DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 19/046,689, filed 02/06/2025, claims priority from U.S. Provisional Application 63/550,417, filed 02/06/2024.
The effective filing date is after the AIA date of March 16, 2013, and so the application is being examined under the “first inventor to file” provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Application
This Non-Final Office Action is in response to Applicant’s communication of 02/06/2025.
Claims 1-20 are pending, of which claims 1 and 14 are independent.
All pending claims have been examined on the merits.
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 non-statutory subject matter. The claimed invention is directed to an abstract idea, without “significantly more”.
Based on the flowchart in MPEP § 2106, Step 1 of the Alice/Mayo analysis is: “Is the claim to a process, machine, manufacture or composition of matter?”
In regards to Step 1 of the Alice/Mayo analysis, independent claim 1 is an apparatus claim, and independent claim 14 is a method claim.
For the sake of compact prosecution, we continue with the Alice/Mayo “abstract idea” analysis.
Step 2A, prong 1 of the Alice/Mayo analysis is: “Does the claim recite a law of nature, a natural phenomenon (product of nature), or an abstract idea?”
In regards to Step 2A, prongs 1 and 2 of the Alice/Mayo analysis, the abstract idea elements recited in independent claim 1 are shown in italic font. (The “additional elements” and “extra solution steps” are shown in italic and underlined font):
1. An apparatus for real-time dynamic pricing, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive real-time data, wherein the real-time data comprises a user behavior parameter;
evaluate the user behavior parameter, wherein evaluating the user behavior parameter comprises:
generating a risk assessment module using real-time data, wherein the risk assessment module is configured to evaluate risk factors;
adjust an insurance premium parameter, wherein the adjusting insurance premium parameters comprises:
generating a dynamic pricing module using the risk factors, wherein the dynamic pricing module is configured to adjust the insurance premium parameter; and
dynamically modifying exposure of an insurer corresponding to the insurance premium parameter by generating a term, wherein the term transfers some risk to a reinsurer based on real-time assessments of behavior and risk factors as a function of the user behavior parameter; and
communicate the adjusted insurance premium parameter to a user.
More specifically, claims 1-20 recite an abstract idea: “Certain Methods of Organizing Human Activity", specifically “Commercial or Legal Interactions (Including Agreements in the form of Contracts; Legal Obligations; Advertising, Marketing, or Sales Activities or Behaviors; Business Relations)”, as discussed in MPEP §2106(a)(2) Parts (I) and (II), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The “Commercial or Legal Interactions” elements include:
“evaluate the user behavior parameter, wherein evaluating the user behavior parameter comprises: generating a risk assessment module using real-time data, wherein the risk assessment module is configured to evaluate risk factors”.
“adjust an insurance premium parameter, wherein the adjusting insurance premium parameters comprises: generating a dynamic pricing module using the risk factors, wherein the dynamic pricing module is configured to adjust the insurance premium parameter; and dynamically modifying exposure of an insurer corresponding to the insurance premium parameter by generating a term, wherein the term transfers some risk to a reinsurer based on real-time assessments of behavior and risk factors as a function of the user behavior parameter”.
Moreover, claims 1-20 recite “Mathematical Concepts", specifically “Mathematical Relationships”, “Mathematical Formulas or Equations”, and “Mathematical Calculations”, as discussed in MPEP §2106.04(a)(2) Part (IV), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The mathematic elements include:
“evaluate risk factors”, and
“dynamically modifying exposure of an insurer corresponding to the insurance premium parameter by generating a term, wherein the term transfers some risk to a reinsurer based on real-time assessments of behavior and risk factors as a function of the user behavior parameter”.
The “additional elements” include: “at least a processor”, and “a memory”.
Moreover, “additional extra-solution elements” include: “receive real-time data, wherein the real-time data comprises a user behavior parameter”, “communicate the adjusted insurance premium parameter to a user”, and “wherein the memory contains instructions configuring the at least a processor”.
Step 2A, prong 2 of the Alice/Mayo analysis is “Does the claim recite additional elements that integrate elements that integrate the judicial exception into a practical application?”
