DETAILED ACTION
1. This office action is in response to the communicated dated 06 August 2026 concerning application number 18/910,891 effectively filed on 09 October 2024.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Information Disclosure Statement
3. The Information Disclosure Statement submitted on 06 August 2026 has been considered by the Examiner.
Status of Claims
4. Claims 1-20 are pending, of which claims 1, 5-6, 8, and 13-20 have been amended; and claims 1-20 are under consideration for patentability.
Response to Arguments
5. Applicant’s arguments dated 06 August 2026, referred to herein as “the Arguments”, have been fully considered, but they are not persuasive in view of the new grounds of rejection necessitated by Applicant’s amendments to the claims.
The Examiner has addressed the amended limitations within the updated text below.
Applicant’s arguments are primarily directed to the amendment which recites the use of a clustering algorithm that is applied to a plurality of parameters to identify a first patient state among a plurality of patient states. The Examiner has introduced a secondary reference by Lipsky to address the amended limitation. However, Applicant made conclusionary statement on page 10 of the Arguments which states that Sharma’s does not define a plurality of patient states. The Examiner respectfully submits that Sharma may not utilize a clustering algorithm to identify each of the patient states, but at the very least, Sharma teaches a plurality of patient states. For example, Sharma teaches the pain therapy device 100 comprising a therapy analysis engine 104 which makes a determination whether the measured physiological data (e.g., blood-oxygen levels, rate of blood flow, and tissue thickness) indicates a patient state consisting of higher levels of pain or a patient state consisting of reduced levels of pain (e.g., faster bone healing) ([0051, 0070-0073]). In this case, the therapy analysis engine 104 is identifying the patient’s state of pain to determine if the pain therapy is satisfactory or unsatisfactory ([0051, 0070-0073]). Specifically, the claimed “patient states” can be interpreted broadly, as Applicant’s claims do not define if the patient states are specific types of conditions, disorders, or diseases. Furthermore, Applicant’s specification defines the plurality of patient states as pain states ([specification: 0031]). As stated previously above, Sharma teaches the plurality of patient states to be different pain states or levels ([0051, 0070-0073]). Thus, the Examiner respectfully maintains that Sharma teaches a plurality of patient states.
Applicant argues that Skelton does not explicitly teach the amendment which recites the clustering solution being mapped to the plurality of parameters consisting of pain, medication, activities of daily living, mood, sleep, alertness, or mobility (page 11 of the Arguments). The Examiner respectfully submits that Skelton is not replied for teaching the amended limitation. However, the Examiner has introduced the secondary reference by Lipsky to address the amended limitations.
Claim Objections
6. Claims 6 and 17 are objected to because of the following informalities.
Claims 6 and 17 contain minor typographical errors.
Claim 6, lines 2-5: The Examiner suggests changing “wherein the plurality of parameters includes each of the pain parameter, a medication parameter, an activities of daily living parameter, a mood parameter, a sleep parameter, an alertness parameter, and a mobility parameter, the pain parameter including subjective information provided, via the user interface, by the patient” to “wherein the plurality of parameters includes each of the pain parameter, the medication parameter, the activities of daily living parameter, the mood parameter, the sleep parameter, the alertness parameter, and the mobility parameter, the pain parameter including subjective information provided, via the user interface, by the patient.”
Claim 17, lines 2-6: The Examiner suggests changing “wherein the plurality of parameters includes each of the pain parameter, a medication parameter, an activities of daily living parameter, a mood parameter, a sleep parameter, an alertness parameter, and a mobility parameter, the pain parameter including subjective information provided, via the user interface, by the patient” to “wherein the plurality of parameters includes each of the pain parameter, the medication parameter, the activities of daily living parameter, the mood parameter, the sleep parameter, the alertness parameter, and the mobility parameter, the pain parameter including subjective information provided, via the user interface, by the patient.”
Appropriate correction is required.
Claim Rejections - 35 USC § 103
7. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
8. Claims 1-5, 7-8, 13-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 2018/0140835 A1) in view of Lipsky et al. (WO 2023/064315 A1, with citations to the corresponding US Publication No. 2025/0006370 A1).
