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 .
Status of the Claims
Claims 1-5, 8-18, 20, 24 and 25 are all the claims pending in the application.
Claims 1, 9, and 11-13 are amended.
Claims 25 is new.
Claims 1-5, 8-18, 20, 24 and 25 are rejected.
The following is a Non-Final Office Action in response to amendments and remarks filed August 5, 2025.
Response to Arguments
Regarding the 101 rejections, the rejections are maintained for the following reasons. Applicant asserts the present claims are analogous to claim 3 of Example 37 because the claims solve various problems like the inability to identify and utilize relationships between various data types and inability to systematically identify potential risks, etc., citing ¶[0051] of the present application. Examiner respectfully does not find this assertion persuasive because these problems are not technical problems. That is, showing an improvement involves showing a technical solution to technical problem, see MPEP 2106.05(a). However the problems identified in ¶[0051] of the present application are logistical or organizational problems (i.e., failing to identify relationships or systematically identify potential risks are logistical or organizational problems), not technical problems. Thus the present claims do not reflect an improvement because the claims are not solving technical problem.
Accordingly the 101 rejections are maintained, please see below for the new rejection of the claims as amended.
Regarding the 103 rejections, Applicant first asserts the rejections should be withdrawn because Patel does not teach the claimed third time or location. Examiner respectfully does not find this assertion passive because one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). That is, Applicant has not explained how or why the other references teach the claimed third time or location and Examiner finds the other references do teach such a concept.
Second, Applicant first asserts the rejections should be withdrawn because Im does not teach the first activity being different from the second activity. Examiner respectfully does not find this assertion passive because one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). That is, Applicant has not explained how or why the other references teach the claimed third time or location and Examiner finds the other references do teach such a concept.
Please see below for the complete rejections of the claims as amended.
Additionally, please note, the previously cited Billman reference has been replaced with another, similar Billman reference for the sake of clarity.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
Claim Objections
Claims 1, 12, and 13 objected to because of the following informalities: the claims repeat the word “at” and should be amended to recites (emphasized) “…the at least one of the first user activity or the second user activity at [[at]] least one of the third time or the third location …”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-5, 8-18, 20, 24 and 25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding the independent claims, claims 1, 12, and 13 recite the newly amended limitations (emphasized):
…a risk mitigation recommendation comprising a risk mitigation activity associated with at least one of a third time or a third location to perform at least one of the first user activity or the second user activity that mitigates at least one potential risk associated with at least one of the first user activity or the second user activity…
…identify that the risk mitigation activity was performed based upon additional user activity data including at least one of time data or location data associated with the user performing the at least one of the first user activity or the second user activity at at least one of the third time or the third location…
however there is no discussion, throughout the entirety of the specification and drawings, of the risk mitigation activity being the claimed first or second activities. That is, the Specification teaches analyzing risks associated with activities (e.g., playing soccer, volunteering, biking, etc., ¶¶[0128]-[0129], [0131] of the Specification as filed). However the Specification does not contemplate doing these activities as a risk mitigation. As such, the Examiner asserts this as evidence that the newly amended claims are new matter.
Accordingly, claims 1, 12, and 13 are rejected under 112(a). Claims 2-5, 8-11, 14-18, 20, 24 and 25 do not rectify this issue and accordingly are rejected due to their dependencies.
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 1-5, 8-18, 20, 24 and 25 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 (i.e., process, machine, manufacture, or composition of matter). Applying Step 1 to the claims it is determined that: claims 1-5, 8-11, 13-18, 20, 24 and 25 are directed to a machine; and claim 12 is directed to a process. Therefore, we proceed to Step 2.
