DETAILED ACTION
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 Claims
This action is in reply to the application filed on 2/27/2025, wherein:
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a system and method for calculating a parameter for a product or service which is considered a judicial exception because it falls under Certain Methods of Organizing Human Activity such as commercial or legal interactions, including marketing, and sales activities or behaviors. This judicial exception is not integrated into a practical application as discussed below and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
This rejection follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed Reg 4, January 7, 2019, pp. 50-57 (“2019 PEG”)(MPEP 2106).
Analysis
Step 1 (Statutory Categories) – 2019 PEG pg. 53 (See MPEP 2106.03)
Claims 1-20 are directed to the statutory category of a process, machine, or manufacture.
Step 2A, Prong 1 (Do the claims recite an abstract idea?) – 2019 PEG pg. 54 (See MPEP 2106.04(a)-(c))
For independent claims 1, 11, and 20, the claims recite an abstract idea of: calculating a parameter for a product or service. The steps of independent claim 1 recite the abstract idea (in bold below) of: A computer-implemented method comprising: receiving a request for a personalized parameter of a product or service, the request including information regarding the requestor; applying the requestor information as input to a data processing model, wherein the data processing model was precompiled by a process comprising: receiving model parameters; building a dependency tree using the model parameters, wherein the dependency tree represents dependencies between model parameters of the data processing model; and compiling the data processing model using the parameters and the dependency tree; receiving the personalized parameter as output from the data processing model; and providing the personalized parameter for display. Independent claims 11 and 20 recite similar steps that recite the abstract idea. Independent claims 1, 11, and 20, as drafted, are a process that, under the broadest reasonable interpretation, covers Certain Methods of Organizing Human Activity, since they recite commercial or legal interactions, including marketing, and sales activities or behaviors. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of additional elements including generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Other than reciting the abstract idea, the independent claims recite additional elements including generic computer components such as “computer-implemented, a data processing model, a dependency tree, a non-transitory computer-readable medium comprising computer program code executed by a computing system, and a computer system comprising one or more processors executing program codes stored on one or more non-transitory computer-readable media”, and nothing in the claims precludes the steps from being performed as a method of organizing human activity. Accordingly, the independent claims recite an abstract idea.
Dependent claims 2-10, and 12-19 recite similar limitations as independent claims 1, 11, and 20; and when analyzed as a whole are held to be patent ineligible under 35 U.S.C 101 because the additional recited limitations only refine the abstract idea further. Other than reciting the abstract idea, the dependent claims recite similar additional elements including generic computer components as the independent claims, such as “computer implemented, the data processing model, a ROC, the dependency tree, a database, and the non-transitory computer-readable medium”. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Step 2A, Prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?) – 2019 PEG pg. 54 (See MPEP 2106.04(d)-(c))
This judicial exception is not integrated into a practical application. In particular, independent claims 1, 11, and 20 only recite the additional elements of “computer-implemented, a data processing model, a dependency tree, a non-transitory computer-readable medium comprising computer program code executed by a computing system, and a computer system comprising one or more processors executing program codes stored on one or more non-transitory computer-readable media”. A plain reading of the Figures and associated descriptions in the specification reveals that generic processors may be used to execute the claimed steps. The additional elements are recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)) and limits the judicial exception to a particular environment (See MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component and limiting the judicial exception to a particular environment doesn’t integrate the abstract idea into a practical application in Step 2A. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Hence, independent claims 1, and 14 are directed to an abstract idea.
Dependent claims 2-10, and 12-19, recite similar additional elements as the independent claims including generic computer components, such as “computer implemented, the data processing model, a ROC, the dependency tree, a database, and the non-transitory computer-readable medium”. The judicial exception is not integrated into a practical application because the additional elements in the dependent claims are also recited at a high-level of generality such that it amounts to more no more than mere instructions to apply the exception using generic computer components. Therefore, the additional elements do not integrate the abstract idea into a practical application because they also do not impose any meaningful limits on practicing the abstract idea. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement of the functioning of a computer system itself; the claims do not effect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) – 2019 PEG pg. 56 (See MPEP 2106.05)
Independent claims 1, 11, and 20 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 recited additional elements amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) and limits the judicial exception to the particular environment of computers (See MPEP 2106.05(h)). The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the function of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept in Step 2B.
