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 .
DETAILED ACTION
Applicant filed an amendment on 3/5/26.
Claims 1-15 are pending examination, Claims 1 and 15 are amended. After careful consideration of applicant arguments and amendments, the examiner finds them to be moot and/or non-persuasive. This action is a Final Rejection.
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 1-15 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without more.
Claim 1 is independent and is considered a system type claim which is a statutory class.
The limitations under their broadest reasonable interpretation covers the performance of the limitation as certain methods of organizing human activity. The claims are directed to insurance analysis calculations (in summary). The calculations related to insurance are a fundamental economic practice.
The following elements are found to be abstract;
Enable… the user to enter targeting criteria and exclusions using the …; the user to enter one or more factors for the system to track for an insured using the …; enable, by …, the user to select one or more time periods for factor evaluation for the insured; execute, by the one or more processors, a risk determination based on the one or more factors; output, …, a premium for the insured based on the risk determination; monitor …, a change in the one or more factors from a first time period to a second time period; and change, … the premium for the insured based on the change in the one or more factors.
Here the non abstract elements are the computer, GUI and one or more processors.
By amendment, “without human intervention” is added premium changing and further the limitation that the monitoring step is automated.
Claims 2-15 do not add further technical steps.
The recitation of generic computing elements in a claims do not necessarily preclude the claim form reciting an abstract idea. Improvement of the computer versus automating known elements is why merely doing something on a computer may not be persuasive.
The judicial exception is not integrated into a practical application. In particular the claims recite the additional elements such as computer, processor and GUI which are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computing components.
The claims do not contain additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and as an ordered combination they do not add significantly more known as inventive concept. While applicant specification indicates it might be a special purpose computer 0031, 055, it appears that a general purpose computer is also contemplated 0031.
Claims 2-15 do not further improve claim 1 from a 35 USC 101 standpoint. Thus claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
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) 1-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication to Hayward 20210256615 in view of US Patent to Kumar, 12079851
As per claim 1, Hayward discloses;
display, by the one or more processors,
a graphical user interface (GUI); enable, by the one or more processors, a user
to enter one or more parameters related to
Hayward (fig. 1 a user interface for insurance)
one or more underwriting functions using the GUI;
Hayward(0043, and 0056, AI driven underwriting)
execute, by the one or more processors, a risk determination based on the one or more factors;
output, by the one or more processors, a premium for the insured based on the risk determination;
Hayward(0056, premium for the initial and the reduced level of risk)
Automatically monitor, by the one or more processors, a change in the one or more factors from a first time period to Hayward(0045, automatic monitor data)
a second time period; and change, by the one or more processors, the premium for the insured based on the change in the one or more factors without human intervention.
Hayward(0056, something the user could do to reduce risk, 0045, automatic monitoring is without human intervention)
Hayward does not explicitly disclose what Kumar teaches;
enable, by the one or more processors, the user to enter targeting criteria
and exclusions using the GUI; enable, by the one or more processors, the user to enter one or more factors for the system to track for an insured using the GUI;
enable, by the one or more processors,
the user to select one or more time periods for factor evaluation for the insured
Kumar(col 4 lines including 55-65, it is noted that the “targeting criteria” is not well defined by applicant spec and could be various personal data relative the insured or potential insured)
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the insurance disclosure of Hayward with the onboarding data collection of Kumar for the motivation of “identifying user falsified information” for example. (Col 1. Lines 20-26)
As per claim 2, Hayward discloses;
The system of claim 1, wherein the one or more non-transitory computer readable media include further program instructions stored thereon that when executed cause the one or more computers to: execute, by the one or more processors, weightings for each of the one or more factors.
Miller Col 58, line 65
Hayward(0105, importance weighting)
As per claim 3 Hayward discloses; The system of claim 2, wherein the one or more non-transitory computer readable media include further program instructions stored thereon that when executed cause the one or more computers to: enable, by the one or more processors, the user to specify the weightings using the GUI. Hayward(0105, importance weighting user definable)
As per claim 4, Hayward discloses;
The system of claim 2, wherein the one or more non-transitory computer readable media include further program instructions stored thereon that when executed cause the one or more computers to: execute, by the one or more processors, a weighting determination for the one or more factors based on one or more predictions from an artificial intelligence model.