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 (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Independent claim 1 recites an abstract idea. Specifically, independent claim 1 recites an abstract idea in the limitations (emphasized)1:
… at least one processor in communication with at least one memory device, at least one database, and at least one user computer device, the at least one processor programmed to:
receive a user risk profile including user activity data, the user activity data including a first user activity associated with a first time and a first location and a second user activity associated with a second time and a second location, the first user activity being different from the second user activity;
input the user activity data to a trained machine learning model configured to determine one or more potential risks based upon the user activity data, wherein the trained machine learning model is trained based upon historical city data comprising criminal activity data associated with at least one of the first location or the second location
receive an output from the trained machine learning model indicating that a total risk score for the user risk profile and associated with the first time and the first location and the second time and the second location satisfies a threshold risk score;
based upon the output, generate a risk mitigation output including a risk mitigation recommendation comprising a risk mitigation activity associated with at least one of a third time or a third location to perform at least one of the first user activity or the second user activity that mitigates at least one potential risk associated with at least one of the first user activity or the second user activity;
transmit the risk mitigation output to a service provider computing device, wherein the risk mitigation recommendation comprises a recommended action for updating an insurance policy associated with the user based on the output;
identify that the risk mitigation activity was performed based upon additional user activity data including at least one of time data or location data associated with the user performing the at least one of the first user activity or the second user activity at at least one of the third time or the third location;
and based upon identifying that the risk mitigation activity was performed, cause the total risk score for the user risk profile to be lowered.
These limitations recite an abstract idea because these limitations encompass fundamental economic principles (i.e., providing insurance or risk assessment). That is, these limitations essentially encompass assessing risks associated with a person, suggesting methods to reduce risk, determining insurance rates based on the risks and actions of the person, and ensuring the person’s actions minimizes the risk, which are all parts of the insuring process. Claims that encompass fundamental economic principles fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Claims 1, 12, and 13 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 limitations that integrate the abstract idea into a practical application.
The additional elements of independent claim 1 do not integrate the abstract idea into a practical application. Independent claim 1 recites an abstract idea in the limitations (emphasized):
… at least one processor in communication with at least one memory device, at least one database, and at least one user computer device, the at least one processor programmed to:
receive a user risk profile including user activity data, the user activity data including a first user activity associated with a first time and a first location and a second user activity associated with a second time and a second location, the first user activity being different from the second user activity;
input the user activity data to a trained machine learning model configured to determine one or more potential risks based upon the user activity data, wherein the trained machine learning model is trained based upon historical city data comprising criminal activity data associated with at least one of the first location or the second location;
receive an output from the trained machine learning model indicating that a total risk score for the user risk profile and associated with the first time and the first location and the second time and the second location satisfies a threshold risk score;
based upon the output, generate a risk mitigation output including a risk mitigation recommendation comprising a risk mitigation activity associated with at least one of a third time or a third location to perform at least one of the first user activity or the second user activity that mitigates at least one potential risk associated with at least one of the first user activity or the second user activity;
transmit the risk mitigation output to a service provider computing device, wherein the risk mitigation recommendation comprises a recommended action for updating an insurance policy associated with the user based on the output;
identify that the risk mitigation activity was performed based upon additional user activity data including at least one of time data or location data associated with the user performing the at least one of the first user activity or the second user activity at at least one of the third time or the third location;
and based upon identifying that the risk mitigation activity was performed, cause the total risk score for the user risk profile to be lowered.
These additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of the processor, memory, database, and service provider computing device, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Second, the additional elements of receiving user activity data, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving data (e.g., receiving user input), 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).
Third, the additional elements of inputting data into a machine learning model, training the machine learning model based on criminal data, and utilizing the machine learning model to determine the risk, when considered individually or in combination, do not integrate the abstract idea into a practical application because the use of machine learning is claimed sufficiently broadly such that it amounts to no more than mere instructions to apply the exception.
Fourth, the additional elements of transmitting the risk mitigation output, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data (i.e. sending text 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 12 and 13 recite similar additional elements as claim 1 and further recite "a service provider computing device” and a "non-transitory computer readable medium having computer-executable instructions embodied thereon", respectively. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Claims 1, 12, and 13 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, 12, and 13 are not patent eligible.
Dependent Claims
The dependent claims are all rejected for the following reasons.
Claims 2 and 4 recite the same abstract idea as the independent claims because the risk mitigation being a risk alert or risk mitigation instructions is a part of the insurance process.
Claims 2 and 4 further recite the additional elements of transmitting the risk alert or risk mitigation instructions to an external computer device. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data (i.e. as two computers communicating with one another), 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 3 and 5 recite the additional elements of displaying the risk alert or precautionary measures to a user device. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of displaying data.