In regards to Step 2A, prong 2 of the Alice/Mayo analysis, this abstract idea is not integrated into a practical application, because:
The claim is directed to an abstract idea with additional generic computer elements. The generically recited computer elements (“at least a processor”, and “a memory”) do not add a meaningful limitation to the abstract idea, because they amount to simply implementing the abstract idea on a computer. The claim amounts to adding the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The extra-solution activities (“receive real-time data, wherein the real-time data comprises a user behavior parameter”, “communicate the adjusted insurance premium parameter to a user”, and “wherein the memory contains instructions configuring the at least a processor”) do not add a meaningful limitation to the method, as they are insignificant extra-solution activity;
The combination of the abstract idea with the additional elements (generically recited computer elements), and/or with the extra-solution activities, does not integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo analysis is: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
In regards to Step 2B of the Alice/Mayo analysis, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, because:
When considering the elements "alone and in combination" (“at least a processor”, and “a memory”), they do not add significantly more (also known as an "inventive concept") to the exception, because they amount to simply implementing the abstract idea on a computer. Instead, they merely add the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
In regards to the extra solution activities (“receive real-time data, wherein the real-time data comprises a user behavior parameter”, “communicate the adjusted insurance premium parameter to a user”, and “wherein the memory contains instructions configuring the at least a processor”), these are recognized as such by the court decisions listed in MPEP § 2106.05(d).
More specifically, in regards to the “storing” step (“wherein the memory contains instructions configuring the at least a processor”), see the court cases Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory); and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (storing and retrieving information in memory).
More specifically, in regards to the “receiving” and “communicating” steps, see the court cases OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and (presenting offers and gathering statistics), OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Moreover, in regards to “apply it”, according to MPEP § 2106.05(f)(2):
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The Examiner holds that the independent claims “use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)” or “simply add a general purpose computer or computer components after the fact to an abstract idea”.
Independent claim 14 is rejected on the same grounds as independent claim 1.
All dependent claims are also rejected, because they merely further define the abstract ideas recited in the independent claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-11 and 13-20 are rejected under 35 U.S.C. §§102(a)(1) and (a)(2) as being anticipated by US 2018/0300816 A1 to Perl et al. (“Perl”. Eff. Filed on Mar. 17, 2016. Published on Oct. 18, 2018).
In regards to claim 1,
1. An apparatus for real-time dynamic pricing, the apparatus comprising:
at least a processor; and
(See Perl, para. [0024]: “In addition to the system, as described above, and the corresponding method, the present invention also relates to a computer program product that includes computer program code means for controlling one or more processors of the control system such that the control system performs the proposed method; and it relates, in particular, to a computer program product that includes a computer-readable medium that contains the computer program code means for the processors.”)
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
(See Perl, claim 12: “The telematics system according to claim 1, wherein at least some of the mobile telematics devices comprise a memory to store processor-driving operation code.”)
receive real-time data, wherein the real-time data comprises a user behavior parameter;
(See Pearl, para. [0012]: “The present invention with the machine-leaning based system, which is enhanced by contextual data, is able to provide best and highest optimized technical solution to the real-time adapted multi-tier risk-transfer system. It allows to capture and control the scores driver behavior, and compare its behavior within the technical operation and context. It allows to automatically capture scores risks according to location or trip, and to automatically analyze and react on data related to the need of value added services, as e.g. accident notifications and/or feedback to the driver and/or automated fleet risk reporting and/or automated and dynamically optimized underwriting etc.). The telematics based feedback means of the system may e.g. comprise a dynamic alert feed via a data link to the motor vehicle's mobile telematics device, wherein the machine-learning based telematics circuit heads up device alerts drivers immediately to a number of performance measures including e.g. high RPM, i.e. high revolutions per minute as a measure of the frequency of the motor rotation of the motor vehicle's engine, unsteady drive, unnecessary engine power, harsh acceleration, road anticipation, and/or ECO drive.”)
evaluate the user behavior parameter, wherein evaluating the user behavior parameter comprises:
generating a risk assessment module using real-time data, wherein the risk assessment module is configured to evaluate risk factors;
(See Pearl, para. [0019]: “In still another alternative embodiment, the machine-learning based telematics circuit comprises means for processing risk-related motor vehicle data and for providing data regarding the likelihood of said risk exposure for one or a plurality of the pooled risk exposed motor vehicle, in particular, based on the risk-related motor vehicle data, and wherein the receipt and preconditioned storage of payments from the risk exposed motor vehicles for the pooling of their risks can be dynamically determined based on the total risk and/or the likelihood of risk exposure of the pooled risk exposed motor vehicles. This alternative embodiment has, inter alia, the advantage that the operation of the first and/or second resource pooling system can be dynamically adjusted to changing conditions in relation to the pooled risk, such as a change of the environmental conditions or risk distribution, or the like, of the pooled motor vehicles. A further advantage is that the system does not require any manual adjustments, when it is operated in different environments, places or countries, because the size of the payments of the risk exposed motor vehicles is directly related to the total pooled risk. However, it is important to note, that the present invention does not necessarily have to lead to adjusted pricing or premiums. For example, it could also automatically provide coupons to drivers that drive well, or that nothing at all changes but that the system uses the telematics data to automatically decide if the risk-transfer is continued the next year.”)