Hayward(0103-0105)
As per claim 5, Hayward discloses; The system of claim 2, wherein the sum of all weighting must equal 100. Hayward(0103-0105, it’s noted that the sum of weightings logically is 100% in 0135-6 it’s more obvious. The examiner checked the internet if overall risk scores as discussed in Hayward would imply total or normalized to 100 and it appears that this is the most common interpretation)
As per claim 6, Hayward discloses; The system of claim 5, wherein a heart rate zone factor is assigned a higher weighting than a body mass index factor.
Hayward(0102 heart data may be considered)
As per claim 7 Hayward discloses; The system of claim 6, wherein a body mass index factor is assigned a higher weighting than a sleep time factor.
Hayward(0124 BMI data may be considered, the weighting is arbitrary and can be set how the user desires)
As per claim 8 Hayward discloses; The system of claim 7, wherein t a sleep time factor is assigned a higher weighting than a tracked steps factor. Hayward(0042 sleep monitor)
As per claim 9 Hayward discloses; The system of claim 1, wherein the change in the one or more factors include a change in a life event of the insured.
Hayward(0057 lifestyle)
As per claim 10 Hayward discloses; The system of claim 9, wherein the change in the life event includes one or more of a name change, a marital status change, and a family addition.
Hayward(0133, having a child, is one of )
As per claim 11 Hayward discloses; The system of claim 9, wherein the change in the life event includes one or more of a department of motor vehicle record change, a
financial institution record change, and a reoccurring purchase change. Hayward(one or more could be just one…. 0058 various conditions of the users life style are considered)
As per claim 12 Hayward discloses;. The system of claim 9, wherein the change in the life event includes one or more of a change in weekly activity. (like a life event)
Miller(col. 57 lines 15-20)
Hayward(0141 activity like exercise or other lifestyle changes))
As per claim 13 Hayward discloses; The system of claim 1, wherein the risk determination includes a mortality risk. (0133), see also Miller (col 19 lines 60-65)
Claim(s) 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication to Hayward 20210256615 in view of US Patent to Kumar, 12079851 further in view of US Patent to Miller 12266018
As per claim 14 Hayward and Kumar does des not explicitly disclose what Miller teaches;
The system of claim 9, wherein the one or more non-transitory computer readable media include further program instructions stored thereon that when executed cause the one or more computers to: add, by the one or more processors, the insured to a risk pool based on the risk determination.
Miller(col. 20 lines 20-25 risk pool, new members can be added fig.12)
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the disclosure of Hayward and Kumar with the insurance teachings of Miller for the motivation of “analyzing various risks ) col. 4 lines 45-50)
As per claim 15 Hayward and Kumar do not explicitly disclose what Miller teaches; The system of claim 14, wherein the mortality risk includes a mortality risk of the risk pool.
Miller(col. 20 lines 20-25 risk pool, a known concept for insurance)
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the disclosure of Hayward and Kumar with the insurance teachings of Miller for the motivation of “analyzing various risks) col. 4 lines 45-50)
Response to Arguments
Applicant filed an amendment on 3/5/26.
Claims 1-15 are pending examination, Claims 1 and 15 are amended. After careful consideration of applicant arguments and amendments, the examiner finds them to be moot and/or non-persuasive. This action is a Final Rejection.
Claim Rejections - 35 U.S.C. § 101
Claim 1 has been amended to emphasize that the claimed system (i) automatically monitors changes in tracked factors across time periods and (ii) changes the premium based on those changes without human intervention. When properly considered as a whole, the amended claims are patent eligible.
I. Step 2A, Prong One - The Amended Claims Are Not Directed to an Abstract Idea
The rejection characterizes the claims as "insurance analysis calculations" and "a fundamental economic practice" and treats the recited operations as methods of organizing human activity. Applicant respectfully submits that amended claim 1 is directed to a computer- implemented automated monitoring-and-control system operating over time on monitored data, not to an abstract idea.
