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 .
Status of Claims
The following claim(s) is/are pending in this office action: 1-3, 5-8, 10, 12-17, 21-23, 25
The following claim(s) is/are amended: 1, 8, 15, 21-22
The following claim(s) is/are new: -
The following claim(s) is/are cancelled: 4, 9, 11, 18-20, 24
Claim(s) 1-3, 5-8, 10, 12-17, 21-23, 25 is/are rejected.
Previous Rejections Withdrawn
The 35 USC 112(d) rejection to claim(s) 9 is/are withdrawn based on the amendment.
Response to Arguments
Applicant’s arguments filed in the amendment filed 11/11/2025, have been fully considered but are moot in view of new grounds of rejection. The reasons set forth below.
Applicant’s Invention as Claimed
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-3, 5-8, 10, 12-17, 21-23, 25 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim(s) 1-3, 5-8, 10, 12-17, 21-23, 25 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to observation and judgment or a method of organizing human activity without significantly more. Claim 1 is representative of all claims. The claim(s) recite(s) “a first token which includes the detected data set representing at least one behavior of the caregiver in an environment; a second token which includes a representation of at least one predicted behavior of the caregiver in the environment; at least one incentive specification indicative of a previously identified incentive which influences the caregiver’s behavior” which are observations and “detects a misalignment between the first token and the second token, determines a new or modified incentive for the caregiver to align the first token to the second token” which is a judgment. Further, the limitations also perform the act of managing caregivers. This judicial exception is not integrated into a practical application because the claims merely command the observation and judgment or the managing be done on a computer with artificial intelligence, which improves the ineligible subject matter, not the computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the remaining features are conventional computer hardware used to gather, transmit, store and process data and they do not impart eligibility. The additional feature of “and sends a notification to an involved party indicative of the new or modified incentive” is insignificant post-solution activity. The additional feature of configuring a sensor is conventional.
Claims not specifically mentioned are rejected by virtue of dependency and because they do not obviate the above-recited deficiencies.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim(s) 1-3, 5-8, 10, 12-17 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claim 1 is representative and claims “configure a sensor in the plurality of sensors to provide additional data on occurrence of an outcome that suggests the misalignment.” The term is indefinite because the boundaries of “occurrence of an outcome that suggests the misalignment is vague.
The above cited rejections are merely exemplary.
The Applicant(s) are respectfully requested to correct all similar errors.
Claims not specifically mentioned are rejected by virtue of their dependency.
Claim Rejections - 35 USC § 103
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 1-3, 5-8, 10, 12-17, 21-23, 25 are rejected under 35 U.S.C. 103 as being unpatentable over Kapoustin (US Pub. 2020/0137357) in view of Yagnyamurthy (US Pub. 2016/0171180) in view of Sandholm (US Pub. 2020/0279650) and further in view of Ashford (US Pat. 7,389,245).
With respect to Claim 1, Kapoustin teaches a system to determine a response to behavior of a caregiver, comprising: a plurality of environmental sensors configured to monitor the caregiver resulting in a detected data set, (paras. 17-20, 49-51, 54-57, 59; system checks for caregiver fraud and patient abuse by using a wearable device with a multitude of sensors that collect geospatial, thermal, biometric and biomechanical data. Para. 46; system tracks caregiver during patient interactions. Paras. 60-62; device uses GPS and geofenced areas for the patients home that turn the device on. Para. 72; other sensing devices in the environment.)
and to provide a first token which includes the detected data set representing at least one behavior of the caregiver in an environment; (paras. 64-67; device monitors the general physical and emotional state of the caregiver and transmits it to cloud monitoring system. Para. 72; other sensing devices track caregiver arrival and departure time. Paras. 23, 51; current measurements are compared to baseline measurements.)
and at least one hardware processing unit to (para. 52; processor.)
configure a sensor in the plurality of sensors to provide additional data on occurrence of an outcome that suggests the misalignment; (para. 25; auto activation of the data collection process including geofencing triggers. Paras. 58-59; device battery. Para. 61-63; cloud system monitoring station can control record on/off camera states. System powers features on/off to perform power management. Paras. 66-67; real time data monitoring and analysis to ensure that abuse is not happening. See also Yagnyamurthy, para. 37-41; configuring monitoring devices/sensors. Para. 54; wellness server may provide updated configuration information to the user device. Therefore it would have been obvious to one of ordinary skill prior to the effective filing date to activate fewer sensors to reduce power usage and activate or reconfigure sensors to provide additional data if analysis points to possible fraud or abuse.)