Regarding claims 1 and 13, Sharma teaches a method ([abstract]) and a non-transitory machine-storage medium embodying instructions that, when executed by a machine, cause the machine to perform operations ([0056-0057]) comprising:
using at least one medical device to treat a condition by delivering a therapy to a patient (the pain therapy device 100 delivers therapeutic energy (e.g., electrical stimulation, heat therapy, or ultrasound therapy) to treat tissue and/or bone conditions (e.g., rheumatoid arthritis or osteoarthritis) which consist of pain [abstract, 0030, 0033, 0050]);
receiving, from a user via a user interface, criteria related to one or more goals to accomplish while receiving the therapy to treat the condition (the patient may interact with the interface of the patient computing device 120 to provide desired physiological responses, feedback, and/or updated parameters during the treatment to achieve a satisfactory pain management, bone healing, bone healing, or other treatment outcome [0011-0012, 0048, 0110]. For example, the patient may indicate a high pain score (e.g., values of 7-10) for bone pain and the treatment may be automatically adjusted until the patient selects a lower pain score (e.g., values of 0-3) for bone pain [0010-0011, 0048, 0050]);
translating, by at least one hardware processor (the pain therapy device 100 includes a processor to execute the device’s functions [0056-0057]), the criteria related to the one or more goals to a state-based classification defining a plurality of patient states defined by a plurality of parameters (the pain therapy device 100 comprises a therapy analysis engine 104 (e.g., application or software) that monitors the patient’s state in response to the patient feedback from the patient computing device 120 [0050-0051, 0070-0071]. Specifically, the therapy analysis engine 104 monitors the patient state (e.g., higher levels of pain or reduced levels of pain) by measuring physiological parameters (e.g., blood-oxygen levels, rate of blood flow, and tissue thickness) [0051, 0071-0072]. Furthermore, the therapy analysis engine 104 may adjust the therapeutic parameters based on the measured physiological parameters to achieve the desired pain management outcome [0051, 0070-0072]);
wherein the plurality of parameters includes at least a pain parameter and a medication parameter (the therapy analysis engine 104 monitors the patient state (e.g., higher levels of pain or reduced levels of pain) by measuring physiological parameters (e.g., blood-oxygen levels, rate of blood flow, and tissue thickness) [0051, 0071-0072]. Furthermore, the therapy analysis engine 104 monitors the dosage of medicine that is applied to the patient [0070])
receiving a first set of parameter values ([0051, 0070-0071]);
identifying a first patient state from among the plurality of patient states using the first set of parameter values (the therapy analysis engine 104 makes a determination whether the measured physiological parameters indicate a patient state consisting of higher levels of pain or a patient state consisting of reduced levels of pain (e.g., bone healing) [0071-0073]); and
generating an output including the first patient state (the therapy analysis engine 104 is configured to output the patient’s state (e.g., reduced levels of pain) and the associated parameters (e.g., the measured physiological parameters) to the patient’s analytic database [0071-0072]).
However, Sharma does not explicitly teach wherein each patient state of the plurality of patient states being defined by a cluster solution generated by a clustering algorithm to the plurality of parameters that includes at least the pain parameter and at least two of the medication parameter, an activities of daily living parameters, a mood parameter, a sleep parameter, an alertness parameter, or a mobility parameter; and
identifying the first patient state among the plurality of patient states by mapping the first set of parameter values to the cluster solution.
The prior art by Lipsky is analogous to Sharma, as they both a system that monitoring or evaluating a pain parameter of a patient ([abstract, 0023, 0073, 0085-0086]).