Here applying automatic to the claims would not be persuasive without elements that technically improve the computer or the automation aspect. Otherwise the applicant could claim to calculate the insurance amount automatically. The general idea was to show an improvement of the computer the interface, reduced storage usage etc. In this case a specialized user interface that incorporates AI or machine learning might be the best way to improve the computer technology.
Amended claim 1 recites a particular machine-implemented workflow that, after configuration via a GUI, automatically monitors changes in one or more tracked factors from a first time period to a second time period, and changes the premium based on the detected change without human intervention. These limitations make clear that the claim's focus is an automated, time-based monitoring and adjustment system, i.e., a system that performs ongoing monitoring and automatic premium updating as a machine operation rather than any known human mental process or manual underwriting practice.
Here applicant appears to be implying machine learning might be the improvement. However, the AI element is found in claim 4 and as claimed is more of an “apply it” type recitation.
II. Step 2A, Prong Two - Any Alleged Judicial Exception Is Integrated Into a Practical Application
Even if certain limitations are viewed as involving underwriting/risk concepts, amended claim 1 integrates any such concepts into a practical application by requiring automatic monitoring across time periods and automatic premium change without human intervention. These are not
mere "instructions to apply" an idea; they impose concrete operational constraints on how the system functions in the real world:
* the system tracks defined factors for an insured;
* the system evaluates changes between discrete time periods; and
* the system automatically updates a premium based on detected changes, without requiring
a human underwriter to perform the monitoring or adjustment.
This is a practical application that improves the functioning of an underwriting platform by enabling continuous and automated premium updating over time, rather than a static, manual, or episodic recalculation.
Here practical application would have to be directed to improving the technology rather than the business process. In this case applicant appears to be applying AI or machine learning to a traditional process and attempting to do it by the computer. However, the best approach would be to go for an improvement to machine learning and a better user interface.
III. Step 2B - The Amended Claims Recite Significantly More Than Any Alleged Abstract Idea
The rejection asserts that the non-abstract elements are only generic computing components (computer/processor/GUI), and that the claims lack an inventive concept when considered individually and as an ordered combination. Applicant respectfully submits that the amended claim set recites "significantly more" because the ordered combination requires a specific automated operation (automatic monitoring across time periods and premium modification without human intervention) that meaningfully limits the claim to a particular implementation of an automated underwriting update mechanism.
In other words, the claim is not merely "calculating risk" on a generic computer; it is directed to an automated system configured to perform ongoing monitoring and premium changes without human intervention, which is a substantive functional limitation on the claimed system.
Here practical application of technology is not satisfied by merely adding generic computing elements and then claiming that the invention is automatic.
Claim 2 (executing weightings for each factor) – does not by itself improve the technology but might as part of a improved interface.
adds a defined computational structure for evaluating risk using factor weightings, which constrains the system to a weighted-factor implementation rather than an unbounded, abstract "risk assessment."
It is noted that execute “weightings” is a rather generic recitation though claims 3-8 appear to further distinguish the weighting process.
Claim 2 (user specifies the weightings using the GUI) adds a specific configuration mechanism tying weighting values to a user-configurable interface, reinforcing that the claim set
is directed to a configurable computerized system with defined configuration inputs and processing behavior.
Claim 2 is more of an apply it argument.
Claim 3 is more of a specialized input claim but, the claim is still rather generic in that it enables inputs of weightings.
Claim 3 is more of a user input “capability”
Claim 4 (weighting determination based on AI-model predictions) further narrows the system to implementations that use predictions from an artificial intelligence model to determine weightings, adding a concrete, computer-implemented technique for generating weights, beyond human judgment or manual rule setting.
Claim 4 contains AI though it does not utilize the AI specifically for an improvement.
Claim 5 (sum of all weightings equals 100) imposes a normalization constraint on the weighting scheme, which is a concrete mathematical/structural limitation that governs system operation and prevents purely subjective or unconstrained weighting.
