DETAILED ACTION
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 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.
This Office Action is response to the preliminary amendment filed on 09/25/24.
Claims 1-58 have been canceled.
Claims 59-124 are pending.
Information Disclosure Statement
The references listed in the Information Disclosure Statement filed on 09/17/24 have been considered by the examiner (see attached PTO-1449 form or PTO/SB/08A and 08B).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 59 and 88 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-54 of U.S. Patent No. 12739596. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in the present application are transparently found in the U.S. 12739596 with obvious wording variations.
Instant Application
U.S. 12739596
59. A method for identifying trigger risk(s), undesirable behavior(s), desirable behavior(s), substance use, substance abuse, and/or addictive activity(ies) of one or more entity(ies) and determining and implementing action(s) for reducing triggers risk(s), pre-empting and/or mitigating undesirable behavior(s) and/or encouraging and/or facilitating desirable behavior(s), and/or pre-empting and/or mitigating substance use, substance abuse, and/or addictive activity(ies), the method comprising utilizing sensor(s), sensor array(s), device(s), system(s), and/or network(s) and a plurality of digital and/or physical constructs and/or engines: wherein the plurality of digital and/or physical constructs and/or engines are operable providing capabilities including: sensor(s), sensor array(s), device(s), system(s), and/or network(s) detection, interfacing, and management;
behavior(s) and/or trigger(s) and/or context(s) detection and/or determination; substance use, substance abuse, undesirable behavior detection, determination, and/or inference; contextual determination and/or analysis; individual triggers, combination of at least two triggers, and/or related trigger(s) analysis; speech and/or facial recognition analysis; support resource(s) availability and management;
motivation(s)/ethic(s)/moral(s) and/or disincentives and/or incentives analysis; determination and/or formulation of risk(s) associated with one or more triggers and/or use of substance(s) and/or involvement in undesirable behavior; action(s) determination, integration, implementation, and management; user interface(s) determination and management; and feedback data collection and analysis about the action(s) involving the user and/or equivalent capabilities; wherein the method includes providing a plurality of data from the sensor(s), sensor array(s), device(s), system(s), and/or network(s) to the plurality of digital and/or physical constructs and/or engines.
1. A system comprising a plurality of different devices, sensors, sensor arrays, and/or communications networks, the system configured to determine, through a plurality of measurements/readings taken by the plurality of different devices, sensors, sensor arrays, and/or communications networks and/or inferred through information from system inputs, behavior(s) of at least one entity and context(s) associated with the behavior(s) of the at least one entity, wherein:
the system is further configured to assess, evaluate, and predict, within a predetermined time frame, a risk or trending risk of a behavior(s) and associated context(s) by the at least one entity; the system is configured to, without requiring manual human intervention, dynamically and adaptively determine:
a reward for incentivizing behavior for an associated context of the at least one entity based at least in part on a computed decrease that the incentivized behavior would have on the predicted risk or trending risk of the behavior(s) by the at least one entity; and/or a disincentive for disincentivizing behavior for an associated context of the at least one entity based at least in part on a computed increase that the disincentivized behavior would have on the predicted risk or trending risk of the behavior(s) by the at least one entity; the dynamic and adaptive determining includes automatically selecting a type, level, and amount of the reward and/or the disincentive responsive to how, where, and by how much the predicted risk or trending risk of the behavior(s) by the at least one entity changes as determined from sensor-detected trigger(s) and contextual features.
For claim 88, the claim has features which are similar in claim 59. Therefore, the claim can compare as above.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 59-62, 64-76, 78-86, 88-91, 93-105, 107-115, 117-118, 120-122 and 124 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hamalainen et al. (U.S. 20180140241).