adjust an insurance premium parameter,
(See Pearl, para. [0019]: “In still another alternative embodiment, the machine-learning based telematics circuit comprises means for processing risk-related motor vehicle data and for providing data regarding the likelihood of said risk exposure for one or a plurality of the pooled risk exposed motor vehicle, in particular, based on the risk-related motor vehicle data, and wherein the receipt and preconditioned storage of payments from the risk exposed motor vehicles for the pooling of their risks can be dynamically determined based on the total risk and/or the likelihood of risk exposure of the pooled risk exposed motor vehicles. This alternative embodiment has, inter alia, the advantage that the operation of the first and/or second resource pooling system can be dynamically adjusted to changing conditions in relation to the pooled risk, such as a change of the environmental conditions or risk distribution, or the like, of the pooled motor vehicles. A further advantage is that the system does not require any manual adjustments, when it is operated in different environments, places or countries, because the size of the payments of the risk exposed motor vehicles is directly related to the total pooled risk. However, it is important to note, that the present invention does not necessarily have to lead to adjusted pricing or premiums. For example, it could also automatically provide coupons to drivers that drive well, or that nothing at all changes but that the system uses the telematics data to automatically decide if the risk-transfer is continued the next year.”)
The Examiner interprets Pearl’s para. [0019] as teaching that Pearl’s “machine-learning based telematics circuit [that] comprises means for processing risk-related motor vehicle data and for providing data regarding the likelihood of said risk exposure for one or a plurality of the pooled risk exposed motor vehicle[s]” can (but does not necessarily have to) “lead to adjusted [insurance] pricing or premiums”.
wherein the adjusting insurance premium parameters comprises:
generating a dynamic pricing module using the risk factors, wherein the dynamic pricing module is configured to adjust the insurance premium parameter; and
(See Pearl, para. [0006]: “For example, an underwriter might ask the insurance agent to indicate how often, where and to which time a motor vehicle is mainly used or other data as age of the motor vehicle and indented use (transportation etc.). Only after such additional information is determined, an appropriate risk analysis can be performed by the insurer to process adapted underwriting decision, and/or premium pricing.”)
(See Pearl, para. [0007]: “Integrated telematics may offer new technological fields, in particular in monitoring and steering by means of centralized expert systems, as e.g. in the risk-transfer technology far more accurate and profitable pricing models provided by such automated expert systems. This would create a huge advantage, in particular for real-time and/or usage-based and/or dynamically operated systems. The advantage of such telematics systems is not restricted to risk transfer rather as also advantages e.g. in fleets' management that monitor employees' driving behavior via telematics improving asset utilization, reduce fuel consumption and improve safety etc. etc. Other fields may also benefit form such integrated telematics systems, as state and local governments needs striving to improve fuel consumption, emissions and highway safety. Some states, for example, recently issued dynamic pay-as-you-drive (PAYD) regulations, which on the other side allows insurers to offer drivers insurance rates based on actual versus estimated miles driven. It's a financial incentive to drive less.”)
(See Pearl, para. [0008]: “Already now, the telematics technology provides features as an accelerometer allowing to assess drivers' style and behavior, thus expanding the risk factors normally tracked from the current 40 to more than 100.”)