Claim 5 is a limitation rather than a technical improvement.
Claims 6-8 (relative weighting relationships among specific factors) further constrain the system to implementations in which specific factor categories (e.g., heart rate zone, BMI, sleep time, tracked steps) are assigned weightings in defined relative relationships, reinforcing that the claims are not directed to an abstract concept at a high level, but to a particularized weighted-factor framework with concrete factor types and defined weighting priorities.
Claims 6-8 do not contain a technical improvement directed to the computer or interface.
Claim 9 (change includes a life event) expands the monitored "change" beyond generic factor drift to include detection/monitoring of a defined category of change (life events), reinforcing that the monitoring step has concrete tracked change-types, not an abstract or open- ended "monitoring" notion.
Claim 9 is more directed to the abstract idea.
Claims 10-12 (specific life event categories) further narrow the life-event monitoring to concrete event types (e.g., name/marital status/family addition; DMV/financial institution/recurring purchase changes; weekly activity changes). These limitations tie the monitoring to specific, objective categories of data change that the system monitors across time periods to trigger premium adjustments, reinforcing the automation and practical application aspects of the claim set.
Claims 10-12 are also directed/related to calculating insurance amounts.
Claims 13-25 likewise do not add any technical elements.
Claim Rejections - 35 U.S.C. Q 103
Claim(s) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 20210256615 to Hayward (hereinafter "Hayward") in view of US Patent 12079851 to Kumar,
(hereinafter "Kumar").
Claim(s) 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayward in view of Kumar, and further in view of US Patent 12266018 to Miller, (hereinafter "Miller").
Applicant respectfully traverses the rejection of claim 1. The combination of Hayward and Kumar fails to teach or suggest, at least, the automatic time-period-to-time-period monitoring of factor changes and the premium change based on those changes, as recited in amended claim 1.
Claim 1 requires that the system "...automatically monitor ... a change in the one or more factors from a first time period to a second time period."
The rejection cites Hayward for "...monitor ... a change ... from a first time period to a second time period..." and characterizes it as "something the user could do to reduce risk." However, Hayward's actual "monitoring" disclosure is expressly tied to monitoring user activity associated with intervening actions (i.e., monitoring whether the user performs suggested actions), not monitoring changes in factors across a first time period and a second time period.
Hayward's operative monitoring language states:
"...monitor user activity associated with the one or more identified intervening actions to
collect user activity monitoring data..." (Hayward, claim 8)
and further explains that the monitoring supports retraining and predicting whether the user will continue actions during a future time period:
"...apply the trained machine learning analytics model to the user activity monitoring data
to determine a likelihood of whether the user will continue to execute the one or more intervening
actions during the future time period."
This is materially different from automatically monitoring a change in underwriting factors from a first time period to a second time period (e.g., detecting a change in tracked factors between two evaluation windows). The cited Hayward passages concern monitoring performance of suggested actions and using that monitoring data for machine learning (ML) retraining/likelihood prediction, not monitoring factor changes across two time periods as claimed.
Here Hayward 0141-0145 appears to monitor the user for various factors that are charging over time. For example tracing activity data to see if the user continues to be active for example and follows mitigation suggestions. Here the time window of applicant if that is the argument is not specific and merely any time window.
Kumar likewise does not fill this gap. Kumar's disclosures relied upon are centered on onboarding and retrieval/analysis of personal information (including for fraud mitigation), and describe retrieving data "for a predetermined time period," rather than monitoring a change from a first time period to a second time period:
"retrieve step count data... sleep data, heart rate data... etc. for a predetermined time
period. The predetermined time period may be hours, days, weeks..." (Kumar, col. 19, lines 61-67)
A "predetermined time period" for retrieval is not the same as the claimed automatic monitoring of changes between two time periods, and nothing in the cited Kumar disclosure teaches a first time period to a second time period change-monitoring operation for underwriting factors.
Accordingly, the combination fails to teach the claimed automatic monitoring of factor changes from a first time period to a second time period.