For claim 59, Hamalainen et al. disclose a method for identifying trigger risk(s), undesirable behavior(s), desirable behavior(s), substance use, substance abuse, and/or addictive activity(ies) of one or more entity(ies) and determining and implementing action(s) for reducing triggers risk(s), pre-empting and/or mitigating undesirable behavior(s) and/or encouraging and/or facilitating desirable behavior(s), and/or pre-empting and/or mitigating substance use, substance abuse, and/or addictive activity(ies), the method comprising utilizing sensor(s), sensor array(s), device(s), system(s), and/or network(s) and a plurality of digital and/or physical constructs and/or engines:
wherein the plurality of digital and/or physical constructs and/or engines are operable providing capabilities including:
sensor(s), sensor array(s), device(s), system(s), and/or network(s) detection, interfacing, and management (at least [0008], [0039]-[0040] and [0055]. The system comprises a server unit 301 and a measurement device 303, communicating through a wireless connection 302. The central server unit 301 requests an individual to conduct a measurement using the measurement device 303. In an example embodiment the measurement should be conducted within a predetermined time. The measurement device 303 then reports back the result of the measurement to the central server unit 301 using the wireless connection 302, either the measured value or that the measurement was not conducted as requested. Upon the central server unit 301 receiving a result, an estimate of the risk for relapse of addictive behavior is calculated. If the risk for relapse exceeds a predetermined threshold, the central server unit attempts to notify a third party recipient 304, and possibly also the individual (either using an encouraging message or a disturbing action, both for the purpose to disrupt the trend and stop the anticipated relapse));
behavior(s) and/or trigger(s) and/or context(s) detection and/or determination (at least [0008]-[0009]. A measurement device configured to enable estimation of a risk of relapse of addictive behavior of an individual, the addictive behavior being related to exposure to an addictive stimulus, wherein the measurement device is configured to, at multiple points in time, perform at least one of measuring the individual's exposure to the stimulus and providing a questionnaire to the individual, subsequently collecting the individual's questionnaire answers; and transmit a result from at least one of the stimulus exposure measurement and the questionnaire answers to a central server unit to enable estimation of a risk of relapse. Upon receiving the result, estimate a risk of relapse based on a combination of the received result and historic results; and if the estimated risk is greater than a predefined level, transmit a message to a third party.);
substance use, substance abuse, undesirable behavior detection, determination, and/or inference (at least [0008]-[0009] and [0026]. A measurement device configured to enable estimation of a risk of relapse of addictive behavior of an individual, the addictive behavior being related to exposure to an addictive stimulus, wherein the measurement device is configured to, at multiple points in time, perform at least one of measuring the individual's exposure to the stimulus and providing a questionnaire to the individual, subsequently collecting the individual's questionnaire answers; and transmit a result from at least one of the stimulus exposure measurement and the questionnaire answers to a central server unit to enable estimation of a risk of relapse. Upon receiving the result, estimate a risk of relapse based on a combination of the received result and historic results; and if the estimated risk is greater than a predefined level, transmit a message to a third party.)
contextual determination and/or analysis (at least [0008]-[0009], [0026] and [0043]. Upon the central server unit receiving the result, estimating S60 a risk of relapse based on a combination of the received result and historic result);
individual triggers, combination of at least two triggers, and/or related trigger(s) analysis (at least [0008]-[0009], [0026], [0043] and [0139]. Upon the central server unit receiving the result, estimating S60 a risk of relapse based on a combination of the received result and historic result. Furthermore, individual A suffers from a confirmed relapse around treatment day 40 and reports consistently increasing MI. This individual may be at risk for relapse when mood is better than average, but in this example MI was not capable of identifying the onset of a relapse.);
speech and/or facial recognition analysis (at least [0074]. Other possible methods for identifying the individual who is operating the measurement device includes, but is not limited to, conducting a retinal or iris scan during measurement, analyzing a voiceprint of the individual or reading a fingerprint during measurement.);
support resource(s) availability and management (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. The present invention can be applied as a monitoring tool where the intoxication history is captured and stored, and in cases where an individual is determined to be intoxicated a message can be sent to the health care provider. The present invention is thus capturing relapse of disease in an early stage, which allows health care providers, family members, and other related individuals to become informed so as to attempt to interrupt the relapse. The present invention may, when used as a monitoring tool, be supplemented with a series of other functions, such as support e.g. in the form of encouraging text messages or e-mails, possibly synchronized with times or geographic locations where risk for relapse is high (for example Friday evening in a restaurant area).);
motivation(s)/ethic(s)/moral(s) and/or disincentives and/or incentives analysis (at least [0055]. Upon the central server unit 301 receiving a result, an estimate of the risk for relapse of addictive behavior is calculated. If the risk for relapse exceeds a predetermined threshold, the central server unit attempts to notify a third party recipient 304, and possibly also the individual (either using an encouraging message or a disturbing action, both for the purpose to disrupt the trend and stop the anticipated relapse).);
determination and/or formulation of risk(s) associated with one or more triggers and/or use of substance(s) and/or involvement in undesirable behavior (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. The central server attempts to determine the likelihood that the individual is at risk for relapse each time new data is submitted to the central server. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.);
action(s) determination, integration, implementation, and management (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. The central server attempts to determine the likelihood that the individual is at risk for relapse each time new data is submitted to the central server. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.);
user interface(s) determination and management (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. The central server attempts to determine the likelihood that the individual is at risk for relapse each time new data is submitted to the central server. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.); and
feedback data collection and analysis about the action(s) involving the user and/or equivalent capabilities (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. The central server attempts to determine the likelihood that the individual is at risk for relapse each time new data is submitted to the central server. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.);
wherein the method includes providing a plurality of data from the sensor(s), sensor array(s), device(s), system(s), and/or network(s) to the plurality of digital and/or physical constructs and/or engines (at least [0055]. The system comprises a server unit 301 and a measurement device 303, communicating through a wireless connection 302. The central server unit 301 requests an individual to conduct a measurement using the measurement device 303. In an example embodiment the measurement should be conducted within a predetermined time. The measurement device 303 then reports back the result of the measurement to the central server unit 301 using the wireless connection 302, either the measured value or that the measurement was not conducted as requested. Upon the central server unit 301 receiving a result, an estimate of the risk for relapse of addictive behavior is calculated. If the risk for relapse exceeds a predetermined threshold, the central server unit attempts to notify a third party recipient 304, and possibly also the individual (either using an encouraging message or a disturbing action, both for the purpose to disrupt the trend and stop the anticipated relapse).)
