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, 15-17, 21-23, 25-28
The following claim(s) is/are amended: 1, 8, 15, 21
The following claim(s) is/are cancelled: 4, 9, 11-14, 18-20, 24
The following claim(s) is/are new: 26-28
Claim(s) 1-3, 5-8, 10, 15-17, 21-23, 25-28 is/are rejected. This rejection is FINAL.
Previous Rejections Withdrawn
The 35 USC 112(b) rejection to claim(s) 1-3, 5-8, 10, 12-17 is/are withdrawn based on the amendment.
Response to Arguments
Applicant’s arguments filed in the amendment filed 5/20/2026, 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, 25-28 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, 25-28 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 “detecting a misalignment of the incentives likely to produce noncompliant or fraudulent caregiver behavior.” The term is indefinite because the boundaries of “likely to produce noncompliant or fraudulent” behavior 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, 15-17, 21-23, 25-28 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 the current behavior or future behavior of the caregiver to confirm whether misalignment is occurring; (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. para. 72; plurality of devices with a plethora of sensors. 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 Claim 10, it is substantially similar to Claim 3 and is 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 the current behavior or future behavior of the caregiver to confirm whether the noncompliant or fraudulent caregiver behavior is occurring; (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. para. 72; plurality of devices with a plethora of sensors. 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 noncompliant or fraudulent 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.
With respect to Claim 26, modified Kapoustin teaches the method of Claim 21, and Kapoustin also teaches wherein configuring the sensor comprises configuring the sensor to provide more detailed, focused, or enhanced sensing of the caregiver behavior. (para. 51; if system detects abuse it alerts for verification to take action or continue monitoring. paras. 10-12, 17; system seeks to improve fraud and abuse investigations by not waiting until after the fact by getting actionable real-time information. Para. 61-63; cloud system monitoring station can control record on/off camera states. para. 72; plurality of devices with a plethora of sensors. See also Yagnamurthy, Fig. 7a, para. 102; sensor for blood pressure monitoring and separate sensor for user’s heart rate. para. 40; configuring wellness application and related device for receiving and/or utilizing user information. para. 54; server may provide updated configuration to user device. Therefore, it would have been obvious to one of ordinary skill to activate additional sensors to give more detailed or enhanced sensing of caregiver behavior to perform real-time investigations if possible abuse exists.)
With respect to Claim 27, modified Kapoustin teaches the method of Claim 21, and Kapoustin also teaches wherein configuring the sensor comprises invoking another sensor to confirm whether the misalignment is occurring. (para. 51; if system detects abuse it alerts for verification to take action or continue monitoring. paras. 10-12, 17; system seeks to improve fraud and abuse investigations by not waiting until after the fact by getting actionable real-time information. Para. 61-63; cloud system monitoring station can control record on/off camera states. para. 72; plurality of devices with a plethora of sensors. See also Yagnamurthy, Fig. 7a, para. 102; sensor for blood pressure monitoring and separate sensor for user’s heart rate. para. 40; configuring wellness application and related device for receiving and/or utilizing user information. para. 54; server may provide updated configuration to user device. Therefore, it would have been obvious to one of ordinary skill to activate additional sensors to give more detailed or enhanced sensing of caregiver behavior to perform real-time investigations if possible abuse exists.)
With respect to Claim 28, modified Kapoustin teaches the method of Claim 21, and Kapoustin also teaches wherein the additional data create a more comprehensive data set representation of the caregiver behavior. (Examiner asserts this is not a structural limitation and therefore requires no additional teaching. Regardless, see Kapoustin, para. 51; if system detects abuse it alerts for verification to take action or continue monitoring. paras. 10-12, 17; system seeks to improve fraud and abuse investigations by not waiting until after the fact by getting actionable real-time information. Para. 61-63; cloud system monitoring station can control record on/off camera states. para. 72; plurality of devices with a plethora of sensors. See also Yagnamurthy, Fig. 7a, para. 102; sensor for blood pressure monitoring and separate sensor for user’s heart rate. para. 40; configuring wellness application and related device for receiving and/or utilizing user information. para. 54; server may provide updated configuration to user device. Therefore, it would have been obvious to one of ordinary skill to activate additional sensors to give more detailed or enhanced sensing of caregiver behavior to perform real-time investigations if possible abuse exists.)
Remarks
Applicant argues at Remarks, pg. 7 that the amendment fixes the previous 112b. Examiner agrees and withdraws that ground of rejection.
Applicant argues at Remarks, pgs. 7-9 that the claims are not a judicial exception. Applicant asserts the claims are a practical application under Step 2A, Prong Two because they configure a sensor “to provide additional data” in response to detecting a misalignment. Applicant asserts the claims are like Example 42 of the PEG. Applicant does not explain how the instant claims are like claims which translated non-standardized information into standardized information to all for sharing regardless of initial format.
Applicant further argues that the new claims reinforce eligibility. But again, all the claims do is configure a sensor to provide data, which is the basic functioning of a sensor. Invoking a sensor to “provide additional data” could be nothing more than turning on a sensor that was previously off. Similarly, turning on a second sensor “provide[s] more detailed, focused or enhanced sensing of the caregiver behavior” and therefore “create[s] a more comprehensive data set representation of the caregiver behavior.” Similarly, since the second sensor senses the same reality as the first sensor, the usage of the sensor will “confirm whether the misalignment is occurring.”