But Kapoustin does not explicitly teach predicted behavior.
Yagnyamurthy, however, does teach a second token which includes a representation of at least one predicted behavior of the caregiver in the environment; (paras. 70, 75-76; system uses trend analysis and machine learning to engage in predictive analytics about future behavior. It would have been obvious to one of ordinary skill prior to the effective filing date to model the behavior of the caregiver as well as the person under care in order to incentivize both to make the patient well and to avoid patient abuse and fraud. Further, see below for incentivizing caregivers in particular.)
receives, the first token, the second token, and at least one incentive specification indicative of a previously identified incentive which influences the caregiver’s behavior, (para. 76; system determines normal wellness behavior of the user. System predicts future wellness behavior of user that they will have high blood pressure. System provides motivations to encourage a healthier lifestyle. Para. 79-80; health related incentive to move toward a healthier lifestyle.)
detects a misalignment between the first token and the second token, determines a new or modified incentive for the caregiver to align the first token to the second token, and sends a notification to an involved party indicative of the new or modified incentive. (para. 25; system determines that a wellness behavior is not being modified by the incentive, so system modifies the incentive and displays the new incentive to the user. Paras. 97-100; Free gym membership does not cause user to work out, so system tries other motivation. User is to run 1km every day, but only runs 1k every other day. System instructs user to run .5km or more each day in attempt to get user to run every day.)
It would have been obvious to one of ordinary skill prior to the effective filing date to combine the system of Kapoustin with the predicted behavior in order to prevent patient abuse before it happens.
But modified Kapoustin does not explicitly teach a prediction engine.
Sandholm, however, does teach a non-transitory computer-readable storage medium configured to store (First see Kapoustin, paras. 16, 20; secure cloud storage. Then see Sandholm, para. 19; non-transitory computer readable medium.)
execute a prediction engine which: (Yagnyamurthy teaches using machine learning for predicting the future wellness position of a user and provides an incentive for diverting from that outcome, see paras. 76-80. Yagnyamurthy can also detect when incentives are not working and modify them, see para. 25, 97-100. But the claim requires a prediction engine (which in some embodiments is a game theory engine) to detect the misalignment. Therefore in addition see Sandholm, paras. 14, 27-31, 48; system uses game theory in modelling treatment of a disease. It would have been obvious to one of ordinary skill prior to the effective filing date to apply the game theory engine of Sandholm to the incentive modification of Yagnyamurthy in order to predict what incentive modifications would be the most effective.)
It would have been obvious to one of ordinary skill prior to the effective filing date to combine the system of modified Kapoustin with the game theory predictive engine in order to improve the course of treatment by identifying ways to successfully attack the problem. (Sandholm, paras. 2-6)
But modified Kapoustin does not explicitly teach an incentive which influences the caregiver’s behavior.
Ashford, however, does teach at least one incentive specification indicative of a previously identified incentive which influences the caregiver’s behavior; determines a new or modified incentive for the caregiver to align the first token to the second token, (Examiner does not think that monetary pay to incentivize caregivers is different in kind than monetary part to incentivize patients. Consequently, Examiner thinks Ashford is unnecessary. Regardless, to compact prosecution Examiner will cite that incentives for caregiver-specific behavior was known, see Ashford, col. 6, ln. 47 to col. 8, ln. 32; system incentivizes doctors to cost save by providing increased incentives for those with methods that result in lower costs to the payor.)
It would have been obvious to one of ordinary skill prior to the effective filing date to combine the system of modified Kapoustin with the incentives to caregivers to influence caregiver actions.
With respect to Claim 2, modified Kapoustin teaches the system of Claim 1, and Yagnyamurthy also teaches wherein a machine learning model converts the detected data set into the first token. (para. 70; preprocessing for model construction such as feature selection.)
The same motivation to combine as the independent claim applies here.
With respect to Claim 3, modified Kapoustin teaches the system of Claim 1, and Yagnyamurthy also teaches wherein the prediction engine includes a machine learning model. (paras. 28, 57; machine learning models.)
