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 .
This action is in response to the amendments filed on December 10, 2025. Claims 1, 9, and 15 are amended; and claims 1-20 are pending and examined below.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lach et al. (U.S. 2022/0223286) in view of Jain et al. (U.S. Patent No. 11,328,796).
With regard to claim 1, Lach teaches a method ([abstract]), comprising:
receiving user interaction from a user via a graphical user interface (GUI) of a computing device (Fig. 5; Figs. 7-15; [abstract] exchanging information among components of a smart health system with mobile devices and/or smartwatches in regards to a patient and caregiver dyad);
in response to receiving the user interaction, collecting a set of user data associated with the user ([0007] A benefit of smart health is the ability to collect a wide range of relevant data passively, minimizing invasiveness and burden—an important consideration for patients and family caregivers coping with the stressors of advanced cancer);
generating a first stress score ([0106] 4) an identified primary ‘family’ caregiver; (note: we interpret ‘family’ here in the broadest sense as an informal caregiver who lives full-time with the patient and is involved with their day-to-day care); 5) scores of 6 or higher on NIH PROMIS Cancer Pain Interference scale measures or the Pain Intensity Numeric Rating Scale) by processing the set of user data using a stress model ([0106] The Dyadic Stress Model posits that life stressors, such as cancer, have a reciprocal impact on patients and their caregivers For example, understanding how patient pain may affect caregiver sleep, and vice versa, is one of the key aspects of this proposal); and
in response to determining that the first stress score satisfies one or more defined criteria ([0106] Sample size is stratified by site as we hypothesize dyads enrolled in hospice and those not enrolled in hospice will be different (see below). Sample size is determined by the minimum number of total pain events per dyad needed for analysis (n=50), and the length of deployment needed to achieve this number):
generating a first prompt for the user, wherein the first prompt requests additional user interaction, as compared to a default prompt (Fig. 5; [0042] to collect active data that requires participant (patient and caregiver) to directly interface with the system by answering questions or marking an event; [0105] dyads will mark and characterize pain events on the smart watch and answer daily survey questions); and
outputting the first prompt via the GUI (Fig. 5; [0105] dyads will mark and characterize pain events on the smart watch and answer daily survey questions). However, Lach does not specifically teach:
- generating a second stress score by processing a second set of user data using the stress model; and
- in response to determining that the second stress score does not satisfy the one or more defined criteria, outputting the default prompt via the GUI
Jain teaches a device that receives data indicating machine learning techniques to enhance the process of defining and managing cohorts for scientific research and provide tools for performing research of such ([abstract]; [col. 1, lines 20-40]). Jain also teaches generating a second stress score ([col. 3, lines 25-40] a confidence score, for each of one or more types of potential study outcomes; [col. 6, lines 30-38] the prediction can include a confidence score generated by a machine learning model; [col. 9, lines 5-9] the output from the one or more models is a confidence score; [col. 9, lines 30-51] the set of feature scores is based on sensor data that is acquired by one or more sensors during one or more activities of the subject or that indicates one or more attributes of the subject… a score for each of a plurality of actions to cause the subject to satisfy the selection criterion, the scores being based on data in the database indicating actions of other subjects with respect to the selection criterion) by processing a second set of user data using the stress model ([col. 28, lines 15-25] if participant data indicates a high percentage of survey responses related to exercise-related stress, then the machine learning models can derive new survey questions that focus on identifying user lifestyles that tend to produce exercise-related stress); and in response to determining that the second stress score does not satisfy the one or more defined criteria (Fig. 11, 1108; Fig. 16, 1608; [abstract] determine a second set of candidates that satisfy a subset of the selection criteria and are determined to not satisfy a same one or more criteria of the selection criteria; [col. 11, lines 65- col. 1, lines 15] The system can identify subjects that do not satisfy cohort selection criteria and identify which subjects have the lowest burden to become eligible according to the cohort selection criteria), outputting the default prompt via the GUI ([abstract] computing devices provide output data through the interface… data indicating the one or more selection criteria not satisfied by the members of the second set of candidates; [col. 32, lines 1-10] determining a second set of candidates classified as not satisfying a same one or more of the selection criteria (1108), and providing data indicating the first set of candidates and the second set of candidates for output through the interface (1110); [col. 32, lines 20-25] the selection criteria can include keywords that identify desired attributes for candidates, such as, a diagnosed medical condition (e.g., diabetes), physiological test data completed (e.g., blood test), a desired age requirement (e.g., older than 30 years old)). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified a system for a patient and/or caregiver of a health system device as taught by Lach, with the second stress score and performing data analysis as taught by Jain, to have achieved a system and method for machine learning models which indicate exercise-related stress and based on inputs and determine and manage participants of the criteria.
