in need thereof, or non-responsiveness to challenged-regimens, and/or maximizing the effect of regimens or maneuvers (Para. [0029]), the method comprising:
receiving a plurality of physiological or pathological parameters of the subject ([0029] teaches on receiving a plurality of physiological and/or pathological patterns of inherent variability of a subject with additional parameters related to a target system and related to the subject, interpreted as “physiological parameters of the subject”; per [0069] “target systems” are understood to include any organ in the body, e.g., brain, heart, kidney, leg, tissue, muscles, etc.)) and/or information from the subject, a team and/or a company, and/or a device;
applying an open or a closed loop machine learning algorithm on the plurality of physiological or pathological parameters ([0029] teaches on applying a closed-loop machine learning algorithm to a plurality of target system parameters which is interpreted as patient physiological parameters, as [0029] also discloses that the physiological patterns of inherent variability of a subject may be “related to a target system and to the subject” and [0069] teaches on target systems including organs, tissues, muscle, etc.));
determining output parameters relating to subject, team and/or company-specific challenged regimens ([0029] teaches on determining subject-specific output parameters relating to at least one target system function; [0031] teaches on using the method for improving function and/or performance and for individualizing treatment regimens and/or devices and for improving their efficacy for reaching goals, using at least one of the subject-specific output parameters – interpreted as output parameters relating to subject challenged regimens, where a “challenged regimen” is interpreted the treatment regimen of the subject), for facilitating a continuous improvement of the regimen or device-based maneuver or stimulation, wherein the output parameters comprise regimen or maneuver parameters, thereof ([0031] teaches on using the method for improving function and/or performance and for individualizing treatment regimens and/or devices and for improving their efficacy for reaching goals, using at least one of the subject-specific output parameters – interpreted as output parameters relating to subject challenged regimens, where a “challenged regimen” is interpreted the treatment regimen of the subject; paras. [0085], [0087], [0089], [0091], [0093] teach on various examples of patients with different medical conditions (e.g., diabetes or heart failure) in which the dosage/timing of administration of their respective treatments are altered so the regularity of dosage/timing is varied from dose to dose in order to prevent loss of effect with a constant administration regimen; Examiner interprets varying the time and dosage of a regimen to read out “output parameters” which comprise “regimen parameters”);
utilizing a subject, team and/or company-tailored, continuously or semi-continuously randomization-based or non-randomization-based algorithm, for continually improving performance ([0029] teaches on utilizing the subject-specific output parameters to improve the at least one target system performance by using the machine learning algorithm by applying a subject-tailored continuously or semi continuously variability patterns to facilitate improvement of the target system; [0076] teaches on the algorithm identifying methods for quantifying patterns of variability such that a better end response is noted; the factors/numbers generated from one or more of the inherent individualized patterns are incorporated into the operating system to determine dosage and time interval ranges for drug administration; these numbers are used for generating a personalized-type of irregularity to improve function of the operating system (interpreted as “improving performance”, as the claim does not explicitly recite which performance is improved; Examiner interprets it to be improving performance of “target system” such as organ, skin, muscle, heart, etc. per [0069]); [0076] further teaches on irregularity being a “random perturbation” with the power of randomness which is exploited to enhance system functions, which is interpreted as the algorithm being “randomization-based”); and
utilizing a subject-tailored, continuously or semi-continuously developing randomization-based or non-randomization-based algorithm (paras. [0029], [0076] as explained in preceding limitation, teach on a subject-tailored continuously randomization-based algorithm; [0021] teaches on an embodiment using a “continuously or semi-continuously developing” algorithm) ([0031] teaches on using the method for improving function and/or performance and for individualizing treatment regimens and/or devices and for improving their efficacy for reaching goals, using at least one of the subject-specific output parameters – interpreted as output parameters relating to subject challenged regimens, where a “challenged regimen” is interpreted the treatment regimen of the subject; paras. [0085], [0087], [0089], [0091], [0093] teach on various examples of patients with different medical conditions (e.g., diabetes or heart failure) in which the dosage/timing of administration of their respective treatments are altered so the regularity of dosage/timing is varied from dose to dose in order to prevent loss of effect with a constant administration regimen; Examiner interprets varying dosage/timing to prevent loss of effect to be “relevant to the target for improving function”, where the “target” is understood to be a target organ system per [0069])).