Claim 8 recites the additional elements of the instruction alter the operations of the user device. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data (i.e. as two computers communicating with one another), 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).
Claim 9 essentially recites the additional elements of an insurance provider computing device altering the insurance policy. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Claim 10 essentially recites repeating the steps of claim 1 to generate an updated user risk profile and a second mitigation output. Examiner finds no reason repeating the steps of claim 1 would render the claim patent eligible and as such claim 10 is rejected under 35 USC 101 for similar reasons as claim 1.
Claim 11 recites the same abstract idea as the independent claims because determining outcomes and risk scores is a part of the insurance process.
Claims 14-16 recite the same abstract idea as the independent claims because identifying behaviors to flag potential risks and assigning potential risk scores is a part of the insurance process.
Claim 14 further recites the additional elements of storing the plurality of behaviors in a risk user profile. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of storing data (i.e. storing user input), 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 17 and 18 recite the same abstract idea as the independent claims because recommending the claimed different risk mitigations is a part of the insurance process.
Claims 20 and 24 recite the same abstract idea as the independent claims because changing insurance premium based on people’s behaviors is a part of the insurance process.
Claim 24 further recites the additional elements of transmitting an recommendation. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of sending data (i.e. sending text 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).
Claim 25 recites the additional elements of training the machine learning model based real time criminal data. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the use of machine learning is claimed sufficiently broadly such that it amounts to no more than mere instructions to apply the exception.
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) 1-5, 8-14, 17, 18, 20, 24 and 25 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 Billman, US Pub. No. 2014/0257862herein referred to as "Billman", further in view of Patel, US Pub. No. 2018/0146354, herein referred to as “Patel”, further in view of Im, US Pub. No. 2020/0200944, herein referred to as “Im”.
Regarding claim 1, Mehta teaches:
at least one processor in communication with at least one memory device, at least one database, and at least one user computer device, the at least one processor programmed to (processor, memory, e.g., ¶¶[0008], [0195], [0198], computer, ¶[0196], and database ¶[0211]):
receive a user risk profile including user activity data, the user activity data including a first user activity associated with a first time and a first location and a second user activity associated with a second time and a second location, the first user activity being different from the second user activity (tracks users current and past location and time, and various modes of travel, e.g., automobile, train, bike, ¶¶[0008], [0010], [0097]-[0098]);
input the user activity data to a trained machine learning model configured to determine one or more potential risks based upon the user activity data (risk prediction is based on a trained machine learning algorithm, ¶[0034]; see also ¶[0133] and Fig. 5 discussing self-learning scheme);
wherein the trained machine learning model is trained based upon historical city data comprising criminal activity data associated with at least one of the first location or the second location (emergency data includes police emergency like robbery, kidnapping, etc., ¶[0088], and emergency data is input as training data, ¶¶[0134]-[0135]; see also ¶[0158] discussing normalizing criminal risk based on location);
receive an output from the trained machine learning model indicating that a total risk score for the user risk profile and associated with the first time and the first location satisfies a threshold risk score (warnings are sent based on risk prediction exceeding a threshold, e.g., ¶[0047]);
based upon the output, generate a risk mitigation output including a risk mitigation recommendation comprising a risk mitigation activity associated with at least one of a third time or a third location to perform at least one of the first user activity or the second user activity that mitigates at least one potential risk associated with at least one of the first user activity or the second user activity (sends warnings to subject, e.g., ¶[0111], and warnings include preventative measures, ¶¶[0036], [0112], like suggesting an alternate driving route or mode of transportation, ¶[0102], [0168]),
transmit the risk mitigation output to a service provider computing device (sends warnings to user mobile devices ¶¶[0047], [0102]);
the user performing the at least one of the first user activity or the second user activity at at least one of the third time or the third location (sends warnings to subject, e.g., ¶[0111], and warnings include preventative measures, ¶¶[0036], [0112], like suggesting an alternate driving route or mode of transportation, ¶[0102], [0168])
However Mehta does not teach but Billman does teach:
wherein the risk mitigation recommendation comprises a recommended action for updating an insurance policy associated with the user based on the output (offers insurance and adjusts premium based on number of mitigated items and whether mitigation work has been completed, ¶¶[0116]-[0117] and Fig. 5; see also e.g., ¶¶[0089]-[0090] discussing risk mitigations recommendations).