dynamically modifying exposure of an insurer corresponding to the insurance premium parameter by generating a term, wherein the term transfers some risk to a reinsurer based on real-time assessments of behavior and risk factors as a function of the user behavior parameter; and
(See Pearl, para. [0012]: “The present invention with the machine-leaning based system, which is enhanced by contextual data, is able to provide best and highest optimized technical solution to the real-time adapted multi-tier risk-transfer system. It allows to capture and control the scores driver behavior, and compare its behavior within the technical operation and context. It allows to automatically capture scores risks according to location or trip, and to automatically analyze and react on data related to the need of value added services, as e.g. accident notifications and/or feedback to the driver and/or automated fleet risk reporting and/or automated and dynamically optimized underwriting etc.). The telematics based feedback means of the system may e.g. comprise a dynamic alert feed via a data link to the motor vehicle's mobile telematics device, wherein the machine-learning based telematics circuit heads up device alerts drivers immediately to a number of performance measures including e.g. high RPM, i.e. high revolutions per minute as a measure of the frequency of the motor rotation of the motor vehicle's engine, unsteady drive, unnecessary engine power, harsh acceleration, road anticipation, and/or ECO drive.”)
communicate the adjusted insurance premium parameter to a user.
(See Pearl, para. [0012]: “Clearly, even the most experienced drivers can benefit from having their driving behavior dynamically analyzed and improved. The vehicle telematics system 1 provides the opportunities for improvement dynamically and in real-time, i.e. as and when they happen, related to the driver's risk behavior. Providing instant feedback to drivers through heads up training aids and get information sent straight to the mobile telematics device, ensures a two pronged approach to correcting risky (and often expensive) driving habits. Thus, the telematics system 1 not only allows to mutually optimize the operational parameters of the first and second risk transfer system, but also optimize the risk and/or risk behavior on the level of the risk exposed motor vehicles. No prior art system allows such an integral, real-time optimization. As another value added service, the telematics system 1 can e.g. dynamically generated fleet risk reports of selected motor vehicles. Such fleet reports automated generated by the telematics system 1 provide a new approach to share and compare driver statistics. Additional advantages follow as a direct effect of such reports, as automated reward generation of top performers or identification of drivers who need extra training, etc. The proposed invention with e.g. prefunding telematics enabled (re)insurance means will stimulate the carriers (first-tier risk-transfer systems) to provide its telematics and claims history to the second-tier risk-transfer system in order to continually improve its scoring service, which in turn benefits carrier in helping reduce costs and combined ratio.”)
In regards to claim 2,
2. The apparatus of claim 1 further configured for:
storing a plurality of pre-arranged policy modifications; and
activating at least a policy modification of the plurality of pre-arranged policy modifications.
(See Pearl, para. [0019]: “In still another alternative embodiment, the machine-learning based telematics circuit comprises means for processing risk-related motor vehicle data and for providing data regarding the likelihood of said risk exposure for one or a plurality of the pooled risk exposed motor vehicle, in particular, based on the risk-related motor vehicle data, and wherein the receipt and preconditioned storage of payments from the risk exposed motor vehicles for the pooling of their risks can be dynamically determined based on the total risk and/or the likelihood of risk exposure of the pooled risk exposed motor vehicles. This alternative embodiment has, inter alia, the advantage that the operation of the first and/or second resource pooling system can be dynamically adjusted to changing conditions in relation to the pooled risk, such as a change of the environmental conditions or risk distribution, or the like, of the pooled motor vehicles. A further advantage is that the system does not require any manual adjustments, when it is operated in different environments, places or countries, because the size of the payments of the risk exposed motor vehicles is directly related to the total pooled risk. However, it is important to note, that the present invention does not necessarily have to lead to adjusted pricing or premiums. For example, it could also automatically provide coupons to drivers that drive well, or that nothing at all changes but that the system uses the telematics data to automatically decide if the risk-transfer is continued the next year.”)
The Examiner interprets Pearl’s para. [0019] as teaching that “adjusted pricing or premiums”, “provide coupons to drivers that drive well”, and “automatically decide if the risk-transfer [insurance policy] is continued the next year” are different possible “policy modifications”.
In regards to claim 3,
3. The apparatus of claim 2, wherein activating the at least a policy modification further comprises:
detecting a hazardous condition; and
activating the at least a policy modification as a function of the detection.
(See Pearl, para. [0012]: “The present invention with the machine-leaning based system, which is enhanced by contextual data, is able to provide best and highest optimized technical solution to the real-time adapted multi-tier risk-transfer system. It allows to capture and control the scores driver behavior, and compare its behavior within the technical operation and context. It allows to automatically capture scores risks according to location or trip, and to automatically analyze and react on data related to the need of value added services, as e.g. accident notifications and/or feedback to the driver and/or automated fleet risk reporting and/or automated and dynamically optimized underwriting etc.). The telematics based feedback means of the system may e.g. comprise a dynamic alert feed via a data link to the motor vehicle's mobile telematics device, wherein the machine-learning based telematics circuit heads up device alerts drivers immediately to a number of performance measures including e.g. high RPM, i.e. high revolutions per minute as a measure of the frequency of the motor rotation of the motor vehicle's engine, unsteady drive, unnecessary engine power, harsh acceleration, road anticipation, and/or ECO drive.”)