With regard to claim 2, the limitations are addressed above and Lach teaches wherein the set of user data comprises workload information for a current job shift that the user is currently working ([0103]-[0104] Preliminary Work: This proposal is related to, among other things, system-level approaches to improve cancer pain management. An embodiment of the present inventor's work includes, among other things: [0104] 1) BESI-Cancer (BESI-C) Pilot Study #1—Designing BESI-C: In an embodiment, the present inventor envisioned BESI-C for the unique needs of patients with cancer and their family caregivers, with a focus on advanced cancer pain).
With regard to claim 3, the limitations are addressed above and Lach teaches wherein the workload information comprises one or more of:
a duration of the current job shift,
an amount of time that has elapsed during the current job shift,
an amount of time that remains during the current job shift, or
a current time ([abstract] delivering in-situ real-time personalized intervention(s) for a patient and/or caregiver; [0048] a system for delivering in-situ real-time personalized intervention for a patient coping with cancer or non-cancer pain management or other cancer-related or other disease-related symptoms).
With regard to claim 4, the limitations are addressed above and Lach teaches wherein:
the user is a healthcare worker ([abstract] exchanging information among components of a smart health system with mobile devices and/or smartwatches in regards to a patient and caregiver dyad; [0010] can be generated and communicated for appropriate action to be undertaken by the caregiver, patient, both the caregiver and patient, and/or health care provider, as well as to cloud services and electronic health records (EHR)), and
the workload information comprises a set of acuity scores for a set of patients being cared for by the user during the current job shift ([0106] scores of 6 or higher on NIH PROMIS Cancer Pain Interference scale measures or the Pain Intensity Numeric Rating Scale…recruit up to 25 healthcare providers from each site (total of 50 participants); this sample size is based upon the number of palliative care/oncology staff at the two clinical sites and on work exploring data visualizations with participants).
With regard to claim 5, the limitations are addressed above and Lach teaches wherein the set of acuity scores is generated by, for each patient in the set of patients ([0106] scores of 6 or higher on NIH PROMIS Cancer Pain Interference scale measures or the Pain Intensity Numeric Rating Scale…recruit up to 25 healthcare providers from each site (total of 50 participants); this sample size is based upon the number of palliative care/oncology staff at the two clinical sites and on work exploring data visualizations with participants), processing corresponding patient data using a machine learning model trained to predict patient acuity ([0110] Data Analysis: An embodiment will use principles of signal processing and machine learning to develop comprehensive digital phenotypes of advanced cancer pain in the home setting from three unique viewpoints—patients with advanced cancer; family caregivers; and patient-family caregiver dyads—nested within two groups).
With regard to claim 6, the limitations are addressed above and Lach teaches wherein generating the first prompt comprises:
identifying one or more at-risk patients, from the set of patients, based on patient data ([0106] Oncology/Palliative Care Healthcare Providers (Aim 2). Inclusion criteria for Group 2 includes healthcare providers age 18 and older involved in the clinical care of patients with advanced cancer pain…recruit up to 25 healthcare providers from each site (total of 50 participants); this sample size is based upon the number of palliative care/oncology staff at the two clinical sites and on work exploring data visualizations with participants); and
indicating the one or more at-risk patients in the first prompt (Fig. 5).