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 risk analysis based insurance of Billman 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 emergencies predicted by Mehta would be useful for determining insurance pricing and accordingly would have modified Mehta to generate insurance recommendations, e.g., as taught by Billman.
However the combination of Mehta and Billman does not teach but Patel does teach:
identify that the risk mitigation activity was performed based upon additional user activity data including at least one of time data or location data associated with the user (sends information on safe locations to users, ¶[0043], and determines if the users are at the safe location based on their location data, ¶[0045]; see also Fig. 3 summarizing process; and ¶[0081] discussing insurance);
and based upon identifying that the risk mitigation activity was performed, cause the total risk score for the user risk profile to be lowered (value of risk mitigation work is captured statistically as a reduction of possible risk, ¶[0060]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with the risk analysis and insurance purchasing of Mehta and Billman 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 and Billman 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 Billman to determine if the users are in a safe location as taught by Patel.
However the combination of Mehta, Billman and Patel does not teach but Im does teach:
receive a user risk profile including user activity data, the user activity data including a first user activity associated with a first time and a first location and a second user activity associated with a second time and a second location (schedule data includes current location and activity information acquired from a previous activity pattern by day of week or by location such as a commuting pattern, ¶[0029]);
receive an output from the trained machine learning model indicating that a total risk score for the user risk profile and associated with the first time and the first location and the second time and the second location satisfies a threshold risk score (predicts dangerous situation based on user’s travel path e.g., ¶¶[0020], [0030] and Fig. 3).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with the risk analysis and insurance purchasing with the personal security monitoring of Mehta, Billman and Patel with the travel risk prediction of Im 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 and Billman would not only be interested in monitoring emergency situations but would also be interested in predicting if the user is likely to enter an emergency situations and accordingly would have modified Mehta, Billman and Patel to predict if a user will enter a dangerous situation, e.g., as taught by Im.
Regarding claim 2, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Mehta further teaches:
wherein the risk mitigation output is a risk alert and the at least one processor is further configured to transmit the risk alert an external computer device (sends warnings to subject devices, e.g., ¶¶[0100], [0105], [0111] and Fig. 2; see also e.g., ¶[0196] discussing types of devices).
Regarding claim 3, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 2 and Mehta further teaches:
wherein the external computer device is a user computer device (sends warnings to subject mobile devices, e.g., ¶[0100] and Fig. 2)
and wherein the risk alert causes the user computer device to display a notification to the user (warnings are sent via text message, ¶[0220]).
Regarding claim 4, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Mehta further teaches:
wherein the risk mitigation output comprises risk mitigation instructions (warnings include information for mitigating the emergency, ¶¶[0036], [0049])
and wherein the at least one processor is further configured to transmit the risk mitigation instructions to an external computer device (sends warnings to subject mobile devices, e.g., ¶[0100] and Fig. 2).
Regarding claim 5, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 4 and Mehta further teaches:
wherein the external computer device is a user computer device (sends warnings to subject devices, e.g., ¶¶[0100], [0105], [0111] and Fig. 2; see also e.g., ¶[0196] discussing types of devices)
and wherein the risk mitigation recommendation contains precautionary measures intended for the user (warnings include preventative measures, ¶¶[0036], [0112]).
Regarding claim 8, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 4 and Mehta further teaches:
wherein the external computer device is the user computer device (sends warnings to subject mobile devices, e.g., ¶[0100] and Fig. 2),
and wherein the risk mitigation instructions are configured to cause the user computer device to alter its operations (warnings are sent via text message, ¶[0220]).
Regarding claim 9, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 4 and Billman further teaches:
wherein the external computer device is the service provider computing device, wherein the service provider computing device is associated with an insurance provider (insurance management system, e.g., ¶[0036], Fig. 1);
and wherein the risk mitigation instructions are configured to cause the service provider computing device to alter the insurance policy associated with the user based on the potential risk (alters insurance premium based on modification of mitigatable items, ¶[0117] and Fig. 5).
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 risk analysis based insurance of Billman 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 emergencies predicted by Mehta would be useful for determining insurance pricing and accordingly would have modified Mehta to generate insurance recommendations, e.g., as taught by Billman.