In regards to claim 4,
4. The apparatus of claim 3, wherein activating the at least a policy modification further comprises:
providing the at least a policy modification to the user;
… and
activating the at least a policy modification as a function of the user input.
(See Pearl, para. [0019]: “In still another alternative embodiment, the machine-learning based telematics circuit comprises means for processing risk-related motor vehicle data and for providing data regarding the likelihood of said risk exposure for one or a plurality of the pooled risk exposed motor vehicle, in particular, based on the risk-related motor vehicle data, and wherein the receipt and preconditioned storage of payments from the risk exposed motor vehicles for the pooling of their risks can be dynamically determined based on the total risk and/or the likelihood of risk exposure of the pooled risk exposed motor vehicles. This alternative embodiment has, inter alia, the advantage that the operation of the first and/or second resource pooling system can be dynamically adjusted to changing conditions in relation to the pooled risk, such as a change of the environmental conditions or risk distribution, or the like, of the pooled motor vehicles. A further advantage is that the system does not require any manual adjustments, when it is operated in different environments, places or countries, because the size of the payments of the risk exposed motor vehicles is directly related to the total pooled risk. However, it is important to note, that the present invention does not necessarily have to lead to adjusted pricing or premiums. For example, it could also automatically provide coupons to drivers that drive well, or that nothing at all changes but that the system uses the telematics data to automatically decide if the risk-transfer is continued the next year.”)
The Examiner interprets Pearl’s para. [0019] as teaching that “adjusted pricing or premiums”, “provide coupons to drivers that drive well”, and “automatically decide if the risk-transfer [insurance policy] is continued the next year” are different possible “policy modifications”.
receiving a user input indicating a desire to perform the policy modification;
(See Pearl, para. [0019]: “The present invention can also exclusively be used for automatically providing and activating adapted and/or specifically selected value added services, as e.g. accident notifications and/or feedback to the driver and/or automated fleet risk reporting and/or automated and dynamically optimized underwriting etc. Thus, the present invention allows an adaption of the risk of the first risk-transfer tier or system as well as risk on level of the insured motor vehicles (e.g. by risk-based driver feedback in real-time) and/or the second risk-transfer tier or system. There is no prior art system, allowing such an optimization and/or adaption. The driver feedback can e.g. be generated by comparing the driver's profile and pattern with other driver's profiles or pattern at the same location and/or comparable conditions.”)
In regards to claim 5,
5. The apparatus of claim 3, wherein activating the at least a policy modification further comprises:
providing the policy modification to the user;
… and
activating the at least a policy modification as a function of the determination.
(See Pearl, para. [0019]: “In still another alternative embodiment, the machine-learning based telematics circuit comprises means for processing risk-related motor vehicle data and for providing data regarding the likelihood of said risk exposure for one or a plurality of the pooled risk exposed motor vehicle, in particular, based on the risk-related motor vehicle data, and wherein the receipt and preconditioned storage of payments from the risk exposed motor vehicles for the pooling of their risks can be dynamically determined based on the total risk and/or the likelihood of risk exposure of the pooled risk exposed motor vehicles. This alternative embodiment has, inter alia, the advantage that the operation of the first and/or second resource pooling system can be dynamically adjusted to changing conditions in relation to the pooled risk, such as a change of the environmental conditions or risk distribution, or the like, of the pooled motor vehicles. A further advantage is that the system does not require any manual adjustments, when it is operated in different environments, places or countries, because the size of the payments of the risk exposed motor vehicles is directly related to the total pooled risk. However, it is important to note, that the present invention does not necessarily have to lead to adjusted pricing or premiums. For example, it could also automatically provide coupons to drivers that drive well, or that nothing at all changes but that the system uses the telematics data to automatically decide if the risk-transfer is continued the next year.”)