For claim 60, Hamalainen et al. disclose the method of claim 59, wherein: the plurality of data is from the sensor(s), sensor array(s), device(s), system(s), and/or network(s) that are providing measurement(s), metric(s), and/or other information about and/or are associated with the one or more entity(ies) and/or one or more entity(ies) physiological, potential degree of impairment, and/or physical and/or mental status, state, condition, and/or environment; and the method includes providing the plurality of data from the sensor(s), sensor array(s), device(s), system(s), and/or network(s) to at least one of a behavioral, trigger, and/or contextual analysis capability and/or a trigger and/or behavioral risk determination capability of the plurality of digital and/or physical constructs and/or engines (at least [0046]-[0051]. Supplying an individual with a measurement device capable of measuring the exposure to stimuli and presenting questions to a user and collecting answers.)
For claim 61, Hamalainen et al. disclose the method of claim 59, wherein the method includes using the plurality of digital and/or physical constructs and/or engines, including trigger(s), behavior(s), and/or contexts(s) analysis(es) capability and/or trigger, substance use, substance abuse, and/or undesirable behavior risk determination capability thereof, for analyzing the plurality of data from the sensor(s), sensor array(s), device(s), system(s), and/or network(s) and historical data about and/or associated with the entity(ies) to identify, determine, calculate, hypothesize, and/or predict potential indicators of a trigger(s) and/or substance use and/or substance abuse and/or undesirable behavior(s) having occurred, occurring, and/or potentially occurring (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 62, Hamalainen et al. disclose the method of claim 61, wherein upon an indication of a trigger(s) and/or substance use and/or substance abuse and/or undesirable behavior(s) of the entity(ies) having occurred, occurring, and/or potentially occurring, the method includes implementing at least one scenario for the entity(ies) concurrently with the indication, within a defined time range, within a reasonable amount of time, and/or while the context(s) involved is relatively unchanged in relation to the entity(ies) data being collected by the sensor(s), sensor array(s), device(s), system(s), and/or network(s), the at least one scenario potentially supporting the entity(ies) in improving and/or mitigating the trigger risk, substance use, substance abuse, and/or undesirable behavior(s), the at least one scenario including one or more of: an action(s) and/or alert(s) to the entity(ies) or support resource(s), including 3.sup.rd party interface(s) to application(s) and/or system(s), and/or support resource(s)/3.sup.rd party interface(s) use of a physical and/or virtual instance of a support resource/3.sup.rd party interface-related action; and/or identification, determination, formulation, and/or facilitation of an activity that can potentially support the entity(ies) in improving and/or mitigating the trigger risk, substance use, substance abuse, and/or undesirable behavior(s) (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 64, Hamalainen et al. disclose the method of claim 62, wherein the method includes using at least one data value associated with the support resource(s)/3.sup.rd party interface(s) logistical availability and/or trigger and/or context suitability for providing physical and/or mental assistance to the entity(ies) in pre-empting/mitigating the past occurrence, occurring, and/or potentially occurring trigger, substance use, substance abuse, and/or undesirable behavior and/or encouraging/facilitating desirable behavior with the support resource(s), including 3.sup.rd party interface(s) to application(s) and/or system(s), availability and/or suitability including one or more of information about the support resource(s) and 3.sup.rd party interface(s) to application(s) and/or system(s) physical and/or virtual proximity to the entity(ies), similarity in context(s) to the context that entity(ies) historically, currently, and/or may encounter in the future, and/or similarity to past assistance provided by the support resource(s) and 3.sup.rd party interface(s) to application(s) and/or system(s), to the entity(ies) and/or other entity(ies) that share, are the same, and/or are common to at least one of another entity(ies) demographic, trigger, contextual, logistical, app usage, user interface, and/or resource characteristics (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 65, Hamalainen et al. disclose the method of claim 62, wherein implementing at least one scenario includes identifying the user interface(s) that can be used by the entity(ies) and/or support resource(s), including 3.sup.rd party interface(s) to application(s) and/or system(s), in implementing at least one action that can potentially support the entity(ies) in improving and/or mitigating the trigger risk, substance use, substance abuse, and/or undesirable behavior(s) (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. When combining the conscious and motoric information on the same subject for the purpose of producing a more reliable estimate of the true condition of the individual. It is beneficial to gather baseline information about each individual to make possible comparisons of current Questionnaire Answers (QA) and current Questionnaire Motorics (QM) so that deviations from the normal state of an individual can be evaluated. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 66, Hamalainen et al. disclose the method of claim 59, wherein the method includes analyzing at least one action and associated scenario to hypothesize, project, and/or predict an impact of the at least one action on a trigger risk, substance use, substance abuse, and/or undesirable behavior of the entity(ies) historically, currently, or in the future (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. The central server attempts to determine the likelihood that the individual is at risk for relapse each time new data is submitted to the central server. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party.