Lipsky teaches wherein each patient state of the plurality of patient states being defined by a cluster solution generated by a clustering algorithm to the plurality of parameters that includes at least the pain parameter and at least two of an activities of daily living parameters, a sleep parameter, or a mobility parameter (the unsupervised clustering algorithm utilizes a combination of different parameter data (e.g., pain data, sleep data, walking data, morning stiffness data, and running data) to classify or determine each of the patient states (e.g., state of a patient’s disease or condition) [0083, 0085-0086]. For example, the unsupervised clustering algorithm may utilize the combination of parameter data to determine if the patient has a state of active lupus (first patient state) or if the patient has a state of inactive lupus (second patient state) [0085-0086]); and
identifying the first patient state among the plurality of patient states by mapping the first set of parameter values to the cluster solution (the clustering algorithm utilizes the combination of input parameter data values (e.g., pain data values, sleep data values, walking data values, morning stiffness data values, and running data values) to determine if the patient has a state of active lupus or if the patient has a state of inactive lupus [0083, 0085-0086]);
and generating an output including the first patient state (the unsupervised clustering algorithm or model generates an output which classifies the patient’s state (e.g., state of active lupus or state of inactive lupus) [0083, 0085-0086]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify Sharma’s state-based classification to utilize a clustering algorithm that is applied to a plurality of parameter values to identify a first patient state from a plurality of patient states and generate an output including the first patient state, as taught by Lipsky. This modification is beneficial, as the clustering algorithm utilizes the combination of input parameter data values (e.g., pain data values, sleep data values, walking data values, morning stiffness data values, and running data values) to generate an output that indicates if the patient has a state of active lupus or if the patient has a state of inactive lupus (see paragraphs [0083, 0085-0086] by Lipsky).
Regarding claims 2 and 14, Sharma teaches wherein the plurality of parameters includes at least one physiological parameter ([0051, 0070-0071]).
Regarding claims 3 and 15, Sharma teaches receiving, from the patient via the user interface, at least one of:
a patient-supplied ranking of each patient state among the plurality of patient states or a patient-supplied ranking of well-being (the patient may interact with the interface of the patient computing device 120 to provide desired physiological responses, feedback, and/or updated parameters during the treatment to achieve a satisfactory pain management, bone healing, bone healing, or other treatment outcome [0011-0012, 0048, 0110]. For example, the patient may indicate a high pain score (e.g., values of 7-10) for bone pain and the treatment may be automatically adjusted until the patient experiences a lower pain score (e.g., values of 0-3) for bone pain [0011-0011, 0048, 0050]).
Regarding claim 4, Sharma teaches wherein the criteria includes patient-defined criteria, clinician-defined criteria, application-defined criteria (the patient may interact with the interface of the patient computing device 120 to provide desired physiological responses, feedback, and/or updated parameters during the treatment to achieve a satisfactory pain management, bone healing, bone healing, or other treatment outcome [0011-0012, 0048, 0110]. Furthermore, the physician may interact with the interface of the physician computing device 110 for receiving feedback to adjust the applied pain management therapy [0048]. Lastly, the therapy analysis engine 104 (e.g., application or software) makes a determination whether the measured physiological parameters indicate a patient state consisting of higher levels of pain or a patient state consisting of reduced levels of pain (e.g., bone healing) [0045, 0051, 0071-0073]).
Regarding claims 5 and 16, Sharma in view of Lipsky suggests the method of claim 1 and the non-transitory machine-storage medium of claim 15. Sharma teaches receiving a second set of parameter values (the therapy analysis engine 104 monitors the patient state (e.g., higher levels of pain or reduced levels of pain) during each treatment by measuring physiological parameters (e.g., blood-oxygen levels, rate of blood flow, and tissue thickness) [0051, 0071-0072]. Furthermore, the therapy analysis engine 104 adjusting the therapeutic parameters based on the measured physiological parameters to achieve the desired pain management outcome [0051, 0070-0072]);
identifying a second patient state from among the plurality of patient states using the second set of parameter values (the therapy analysis engine 104 makes a determination whether the measured physiological parameters indicate a patient state consisting of higher levels of pain or a patient state consisting of reduced levels of pain (e.g., bone healing) [0071-0073]); and
generating a comparison state set based on the first set of parameter values and the second set of parameter values (as stated previously in claim 1, the therapy analysis engine 104 is configured to output the patient’s state (e.g., reduced levels of pain) and the associated first set of parameters (e.g., the first set of measured physiological parameters and first set of therapy parameters) to the patient’s analytic database [0051, 0071-0072]. Furthermore, the therapy analysis engine 104 can compare the second set of parameters (e.g., physiological and therapeutic parameters) monitored during the treatment to the previous first set of parameters which were stored in the patient’s analytic database [0012, 0071-0073]. This allows for matching the second set of parameters with the previous first set of parameters which had produced the desirable patient state (e.g., reduced levels of pain) [0012, 0071-0073]).
Sharma does not explicitly teach identifying the second patient state among the plurality of patient states by mapping the second set of parameter values to the cluster solution.
