Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention. Applying Step 1 to the claims it is determined that: claims 1-18 are directed to a machine; and claims 19 and 20 are directed to a process.
Independent Claims
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
The independent claims recite an abstract idea. Specifically, independent claim 1 recites an abstract idea in the limitations (emphasized):
…train a machine learning model using at least i) historical systems data comprising a layout of security systems in a geographic area, ii) historical scheduled event data comprising historical scheduled events in the geographic area, and iii) one or more historical security occurrences associated with the historical scheduled events;
receive new systems data from at least one services computer system, the new systems data identifying a geographic location of a scheduled event in the geographic area and a time that the scheduled event is scheduled to occur;
input the new systems data into the machine learning model;
receive an output from the machine learning model that identifies at least one potential security occurrence associated with the scheduled event;
identify at least one person potentially impacted by the at least one potential security occurrence based at least in part upon the geographic location of the scheduled event and at least one individual profile associated with the at least one person indicating that at least one scheduled or predicted commute of the at least one person includes travel proximate to the geographic location; and
transmit security data associated with the at least one potential security occurrence and including a risk mitigating recommendation to a computing device associated with the at least one person, wherein the security data causes initiation of at least one recommended action to be taken proximate to the time that the scheduled event is scheduled to occur, and wherein the at least one recommended action increases a likelihood of the person avoiding the at least one potential security occurrence.
These limitations recite an abstract idea because these limitations encompass managing personal behavior or interactions between people (i.e., following rules or instructions). These limitations encompass managing personal behavior or interactions between people (i.e., following rules or instructions) because these limitations encompass identifying people at potential risk and providing instructions for avoiding the risk (e.g., organizing an evacuation). Claims that encompass managing personal behavior or interactions between people fall within the “Certain Methods of Organizing Human Activity”. Claims 1, 10 and 19 recite an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. Claim 1 recites the additional elements (emphasized):
…train a machine learning model using at least i) historical systems data comprising a layout of security systems in a geographic area, ii) historical scheduled event data comprising historical scheduled events in the geographic area, and iii) one or more historical security occurrences associated with the historical scheduled events;
receive new systems data from at least one services computer system, the new systems data identifying a geographic location of a scheduled event in the geographic area and a time that the scheduled event is scheduled to occur;
input the new systems data into the machine learning model;
receive an output from the machine learning model that identifies at least one potential security occurrence associated with the scheduled event;
identify at least one person potentially impacted by the at least one potential security occurrence based at least in part upon the geographic location of the scheduled event and at least one individual profile associated with the at least one person indicating that at least one scheduled or predicted commute of the at least one person includes travel proximate to the geographic location; and
transmit security data associated with the at least one potential security occurrence and including a risk mitigating recommendation to a computing device associated with the at least one person, wherein the security data causes initiation of at least one recommended action to be taken proximate to the time that the scheduled event is scheduled to occur, and wherein the at least one recommended action increases a likelihood of the person avoiding the at least one potential security occurrence.
These additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of training the machine learning model, inputting the data into the model, and receiving an output for the model, when considered individually or in combination, do not integrate the abstract idea into a practical application because the training and use of the machine learning is recited sufficiently generally and broadly such that it amounts to no more than mere instructions to apply the exception.
Second, the additional elements of receiving the new systems data and transmitting the security data, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass generic computer functions of receiving and sending data, see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application). Claims 1, 10 and 19 are directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claims 1, 10 and 19 are not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract idea for the following reasons.
Claims 2 and 11 recite the same abstract idea as the independent claims because determining safety actions at a proximate time is a part of identifying people at potential risk and providing instructions for avoiding the risk (e.g., monitoring an evacuation).
Claims 3, 5, 12, and 14 recite the same abstract idea as the independent claims because changing levels of risk is a for a person is a part of identifying people at potential risk and providing instructions for avoiding the risk (e.g., organizing an evacuation and identifying those who have been safely evacuated).
Claims 4 and 13 recite the additional elements of training the machine learning model by defining function coefficients. These additional elements, when considered individually or in combination, do not integrate the abstract into a practical application because the additional elements are recited sufficiently generally and broadly (e.g., as determining weighting) such that it amounts to no more than mere instructions to apply the exception.