Amended claim 1 now further requires changing the premium "...based on the change in the one or more factors without human intervention." Hayward discloses calculating premiums associated with risk levels and transmitting those premiums for presentation:9
Between two time periods is argued. However, on review of applicant specification, 0065 “define the evaluation time period for example…. There is not specific time period required in the claim and thus any time period could be compared. In Hayward the past is compared to the future at least. In Hayward 0124-6, user activity such as exercise and changes in the ament are tracked and compared to changes over time in the BMI etc. These periodic changes are used to predict the future. Thus, applicant does not specify a specific time period limitation so broadly Hayward would anticipate this.
"calculate a first insurance premium..." (Hayward, claim 10)
"calculate a second insurance premium..." (Hayward, claim 10)
"transmit the first and the second insurance premium... for presentation to the user."
(Hayward, claim 10)
This is an output/presentation of premiums corresponding to risk levels, not a change to an insured's premium triggered by monitored factor changes: Hayward is silent on doing so "without human intervention." In fact, Hayward's approach is framed around suggesting "intervening actions" for the user to reduce risk and then monitoring user activity associated with those actions, not an automated premium-change loop based on tracked factor changes across time periods.
Kumar, as applied, also does not teach the missing "without human intervention" premium change. Kumar discusses fraud mitigation including post-issuance "spot-check" and possible cancellation:
"...the system may engage in a spot-check... after an individual has obtained life
insurance... prompt the user to provide validating information on the spot...If the validating
information does not match expected information, then the system may... cancels the insurance
policy."
This is not an automated premium change based on factor changes, and not a teaching of changing a premium based on changes in tracked factors without human intervention as claimed.
Accordingly, the combination fails to teach the claimed premium change based on monitored factor changes without human intervention.
For the reasons above, particularly the failure of the applied art, alone or in combination, to teach or suggest (i) automatically monitoring factor changes from a first time period to a second time period and (ii) changing the premium based on those changes without human intervention, Applicant respectfully submits that claim 1 is not rendered obvious by Hayward in view of Kumar. Withdrawal of the § 103 rejection of claim 1 is respectfully requested.
Applicant also respectfully traverses the rejection of claim 2 under 35 U.S.C. § 103 over Hayward in view of Kumar.
Here citations from Hayward’s claims do not limit the teachings of Hayward as the specification is also considered when using a reference to anticipate a claim. In regards to Kumar the argued elements are not attributed to Kumar.
Claim 2 as claimed requires execute weightings which is not limited extensively by the claim. “execute weightings” is reasonably open ended. How does one execute a weighting?
Applicant respectfully traverses the rejection of claim 3 under 35 U.S.C. § 103 over Hayward in view of Kumar. Claim 3 depends from claim 2 and further requires that the program
instructions, when executed, cause the one or more computers to "enable, by the one or more processors, the user to specify the weightings using the GUI." The applied references, as cited by the Office, do not teach this limitation.
The Office cites Hayward "(0105, importance weighting user definable)." However, Hayward, par. 0105, does not disclose a GUI that enables a user to specify weightings.
Claim 3 enables or allows specific weighing’s… Hayward allows the user to specify weightings as well. In 0070 the user can input data.
Kumar does not cure these deficiencies. As applied in the rejection, Kumar is relied upon for onboarding/data collection teachings, and it does not disclose enabling a user to specify factor weightings using a GUI in the manner required by claim 3. Accordingly, the applied combination fails to teach or suggest the limitation of claim 3 requiring enabling the user to specify the weightings using the GUI.
Here the invention of Hayward allows the user to input various factors that have weight. Unless a specific weighting is claimed, then any weighting would be acceptable.
Applicant further traverses the rejection of claim 4 under 35 U.S.C. § 103 over Hayward in view of Kumar. Claim 4 depends from claim 2 and further requires that the program instructions, when executed, cause the one or more computers to "execute, by the one or more processors, a weighting determination for the one or more factors based on one or more predictions from an artificial intelligence model." The applied references, as cited by the Office, do not teach this limitation as claimed.