However, Lipsky teaches identifying the second patient state among the plurality of patient states by mapping the second set of parameter values to the cluster solution (the unsupervised clustering algorithm utilizes a combination of different parameter data (e.g., pain data, sleep data, walking data, morning stiffness data, and running data) to classify or determine each of the patient states (e.g., state of a patient’s disease or condition) [0083, 0085-0086]. For example, the clustering algorithm utilizes the combination of input parameter data values (e.g., pain data values, sleep data values, walking data values, morning stiffness data values, and running data values) to determine if the patient has a state of active lupus (first patient state) or if the patient has a state of inactive lupus (second patient state) [0083, 0085-0086]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the cluster solution suggested by Sharma in view of Lipsky to be applied to the second set of parameter values to identify a second patient state from a plurality of patient states, as further taught by Lipsky. This modification is beneficial, as the clustering algorithm utilizes the combination of input parameter data values (e.g., pain data values, sleep data values, walking data values, morning stiffness data values, and running data values) to generate an output that indicates if the patient has a state of active lupus (first patient state) or if the patient has a state of inactive lupus (second patient state) (see paragraphs [0083, 0085-0086] by Lipsky).
Regarding claims 7 and 18, Sharma teaches determining a multi-step pathway between the plurality of patient states based at least in part on the criteria related to the one or more goals to accomplish while receiving the therapy (the Examiner respectfully submits that the therapy analysis engine 104 utilizes multi-step pathway information (e.g., pain scores ranging from 0 to 10 or different types of emoticons) to assess the patient state of pain during the pain management treatment [0048, 0050-0051]. Specifically, the therapy analysis engine 104 may receive the multi-step pathway information from the patient’s computing device 120 which consist of pain scores (e.g., values from 0 to 10) or different emoticons indicative of the patient’s state of pain [0048, 0050-0051]. For example, the patient may indicate a high pain score (e.g., values of 7-10) for bone pain and the therapy analysis engine 104 may automatically adjust the treatment until the patient selects a lower pain score (e.g., values of 0-3) for bone pain [0011, 0048, 0050-0051]).
Regarding claim 8, Sharma teaches wherein the multi-step pathway between the plurality of patient states is a non-sequential pathway from the first patient state to a last patient state, the last patient state being an optimal patient state (as stated previously in claim 7, the therapy analysis engine 104 may receive the multi-step pathway information from the patient’s computing device 120 which consist of pain scores (e.g., values from 0 to 10) or different emoticons indicative of patient’s state of pain [0048, 0050-0051]. The Examiner respectfully submits that the patient can select any of the pain scores ranging from 0 to 10 in a non-sequential order to indicate their current state of pain [0048, 0050-0051]. For example, during the treatment, the patient may select a pain score of “8” (e.g., intense pain) as the first input, a pain score of “3” (e.g., minor pain) as the second input, and a pain score of “0” (e.g., no pain) as the last input [0050-0051]. The Examiner further submits that the pain score of 0 (e.g., no pain) is considered to be the optimal patient state [0050-0051]).