Ilan does not teach, but Sim, which is directed to a smart coach for enhancing personal productivity, teaches: utilizing an algorithm capable of mixing two or more work tasks, relevant to the target for improving function ([0033] teaches on a task optimizer module determining an optimized order of tasks for completion; the task optimizer may utilize one or more machine learning techniques (“algorithm”) to provide optimization based on a user optimization goal, an efficiency goal, where providing an order of tasks where the tasks having a large degree of mental difficulty are spaced apart from one another to optimize efficiency; as Applicant has not defined what “mixing two or more work tasks” entails, Examiner is interpreting the applied reference of recommending an order to accomplish tasks to read on the broadest reasonable interpretation as it teaches on how different work tasks are combined (“mixed”) to improve efficiency (function); Examiner interprets the system of Sim identifying tasks with “mental difficulty” to infer the “brain” as the target system).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Ilan with these teachings of Sim, to identify how to combine (“mix”) two or more work tasks relevant to improving function, with the motivation of maximizing personal productivity, promoting an overall positive emotional disposition to the task list, and attempting to ensure long-term achievement of goals (Sim [0003]).
Regarding Claim 2, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses . further comprising updating output parameters comprising, challenged-regimens-related parameters ([0085] teaches on introducing irregularity into the treatment regimens of individuals with drug-resistant epilepsy; [0087] teaches on patients treated with Zoloft and suffering from a partial loss of effect being enrolled in a study to determine the effect of introducing irregularity into the dose and times of administration; see also [0089], [0091], [0093] which further teach on introducing irregularity into dosage/administration timing of different drugs for different medical conditions for loss of therapeutic effect; Examiner interprets the irregular dose/administration times to read as output parameters comprising “challenged regimens”-related parameters); and/or device-generated maneuver or stimulation parameters comprising amplitude, frequency, interval, and/or duration; or any combinations thereof, comprising updating changes and alterations in each of the parameters, which are of relevance to target performance.
Regarding Claim 3, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses further comprising determining challenged regimens ([0079] teaches on determining type, dose and mode of administration of anti-tumor therapies, which is interpreted as “determining challenged regimens”) and/or maneuvers or stimulation parameters.
Regarding Claim 4, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses further comprising updating regimen parameters based on data being continuously or semi-continuously learned from user(s) ([0067] teaches on input data from a user being processed on continuous or semi-continuous basis using a closed-loop system to generate an improved algorithm being transformed into new output; [0071] teaches on a user being instructed to measure their organ function and/or regimen; processing circuitry is designed for a continuous or semi-continuous closed loop data input and output, wherein algorithm output and/or device-generated maneuvers or stimulation parameters are adjusted based on the input information and data; [0106] teaches on an example of a dialysis regimen being continuously or semi continuously altered based on individualized variability patterns and/or any other individualized data and/or data from other patients with acute or chronic kidney diseases).
Regarding Claim 5, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses wherein the machine learning algorithm further considers personal and/or group and/or company data selected from sources comprising subject, team and/or company performance, function-related scores, parameters relevant to performance, age, weight, tasks, gender, ethnicity, geography, pathological history and/or state, temperature, metabolic rate, brain function, health status, heart, lung muscle function, blood tests, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or the task and/or the subject, team and/or company ([0032] teaches on the machine learning algorithm taking into account “personal data”, wherein at least one of the physiological and/or pathological parameters is obtained from a sensor (reads on personal data selected from sources comprising subject physiological or pathological biomarkers or parameters that can be measured which are directly or indirectly associated with the subject; [0061]-[0062] teaches on the user (subject) providing input with related or non-related inherent variability parameters with or without additional biomarkers or parameters or combinations of regimens, wherein the user inputs include “all types of physiological and pathological parameters, personal environmental parameters, which are directly or indirectly relevant to target system regimens and procedures).
Regarding Claim 6, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses wherein at least one of the physiological or pathological parameters is obtained from a sensor ([0032] teaches on at least one of the physiological and/or pathological parameters is obtained from a sensor).
Regarding Claim 7, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses wherein the subject/team/company challenged- regimen, is based on a deep machine learning closed loop of irregularity ([0023] teaches on the machine learning capabilities including closed-loop deep learning; [0029] teaches on “irregularity” generating algorithms), regularity, randomization, or non-randomization.