For claim 67, Hamalainen et al. disclose the method of claim 66, wherein if the analysis predicts that the at least one action and associated scenario will not successfully produce a meaningful or positive result/impact on the trigger risk historically, currently, or in the future, the method includes changing one or more parameters to identify determine, and/or formulate at least one other action that can potentially produce a meaningful or positive result/impact on the trigger risk historically, currently, or in the future (at least [0033], [0052], [0073], [0089] and [0104]-[0109]. By combining intoxication measures of different time-frames like e.g. STI, MTI, LTI, QA, QM and/or other data a reliable measure of the likelihood of being at risk for relapse can be created. Other data includes, but is not limited to, time of day, day of week, result of the previous measurement, elapsed time since the previous measurement, number of measurements that have been missed in the recent past, and similar.)
For claim 68, Hamalainen et al. disclose the method of claim 66, wherein if the analysis predicts that the at least one action and associated scenario will successfully produce a meaningful or positive result/impact on the trigger risk, substance use, substance abuse, and/or undesirable behavior historically, currently, or in the future, the method includes implementing the at least action using one or more sensor(s), sensor array(s), device(s), system(s), communications network(s), alert(s), support resource(s), including 3.sup.rd party interface(s) to application(s) and/or system(s), and/or user interface(s) (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 69, Hamalainen et al. disclose the method of claim 68, wherein the method includes implementing the at least action using one or more alert(s) to inform a recipient(s), suggest response(s) by the recipient(s), and/or trigger systemic and/or human action(s) (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 70, Hamalainen et al. disclose the method of claim 66, wherein the at least one action and associated scenario is generated by, identified within, associated/related to, interpolated and/or extrapolated from at least one action-related data set (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The measurement device 303 then reports back the result of the measurement to the central server unit 301 using the wireless connection 302, either the measured value or that the measurement was not conducted as requested. Upon the central server unit 301 receiving a result, an estimate of the risk for relapse of addictive behavior is calculated. If the risk for relapse exceeds a predetermined threshold, the central server unit attempts to notify a third party recipient 304, and possibly also the individual (either using an encouraging message or a disturbing action, both for the purpose to disrupt the trend and stop the anticipated relapse).)
For claim 71, Hamalainen et al. disclose the method of claim 59, wherein the method includes monitoring, via the plurality of digital and/or physical constructs and/or engines, the entity(ies) behaviors and associated context(s) and the sensor(s), sensor array(s), device(s), system(s), network(s), and/or support resource(s), including 3.sup.rd party interface(s) to application(s) and/or system(s), to collect data, provide feedback, and perform analysis about impact(s) of action(s) on a trigger risk, substance use, substance abuse, and/or undesirable behavior of the entity(ies) historically, currently, or in the future (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 72, Hamalainen et al. disclose the method of claim 71, wherein the method includes a learning capability of the plurality of digital and/or physical constructs and/or engines using the analysis of the data, feedback, and analysis about the impact(s) of the action(s) to identify additions, deletions, and/or modifications to: the type of data collected by the sensor(s), sensor array(s), device(s), system(s), and/or network(s); the number of sensor(s), sensor array(s), device(s), system(s), and/or network(s) to use; data collection mechanisms; frequency of use; and/or other data collection-related configuration parameters future (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The central server unit is typically a computer which comprises a program capable of regularly, or with irregular time intervals, requesting an individual to make measurement, storing measurement results, and estimating if present data is indicative of the individual being clean. The purpose of repeatedly requesting the individual to make measurements is to collect a time-line of stimuli exposure data, such as intoxication data. Such a stimuli exposure history can reveal onset patterns, frequency, duration and intensity of stimuli exposure. Such information is valuable when assessing the likelihood that an individual is clean. For example, if stimuli exposure history indicates that an individual is highly exposed to stimuli approximately once per month with onset in the evening, but the duration is only one day, it may be sufficiently accurate to rely on an evening measurement previous day until the afternoon present day. As another example, if a different individual has a stimuli exposure history which indicates frequent stimuli exposures or intoxications at mild intensity during 3-5 days, a new measurement may be requested multiple times per day.)