Regarding claim 20, Sharma teaches a system for managing pain of a patient (the pain therapy device 100 delivers therapeutic energy (e.g., electrical stimulation, heat therapy, or ultrasound therapy) to treat tissue and/or bone conditions (e.g., rheumatoid arthritis or osteoarthritis) which consist of pain [abstract, 0030, 0033, 0050]), the system comprising:
one or more processors ([0056-0057]); and
one or more memory storing instructions, which when executed by the one or more processors ([0056-0057]), cause the one or more processors to perform operations that:
receive, from a user via a user interface, criteria related to one or more goals to accomplish while receiving therapy to treat a condition (the patient may interact with the interface of the patient computing device 120 to provide desired physiological responses, feedback, and/or updated parameters during the treatment to achieve a satisfactory pain management, bone healing, bone healing, or other treatment outcome [0011-0012, 0048, 0110]. For example, the patient may indicate a high pain score (e.g., values of 7-10) for bone pain and the treatment may be automatically adjusted until the patient selects a lower pain score (e.g., values of 0-3) for bone pain [0010-0011, 0048, 0050]);
translate, by at least one hardware processor (the pain therapy device 100 includes a processor to execute the device’s functions [0056-0057]), the criteria related to the one or more goals to a state-based classification defining a plurality of patient states defined by a plurality of parameters (the pain therapy device 100 comprises a therapy analysis engine 104 (e.g., application or software) that monitors the patient’s state in response to the patient feedback from the patient computing device 120 [0050-0051, 0070-0071]. Specifically, the therapy analysis engine 104 monitors the patient state (e.g., higher levels of pain or reduced levels of pain) by measuring physiological parameters (e.g., blood-oxygen levels, rate of blood flow, and tissue thickness) [0051, 0071-0072]. Furthermore, the therapy analysis engine 104 may adjust the therapeutic parameters based on the measured physiological parameters to achieve the desired pain management outcome [0051, 0070-0072]);
wherein the plurality of parameters includes at least a pain parameter and a medication parameter (the therapy analysis engine 104 monitors the patient state (e.g., higher levels of pain or reduced levels of pain) by measuring physiological parameters (e.g., blood-oxygen levels, rate of blood flow, and tissue thickness) [0051, 0071-0072]. Furthermore, the therapy analysis engine 104 monitors the dosage of medicine that is applied to the patient [0070])
receiving a first set of parameter values ([0051, 0070-0071]);
identifying a first patient state from among the plurality of patient states using the first set of parameter values (the therapy analysis engine 104 makes a determination whether the measured physiological parameters indicate a patient state consisting of higher levels of pain or a patient state consisting of reduced levels of pain (e.g., bone healing) [0071-0073]); and
generating an output including the first patient state (the therapy analysis engine 104 is configured to output the patient’s state (e.g., reduced levels of pain) and the associated parameters (e.g., the measured physiological parameters) to the patient’s analytic database [0071-0072]).
However, Sharma does not explicitly teach wherein each patient state of the plurality of patient states being defined by a cluster solution generated by a clustering algorithm to the plurality of parameters that includes at least the pain parameter and at least two of the medication parameter, an activities of daily living parameters, a mood parameter, a sleep parameter, an alertness parameter, or a mobility parameter; and
identifying the first patient state among the plurality of patient states by mapping the first set of parameter values to the cluster solution.
The prior art by Lipsky is analogous to Sharma, as they both a system that monitoring or evaluating a pain parameter of a patient ([abstract, 0023, 0073, 0085-0086]).
Lipsky teaches wherein each patient state of the plurality of patient states being defined by a cluster solution generated by a clustering algorithm to the plurality of parameters that includes at least the pain parameter and at least two of an activities of daily living parameters, a sleep parameter, or a mobility parameter (the unsupervised clustering algorithm utilizes a combination of different parameter data (e.g., pain data, sleep data, walking data, morning stiffness data, and running data) to classify or determine each of the patient states (e.g., state of a patient’s disease or condition) [0083, 0085-0086]. For example, the unsupervised clustering algorithm may utilize the combination of parameter data to determine if the patient has a state of active lupus (first patient state) or if the patient has a state of inactive lupus (second patient state) [0085-0086]); and
identifying the first patient state among the plurality of patient states by mapping the first set of parameter values to the cluster solution (the clustering algorithm utilizes the combination of input parameter data values (e.g., pain data values, sleep data values, walking data values, morning stiffness data values, and running data values) to determine if the patient has a state of active lupus or if the patient has a state of inactive lupus [0083, 0085-0086]);
and generating an output including the first patient state (the unsupervised clustering algorithm or model generates an output which classifies the patient’s state (e.g., state of active lupus or state of inactive lupus) [0083, 0085-0086]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify Sharma’s state-based classification to utilize a clustering algorithm that is applied to a plurality of parameter values to identify a first patient state from a plurality of patient states and generate an output including the first patient state, as taught by Lipsky. This modification is beneficial, as the clustering algorithm utilizes the combination of input parameter data values (e.g., pain data values, sleep data values, walking data values, morning stiffness data values, and running data values) to generate an output that indicates if the patient has a state of active lupus or if the patient has a state of inactive lupus (see paragraphs [0083, 0085-0086] by Lipsky).
9. Claims 9-12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma in view of Lipsky et al., further in view of Skelton et al. et al. (US 2010/0280440 A1).