Regarding Claim 8, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses notifying the subject /team/company in real-time ([0033] teaches on providing to the subject, a recommended regimen or changes thereto; [0034] teaches on providing the recommended regimen or changes thereto in real time; Examiner submits that as instant Claim 8 nor parent Claim 1 disclose what the “notification” to the subject/team/company pertains to and as such, interprets providing “a recommended regimen or changes thereto” to a subject to read on the broadest reasonable interpretation).
Regarding Claim 9, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses further comprising challenged- regimens and/or maneuvers or stimulating-generating devices to evoke a reaction by a form of external, wearable, swallowed and/or implanted device associated with improving function ([0069] teaches on a challenged-functional regimen output device and/or maneuver/stimulator (interpreted as “maneuvers or stimulating-generating devices”) which is configured to “provide function regimen alert output to a target system (such as brain, heart, kidney, leg, hand and the like and any tissue, such as muscles, connective tissue, epithelial cells or nerves”, to achieve a desired goal as determined by the user, or by others, or a physiological effect for improving of system function; Examiner interprets the “maneuver/stimulator” device to read on “external device” which evokes an action associated with improving function).
Regarding Claim 10, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses further comprising administering the challenged regimen to the subject/team/company ([0085], [0087], [0089], [0091], [0093], [0095] teach on various embodiments of administering a drug/treatment with irregularity in the treatment regimen, which is interpreted as reading on the broadest reasonable interpretation of the “challenged regimen” as this term does not appear to be explicitly defined by the instant specification).
Regarding Claim 11, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses for improving function in healthy subjects who wish to improve performance ([0024], teaching on the method may be used for “improving organ function in healthy subjects who wish to improve muscle, heart, lung, skin, brain on any other tissue/organ/organs performance”), and/or for reaching a better target, for prevention or slowing down of aging processes, and /or for improving the effect of anti-aging drugs, maneuvers and/or techniques.
Regarding Claim 12, Ilan/Sim teach the limitations of Claim 1. Ilan further discloses where challenged regimens are utilized in combination with device-generated maneuvers or stimulation parameters, or with regimens of conditions wherein enhanced functioning is required, or for improving performance, for prevention or overcoming of adaptation to chronic regimens ([0085], [0087], [0089], [0091], [0093], [0095] teach on various embodiments of administering a drug/treatment with irregularity in the treatment regimen; specifically, [0085] teaches on introducing irregularity into the treatment regimens of individuals with drug-resistant epilepsy; [0087] teaches on patients treated with Zoloft and suffering from a “partial loss of effect” being enrolled in a study to determine the effect of introducing irregularity into the dose and times of administration; see also [0089], [0091], [0093] which further teach on introducing irregularity into dosage/administration timing of different drugs for different medical conditions for loss of therapeutic effect; Examiner interprets the irregular dose/administration times to read on challenged regimens which are used for “improving performance for prevention/overcoming adaptation to chronic regimens”, e.g., improving performance of a treatment/therapy for preventing adaptation to a chronic regimen by varying dosing and timing), or for continuously overcoming partial/complete loss of an effect of these regimens, and/or for improving the beneficial effects of a regimen ([0085], [0087], [0089], [0091], [0093], [0095] teach on various embodiments of administering a drug/treatment with irregularity in the treatment regimen; specifically, [0085] teaches on introducing irregularity into the treatment regimens of individuals with drug-resistant epilepsy; [0087] teaches on patients treated with Zoloft and suffering from a “partial loss of effect” being enrolled in a study to determine the effect of introducing irregularity into the dose and times of administration; see also [0089], [0091], [0093] which further teach on introducing irregularity into dosage/administration timing of different drugs for different medical conditions for loss of therapeutic effect; Examiner interprets as continuously overcoming partial/complete loss of an effect of a regimen and improving benefits of a regimen).