The Examiner interprets Pearl’s para. [0019] as teaching that “adjusted pricing or premiums”, “provide coupons to drivers that drive well”, and “automatically decide if the risk-transfer [insurance policy] is continued the next year” are different possible “policy modifications”.
determining that no user input has been received;
(See Pearl, para. [0019]: “The present invention can also exclusively be used for automatically providing and activating adapted and/or specifically selected value added services, as e.g. accident notifications and/or feedback to the driver and/or automated fleet risk reporting and/or automated and dynamically optimized underwriting etc. Thus, the present invention allows an adaption of the risk of the first risk-transfer tier or system as well as risk on level of the insured motor vehicles (e.g. by risk-based driver feedback in real-time) and/or the second risk-transfer tier or system. There is no prior art system, allowing such an optimization and/or adaption. The driver feedback can e.g. be generated by comparing the driver's profile and pattern with other driver's profiles or pattern at the same location and/or comparable conditions.”)
In regards to claim 6,
6. The apparatus of claim 2, wherein activating the at least a policy modification further comprises:
receiving a user input indicating a desire to perform the policy modification; and
activating the at least a policy modification as a function of the user input.
Claim 6 is rejected on the same grounds as claim 4.
In regards to claim 7,
7. The apparatus of claim 1, wherein receiving the real-time data comprises
receiving a data collection module configured to receive real-time data.
(See Perl, para. [0007]: “Integrated telematics may offer new technological fields, in particular in monitoring and steering by means of centralized expert systems, as e.g. in the risk-transfer technology far more accurate and profitable pricing models provided by such automated expert systems. This would create a huge advantage, in particular for real-time and/or usage-based and/or dynamically operated systems. The advantage of such telematics systems is not restricted to risk transfer rather as also advantages e.g. in fleets' management that monitor employees' driving behavior via telematics improving asset utilization, reduce fuel consumption and improve safety etc. etc.”)
In regards to claim 8,
8. The apparatus of claim 1 further comprising a risk mitigation module, wherein the risk mitigation module is configured to measure a risk reduction.
(See Perl, para. [0007]: “Integrated telematics may offer new technological fields, in particular in monitoring and steering by means of centralized expert systems, as e.g. in the risk-transfer technology far more accurate and profitable pricing models provided by such automated expert systems. This would create a huge advantage, in particular for real-time and/or usage-based and/or dynamically operated systems. The advantage of such telematics systems is not restricted to risk transfer rather as also advantages e.g. in fleets' management that monitor employees' driving behavior via telematics improving asset utilization, reduce fuel consumption and improve safety etc. etc. Other fields may also benefit form such integrated telematics systems, as state and local governments needs striving to improve fuel consumption, emissions and highway safety. Some states, for example, recently issued dynamic pay-as-you-drive (PAYD) regulations, which on the other side allows insurers to offer drivers insurance rates based on actual versus estimated miles driven. It's a financial incentive to drive less.”)
In regards to claim 9,
9. The apparatus of claim 1, wherein the dynamic pricing module is configured to perform premium adjustments at regular intervals.
(See Perl, para. [0006]: “In addition to real-time surveillance, it is to be mentioned, that an insurance agent may want to exchange information with a customer associated with insurer for a number of different reasons. However, the information exchange between the customer and the insurer and/or the insurer and the reinsurer mostly is still cumbersome and time-consuming, and thus, risk-transfers provided by such structures typically remain static within a fixed time period agreed upon. For example, an existing or potential consumer may access an insurance agent's web page to determine a yearly or monthly cost of an insurance policy (e.g. hoping to save money or increase a level of protection by selecting a new insurance company).”)
In regards to claim 10,
10. The apparatus of claim 1, wherein the apparatus further comprises an incentive module,
wherein the incentive module is configured to offer additional benefits and rewards to users.
(See Pearl, para. [0019]: “In still another alternative embodiment, the machine-learning based telematics circuit comprises means for processing risk-related motor vehicle data and for providing data regarding the likelihood of said risk exposure for one or a plurality of the pooled risk exposed motor vehicle, in particular, based on the risk-related motor vehicle data, and wherein the receipt and preconditioned storage of payments from the risk exposed motor vehicles for the pooling of their risks can be dynamically determined based on the total risk and/or the likelihood of risk exposure of the pooled risk exposed motor vehicles. This alternative embodiment has, inter alia, the advantage that the operation of the first and/or second resource pooling system can be dynamically adjusted to changing conditions in relation to the pooled risk, such as a change of the environmental conditions or risk distribution, or the like, of the pooled motor vehicles. A further advantage is that the system does not require any manual adjustments, when it is operated in different environments, places or countries, because the size of the payments of the risk exposed motor vehicles is directly related to the total pooled risk. However, it is important to note, that the present invention does not necessarily have to lead to adjusted pricing or premiums. For example, it could also automatically provide coupons to drivers that drive well, or that nothing at all changes but that the system uses the telematics data to automatically decide if the risk-transfer is continued the next year.”)