Regarding claim 9, Sharma in view of Lipsky suggests the method of claim 1. Sharma and Lipsky do not explicitly teach identifying a time period maintained in the first patient state; and
calculating a dwell time percentage based on the time period maintained among the plurality of patient states.
The prior art by Skelton is analogous to Sharma, as they both teach devices that can provide a non-invasive or invasive stimulation to provide pain relief (see paragraph [0033] by Sharma and paragraphs [0030, 0041-0042] by Skelton).
Skelton teaches identifying a time period maintained in the first patient state; and calculating a dwell time percentage based on the time period maintained among the plurality of patient states (the IMD 14 evaluate the evaluate the amount of time the patient was in each posture state to assess the patient’s pain and/or adjust the therapy [0030, 0164, 0166]. Furthermore, the external programmer 20 or computing devices 124A-124N may display the amount of time or percentage of time that patient was in each posture state [0166]. Specifically, the patient may review the posture state information (e.g., time percentages for each posture state) [0166-0167]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the user interface suggested by Sharma in view of Lipsky to consist of identifying a time period and calculating a time percentage among the plurality of patient states, as taught by Skelton. This modification is beneficial, as the time percentage among the patient states may be evaluated to determine the efficacy of the therapy and whether the therapy needs to be adjusted to maintain a desired patient state (see paragraphs [0030, 0164, 0166-0167] by Skelton).
Regarding claim 10, Sharma in view of Lipsky and Skelton suggests the method of claim 9. Skelton teaches mapping the time period maintained in the plurality of patient states to the criteria related to the one or more goals to accomplish while receiving the therapy; generating a recommendations plan for the therapy; and providing the recommendations plan for planning treatment decisions (as stated previously in claim 9, the IMD 14 evaluate the evaluate the amount of time the patient was in each posture state to assess the patient’s pain and/or adjust the therapy [0030, 0164, 0166]. Furthermore, the external programmer 20 or computing devices 124A-124N may display the amount of time or percentage of time that patient was in each posture state to illustrate the efficacy of the treatment to the patient and clinician [0166-0167]. In response to the time percentage information, the IMD 14 may provide recommendations by adjusting the parameters or settings of the therapy programs which can be evaluated by the patient and clinician on the external programmer 20, computing devices 124A-124N, and/or server 122 [0031, 0164, 0166-0167]. Specifically, the patient or clinician may manually adjust the parameters or settings of the therapy program which was recommended by the IMD 14 [0031, 0164, 0166-0167]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the user interface suggested by Sharma in view of Lipsky and Skelton to utilize the time period related to the patients states to generate a recommended plan for therapy, as further taught by Skelton. This modification is beneficial, as the time percentage among the patient states may be evaluated to provide a therapy program that has improve efficacy to maintain a desired patient state (see paragraphs [0031, 0164, 0166-0167] by Skelton).
Regarding claim 11, Sharma in view of Lipsky and Skelton suggests the method of claim 10. Skelton teaches identifying one or more confounding variables among the plurality of parameters, the one or more confounding variables being identified as problematic to success of the recommendations plan for the therapy (in response to the time percentage information, the IMD 14 may provide recommendations by adjusting the parameters or settings of the therapy programs which can be evaluated by the patient and clinician on the external programmer 20, computing devices 124A-124N, and/or server 122 [0031, 0164, 0166-0167]. Specifically, the clinician or patient may evaluate the parameters or settings the recommended therapy programs to possibly identify any problems or issues with the therapy that should be addressed [0031, 0166-0167]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the user interface suggested by Sharma in view of Lipsky and Skelton to identify one or more confounding variables among the plurality of parameters, as taught by Skelton. This modification is beneficial, as it will allow the patient or clinician to address any problems or issues relating to the parameters of the therapy programs which were received from the medical device (see paragraphs [0031, 0164, 0166-0167] by Skelton).