Regarding Claim 13, Ilan discloses: a system for preventing, mitigating and/or treating partial/complete loss of effect due to adaptation to a challenged- regimen and/or used in combination with device-generated maneuvers or stimulation parameters, administered to or used by a subject, team and/or a company in need thereof, or non-responsiveness to regimens, and continuously maximizing the beneficial effect of work regimens, and/or improving function ([0029], [0042] teaching on a computerized system which applies a closed-loop machine learning algorithm), the system being continuous, semi-continuous, conditional or non-continuous closed loop, comprising one or more processing units configured ([0029], [0071], processing circuitry designed for continuous or semi continuous closed-loop data input and output) for:
receiving, a plurality of physiological or pathological parameters of the subject, team and/or company, and/or information therefrom and/or device, or other sources ([0029] teaches on receiving a plurality of physiological and/or pathological patterns of inherent variability of a subject with additional parameters related to a target system and related to the subject, interpreted as “physiological parameters of the subject”; per [0069] “target systems” are understood to include any organ in the body, e.g., brain, heart, kidney, leg, tissue, muscles, etc.);
applying a closed-loop machine learning algorithm on the plurality of physiological or pathological parameters ([0029] teaches on applying a closed-loop machine learning algorithm to a plurality of target system parameters which is interpreted as patient physiological parameters, as [0029] also discloses that the physiological patterns of inherent variability of a subject may be “related to a target system and to the subject” and [0069] teaches on target systems including organs, tissues, muscle, etc.);
determining output parameters relating to subject, team, and/or company-specific challenged-regimens, and/or in combination with device-generated maneuvers/stimulation parameters ([0029] teaches on determining subject-specific output parameters relating to at least one target system function; [0031] teaches on using the method for improving function and/or performance and for individualizing treatment regimens and/or devices and for improving their efficacy for reaching goals, using at least one of the subject-specific output parameters – interpreted as output parameters relating to subject challenged regimens, where a “challenged regimen” is interpreted the treatment regimen of the subject – interpreted as “output parameters relating to subject challenged regiments; “and/or in combination with device generated…parameters is not required per claim construction and/or), for facilitating improvement of work regimens or device-based maneuvers, wherein the output parameters comprise regimen administration parameters, maneuver/ stimulation parameters, or any combination thereof ([0031] teaches on using the method for improving function and/or performance and for individualizing treatment regimens and/or devices and for improving their efficacy for reaching goals, using at least one of the subject-specific output parameters – interpreted as output parameters relating to subject challenged regimens, where a “challenged regimen” is interpreted the treatment regimen of the subject; [0060] teaches on the machine learning model providing updated “functional regimens-relevant parameters and regimens, and/or stimulation or other device-related parameters based on data learned” (output parameters), which is performed for “overall improvement of organ or system performance”, which is interpreted as “facilitating improvement of device-based maneuvers, wherein the output parameters comprise maneuver/stimulation parameters; per claim construction, “work regimens” is not required);
using a subject/group of subjects/company-tailored continuously, semi-continuously, and non-continuous information for developing randomization-based or non-randomization-based algorithms for improving function following challenged regimens and/or any maneuver that can improve the function for continuously improving the performance related to the function of the said subject/group/company ([0021] teaches on providing subject-tailored variability patterns for continuously or semi-continuously developing closed-loop methods and devices/systems for improving system function; [0029] teaches on utilizing the subject-specific output parameters to improve the at least one target system performance by using the machine learning algorithm by applying a subject-tailored continuously or semi continuously variability patterns to facilitate improvement of the target system; Abstract teaches on output parameters which are “continuously, semi-continuously or conditionally” updated based on measurements and inputs, which are interpreted as “subject-tailored information” that is updated continuously, semi-continuously or in a non-continuous method (e.g., conditionally when something occurs or happens); [0076] teaches on the algorithm identifying methods for quantifying patterns of variability such that a better end response is noted; the factors/numbers generated from one or more of the inherent individualized patterns are incorporated into the operating system to determine dosage and time interval ranges for drug administration; these numbers are used for generating a personalized-type of irregularity to improve function of the operating system (interpreted as “improving performance” of the “target system” such as organ, skin, muscle, heart, etc. per [0069]); [0076] further teaches on irregularity being a “random perturbation” with the power of randomness which is exploited to enhance system functions, which is interpreted as the algorithm being “randomization-based”; per claim construction “and/or any maneuver”, this element is not required);
using a subject/team/company-tailored continuously or semi-continuously developing a randomization-based algorithm (paras. [0029], [0076] as explained in preceding limitation, teach on a subject-tailored continuously randomization-based algorithm; [0021] teaches on an embodiment using a “continuously or semi-continuously developing” algorithm) , whether relevant to the task ([0031] teaches on using the method for improving function and/or performance and for individualizing treatment regimens and/or devices and for improving their efficacy for reaching goals, using at least one of the subject-specific output parameters – interpreted as output parameters relating to subject challenged regimens, where a “challenged regimen” is interpreted the treatment regimen of the subject; paras. [0085], [0087], [0089], [0091], [0093] teach on various examples of patients with different medical conditions (e.g., diabetes or heart failure) in which the dosage/timing of administration of their respective treatments are altered so the regularity of dosage/timing is varied from dose to dose in order to prevent loss of effect with a constant administration regimen; Examiner interprets varying dosage/timing to prevent loss of effect to be “relevant to the task”, where the “task” is understood to be a target organ system per [0069])).