Regarding claim 10, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Mehta further teaches:
wherein the at least one processor is further configured to: receive user profile data from a database (obtains subject data like location information from subjects communication devices (e.g., mobile phones), ¶¶0097]-[0098]).
However, Mehta does not explicitly teach:
utilize the trained machine learning model to determine at least one additional potential risk associated with the user based upon at least the user profile data;
generate an updated user risk profile that includes the at least one additional potential risk associated with the user;
and generate a second risk mitigation output based upon at least one of the updated user risk profile or the at least one additional potential risk, wherein the second risk mitigation output includes at least one of a risk alert, a second risk mitigation recommendation, or risk mitigation instructions.
Nevertheless, it would have been obvious at the time of filing to determine at least one additional potential risk, generate an updated user risk profile, and generate a second risk mitigation output because duplication of parts is obvious unless a new and unexpected result is produced, see MPEP 2144.04.VI.B. That is, claim 10 essentially recites repeating the steps in claim 1 for an additional potential risk and a second risk mitigation output. Examiner finds no evidence that repeating the process for an additional potential risk and a second risk mitigation output would produce new and unexpected results.
Regarding claim 11, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Mehta further teaches:
wherein identifying the potential risk comprises determining at least one potential outcome associated with the user (predicts various outcomes like an increase in traffic accidents based on freezing temperatures, ¶[0092])
and determining a risk score for the at least one potential outcome (determines an accuracy score for predictions, ¶[0135]-[0136]).
Regarding claim 12, Mehta teaches:
the method implemented by a computer system including at least one processor in communication with at least one memory device, at least one database, and at least one user computer device, the computer-implemented method comprising, by the at least one processor (processor, memory, e.g., ¶¶[0008], [0195], [0198], computer, ¶[0196], and database ¶[0211]):
receiving a user risk profile including user activity data, the user activity data including a first user activity associated with a first time and a first location and a second user activity associated with a second time and a second location, the first user activity being different from the second user activity (tracks users current and past location and time, and various modes of travel, e.g., automobile, train, bike, ¶¶[0008], [0010], [0097]-[0098]);
inputting the user activity data to a trained machine learning model configured to determine one or more potential risks based upon the user activity data (risk prediction is based on a trained machine learning algorithm, ¶[0034]; see also ¶[0133] and Fig. 5 discussing self-learning scheme);
wherein the trained machine learning model is trained based upon historical city data comprising criminal activity data associated with at least one of the first location or the second location (emergency data includes police emergency like robbery, kidnapping, etc., ¶[0088], and emergency data is input as training data, ¶¶[0134]-[0135]; see also ¶[0158] discussing normalizing criminal risk based on location);
receiving an output from the trained machine learning model indicating that a total risk score for the user risk profile and associated with the first time and the first location satisfies a threshold risk score (warnings are sent based on risk prediction exceeding a threshold, e.g., ¶[0047]);
based upon the output, generate a risk mitigation output including a risk mitigation recommendation comprising a risk mitigation activity associated with at least one of a third time or a third location to perform at least one of the first user activity or the second user activity that mitigates at least one potential risk associated with at least one of the first user activity or the second user activity (sends warnings to subject, e.g., ¶[0111], and warnings include preventative measures, ¶¶[0036], [0112], like suggesting an alternate driving route or mode of transportation, ¶[0102], [0168]),
the user performing the at least one of the first user activity or the second user activity at at least one of the third time or the third location (sends warnings to subject, e.g., ¶[0111], and warnings include preventative measures, ¶¶[0036], [0112], like suggesting an alternate driving route or mode of transportation, ¶[0102], [0168]).
However Mehta does not teach but Billman does teach:
transmitting the risk mitigation output to a service provider computing device, wherein the service provider computing device is associated with an insurance provider (transmits mitigation information to insurance company, ¶[0117]; see also ¶[0036] and Fig. 1 discussing insurance management system),
wherein the risk mitigation output comprises risk mitigation instructions, and wherein the risk mitigation instructions are configured to cause the service provider computing device to alter an insurance policy associated with the user based on the output (offers insurance and adjusts premium based on number of mitigated items and whether mitigation work has been completed, ¶¶[0116]-[0117] and Fig. 5; see also e.g., ¶¶[0089]-[0090] discussing risk mitigations recommendations).