Claims 6, 8, 9, 15, 17, and 18 recite the additional elements of displaying a notification for the security data, transmitting the security data proximate to the event and displaying an alternative route. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass generic computer functions of displaying data, see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claims 7 and 16 recite the same abstract idea as the independent claims because the claimed events are still a part of identifying people at potential risk and providing instructions for avoiding the risk.
Claim 20 recites similar limitations as claims 2 and 3 accordingly is rejected for similar reasons as claims 2 and 3.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehta et al, US Pub. No. 2017/0161614, herein referred to as "Mehta" in view of Patel, US Pub. No. 2018/0146354, herein referred to as “Patel”.
Regarding claim 1, Mehta teaches:
at least one processor and at least one memory device, the at least one processor programmed to (processor and memory, e.g. ¶¶[0008], [0065] and Fig. 1):
train a machine learning model (trained prediction model, e.g., ¶¶[0034], [0135])
using at least i) historical systems data comprising security systems in a geographic area (uses historical data, e.g., ¶[0031], [0040], including number and effectiveness of emergency service providers involved,¶[0087])
ii) historical scheduled event data comprising historical scheduled events in the geographic area (uses historical data, e.g., ¶[0031], [0040], including event data, e.g., ¶[0095]) ,
and iii) one or more historical security occurrences associated with the historical scheduled events (uses historical data, e.g., ¶[0031], [0040], including emergencies associated with events, ¶¶[0038], [0052]);
receive new systems data from at least one services computer system, the new systems data identifying a geographic location of a scheduled event in the geographic area and a time that the scheduled event is scheduled to occur (event data includes time and location of event, ¶[0095]; see also ¶¶[0010], [0096] discussing receiving event data);
input the new systems data into the machine learning model (applies prediction model to event data, e.g., ¶¶[0071], [0078]-[0079]);
receive an output from the machine learning model that identifies at least one potential security occurrence associated with the scheduled event (predicts probability of emergency situations about events, ¶[0052]; see also ¶[0088] noting types of emergencies include security issues like shooting or terrorist attack; and ¶[0141] and Fig.6 discussing prediction associated with Thanksgiving);
identify at least one person potentially impacted by the at least one potential security occurrence based at least in part upon the geographic location of the scheduled event and at least one individual profile associated with the at least one person indicating that at least one scheduled or predicted commute of the at least one person includes travel proximate to the geographic location (determines subject will be within a zone of danger in the future, ¶[0045] and generates risk prediction for an emergency in a location, ¶[0108]; see also ¶[0097] discussing determining future location of subject);
and transmit security data associated with the at least one potential security occurrence and including a risk mitigating recommendation to a computing device associated with the at least one person (sends warnings to subject, e.g., ¶[0111], and warnings include preventative measures, ¶¶[0036], [0112]),
wherein the security data causes initiation of at least one recommended action to be taken proximate to the time that the scheduled event is scheduled to occur, and wherein the at least one recommended action increases a likelihood of the person avoiding the at least one potential security occurrence (warnings include preventative measures, ¶¶[0036], [0112], like alternate driving routes, ¶[0050]. Please note, the security data causing initiation of at least one recommended action does not further limit the scope of the claim because it is only the intended use the security data, see MPEP 2103.I.C. That is, the security data causing initiation of recommended action (e.g., causing people to seek shelter or avoid an area) does not further limit the scope of the claim because it is only the intended use of the security data).
However Mehta does not teach but Patel does teach:
using at least i) historical systems data comprising a layout of security systems in a geographic area (uses shelter location data, ¶[0054], location data of emergency responders, ¶[0071])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation of Mehta with the personal security monitoring of Patel because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Mehta would not only be interested in monitoring emergency situations but would also be interested in monitoring the safety of the people in the region and accordingly would have modified Mehta and Mdeway to determine if the users are in a safe location as taught by Patel.