In regards to claim 11,
11. The apparatus of claim 1, wherein receiving the real-time data comprises receiving the real-time data using a telematic device.
(See Pearl, para. [0012]: “The present invention with the machine-leaning based system, which is enhanced by contextual data, is able to provide best and highest optimized technical solution to the real-time adapted multi-tier risk-transfer system. It allows to capture and control the scores driver behavior, and compare its behavior within the technical operation and context. It allows to automatically capture scores risks according to location or trip, and to automatically analyze and react on data related to the need of value added services, as e.g. accident notifications and/or feedback to the driver and/or automated fleet risk reporting and/or automated and dynamically optimized underwriting etc.). The telematics based feedback means of the system may e.g. comprise a dynamic alert feed via a data link to the motor vehicle's mobile telematics device, wherein the machine-learning based telematics circuit heads up device alerts drivers immediately to a number of performance measures including e.g. high RPM, i.e. high revolutions per minute as a measure of the frequency of the motor rotation of the motor vehicle's engine, unsteady drive, unnecessary engine power, harsh acceleration, road anticipation, and/or ECO drive.”)
In regards to claim 13,
13. The apparatus of claim 1, wherein generating the risk assessment further comprises generating a pricing adjustment.
(See Pearl, para. [0007]: “Integrated telematics may offer new technological fields, in particular in monitoring and steering by means of centralized expert systems, as e.g. in the risk-transfer technology far more accurate and profitable pricing models provided by such automated expert systems. This would create a huge advantage, in particular for real-time and/or usage-based and/or dynamically operated systems. The advantage of such telematics systems is not restricted to risk transfer rather as also advantages e.g. in fleets' management that monitor employees' driving behavior via telematics improving asset utilization, reduce fuel consumption and improve safety etc. etc. Other fields may also benefit form such integrated telematics systems, as state and local governments needs striving to improve fuel consumption, emissions and highway safety. Some states, for example, recently issued dynamic pay-as-you-drive (PAYD) regulations, which on the other side allows insurers to offer drivers insurance rates based on actual versus estimated miles driven. It's a financial incentive to drive less.”)
In regards to claim 14, it is rejected on the same grounds as claim 1.
In regards to claim 15, it is rejected on the same grounds as claim 2.
In regards to claim 16, it is rejected on the same grounds as claim 3.
In regards to claim 17, it is rejected on the same grounds as claim 4.
In regards to claim 18, it is rejected on the same grounds as claim 5.
In regards to claim 19, it is rejected on the same grounds as claim 6.
In regards to claim 20, it is rejected on the same grounds as claim 7.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0300816 A1 to Perl et al. (“Perl”. Eff. Filed on Mar. 17, 2016. Published on Oct. 18, 2018) in view of US 2023/0038428 A1 to O’Sullivan et al. (“O’Sullivan”. Eff. Filed on Aug. 4, 2022. Published on Feb. 9, 2023).
In regards to claim 12, under a conservative interpretation of Perl, it could be argued that Perl does not explicitly teach the following features, which are taught by O’Sullivan:
12. The apparatus of claim 1, the apparatus further comprises a security module, wherein the security module is configured to ensure protection and accuracy of real-time data collected from telematic devices.
(See O’Sullivan, para. [0002]: “The present disclosure generally relates to mobile phones, and more particularly, to the ability of mobile phones to track vehicle (e.g., fleet vehicles) and/or vehicle operator (e.g., fleet vehicle operator) information while ensuring appropriate privacy measures are implemented with regard to surfacing and/or transmitting the tracked information.”)