With regard to claim 7, the limitations are addressed above and Lach teaches wherein the stress model is a trained machine learning model ([0110] Data Analysis: An embodiment will use principles of signal processing and machine learning to develop comprehensive digital phenotypes of advanced cancer pain in the home setting from three unique viewpoints—patients with advanced cancer; family caregivers; and patient-family caregiver dyads—nested within two groups), the method further comprising training the stress model, comprising:
collecting a training set of user data associated with a historic user ([0106] validation by previously conducted dyad interviews (Preliminary Work); [0110] Data Analysis: An embodiment will use principles of signal processing and machine learning to develop comprehensive digital phenotypes of advanced cancer pain in the home setting);
determining a level of stress being experienced by the historic user ([0106] The Dyadic Stress Model posits that life stressors, such as cancer, have a reciprocal impact on patients and their caregivers);
generating a test stress score by processing the training set of user data using the stress model ([0106] scores of 6 or higher on NIH PROMIS Cancer Pain Interference scale measures or the Pain Intensity Numeric Rating Scale…recruit up to 25 healthcare providers from each site (total of 50 participants); this sample size is based upon the number of palliative care/oncology staff at the two clinical sites and on work exploring data visualizations with participants); and
refining the stress model based on a difference between the test stress score and the determined level of stress ([0106] The present inventor considers a ‘pain event’ as when marked by a patient or caregiver, and the one hour time window pre and post the marking of a pain event; any other time period is considered a ‘non-pain’ event. Assuming a conservative coefficient of variation of 0.25 within a dyad, 80% power and alpha of 0.05, we wish to detect any variable that changes by +/−15% during a pain event).
With regard to claim 8, the limitations are addressed above and Lach teaches wherein the additional user interaction comprises at least one of:
(i) selecting a specified visual element of the first prompt prior to dismissing the first prompt (Fig. 5; [0042] to collect active data that requires participant (patient and caregiver) to directly interface with the system by answering questions or marking an event; [0110] Concordance will be dichotomized (yes/no) within 30 minute epochs of time, and logistic regression used to determine if concordance is affected by any measured characteristic (such as severity and perceived burden, medication use, non-pharmacological strategies, mobility, sleep, heartrate and home/room level data) or if concordance improves over time within a dyad, and if mixed effects models detect similar concordance patterns across dyads),
(ii) scrolling from a first portion of the first prompt to a second portion of the first prompt prior to dismissing the first prompt, or
(iii) typing a specified textual string prior to dismissing the first prompt.
With regard to claim 9, Lach teaches a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system (Fig. 2, processor 2010; [0044] the system and method may include a computer, processor, computer network, or computer server (any of which may include cloud platform or cloud services)), cause the processing system to perform an operation ([abstract]) comprising:
receiving user interaction from a user via a graphical user interface (GUI) of a computing device (Fig. 5; Figs. 7-15; [abstract] exchanging information among components of a smart health system with mobile devices and/or smartwatches in regards to a patient and caregiver dyad);
in response to receiving the user interaction, collecting a set of user data associated with the user ([0007] A benefit of smart health is the ability to collect a wide range of relevant data passively, minimizing invasiveness and burden—an important consideration for patients and family caregivers coping with the stressors of advanced cancer);
generating a first stress score ([0106] 4) an identified primary ‘family’ caregiver; (note: we interpret ‘family’ here in the broadest sense as an informal caregiver who lives full-time with the patient and is involved with their day-to-day care); 5) scores of 6 or higher on NIH PROMIS Cancer Pain Interference scale measures or the Pain Intensity Numeric Rating Scale) by processing the set of user data using a stress model ([0106] The Dyadic Stress Model posits that life stressors, such as cancer, have a reciprocal impact on patients and their caregivers For example, understanding how patient pain may affect caregiver sleep, and vice versa, is one of the key aspects of this proposal); and
in response to determining that the first stress score satisfies one or more defined criteria ([0106] Sample size is stratified by site as we hypothesize dyads enrolled in hospice and those not enrolled in hospice will be different (see below). Sample size is determined by the minimum number of total pain events per dyad needed for analysis (n=50), and the length of deployment needed to achieve this number):
generating a first prompt for the user, wherein the first prompt requests additional user interaction, as compared to a default prompt (Fig. 5; [0042] to collect active data that requires participant (patient and caregiver) to directly interface with the system by answering questions or marking an event; [0105] dyads will mark and characterize pain events on the smart watch and answer daily survey questions); and
outputting the first prompt via the GUI (Fig. 5; [0105] dyads will mark and characterize pain events on the smart watch and answer daily survey questions). However, Lach does not specifically teach:
- generating a second stress score by processing a second set of user data using the stress model; and
- in response to determining that the second stress score does not satisfy the one or more defined criteria, outputting the default prompt via the GUI