The Office cites Hayward "(par. 0103-0105)." However, the cited Hayward passages describe (i) selecting training data and training a machine learning model, and (ii) the general concept that training may include defining "weighting" and that the trained model may, over time, "vary the weighing of certain inputs" as additional data becomes available or the dataset is updated. For example, Hayward states:12
"instructions stored in machine learning training module 231 may facilitate ... using
various portions of the dynamic data set as training data for a machine learning analytics model...the overall process of training the machine learning analytics model may include defining the sample
inputs, the importance (e.g., weighting) of these inputs."
Hayward also states in paragraph 0105 that the machine learning process allows "specific predictions to be formulated," and that "[o]ver time ... the trained machine learning model may identify new correlations, vary the weighing of certain inputs, change the inputs, etc., such that ... the accuracy of predictions may be improved."
These disclosures, even if taken at face value, describe weighting in the context of the training framework and subsequent model evolution; they do not teach the specific claim requirement of "execute ... a weighting determination ... based on one or more predictions from an artificial intelligence model." In particular, Hayward does not disclose using one or more AI model predictions as an input to a weighting-determination operation (i.e., "predictions" driving the determination of the weights), as recited in claim 4.
Rather, Hayward describes that weights may be defined as part of training and may be varied over time to improve prediction accuracy, which is materially different from determining weights based on one or more AI predictions.
Here the purpose of the AI is the make inputs into the model and predictions can be used to update the model. Claim 4 furthermore uses the term “based on” which is not necessarily a direct correlation. However, in 0105 the model can predict and then define weightings . Thus applciant may not be persuasive here.
Applicant respectfully traverses the rejection of claim 5 under 35 U.S.C. § 103 over Hayward in view of Kumar. Claim 5 depends from claim 2 and further requires that "the sum of all weighting must equal 100." The applied references, as cited by the Office, do not teach or suggest this specific normalization constraint.
Hayward's cited disclosures are directed to the general concept that inputs may be weighted and that weights may be defined during training or varied over time. For example, Hayward states13
that training "...may include defining the sample inputs, the importance (e.g., weighting) of these inputs..." (Hayward, par. 0105) and that "...the trained machine learning model may ... vary the weighing of certain inputs...." (Hayward, par. 0105). Hayward also explains, in the neural network context, that inputs "...may be weighted..." and that weights may be "...determined during the training process..." (Hayward, par. 0117). None of these disclosures teaches or suggests the specific requirement that the weightings must be normalized such that "the sum of all weighting must equal 100." In other words, Hayward discloses that weights exist and may be learned or varied, but it does not disclose the claimed quantitative constraint that the weights sum to 100 (or any equivalent fixed-sum normalization rule).
Kumar likewise does not cure this deficiency. As applied, Kumar is directed to onboarding/verification and data retrieval/analysis and does not disclose a fixed-sum weighting constraint, nor does it provide any teaching or suggestion that would lead one of ordinary skill to modify Hayward's disclosed weighting concepts to require that "the sum of all weighting must equal 100." The rejection also does not articulate a reasoned basis for adopting this specific normalization requirement in the combined system.
Accordingly, the applied combination fails to teach or suggest claim 5's normalization constraint, and
claim 5 is not rendered obvious by Hayward in view of Kumar. Withdrawal of the § 103 rejection of claim 5 is respectfully requested.
Claim 5 might be more persuasive if it were rolled up as it is a dependent claim.
In addition, Applicant respectfully traverses the § 103 rejection of claims 6-8 over Hayward in view of Kumar.
Claims 6-8 recite specific, ordered relative-weighting constraints among expressly recited factor types:
claim 6 requires that "a heart rate zone factor is assigned a higher weighting than a body mass index factor," -
This element is an arbitrary assignment of weightings. Weightings themselves are arbitrary.
claim 7 requires that "a body mass index factor is assigned a higher weighting than a sleep time factor,"
This element is an arbitrary assignment of weightings. Weightings themselves are arbitrary.
claim 8 requires that "a sleep time factor is assigned a higher weighting than a tracked steps factor."