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 risk analysis based insurance of Billman 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 emergencies predicted by Mehta would be useful for determining insurance pricing and accordingly would have modified Mehta to generate insurance recommendations, e.g., as taught by Billman.
However the combination of Mehta and Billman does not teach but Patel does teach:
identifying that the risk mitigation activity was performed based upon additional user activity data including at least one of time data or location data associated with the user (sends information on safe locations to users, ¶[0043], and determines if the users are at the safe location based on their location data, ¶[0045]; see also Fig. 3 summarizing process; and ¶[0081] discussing insurance);
and based upon identifying that the risk mitigation activity was performed, cause the total risk score for the user risk profile to be lowered (value of risk mitigation work is captured statistically as a reduction of possible risk, ¶[0060]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with the risk analysis and insurance purchasing of Mehta and Billman 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 and Billman 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 Billman to determine if the users are in a safe location as taught by Patel.
However the combination of Mehta, Billman and Patel does not teach but Im does teach:
receiving a user risk profile including user activity data, the user activity data including a first user activity associated with a first time and a first location and a second user activity associated with a second time and a second location (schedule data includes current location and activity information acquired from a previous activity pattern by day of week or by location such as a commuting pattern, ¶[0029]);
receiving an output from the trained machine learning model indicating that a total risk score for the user risk profile and associated with the first time and the first location and the second time and the second location satisfies a threshold risk score (predicts dangerous situation based on user’s travel path e.g., ¶¶[0020], [0030] and Fig. 3).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with the risk analysis and insurance purchasing with the personal security monitoring of Mehta, Billman and Patel with the travel risk prediction of Im 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 and Billman would not only be interested in monitoring emergency situations but would also be interested in predicting if the user is likely to enter an emergency situations and accordingly would have modified Mehta, Billman and Patel to predict if a user will enter a dangerous situation, e.g., as taught by Im.
Regarding claim 13, Mehta teaches:
A non-transitory computer-readable storage medium having computer-executable instructions embodied thereon for analyzing and mitigating risks associated with a user, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to (memory and instructions, ¶¶[0200]-[0201]):
receive a user risk profile including user activity data, the user activity data including a first user activity associated with a first time and a first location and a second user activity associated with a second time and a second location, the first user activity being different from the second user activity (tracks users current and past location and time, and various modes of travel, e.g., automobile, train, bike, ¶¶[0008], [0010], [0097]-[0098]);
input the user activity data to a trained machine learning model configured to determine one or more potential risks based upon the user activity data (risk prediction is based on a trained machine learning algorithm, ¶[0034]; see also ¶[0133] and Fig. 5 discussing self-learning scheme);
wherein the trained machine learning model is trained based upon historical city data comprising criminal activity data associated with at least one of the first location or the second location (emergency data includes police emergency like robbery, kidnapping, etc., ¶[0088], and emergency data is input as training data, ¶¶[0134]-[0135]; see also ¶[0158] discussing normalizing criminal risk based on location);
receive an output from the trained machine learning model indicating that a total risk score for the user risk profile and associated with the first time and the first location satisfies a threshold risk score (warnings are sent based on risk prediction exceeding a threshold, e.g., ¶[0047]);
based upon the output, generate a risk mitigation output including a risk mitigation recommendation comprising a risk mitigation activity associated with at least one of a third time or a third location to perform at least one of the first user activity or the second user activity that mitigates at least one potential risk associated with at least one of the first user activity or the second user activity (sends warnings to subject, e.g., ¶[0111], and warnings include preventative measures, ¶¶[0036], [0112], like suggesting an alternate driving route or mode of transportation, ¶[0102], [0168]),
transmit the risk mitigation output to a service provider computing device (sends warnings to user mobile devices ¶¶[0047], [0102]);
the user performing the at least one of the first user activity or the second user activity at at least one of the third time or the third location (sends warnings to subject, e.g., ¶[0111], and warnings include preventative measures, ¶¶[0036], [0112], like suggesting an alternate driving route or mode of transportation, ¶[0102], [0168]).