Regarding claim 2, the combination of Mehta and Patel teaches all the limitations of claim 1 and Patel further teaches:
determine that the at least one recommended action was performed proximate to the time the scheduled event was scheduled to occur (determines if users are in area of effect, ¶[0041] and sends information on shelters, ¶[0043], and determines if user is at shelter, ¶¶[0045]-[0046]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation of Mehta with the personal security monitoring of Patel because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Mehta would not only be interested in monitoring emergency situations but would also be interested in monitoring the safety of the people in the region and accordingly would have modified Mehta and Mdeway to determine if the users are in a safe location as taught by Patel.
Regarding claim 3, the combination of Mehta and Patel teaches all the limitations of claim 2 and Mehta further teaches:
wherein the at least one individual profile is associated with a first risk level based upon the at least one potential security occurrence (determines level of risk, e.g., against a baseline, ¶[0111]),
and wherein the at least one processor is further programmed to update the at least one individual profile to be associated with a second risk level, wherein the second risk level is lower than the first risk level (updates user risk prediction to show a lower risk, ¶[0053]).
However Mehta does not teach but Patel does teach:
and wherein the at least one processor is further programmed to update the at least one individual profile to be associated with a second risk level based on the at least one recommended action being performed proximate to the time the scheduled event was scheduled to occur, wherein the second risk level is lower than the first risk level (determines if users are at shelter and probably safe, ¶¶[0054]-[0055] and Fig. 3C).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation of Mehta with the personal security monitoring of Patel because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Mehta would not only be interested in monitoring emergency situations but would also be interested in monitoring the safety of the people in the region and accordingly would have modified Mehta and Mdeway to determine if the users are in a safe location as taught by Patel.
Regarding claim 4, the combination of Mehta and Patel teaches all the limitations of claim 1 and Mehta further teaches:
wherein the at least one processor is further programmed to further train the machine learning model by defining function coefficients by at least one of supervised machine learning, unsupervised machine learning, or reinforcement machine learning (determines weights, ¶¶[0128], [0130]; see also ¶¶[0010], [0079] discussing using random forest and logistic regression).
Regarding claim 5, the combination of Mehta and Patel teaches all the limitations of claim 1 and Mehta further teaches:
update at least one user profile associated with the at least one person to be associated with a first risk level based on the at least one person being potentially impacted by the at least one potential security occurrence (determines subject will be within a zone of danger in the future, ¶[0045] and sends warnings to subject, e.g., ¶[0111]).
Regarding claim 6, the combination of Mehta and Patel teaches all the limitations of claim 1 and Mehta further teaches:
wherein the security data comprises a risk alert causes the computing system to display a notification associated with the at least one potential security occurrence (sends warning messages to subject, e.g., ¶¶[0111]-[0112]; see also ¶[0199] discussing display devices).
Regarding claim 7, the combination of Mehta and Patel teaches all the limitations of claim 1 and Mehta further teaches:
wherein the scheduled event is one of a concert, a festival, or a parade (events include concert, festival and parade, ¶[0008]).
Regarding claim 8, the combination of Mehta and Patel teaches all the limitations of claim 1 and Mehta further teaches:
transmit the security data to the computing device proximate to the scheduled event being scheduled (calculates warning based on where subject will be in next 15 minutes, ¶[0045]).
Regarding claim 9, the combination of Mehta and Patel teaches all the limitations of claim 1 and Mehta further teaches:
wherein the computer device comprises a user computing device (devices, e.g., ¶[0196]),
and wherein the at least one recommended action comprises causing the user computing device to display an alternate transportation route determined to be safer than a current transportation route (warnings include preventative measures, ¶¶[0036], [0112], like alternate driving routes, ¶[0050]).
Regarding claims 10-20, claims 10-20 recite similar limitations as claims 1-9 and accordingly are rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jacob US Pat. No. 9,888,371 teaches a similar monitoring system
Duan et al, US Pub. No. 2019/0147538 teaches a similar method of assessing risk
Ramzan, Muhammad, et al. "A review on state-of-the-art violence detection techniques." IEEE Access 7 (2019): 107560-107575 teaches a similar method of using machine learning and security cameras to predict violence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 11-7.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626
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