Jain teaches a device that receives data indicating machine learning techniques to enhance the process of defining and managing cohorts for scientific research and provide tools for performing research of such ([abstract]; [col. 1, lines 20-40]). Jain also teaches generating a second stress score ([col. 3, lines 25-40] a confidence score, for each of one or more types of potential study outcomes; [col. 6, lines 30-38] the prediction can include a confidence score generated by a machine learning model; [col. 9, lines 5-9] the output from the one or more models is a confidence score; [col. 9, lines 30-51] the set of feature scores is based on sensor data that is acquired by one or more sensors during one or more activities of the subject or that indicates one or more attributes of the subject… a score for each of a plurality of actions to cause the subject to satisfy the selection criterion, the scores being based on data in the database indicating actions of other subjects with respect to the selection criterion) by processing a second set of user data using the stress model ([col. 28, lines 15-25] if participant data indicates a high percentage of survey responses related to exercise-related stress, then the machine learning models can derive new survey questions that focus on identifying user lifestyles that tend to produce exercise-related stress); and in response to determining that the second stress score does not satisfy the one or more defined criteria (Fig. 11, 1108; Fig. 16, 1608; [abstract] determine a second set of candidates that satisfy a subset of the selection criteria and are determined to not satisfy a same one or more criteria of the selection criteria; [col. 11, lines 65- col. 1, lines 15] The system can identify subjects that do not satisfy cohort selection criteria and identify which subjects have the lowest burden to become eligible according to the cohort selection criteria), outputting the default prompt via the GUI ([abstract] computing devices provide output data through the interface… data indicating the one or more selection criteria not satisfied by the members of the second set of candidates; [col. 32, lines 1-10] determining a second set of candidates classified as not satisfying a same one or more of the selection criteria (1108), and providing data indicating the first set of candidates and the second set of candidates for output through the interface (1110); [col. 32, lines 20-25] the selection criteria can include keywords that identify desired attributes for candidates, such as, a diagnosed medical condition (e.g., diabetes), physiological test data completed (e.g., blood test), a desired age requirement (e.g., older than 30 years old)). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified a system for a patient and/or caregiver of a health system device as taught by Lach, with the second stress score and performing data analysis as taught by Jain, to have achieved a system and method for machine learning models which indicate exercise-related stress and based on inputs and determine and manage participants of the criteria.
With regard to claim 10, the medium claim corresponds to the method claim 3, respectively, and therefore is rejected with the same rationale.
With regard to claim 11, the medium claim corresponds to the method claim 4, respectively, and therefore is rejected with the same rationale.
With regard to claim 12, the medium claim corresponds to the method claim 6, respectively, and therefore is rejected with the same rationale.
With regard to claim 13, the medium claim corresponds to the method claim 7, respectively, and therefore is rejected with the same rationale.
With regard to claim 14, the medium claim corresponds to the method claim 8, respectively, and therefore is rejected with the same rationale.
With regard to claim 15, the system claim corresponds to the method claim 1, respectively, and therefore is rejected with the same rationale.
With regard to claim 16, the system claim corresponds to the method claim 3, respectively, and therefore is rejected with the same rationale.
With regard to claim 17, the system claim corresponds to the medium claim 11, respectively, and therefore is rejected with the same rationale.
With regard to claim 18, the system claim corresponds to the method claim 6, respectively, and therefore is rejected with the same rationale.
With regard to claim 19, the system claim corresponds to the method claim 7, respectively, and therefore is rejected with the same rationale.
With regard to claim 20, the system claim corresponds to the method claim 8, respectively, and therefore is rejected with the same rationale.
Response to Arguments
In the remarks, Applicant argues that the Lach reference fails to teach generating a first stress score by processing the set of user data using a stress model as well as the newly added claim amendments. Lach teaches a patient and/or caregiver system for delivering real-time interventions [abstract]. Lach provides a scheduled Ecological Momentary Assessment (EMA) to generate a brief series of questions regarding mood, sleep quality, activity level and amount of social interaction as shown in Figure 5. Based on the series of questions, the system rates the score of 6 or higher on a Pain Intensity Numeric Rating Scale and cognitive and physical ability to interact with the study on the smart watch [0106]. Lach gives a stress score of 6 or higher based on the Dyadic Stress Model that measures life stressors and give a reciprocal impact on patients that their caregivers [0106]. This shows that a stress model is being presented and further that a stress score rating of 6 or higher is given. However, Lach does not teach the amended limitations of the claim language. In response, Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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 ANDREA C. LEGGETT whose telephone number is (571)270-7700. The examiner can normally be reached M-F 9am-5pm.
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/ANDREA C LEGGETT/Primary Examiner, Art Unit 2171
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