Here again the weighting is arbitrary. However, if the applicant were to claim these in the independent claims it might overcome the art because it is a specific requirement.
Claim 9 requires that "the change in the one or more factors include a change in a life event of the insured."
No specific argument. A life event could be many changes.
Claims 10-12 further require that the "life event" change include specific enumerated categories, including (i) "one or more of a name change, a marital status change, and a family addition," (ii) "one or more of a department of motor vehicle record change, a financial institution record change, and a reoccurring purchase change," and (iii) "one or more of a change in weekly activity." The cited portions of Hayward and Kumar may discuss that certain types of information exist as inputs or data sources (e.g., demographic information like marital status or children, or external records such as DMV/financial/purchase-related information). However, that is not what the claims require.
The claims require that the monitored "change" itself includes a "life event" change of the insured (i.e., a change-type classification and use of that change within the claimed pipeline) and then require the specific life-event change categories listed in claims 10- 12.
The applied references, as relied upon in the rejection, do not teach "life event" change monitoring as such (and do not teach using those life-event changes as the monitored "change" recited in claim 9), and the rejection does not identify any passage in the applied art disclosing the claimed life-event change construct or the specific enumerated categories in the manner required by claims 9-12. Accordingly, claims 9-12 are not rendered obvious by the applied art.
Claims 10-12 are argued by virtue of dependency. However, as claimed “life event” could be “one of” a list of events. No specific event, just a choice are required.
Claim 13 requires that "the risk determination includes a mortality risk." The rejection's reliance on Hayward for general underwriting/risk does not establish that the claimed risk determination includes a "mortality risk" as recited. The applied art excerpts relied upon in the rejection discuss predicting conditions and determining risk levels and premiums generally, but the rejection does not identify a disclosure in the applied references that teaches a risk determination that includes mortality risk in the claimed sense (i.e., within the recited factor-driven risk determination of claim 1 and its dependents).
To the extent the Office points to mortality-related discussion in Miller, noting there has been no obviousness analysis in the rejection of claim 13 with regard to Miller, the cited discussion is not shown in the rejection as teaching the claimed "risk determination includes a mortality risk" limitation as an element of the risk determination operation of claim 1 and its dependents. Accordingly, claim 13 is not rendered obvious by the applied art as applied.
Here, “see also” means that that the generic concept is found in Hayward. Miller if applied would use the same motivation as provided for claim 14.
Claim 14 (depending from claim 9) requires that the system "add ... the insured to a risk pool based on the risk determination." The applied Hayward/Kumar combination does not teach adding an insured to a "risk pool" based on a computed risk determination; the cited Hayward passages relied upon by the
Office describe determining risk levels and calculating/transmitting premiums, but do not disclose assigning an insured to a risk pool as an additional system action triggered by the risk determination.
Here the combination of references are offered. Miller teaches risk pool which is a common insurance process. Arguing based on risk does not limit the assignment to being in a specific configuration. For example is based on risk to reduce the risk of the pool or increase the risk. There is no specific criteria.
Claim 15 requires that "the mortality risk includes a mortality risk of the risk pool."
Even if the applied art references mortality-related parameters and separately references a risk pool, the rejection must still show that the applied references teach a "mortality risk of the risk pool" (i.e., a pool-level mortality risk), not merely mortality-related considerations and not merely a generic mention of a risk pool.
Here applicant may be arguing the combination of references as Miller is offered to teach risk pool in combination with Kumar and Hayward, this would generally not be persuasive.
Conclusion
The prior art made of record from IP.com and not relied upon is considered pertinent to applicant's disclosure.
Machine Learning-Based Regression Framework to Predict Health Insurance Premiums, PMC, 2022
Blockchain and AI-Empowered Healthcare Insurance Fraud Detection: an Analysis, Architecture, and Future Prospects, IEEE 2022
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE I EBERSMAN whose telephone number is (571)270-3442. The examiner can normally be reached 8:00 am - 5:00 pm Monday-Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRUCE I EBERSMAN/Primary Examiner, Art Unit 3693
Prosecution Insights