Applicant argues that Examiner’s finding that there is no technical problem in configuring a sensor is “incorrect” because “the specification expressly states that an alert or variation can trigger configuration of sensors to verify the variation or deviation, that a sensor can be configured to provide additional data with more detailed, focused, or enhanced sensing capabilities, and that another sensor may be invoked to ascertain whether a misalignment is occurring.” But rather than disputing Examiner’s point, Applicant confirms it. Spec, para. 152 states “One aspect of incentive analytics operations is the capability, through for example environment sensing management systems and/or care processing systems to configure a sensor, device and/or system or set thereof, to provide additional data on an occurrence or sequence of occurrences so as to provide more detailed, focused, enhanced and/or additional sensing capabilities.” That is the disclosure. In other words, the specification provides no technical teaching to solve an alleged technical problem in the configuration of a sensor. Rather, the specification relies upon the ordinary skill in the art to achieve the described result. Configuring a sensor to generate sensed data of the environment is simply using a sensor, not improving the art of sensing. To the extent that the additional data improves the ability to “align” incentives, that is not an improvement in a technical field.
Examiner maintains the rejection to all claims.
Applicant argues at Remarks, pgs. 9-11 that Claim 1 is nonobvious and that “generic citations to prediction, preprocessing, incentives, or configurable monitoring devices do not disclose the presently claimed token-based comparison architecture and responsive sensor reconfiguration.”
Applicant considers Yagnamurthy on its own and asserts it does not teach comparison of first and second tokens or predicting caregiver behavior. Applicant asserts that the mapping for the sensor limitation is “especially weak” because “the rejection does not identify a teaching of [misalignment triggering configuration].” Applicant then argues that Sandholm and Ashford do not cure the problems.
Claim 1 is obvious. Claim 1 uses sensors to identify a current behavior of a caregiver, which is expressly what Kapoustin does. Kapoustin monitors the caregivers because when a caregiver is alone with a patient there is the opportunity for both fraud and abuse. (para. 8) Kapoustin identifies the problem with investigating fraud and abuse is that “detection only occurs after the fact” and therefore Kapoustin’s real-time monitoring seeks to immediately catch fraud and abuse. (para. 11, 17) Kapoustin even monitors (1) the health of the caregiver, (2) the mental/physical stress of the caregiver, (3) blood alcohol levels of the caregiver. (para. 67) It does this because (1) a sick caregiver could infect the patient, (2) a stressed caregiver may abuse the patient and (3) a drunk caregiver may comprise the quality of care. Therefore Kapoustin actually monitors not only for immediate problems (i.e. fraud or abuse currently happening) but immediate statuses that could portend future problems.
Yagnamurthy similarly employs sensors to identify the state of wellbeing of a user, predicts future outcomes based on the state, and employs incentives to influence action. Yagnamurthy therefore expands upon the Kapoustin system that seeks to curtail real-time bad acts or immediately ascertainable future problems by engaging a prediction engine to infer future states. Yagnamurthy further seeks to influence future trajectories by incentivizing action. Contrary to Applicant’s assertion, this is more than enough to render the claim features for which it is cited as obvious, because the broad scope of Applicant’s claims cover any situation in which a current incentive is not sufficient to align current and future behavior. Applicant provides no reasoning that a person of ordinary skill would find presenting incentives in order to align behavior in this particular context (as opposed to any other) to be a nonobvious act.
Further, beyond the fact that Kapoustin specifically seeks to audit and control caregiver behavior and Yagnamurthy seeks to influence wellbeing outcomes, Examiner additionally cited Ashford which particularly incentivizes doctors to use methods that result in lower costs to the payor. Examiner fails to see how a system with sensors that monitors what a caregiver is doing combined with a teaching that seeks to modify what methods a caregiver performs is not directly teaching upon the claim limitations. There is a current behavior of a high cost method being performed, and that “misaligns” with a preferred predicted behavior of using a lower cost method, so the system executes an incentive to align current to predicted behavior.
In short, Examiner disagrees that the art needed a teaching that caregivers can be incentivized to change their behavior.
With respect to configuring a sensor to provide additional data, Examiner similarly disagrees that the claim feature renders the claim nonobvious. Sensors provide data. The natural effect of “configuring a sensor in the plurality of sensors” to sense is that it would “provide additional data.” Both Kapoustin and Yagnamurthy teach systems with multiple sensors that provide different types of information. Consequently, the question is whether it is nonobvious to turn on an additional sensor to confirm what the first sensor is suggesting. Examiner notes that Kapoustin explicitly considers verifying an abuse alert, and to possibly continue monitoring. (para. 51) Yagnamurthy discloses re-analyzing using updated data. (para. 99) But these disclosures are secondary. A person of ordinary skill has ordinary creativity and verification or confirmation is a basic technique that is well within the skill of the art. Applicant essentially argues that absent a specific textual teaching that a person of ordinary skill would have found hypothesis confirmation – a basic tenant of the Scientific Method – nonobvious. Examiner disagrees because Kapoustin explicitly calls for verification and continued monitoring and because the context of accusing someone of crime suggests additional confirmatory data is warranted. An obvious act in response to an alert of the Kapoustin real-time system with a plurality of sensors would be to activate other sensors to provide additional data to verify and further evidence any fraud or abuse that may be occurring.
Examiner maintains the obviousness rejection to all claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS P CELANI whose telephone number is (571)272-1205. The examiner can normally be reached on M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vivek Srivastava can be reached on 571-272-7304. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NICHOLAS P CELANI/Examiner, Art Unit 2449
Prosecution Insights