The same motivation to combine as the independent claim applies here.
With respect to Claim 5, modified Kapoustin teaches the system of claim 1, and Sandholm also teaches wherein the prediction engine employs game theory in the form of a cooperative game, a normal form or extensible form game. (paras. 14, 27-31, 48; system uses game theory in modelling treatment of a disease. paras. 18, 60; normal form. Paras. 18, 61; extensive form.)
The same motivation to combine as the independent claim applies here.
With respect to Claim 6, modified Kapoustin teaches the system of claim 1, and Sandholm also teaches wherein the prediction engine employs a simultaneous or sequential move game. (paras. 32, 36; simultaneous or sequential moves)
The same motivation to combine as the independent claim applies here.
With respect to Claim 7, modified Kapoustin teaches the system of claim 1, and Sandholm also teaches wherein the prediction engine employs a constant sum, zero sum, non-zero-sum game, symmetric or asymmetric game. (para. 56; zero-sum game, non-zero-sum game.)
The same motivation to combine as the independent claim applies here.
With respect to Claim 8, it is substantially similar to Claim 2 and is rejected in the same manner, the same art and reasoning applying.
With respect to Claims 10, 12-14, they are substantially similar to Claims 3, 5-7, respectively, and are rejected in the same manner, the same art and reasoning applying.
With respect to Claim 15, it is substantially similar to Claim 1 and is rejected in the same manner, the same art and reasoning applying.
With respect to Claims 16-17, they are substantially similar to Claims 2-3, respectively, and are rejected in the same manner, the same art and reasoning applying.
With respect to Claim 21, Kapoustin teaches a method, comprising: generating, via a plurality of environmental sensors, a detected data set; (paras. 17-20, 49-51, 54-57, 59; system checks for caregiver fraud and patient abuse by using a wearable device with a multitude of sensors that collect geospatial, thermal, biometric and biomechanical data. Para. 46; system tracks caregiver during patient interactions. Paras. 60-62; device uses GPS and geofenced areas for the patients home that turn the device on. Para. 72; other sensing devices in the environment.)
determining, based on the detected data set, a current behavior of a caregiver; (paras. 64-67; device monitors the general physical and emotional state of the caregiver and transmits it to cloud monitoring system. Para. 72; other sensing devices track caregiver arrival and departure time. Paras. 23, 51; current measurements are compared to baseline measurements.)
configuring a sensor in the plurality of sensors to provide additional data on occurrence of the current behavior or future behavior; (para. 25; auto activation of the data collection process including geofencing triggers. Paras. 58-59; device battery. Para. 61-63; cloud system monitoring station can control record on/off camera states. System powers features on/off to perform power management. Paras. 66-67; real time data monitoring and analysis to ensure that abuse is not happening. See also Yagnyamurthy, para. 37-41; configuring monitoring devices/sensors. Para. 54; wellness server may provide updated configuration information to the user device. Therefore it would have been obvious to one of ordinary skill prior to the effective filing date to activate fewer sensors to reduce power usage and activate or reconfigure sensors to provide additional data if analysis points to possible fraud or abuse.)
But Kapoustin does not explicitly teach detecting misalignment.
Yagnyamurthy, however, does teach analyzing at least the current behavior of the caregiver to determine one or more incentives for the caregiver; (para. 76; system determines normal wellness behavior of the user. System predicts future wellness behavior of user that they will have high blood pressure. System provides motivations to encourage a healthier lifestyle. Para. 79-80; health related incentive to move toward a healthier lifestyle. It would have been obvious to one of ordinary skill prior to the effective filing date to model the behavior of the caregiver as well as the person under care in order to incentivize both to make the patient well and to avoid patient abuse and fraud. Further, see below for incentivizing caregivers in particular.)
detecting a misalignment of the incentives likely to produce adverse caregiver behavior; and proposing a new or modified incentive to avert, diffuse, or mitigate the misalignment. (para. 25; system determines that a wellness behavior is not being modified by the incentive, so system modifies the incentive and displays the new incentive to the user. Paras. 97-100; Free gym membership does not cause user to work out, so system tries other motivation. User is to run 1km every day, but only runs 1k every other day. System instructs user to run .5km or more each day in attempt to get user to run every day.)