However Mehta does not teach but Billman does teach:
wherein the risk mitigation recommendation comprises a recommended action for updating an insurance policy associated with the user based on the output (offers insurance and adjusts premium based on number of mitigated items and whether mitigation work has been completed, ¶¶[0116]-[0117] and Fig. 5; see also e.g., ¶¶[0089]-[0090] discussing risk mitigations recommendations).
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 risk analysis based insurance of Billman 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 emergencies predicted by Mehta would be useful for determining insurance pricing and accordingly would have modified Mehta to generate insurance recommendations, e.g., as taught by Billman.
However the combination of Mehta and Billman does not teach but Patel does teach:
identify that the risk mitigation activity was performed based upon additional user activity data including at least one of time data or location data associated with the user (sends information on safe locations to users, ¶[0043], and determines if the users are at the safe location based on their location data, ¶[0045]; see also Fig. 3 summarizing process; and ¶[0081] discussing insurance);
and based upon identifying that the risk mitigation activity was performed, cause the total risk score for the user risk profile to be lowered (value of risk mitigation work is captured statistically as a reduction of possible risk, ¶[0060]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with the risk analysis and insurance purchasing of Mehta and Billman 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 and Billman 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 Billman to determine if the users are in a safe location as taught by Patel.
However the combination of Mehta, Billman and Patel does not teach but Im does teach:
receive a user risk profile including user activity data, the user activity data including a first user activity associated with a first time and a first location and a second user activity associated with a second time and a second location (schedule data includes current location and activity information acquired from a previous activity pattern by day of week or by location such as a commuting pattern, ¶[0029]);
receive an output from the trained machine learning model indicating that a total risk score for the user risk profile and associated with the first time and the first location and the second time and the second location satisfies a threshold risk score (predicts dangerous situation based on user’s travel path e.g., ¶¶[0020], [0030] and Fig. 3).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with the risk analysis and insurance purchasing with the personal security monitoring of Mehta, Billman and Patel with the travel risk prediction of Im 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 and Billman would not only be interested in monitoring emergency situations but would also be interested in predicting if the user is likely to enter an emergency situations and accordingly would have modified Mehta, Billman and Patel to predict if a user will enter a dangerous situation, e.g., as taught by Im.
Regarding claim 14, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Mehta further teaches:
configured to: identify a plurality of behaviors associated with the user (gathers subject data including location, speed, mode of transportation, etc., ¶¶[0097]-[0099]);
store the plurality of behaviors in the user risk profile (stores subject data, ¶[0046]);
and flag at least one behavior of the plurality of behaviors as being potentially risky and include the at least one behavior in the at least one potential risk (sends warnings to users who are located within the scope of a risk prediction, ¶[0047]).
Regarding claim 17, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Mehta further teaches:
wherein the risk mitigation activity comprises an alternate transportation route (warnings include suggestions for an alternate route, ¶¶[0050], [0102]; see also ¶[0007] discussing analyzing multi routes and modes of transportation).
Regarding claim 18, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Mehta further teaches:
wherein the risk mitigation activity comprises an alternate means of transportation (warnings include suggestions for an alternate route or taking public transportation, ¶¶[0050], [0102]; see also ¶[0007] discussing analyzing multi routes and modes of transportation).
Regarding claim 20, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Billman further teaches:
and wherein the at least one processor is further configured to lower the insurance premium based on the risk mitigation activity being performed (premium is lowered based on removal or modification of mitigatable items or hazardous conditions, ¶[0115] and Fig. 5).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction with the risk prediction and mitigation of Mehta with adjusting the insurance premiums as taught by Billman 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 it would advantageous for the parties involved in insurance to adjust premiums based on mitigation activities being performed to create an economic incentive to perform the mitigation, i.e., as taught by Billman, thereby encoring safer behavior and lower costs associated with the insurance.