(See O’Sullivan, para. [0004]: “Embodiments of the present disclosure may provide systems and methods for normalizing and securely transmitting information, for instance, telematics and other data, tracked and collected by mobile devices. In this regard, embodiments of the present disclosure may provide a data normalization service capable of normalizing data from any mobile phone in (or from users of) any region for purposes of tracking vehicle information (e.g., for fleet vehicles) and vehicle operator information (e.g., fleet vehicle operator behavior) for purposes of fleet management, insurance services, mobility services, and risk analysis. Further, embodiments of the present disclosure may provide a secure data transmission service capable of ensuring appropriate privacy measures are implemented with regard to surfacing and/or transmitting the tracked information. In aspects, one or more permissions regarding surfacing and/or transmitting the tracked information may be established based on the privacy settings of individuals (e.g., fleet vehicle operators) working under organizations (e.g., fleet management organizations). In aspects, one or more permissions regarding surfacing and/or transmitting the tracked information may be established based on the privacy settings of an individual leasing a vehicle. In aspects, one or more permissions may be configured for differing geographical regions to accommodate differing region-specific privacy regulations. The data normalization service and the secure data transmission service according to embodiments of the present disclosure may be integrated with a vehicle information system and/or data intelligence platforms, and also may be integrated with back-end systems of third parties, such as customers of the vehicle information system and/or data intelligence platforms.”)
(See O’Sullivan, para. [0005]: “One aspect of the present disclosure relates to a method for securely transmitting information, e.g., telematics and other data associated with vehicles (e.g., fleet vehicles) and/or vehicle operators (e.g., fleet vehicle operators). The method may include receiving telematics data from a mobile device associated with a first user (e.g., a fleet vehicle operator). The method may include normalizing the telematics data. The method may include, based on privacy settings derived from the mobile device associated with the first user, receiving permission, from the first user, to share the telematics data. The method may include, based on receiving permission, from the first user, to share the telematics data, transmitting at least a portion of the normalized telematics data to a second user (e.g., a fleet manager and/or a fleet management service).”)
(The Examiner interprets that privacy features that limit the sharing of data help to ensure that it is not corrupted or changed by unknown users)
(See O’Sullivan, para. [0033]: “Data normalization may then occur, and the normalized data may be stored for use by one or more applications and services. By normalizing and processing the data, the platform according to embodiments of the present disclosure may answer questions but also identify questions that users may not realize should be asked, thereby providing business solutions through custom business services, custom applications, intelligent mobility ecosystems, and monetization. It should be appreciated that the platform according to embodiments of the present disclosure may consume all types in all formats including, but not limited to, video, audio, images, text, and/or time series. The datasets may be normalized into a universal data format. The platform according to embodiments of the present disclosure may be seeded with historical data but also take in new data in real time as it is generated.”)
(See O’Sullivan, para. [0035]: “While certain applications and services are depicted/described herein, it should be appreciated that more or fewer applications or services may be provided as part of the mobile telematics data normalization service without departing from the present disclosure. These applications and services may be accessed by, and information may be exchanged with, the multi-regional user base and/or the cloud mobility platform in embodiments of the present disclosure. It should be appreciated that the one or more applications may consume the normalized data and access one or more services without regard from which of the plurality of devices the normalized data originated.”)
(See O’Sullivan, para. [0036]: “FIG. 3 depicts a flow of a device and region agnostic mobile telematics data normalization service according to an embodiment of the present disclosure. The flow begins after login, which may be after signing up or signing in according to embodiments of the present disclosure. After login, a determination may be made as to whether the organization offers TSP. The term “TSP” is used within the connected car industry as a term to categorize telematics service providers who play a role in the connected car value chain centered around secure vehicle to cloud data management. TSPs typically offer services to fleet operators, enabling them to monitor their vehicles and drivers.”)
It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the “Telematics system and corresponding method thereof”, as taught by Perl, with “Normalizing and securely transmitting telematics data”, as taught by O’Sullivan, because both references are in the same art of telematics systems for vehicles, whereas O’Sullivan teaches (See para. [0004]) that “embodiments of the present disclosure may provide a secure data transmission service capable of ensuring appropriate privacy measures are implemented with regard to surfacing and/or transmitting the tracked information”.
Conclusion
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Any inquiry concerning this communication or earlier communications should be directed to Examiner Ayal Sharon, whose telephone number is (571) 272-5614, and fax number is (571) 273-1794. The Examiner can normally be reached from Monday to Friday between 9 AM and 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SPE Christine Behncke can be reached at (571) 272-8103 or at christine.behncke@uspto.gov. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
February 24, 2025