It would have been obvious to one of ordinary skill prior to the effective filing date to combine the method of Kapoustin with the misalignment to allow for incentives to create conforming behavior.
But modified Kapoustin does not explicitly teach predicting one or more future behaviors of the caregiver based at least on the current behavior and the one or more incentives.
Sandholm, however, does teach predicting one or more future behaviors of the caregiver based at least on the current behavior and the one or more incentives; (First see Yagnyamurthy, para. 76; system determines normal wellness behavior of the user. System predicts future wellness behavior of user that they will have high blood pressure. System provides motivations to encourage a healthier lifestyle. Para. 79-80; health related incentive to move toward a healthier lifestyle. Then see Sandholm, paras. 14, 27-31, 48; system uses game theory in modelling treatment of a disease. It would have been obvious to one of ordinary skill prior to the effective filing date to apply the game theory engine of Sandholm to the incentive of Yagnyamurthy in order to predict what incentive modifications would be the most effective.)
It would have been obvious to one of ordinary skill prior to the effective filing date to combine the method of modified Kapoustin with the prediction of future behaviors based on the current behavior and one or more incentives in order to improve the course of treatment by identifying ways to successfully attack the problem. (Sandholm, paras. 2-6)
But modified Kapoustin does not explicitly teach caregiver incentives.
Ashford, however, does teach one or more incentives for the caregiver, (Examiner does not think that monetary pay to incentivize caregivers is different in kind than monetary part to incentivize patients. Consequently, Examiner thinks Ashford is unnecessary. Regardless, to compact prosecution Examiner will cite that incentives for caregiver-specific behavior was known, see Ashford, col. 6, ln. 47 to col. 8, ln. 32; system incentivizes doctors to cost save by providing increased incentives for those with methods that result in lower costs to the payor.)
It would have been obvious to one of ordinary skill prior to the effective filing date to combine the method of modified Kapoustin with the incentives to caregivers to influence caregiver actions.
With respect to Claim 22, modified Kapoustin teaches the method of claim 21, and Yagnyamurthy also teaches wherein at least a behavior of the caregiver is represented by a token. (para. 70; preprocessing for model construction such as feature selection.)
The same motivation to combine as the independent claim applies here.
With respect to Claim 23, modified Kapoustin teaches the method of claim 21, and Yagnyamurthy also teaches wherein at least one of the one or more incentives is represented by a token. (para. 70; preprocessing for model construction such as feature selection.)
The same motivation to combine as the independent claim applies here.
With respect to Claim 25, modified Kapoustin teaches the method of claim 21, and Yagnyamurthy also teaches wherein the incentives include at least one of a financial consideration, a safety consideration, a security consideration, stress management, influence accrual, compliance, time allocation, system optimization, theft, brand management, reputational management, marketing, competition, deception, social advantage, information access control, effort minimization, perception management, maintenance of privacy, emotional management, or behavioral management. (para. 42; monetary and non-monetary incentives.)
The same motivation to combine as the independent claim applies here.
Remarks
Applicant argues at Remarks, pg. 7 that the amended claims are eligible because they configure a sensor and therefore “modifies a real-world system.”
Modifying a real-world system is not a basis for eligibility. All actions carried out by a computer modify a real-world system, yet computer implementation is not sufficient for eligibility. The claims are directed to recognizing a “misalignment” between current and predicted behavior of a caregiver and modifying incentives to align the two. That is an observation on how the caregiver acts and a judgment as to how to incentivize them to change. Further, Examiner now adds organizing human activity as a grounds, as the claims are also fairly described as that. The addition of configuring a sensor does not make the claims directed to something else nor does it constitute a practical application because there is no technical problem in configuring a sensor. Examiner maintains the rejection.
Applicant argues at Remarks, pg. 7 that Claim 9 is cancelled and that fixes the 112(d). Examiner agrees and withdraws the rejection.
Applicant argues at Remarks, pg. 8 that Yagnymurthy and Sandholm analyze patient behavior and the amended claims require analyzing caregiver behavior.
Examiner cites Kapoustin, which analyzes caregiver behavior. Examiner will also, probably unnecessarily, cite Ashford, which teaches incentivizing caregiver action. All claims remain obvious.
All claims are rejected.
Conclusion
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/NICHOLAS P CELANI/Examiner, Art Unit 2449