Regarding claim 24, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Billman further teaches:
wherein the at least one processor is further programmed to transmit a recommendation to an insurance provider computer device (transmits mitigation information to insurance company, ¶[0117]; see also ¶[0036] and Fig. 1 discussing insurance management system)
to lower one or more insurance rates associated with the user based upon the risk mitigation activity being performed (premium is lowered based on removal or modification of mitigatable items or hazardous conditions, ¶[0115] and Fig. 5; see also e.g., ¶¶[0089]-[0090] discussing risk mitigations recommendations).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction with the risk prediction and mitigation of Mehta with adjusting the insurance premiums as taught by Billman 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 it would advantageous for the parties involved in insurance to adjust premiums based on mitigation activities being performed to create an economic incentive to perform the mitigation, i.e., as taught by Billman, thereby encoring safer behavior and lower costs associated with the insurance.
Regarding claim 25, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 1 and Mehta further teaches:
wherein the trained machine learning model is further trained based upon real-time city data comprising real-time criminal activity data associated with at least one of the first location or the second location (predication algorithm is updated in real time, ¶¶[0179],[0191], [0193] see also ¶¶[0134]-[0135] discussing using emergency data is input as training data).
Claim(s) 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Mehta, Billman, Patel and Im further in view of Elhawary et al, US Pub. No. 2019/0343429, herein referred to as “Elhawary”.
Regarding claim 15, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 14 and does not teach but Elhawary does teach:
wherein the at least one processor is further configured to assign a potential risk score to each of the plurality of behaviors (scores various user behaviors like driving, ¶[0177], activities performed by a worker in a day, ¶¶[0193], [0206], [0313]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with insurance purchasing and personal security monitoring of Mehta, Mdeway, Patel and Im with the risk analysis and the safety monitoring of Elhawary 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 behaviors may influence the emergencies predictions (i.e., dangerous users would be more likely to create emergencies), and accordingly would have modified Mehta to score the users (i.e., score how safe they are), e.g., as taught by Elhawary, to account for the users’ behavior when predicting emergencies.
Regarding claim 16, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 14 and does not teach but Elhawary does teach:
wherein the at least one processor is further configured to flag the at least one behavior based on a risk score assigned to the at least one behavior (identifies an activity as high risk based on a score exceedingly a threshold, e.g., ¶¶[0148], [0152], [0158], [0162]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with insurance purchasing and personal security monitoring of Mehta, Mdeway, Patel and Im with the risk analysis and the safety monitoring of Elhawary 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 behaviors may influence the emergencies predictions (i.e., dangerous users would be more likely to create emergencies), and accordingly would have modified Mehta to score the users (i.e., score how safe they are), e.g., as taught by Elhawary, to account for the users’ behavior when predicting emergencies.
Claim(s) 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Mehta, Billman, Patel and Im further in view of Elhawary et al, US Pub. No. 2019/0343429, herein referred to as “Elhawary”.
Regarding claim 15, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 14 and does not teach but Elhawary does teach:
wherein the at least one processor is further configured to assign a potential risk score to each of the plurality of behaviors (scores various user behaviors like driving, ¶[0177], activities performed by a worker in a day, ¶¶[0193], [0206], [0313]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with insurance purchasing and personal security monitoring of Mehta, Mdeway, Patel and Im with the risk analysis and the safety monitoring of Elhawary 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 behaviors may influence the emergencies predictions (i.e., dangerous users would be more likely to create emergencies), and accordingly would have modified Mehta to score the users (i.e., score how safe they are), e.g., as taught by Elhawary, to account for the users’ behavior when predicting emergencies.
Regarding claim 16, the combination of Mehta, Billman, Patel and Im teaches all the limitations of claim 14 and does not teach but Elhawary does teach:
wherein the at least one processor is further configured to flag the at least one behavior based on a risk score assigned to the at least one behavior (identifies an activity as high risk based on a score exceedingly a threshold, e.g., ¶¶[0148], [0152], [0158], [0162]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the emergency prediction and mitigation with insurance purchasing and personal security monitoring of Mehta, Mdeway, Patel and Im with the risk analysis and the safety monitoring of Elhawary 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 behaviors may influence the emergencies predictions (i.e., dangerous users would be more likely to create emergencies), and accordingly would have modified Mehta to score the users (i.e., score how safe they are), e.g., as taught by Elhawary, to account for the users’ behavior when predicting emergencies.
Conclusion
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626
1 Examiner notes the exact language of claims 1, 12, and 13 differ but does not find these differences significantly alter the eligibility analysis and accordingly analyzes the claims concurrently here for the sake of brevity.