Ilan does not teach, but Sim, which is directed to a smart coach for enhancing personal productivity, teaches: an algorithm that mixes two or more tasks, whether relevant to the task ([0033] teaches on a task optimizer module determining an optimized order of tasks for completion; the task optimizer may utilize one or more machine learning techniques (“algorithm”) to provide optimization based on a user optimization goal, an efficiency goal, where providing an order of tasks where the tasks having a large degree of mental difficulty are spaced apart from one another to optimize efficiency; as Applicant has not defined what “mixing two or more work tasks” entails, Examiner is interpreting the applied reference of recommending an order to accomplish tasks (two or more tasks, as it is plural) to read on the broadest reasonable interpretation as it teaches on how different work tasks are combined (“mixed”) to improve efficiency (function), interpreted as being “relevant to the task” as it improves efficiency/function).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Ihan with these teachings of Sim, to identify how to combine (“mix”) two or more work tasks relevant to a task of improving function, with the motivation of maximizing personal productivity, promoting an overall positive emotional disposition to the task list, and attempting to ensure long-term achievement of goals (Sim [0003]).
Regarding Claim 14, Ilan/Sim teach the limitations of 13, Ilan further discloses wherein the machine learning algorithm is further configured to update output parameters comprising challenged-regimen and/or administration, and precisely parameters which are relevant to the regimens which are specific for the task and/or to stimulation signals; based on initial regimens parameters and/or initial stimulation parameters and/or on continuous or semi-continuous information obtained during or following the challenged- working session, and/or a maneuver which can improve the function by overcoming adaptation to maneuvers or regimes ([0085], [0087], [0089], [0091], [0093], [0095] teach on various embodiments of administering a drug/treatment with irregularity in the treatment regimen to patients with different medical conditions, for example, [0087] teaches on patients treated with Zoloft and suffering from partial loss of effect being enrolled in a study to determine the effect of introducing irregularity into the dose and times of administration (“challenged regimen”) – introducing irregularity is interpreted as “update output parameters” for the challenged regimen; [0089] teaches on an example of app that alters the regularity of the dosages and intervals between dosages while keeping them within a range determined by a physician, interpreted as updated output parameters comprising challenged regimen and which parameters are relevant (e.g., intervals between dosages); Examiner interprets “based on initial regimens parameters” to be the standard dosing/timing prior to introducing irregularity).
Regarding Claim 15, Ilan/Sim teach the limitations of 13, Ilan further discloses wherein the machine learning algorithm further considers subject/team/company data selected from the data comprising subject /team/company performance, task-related scores, parameters relevant to performance, and physiological or pathological biomarkers or parameters that can be measured, directly or indirectly associated with the physiological target or with function and/or health status of the subject and/or subject's chronic condition that can be measured, directly or indirectly associated with the target to be achieved continuously ([0032] teaches on the machine learning algorithm taking into account “personal data”, wherein at least one of the physiological and/or pathological parameters is obtained from a sensor (interpreted as reading on broadest reasonable interpretation of “physiological parameters that can be measured and are directly or indirectly associated with the subject – if the sensor data comes from the subject it is interpreted as being “directly associated” with the subject; [0061]-[0062] teaches on the user (subject) providing input with related or non-related inherent variability parameters with or without additional biomarkers or parameters or combinations of regimens, wherein the user inputs include “all types of physiological and pathological parameters, personal environmental parameters, which are directly or indirectly relevant to target system regimens and procedures).
Regarding Claim 16, Ilan/Sim teach the limitations of 13, Ilan further discloses wherein at least one of the physiological or pathological parameters is obtained from a sensor ([0032] teaches on at least one of the physiological and/or pathological parameters is obtained from a sensor).
Regarding Claim 17, Ilan/Sim teach the limitations of claim 13. Ilan further discloses wherein the subject regimen or any type of maneuver/regimen/regimens is irregular ([0085], [0087], [0089], [0091], [0093], [0095] teach on various embodiments of administering a drug/treatment with irregularity in the treatment regimen to patients with different medical conditions, for example, [0087] teaches on patients treated with Zoloft and suffering from partial loss of effect being enrolled in a study to determine the effect of introducing irregularity into the dose and times of administration (“regimen”); [0089] teaches on an example of app that alters the regularity of the dosages and intervals between dosages while keeping them within a range determined by a physician; “altering regularity” of dosages/intervals is interpreted as an “irregular” regimen for a subject).