Regarding claim 12, Sharma in view of Lipsky and Skelton suggests the method of claim 10. Skelton teaches wherein the recommendations plan for the therapy is a closed-loop recommendations plan (In response to the time percentage information, the IMD 14 may provide recommendations by automatically adjusting the parameters or settings of the therapy programs which can be evaluated by the patient and clinician on the external programmer 20, computing devices 124A-124N, and/or server 122 [0031, 0164, 0166-0167]. Specifically, the patient or clinician may manually adjust the parameters or settings of the therapy program which was recommended by the IMD 14 [0164, 0166-0167]. The Examiner respectfully submits that the IMD 14 may operate automatically (e.g., closed loop) to provide recommended the therapy programs which can be optionally evaluated by the clinician or patient [0031, 0166-0167]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the user interface suggested by Sharma in view of Lipsky and Skelton to provide the recommendations plan for therapy in a closed-loop manner, as taught by Skelton. This modification is beneficial, is beneficial as it will allow the medical device to automatically provide the recommended therapy programs to the user without requiring any manual interaction (see paragraphs [0031, 0166-0167] by Skelton).
Regarding claim 19, Sharma in view of Lipsky suggests the machine-storage medium of claim 18. Sharma and Lipsky do not explicitly teach mapping a time period maintained in the plurality of patient states to the criteria related to the one or more goals to accomplish while receiving the therapy; generating a recommendations plan for the therapy; and providing the recommendations plan for planning treatment decisions.
The prior art by Skelton is analogous to Sharma, as they both teach devices that can provide a non-invasive or invasive stimulation to provide pain relief (see paragraph [0033] by Sharma and paragraphs [0030, 0041-0042] by Skelton).
Skelton teaches mapping a time period maintained in the plurality of patient states to the criteria related to the one or more goals to accomplish while receiving the therapy; generating a recommendations plan for the therapy; and providing the recommendations plan for planning treatment decisions (the IMD 14 evaluate the evaluate the amount of time the patient was in each posture state to assess the patient’s pain and/or adjust the therapy [0030, 0164, 0166]. Furthermore, the external programmer 20 or computing devices 124A-124N may display the amount of time or percentage of time that patient was in each posture state to illustrate the efficacy of the treatment to the patient and clinician [0166-0167]. In response to the time percentage information, the IMD 14 may provide recommendations by adjusting the parameters or settings of the therapy programs which can be evaluated by the patient and clinician on the external programmer 20, computing devices 124A-124N, and/or server 122 [0031, 0164, 0166-0167]. Specifically, the patient or clinician may manually adjust the parameters or settings of the therapy program which was recommended by the IMD 14 [0031, 0164, 0166-0167]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the user interface suggested by Sharma in view of Lipsky to utilize a time period related to the patients states to generate a recommended plan for therapy, as taught by Skelton. This modification is beneficial, as the time percentage among the patient states may be evaluated to provide a therapy program that has improve efficacy to maintain a desired patient state (see paragraphs [0031, 0164, 0166-0167] by Skelton).
Allowable Subject Matter
10. Claims 6 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The Examiner has provided an explanation below that describes how the prior art of record fails to suggest the corresponding claims.
Regarding claims 6 and 17, Sharma in view of Lipsky suggests the method of claim 1 and the non-transitory machine-storage medium of claim 13. Sharma teaches wherein the plurality of patient states includes a representation of overall patient health (a pain relief score is indicated or represented on a patient feedback interface 121 [0050, 0052, 0070]), and wherein the plurality of parameters includes the pain parameter ([0051, 0071-0072]) and the medication parameter ([0070]), the pain parameter including subjective information provided, via the user interface, by the patient ([0048, 0050-0051, 0070]).
However, Sharma and Lipsky do not explicitly teach wherein the plurality of parameters includes each of the pain parameter, the medication parameter, the activities of daily living parameter, the mood parameter, the sleep parameter, the alertness parameter, and the mobility parameter. The Examiner respectfully submits that Sharma and Lipsky do not explicitly teach each of the corresponding parameters being used for defining the patient states.
The Examiner further concludes that the prior art does not provide the requisite teaching, suggestion, and motivation to suggest the recited claim limitations. Therefore, the inventive features recited in the pending claims are not disclosed by the prior art and are not suggested by an obvious combination of the most analogous prior art elements.
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The Examiner respectfully submits that the prior art by John (US 2008/0249430 A1) is pertinent to Applicant’s disclosure, as John teaches a clustering algorithm that is applied to pain parameters ([0009, 0026-0028]).
12. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA BRENDON SOLOMON whose telephone number is (571)270-7208. The examiner can normally be reached on 7:30am -4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Niketa Patel can be reached on (571)272-4156. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSHUA BRENDON SOLOMON/Examiner, Art Unit 3792
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