For claim 73, Hamalainen et al. disclose the method of claim 71, wherein the method includes a learning capability of the plurality of digital and/or physical constructs and/or engines using the analysis of the data, feedback, and analysis about the impact(s) of the action(s) to identify additions, deletions, and/or modifications to: the action(s); the support resource(s) including 3.sup.rd party interface(s) to application(s) and/or system(s); the user interface(s); and/or usage variations within and/or configuration of the action(s) including the support resource(s) and/or user interface(s) used in the action(s) that can be potentially used in the future though inclusion in the data sets used by the action generation, identification, association, interpolation, and/or extrapolation capabilities (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The central server unit is typically a computer which comprises a program capable of regularly, or with irregular time intervals, requesting an individual to make measurement, storing measurement results, and estimating if present data is indicative of the individual being clean. The purpose of repeatedly requesting the individual to make measurements is to collect a time-line of stimuli exposure data, such as intoxication data. Such a stimuli exposure history can reveal onset patterns, frequency, duration and intensity of stimuli exposure. Such information is valuable when assessing the likelihood that an individual is clean. For example, if stimuli exposure history indicates that an individual is highly exposed to stimuli approximately once per month with onset in the evening, but the duration is only one day, it may be sufficiently accurate to rely on an evening measurement previous day until the afternoon present day. As another example, if a different individual has a stimuli exposure history which indicates frequent stimuli exposures or intoxications at mild intensity during 3-5 days, a new measurement may be requested multiple times per day.)
For claim 74, Hamalainen et al. disclose the method of claim 59, wherein the method includes implementing at least one action to potentially support the entity(ies) in improving and/or mitigating a trigger risk, substance use, substance abuse, and/or undesirable behavior of the entity(ies) historically, currently, or in the future (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 75, Hamalainen et al. disclose the method of claim 74, wherein implementing the at least one action includes changing the context only or changing the user interface only (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 76, Hamalainen et al. disclose the method of claim 59, wherein: the method includes formulating at least one action to potentially support the entity(ies) in improving and/or mitigating a trigger risk, substance use, substance abuse, and/or undesirable behavior of the entity(ies) historically, currently, or in the future; and formulating at least one action includes changing or more variable(s) within and/or parameters associated within the variable(s) of the entity(ies) motivations, ethics, and/or morals and/or the incentives and/or disincentives of the entity(ies) (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 78, Hamalainen et al. disclose the method of claim 59, wherein the method includes determining a risk of a trigger having occurred, occurring, or potentially occurring by calculating an absolute score and/or relative score in relation to a measurement system and/or comparison to another entity(ies) and/or determination of the entity(ies)'s risk relative to a trigger-related threshold, degree of trigger activation, and/or possible use of substance, observation of certain behavior(s), trend of the possibility of certain behavior(s) (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 79, Hamalainen et al. disclose the method of claim 59, wherein the method includes: upon reaching a certain absolute or relative score, threshold, risk of trigger activation, use or possible use of substance, observation of certain behavior(s), trend of the possibility of certain behavior(s), identifying a certain context(s), context restriction, and/or degree of possible context change, level of risk, risk trend, and/or limitation(s) or requirement(s) associated with potential action(s), available support resource(s) including 3.sup.rd party interface(s) to application(s) and/or system(s), and/or available user interface(s); and identifying, formulating, determining, and/or projecting potential action(s) for pre-empting and/or mitigating the risk of undesirable behavior(s) occurring or potentially occurring, the potential actions including at least one or more of an alert(s), entity(ies) action(s) and/or activity(s), and/or use of support resource(s) with the potential for pre-empting the trigger(s) and/or substance use risk, mitigating an already active trigger and/or substance use-related activity, and/or lowering future risk(s) of the entity(ies) activating the trigger and/or using substance(s) and/or performing undesirable behavior(s) in the future (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 80, Hamalainen et al. disclose the method of claim 59, wherein the method includes using a contextual analysis engine(s) of the plurality of digital and/or physical constructs and/or engines to analyze a plurality of data from the sensor(s), sensor array(s), device(s), system(s), and/or network(s) and historical data about and/or associated with the entity(ies) data to identify, determine, calculate, hypothesize, and/or predict: potential indicators of the context(s) associated with one or more data elements; and the context(s) associated with the data, before, during, and potentially after when the data was collected (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. Upon the central server unit receiving the result, estimating S60 a risk of relapse based on a combination of the received result and historic results. The central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring.)
For claim 81, Hamalainen et al. disclose the method of claim 59, wherein the method includes using one or more contextual data to normalize one or more data elements and/or values from the sensor(s), sensor array(s), device(s), system(s), and/or network(s) such that at least one of the normalized data elements is an input into at least one of the trigger, behavior, and/or context analysis and/or the trigger and/or behavior risk determination capability(ies) of the of the plurality of digital and/or physical constructs and/or engines (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. Upon the central server unit receiving the result, estimating S60 a risk of relapse based on a combination of the received result and historic results. The central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring.)