Regarding Claim 18, Ilan/Sim teach the limitations of claim 13. Ilan further discloses wherein processor configured to notify the subject/team/company regarding regimen and/or device-generated maneuvers/stimulation regimens-relevant parameters, including relevant and irrelevant parameters for administering these regimens([0033] teaches on providing to the subject, a recommended regimen or changes thereto; [0034] teaches on providing the recommended regimen or changes thereto in real time – recommended regimen/changes to a regimen is interpreted as “regimen-relevant parameters”, including “relevant parameters for administering these regimens” where at least paras. [0085], [0087], [0089], [0091], [0093], [0095] teach on various embodiments of varying dosage/timing which are interpreted as “relevant parameters for administering a regimen”; [0058] teaches on including both relevant and irrelevant parameters).
Ilan does not disclose, but Sim further teaches: work-related parameters ([0033] teaches on a task optimizer module determining an optimized order of tasks for completion which is interpreted as “work-related parameters” where providing an order of tasks where the tasks having a large degree of mental difficulty are spaced apart from one another to optimize efficiency).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Ilan/Sim with these teachings of Sim, to include a work-related parameter, with the motivation of maximizing personal productivity, promoting an overall positive emotional disposition to the task list, and attempting to ensure long-term achievement of goals by strategically spacing work tasks having a high degree of mental difficulty (Sim [0003], [0033]).
Regarding Claim 19, Ilan/Sim teach the limitations of Claim 13. Ilan further discloses further comprising a processor configured to use a work regimen and/or to improve function or manipulate/stimulate an organ of the subject/group to evoke a reaction by a form of external, wearable, swallowed and/or an implanted device ([0069] teaches on a challenged-functional regimen output device and/or maneuver/stimulator (interpreted as “maneuvers or stimulating-generating devices”) which is configured to “provide function regimen alert output to a target system (such as brain, heart, kidney, leg, hand and the like and any tissue, such as muscles, connective tissue, epithelial cells or nerves”, to achieve a desired goal as determined by the user, or by others, or a physiological effect for improving of system function; Examiner interprets the “maneuver/stimulator” device to read on “external device” which evokes an action associated with improving function; per claim construction “further comprising a processor configured to use a work regimen and/or to improve function or manipulate/stimulate an organ, Examiner is interpreting that “use a work regimen” is not required and is interpreting the claim as “further comprising a processor configured to improve function or manipulate/stimulate an organ”).
Regarding Claim 20, Ilan/Sim teach the limitations of Claim 13. Ilan further discloses wherein a closed algorithm receives input from a subject, groups of subjects, or companies, for determining a change of challenged regimen relevant to improving the target or non-target function by said regimens ([0064], teaching on receiving inputs from the user and/or other users (subject/group of subjects) to inform the algorithm in a way that enables it to redirect or further define the function of the algorithm (“a change”), device or treatment regimen (e.g., determine a “change of challenged regimen”), following a closed-loop system); [0106] teaches on continuously or semi continuously changing the rate of dialysis and percentage of solutions used based on “feedback received from the patient” where “feedback” is interpreted as the subject input; changes to rate of dialysis/percentage of solutions are interpreted as changes to challenged regimen; the example in [0106] is understood to be improving function of the patient’s kidneys (target system) by said regimen.
Conclusion
Examiner respectfully requests that Applicant provides citations to relevant paragraphs of specification for support for amendments in future correspondence.
The following relevant prior art not cited is made of record:
US Publication 20240320596A1, teaching on systems and methods for utilizing machine learning for burnout prediction and providing recommendations to a user
US Publication 20170258384A1, teaching on a method for detecting and presenting symptoms associated with users
US Publication 20220012666A1, teaching on a system and method for providing a recommendation to an individual for personal productivity efficiency
US Publication 20160180277A1, teaching on modifying work assignments of an agent when burnout is detected to prevent/mitigate the burnout
US20240073170A1, teaching on determining concurrent workload, e.g., a number of tasks, an agent can handle at one time
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/ANNE-MARIE K ALDERSON/Primary Examiner, Art Unit 3682