For claim 82, Hamalainen et al. disclose the method of claim 59, wherein the method includes performing contextual analysis, via a risk assessment engine(s) of the plurality of digital and/or physical constructs and/or engines, using a plurality of normalized data elements and/or values from the sensor(s), sensor array(s), device(s), system(s), and/or network(s) to assess one or more of: the risk of the trigger(s) being activated; a threshold reached; an absolute and/or relative score being reached; a status and/or state obtained; a range being reached, entered, and/or exited; and a trend consisting of at least two normalized data elements and/or values including a historical normalized data element and/or value and a current or relatively current normalized data element and/or value that are usable to assess directional risk according to the associated trend, momentum, associated indicators of the normalized data elements and/or values, associated absolute and/or relative risk scores being approached and/or how rapidly the score(s) are changing, and/or a formulating a projection of risk and/or trend of the trigger being activated based on the normalized data elements and/or values from the sensor(s), sensor array(s), device(s), system(s), and/or network(s) (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The server is then determining (a) if the individual is at elevated risk and (b) a suitable time point for the next request to the individual for presenting a questionnaire. If the central server determines that the individual is, with a high likelihood, at risk for relapse, for example if the risk is above a predefined threshold, a message can be sent to a third party, such as a family member, a health care provider, employer, law enforcement or any other party. This message can be transmitted using the internet or a GSM cell phone network or any other similar network. Additionally, the central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring. As an example, the provided feedback may be transmitted as playing a prerecorded message, or sending a short text message (SMS) to the individual's cell phone, with an encouraging content. As another example, the feedback may be of annoying character, such as vibrating the device or playing an annoying alarm.)
For claim 83, Hamalainen et al. disclose the method of claim 59, wherein the entity(ies) comprises one or more of a human, an animal, a system, a machine, a robot, an artificial intelligence, a virtual agent, a corporation, a business entity, a nation, a network, a driverless vehicle, a connected vehicle, a drone, a governmental entity, a digital and/or physical construct, agent, and/or engine, and combination of two or more thereof (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. Upon the central server unit receiving the result, estimating S60 a risk of relapse based on a combination of the received result and historic results. The central server may, if the risk for relapse is exceeding a predetermined level, instruct the device to provide feedback to the individual, so as to attempt to disturb the pattern indicative of relapse and thereby possibly prevent relapse from advancing or even occurring.)
For claim 84, Hamalainen et al. disclose the method of claim 59, wherein: the plurality of digital and/or physical constructs and/or engines are configured such at least one of the plurality of digital and/or physical constructs and/or engines is operable for providing more than one of the capabilities; the plurality of digital and/or physical constructs and/or engines are configured such at least two of the plurality of digital and/or physical constructs and/or engines are operable collectively to provide at least one of the capabilities; and/or the plurality of digital and/or physical constructs and/or engines comprise at least one digital and/or physical construct and/or engine that is at least one of a digital construct, hardware, network, and/or combination of at least two thereof (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The system comprises a server unit 301 and a measurement device 303, communicating through a wireless connection 302. The central server unit 301 requests an individual to conduct a measurement using the measurement device 303. In an example embodiment the measurement should be conducted within a predetermined time. The measurement device 303 then reports back the result of the measurement to the central server unit 301 using the wireless connection 302, either the measured value or that the measurement was not conducted as requested. Upon the central server unit 301 receiving a result, an estimate of the risk for relapse of addictive behavior is calculated. If the risk for relapse exceeds a predetermined threshold, the central server unit attempts to notify a third party recipient 304, and possibly also the individual (either using an encouraging message or a disturbing action, both for the purpose to disrupt the trend and stop the anticipated relapse).)
For claim 85, Hamalainen et al. disclose the method of claim 59, wherein the support resource(s) for the entity(ies) comprises one or more of a person, persons, group, application, third party, digital construct, and/or institution (at least [0033], [0052], [0073]-[0089] and [0104]-[0109]. The system comprises a server unit 301 and a measurement device 303, communicating through a wireless connection 302. The central server unit 301 requests an individual to conduct a measurement using the measurement device 303. In an example embodiment the measurement should be conducted within a predetermined time. The measurement device 303 then reports back the result of the measurement to the central server unit 301 using the wireless connection 302, either the measured value or that the measurement was not conducted as requested. Upon the central server unit 301 receiving a result, an estimate of the risk for relapse of addictive behavior is calculated. If the risk for relapse exceeds a predetermined threshold, the central server unit attempts to notify a third party recipient 304, and possibly also the individual (either using an encouraging message or a disturbing action, both for the purpose to disrupt the trend and stop the anticipated relapse).)
For claim 86, Hamalainen et al. disclose the method of claim 59, wherein: the method includes determining a trigger(s) using a Trigger Identification and Determination Engine of the plurality of digital and/or physical constructs and/or engines; and the trigger(s) comprises one or more of Ambiguity, Anger, Anxiety (including all forms of phobias), Boredom, Change, Children, Conflict, Depression, Disorder, Embarrassment, Escape, Envy, Excitement, Fun, Frustration, Guilt, Health issues, Holidays, Hunger, Insomnia, Irritation, Job stress, Loneliness, Mid-life Crisis, Money worries, Music, Noise, Overconfidence, Pain, Peer Pressure, feeling Powerful or Powerless, Proximity, Fear of Quitting, Relationship issues, Relatives, Reminders, Rudeness, Self-Esteem (Low and High), Self-Loathing, Sex, Shopping Situations, Social Situations, Special Occasions, Stress, Taste and Smell, Times of Day, being Tired, being “Unfun”, being a Victim, ex-spouses/partners, Yelling, Season or Weather changes, ignorance and fear of the unknown, Disrespect, and Epistemological State(s), including not knowing or being faced with an Unknown, a Known, a Belief with little or no confidence in that Belief, a Belief with high confidence in that Belief but the Belief is wrong, a Belief with high confidence that is correct, a Belief that was correct but has become false over time, a Belief that is true and false at the same time but that Belief is in fact neither true nor false but possibly either, and/or an Unknowable (at least [0090]. Addictive behavior need not be linked to a chemical compound. An individual may become addicted to e.g. gambling, and with the availability of a multitude of on-line gambling web-sites it may be difficult for an individual to stay clean from gambling. Aspects of pathological gambling is discussed in the report “Retrospective and Prospective Reports of Precipitants to Relapse in Pathological Gambling.” by Hodgins, David C. and el-Guebaly, Nady as published in Journal of Consulting and Clinical Psychology, Vol 72(1), February 2004, 72-80. http://dx.doi.org/10.1037/0022-006X.72.1.72. Another example is pathological relationships to food, leading to an un-healthy situation (such as anorexia or obesity).)
For claim 117, Hamalainen et al. disclose the system of claim 88, wherein the plurality of digital and/or physical constructs and/or engines includes a motivation analysis engine configured to be operable for determining a motivation(s) associated with the one or more entity(ies) (at least [0033], [0052]-[0055], [0073]-[0089] and [0104]-[0109]. Upon the central server unit 301 receiving a result, an estimate of the risk for relapse of addictive behavior is calculated. If the risk for relapse exceeds a predetermined threshold, the central server unit attempts to notify a third party recipient 304, and possibly also the individual (either using an encouraging message or a disturbing action, both for the purpose to disrupt the trend and stop the anticipated relapse).)
For claim 118, Hamalainen et al. disclose the system of claim 88, wherein the plurality of digital and/or physical constructs and/or engines includes a plurality of digital and/or physical constructs, agents, and/or engines (at least [0033], [0052]-[0055], [0073]-[0089] and [0104]-[0109]. FIG. 3 is a schematic diagram illustrating an example of a system configured to estimate a risk of relapse of addictive behavior according to an embodiment. The system comprises a server unit 301 and a measurement device 303, communicating through a wireless connection 302. The central server unit 301 requests an individual to conduct a measurement using the measurement device 303. In an example embodiment the measurement should be conducted within a predetermined time. The measurement device 303 then reports back the result of the measurement to the central server unit 301 using the wireless connection 302, either the measured value or that the measurement was not conducted as requested. Upon the central server unit 301 receiving a result, an estimate of the risk for relapse of addictive behavior is calculated. If the risk for relapse exceeds a predetermined threshold, the central server unit attempts to notify a third party recipient 304, and possibly also the individual (either using an encouraging message or a disturbing action, both for the purpose to disrupt the trend and stop the anticipated relapse).)
For claim 120, Hamalainen et al. disclose the system of claim 88, wherein the support resource(s) availability and management includes 3rd party interfaces to applications and/or systems availability and management (at least [0033], [0052]-[0055], [0073]-[0089] and [0104]-[0109]. FIG. 3 is a schematic diagram illustrating an example of a system configured to estimate a risk of relapse of addictive behavior according to an embodiment. The system comprises a server unit 301 and a measurement device 303, communicating through a wireless connection 302. The central server unit 301 requests an individual to conduct a measurement using the measurement device 303. In an example embodiment the measurement should be conducted within a predetermined time. The measurement device 303 then reports back the result of the measurement to the central server unit 301 using the wireless connection 302, either the measured value or that the measurement was not conducted as requested. Upon the central server unit 301 receiving a result, an estimate of the risk for relapse of addictive behavior is calculated. If the risk for relapse exceeds a predetermined threshold, the central server unit attempts to notify a third party recipient 304, and possibly also the individual (either using an encouraging message or a disturbing action, both for the purpose to disrupt the trend and stop the anticipated relapse).)
For claims 88-91, the claims have features which are similar in claims 59-62. Therefore, the claims are also rejected for the same reason in claim 59-62.
For claims 93-105, the claims have features which are similar in claims 64-76. Therefore, the claims are also rejected for the same reason in claim 67-76.
For claims 107-115, the claims have features which are similar in claims 78-86. Therefore, the claims are also rejected for the same reason in claim 78-86.
For claims 121-122, the claims have features which are similar in claims 117-118. Therefore, the claims are also rejected for the same reason in claim 117-118.
For claim 124, the claim has features which are similar in claim 120. Therefore, the claim is also rejected for the same reason in claim 120.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 63 and 92 are rejected under 35 U.S.C. 103 as being unpatentable over Hamalainen et al. (U.S. 20180140241) in view of Mosby (U.S. 20140051043).
For claim 63, Hamalainen et al. do not disclose the method of claim 62, wherein upon indication that the scenario implemented for the entity(ies) does not successfully produce a meaningful or positive result/impact on the trigger risk, substance use, substance abuse, and/or undesirable behavior, the method includes: changing one or more parameters to identify determine, and/or formulate another scenario that can potentially support the entity(ies) in improving and/or mitigating the trigger risk, substance use, substance abuse, and/or undesirable behavior(s); and implementing the another scenario for the entity(ies).
In the same field of endeavor, Mosby discloses that upon indication that the scenario implemented for the entity(ies) does not successfully produce a meaningful or positive result/impact on the trigger risk, substance use, substance abuse, and/or undesirable behavior, the method includes: changing one or more parameters to identify determine, and/or formulate another scenario that can potentially support the entity(ies) in improving and/or mitigating the trigger risk, substance use, substance abuse, and/or undesirable behavior(s); and implementing the another scenario for the entity(ies) (at least [0021]. In the event the patient experiences a relapse, he or she has a window of opportunity within which he or she can post an amount equal to the originally remitted amount, essentially betting himself "double or nothing" regarding his or her own will power. In doing so, the time period during which the patient is monitored also doubles. If the patient is successful (i.e., does not experience a relapse during the monitored time period), the patient receives all of the invested funds. Conversely, if the patient experiences another relapse, the patient can, remit an amount double the originally remitted amount and the time period for monitoring is extended even more. The patient can continue to self-wager "double or nothing" until success occurs (i.e., there is no relapse during the monitoring period).)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Hamalainen et al. as taught by Mosby for purpose of improving his or her health status by ceasing to smoke.
For claim 92, the claim has features which are similar in claim 63. Therefore, the claim is also rejected for the same reason in claim 63.
Claims 77, 106, 119 and 123 are rejected under 35 U.S.C. 103 as being unpatentable over Hamalainen et al. (U.S. 20180140241) in view of Davis et al. (U.S. 20210345925).
For claim 77, Hamalainen et al. do not disclose the method of claim 59, wherein the method includes using artificial intelligence (AI) and/or machine learning in at least one of the plurality of digital and/or physical constructs and/or engines.
In the same field of endeavor, Davis et al. disclose using artificial intelligence (AI) and/or machine learning in at least one of the plurality of digital and/or physical constructs and/or engines (at least [0036]. The data processing device 120 can use natural language processing (NLP) and closed-form indirect questions on data collected through an application on a smart phone or similar apparatus to predict health risks such as depression among peripartum women. Patients can be asked open-ended journal questions each day and can respond by text or voice in an application on a smart phone or similar apparatus. Examples of questions are: 1) “How would you describe your overall mood in the past 24 hours? What had the biggest impact on your mood, and why?” 2) “In looking back at the past 24 hours, what events or interactions stand out? How did they make you feel?” and 3) “What activity or event did you most enjoy in the past 24 hours? What did you enjoy the least? Why?”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Hamalainen et al. as taught by Davis et al. for purpose of identify treatment responsive to a health risk determined from feature data provided by one or more networked data sources.
For claim 119, Hamalainen et al. do not disclose the method of claim 88, wherein at least one of the plurality of digital and/or physical constructs and/or is included within a neural network, a quantum network, and/or a mesh network with at least one or more other digital and/or physical constructs, agents, and/or engines.
In the same field of endeavor, Davis et al. disclose at least one of the plurality of digital and/or physical constructs and/or is included within a neural network, a quantum network, and/or a mesh network with at least one or more other digital and/or physical constructs, agents, and/or engines (at least [0031]. FIG. 1 is a block diagram of an example computing environment 100 for detecting health risks and causing treatment responsive to the detection. Overall, a detection device 110 is used to collect input data from a source of the input data. The input data can include a speech signal, text data, responses to questionnaires, and so forth. The detection device 110 routes the input data to a data processing device 120 for analysis of the input data and the extraction of features from the input data. The routing can be done, for example, over a wired or wireless network 130. The data processing device 120 is configured to analyze the input to extract one or more features of the input data. The data processing device 120 is configured to provide an output representing one or more health risks experienced by the source of the input data (e.g., a patient that provided the input data). The output can include a visual representation of the identified health risks of the patient. The visual representation can include alerts, alarms, etc. that communicate the detection of the health risks to the patient. In some implementations, the visual representation can include one or more interactive controls that facilitate treatment a condition or diseases associated with the health risks that are detected. For example, the visual representation can include a link to another data source (such as a website), additional prompts for information, a control for contacting a physician, and so forth. The visual representation can be displayed, for example, on a display of a client device 140 to physicians and other healthcare providers.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Hamalainen et al. as taught by Davis et al. for purpose of identify treatment responsive to a health risk determined from feature data provided by one or more networked data sources.
For claims 106 and 123, the claims have features which are similar in claims 77 and 119. Therefore, the claims are also rejected for the same reason in claims 77 and 119.
Allowable Subject Matter
Claims 87 and 116 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.
Conclusion
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/DAI PHUONG/Primary Examiner, Art Unit 2644
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