In addition, the dependent claims 2-10, and 12-19 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 of the dependent claims to perform the claimed limitations, amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Similar to the independent claims, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Also, for the same reasoning as the independent claims, the additional elements of the limitations of the dependent claims, when considered individually and as an ordered combination, together do not offer significantly more than the sum of the functions of the elements when each is taken alone and the dependent claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
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 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.
Claims 1-6, 8-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230008057 to Hendry et al. (hereinafter referred to as Hendry), in view of US 9836283 to Pudiyapora et al. (hereinafter referred to as Pudiyapora).
In regards to claim 1, Hendry discloses a computer-implemented method comprising: receiving a request for a personalized parameter of a product or service (fig. 9 process of execution engine handling a user request input via a service running on an insurance hosting system, para. 0017, fig. 9), the request including information regarding the requestor (exemplary user request contains data associated with a particular insurance policy or submission for a future policy based on a submitted application, para. 0066); applying the requestor information as input to a data processing model (FIG. 9 is exemplary process of the execution engine handling a user request input via a service running on an insurance hosting system, para. 0017, fig. 9), wherein the data processing model was precompiled (system 10 includes compiler 12 that translates source code defining an insurance product as a directed graph into a collection of nodes representing either a computation or an input, para. 0027) by a process comprising: receiving model parameters (for each insurance product the compiler 12 executes a series of transformations on the source code, and if the source code is syntactically and semantically valid, the compiler constructs an intermediate graph object representation of the relevant source code that can then be executed by the execution engine 14 via the web-based runtime environment 16, para. 0027); building a dependency using the model parameters (each insurance product is defined using the meta-model of FIG. 2, which results in a structure that is a directed graph made up of a set of nodes, points or vertices connected by directed edges, para. 0033), wherein the dependency represents dependencies between model parameters (compiler verifies the dependencies of the directed graph, para. 0033) of the data processing model (reason defining an insurance product as a directed graph is that relationships between meta-model elements, including products 32a, lines of business 32b, entities 32c, limits 32i, deductibles 32i, features 32d, and computations 34a, are preserved which maintains the semantics described in the source code in the compiled graph object artifact, para. 0033); and compiling the data processing model using the parameters (compiler 12 executes a series of transformations on the source code, the compiler constructs an intermediate graph object representation of the relevant source code that can then be executed by the execution engine 14 via the web-based runtime environment 16, para. 0027) and the dependency (it is noted that when the compiler 12 compiles the source code, the compiler verifies the dependencies of the directed graph, para. 0033); receiving the personalized parameter as output from the data processing model; and providing the personalized parameter for display (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0035). Hendy fails to disclose a dependency tree.
Pudiyapura, In the related field of compiler architecture, teaches a dependency tree (for each of the functions in the list 3002, the cloud mapper 622 determines all of the possible signal paths 30 3102 from the inputs and output of the selected function to primary inputs (for the inputs) and final outputs (for the output) using any one or combination of the other functions in the list 3002, which together form a dependency tree 3104 for that function, col. 50, lines 28-49). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the method of Hendy with the ability to use a dependency tree as taught by the method of Pudiyapura. The motivation for doing so would have been to determine the restrictions and input/output dependencies to map the functions (Pudiyapura, col. 49, lines 59-67).
In regards to claim 2, modified Hendy discloses the computer-implemented method of claim 1, and further discloses wherein the information about the requestor (structure of the data model instance associated with an insurance product defines what input data the insurance carrier will need to collect from a customer in order to service that customer's insurance policy, para. 0030) includes one or more of: a geographic location of the requestor, demographic information of the requestor, a discount the requestor is entitled to, or a parameter for the product or service selected by the requestor (exemplary user request contains data associated with a particular insurance policy or submission for a future policy based on a submitted application, para. 0066).
In regards to claim 3, modified Hendy discloses the computer-implemented method of claim 1, and further discloses wherein the data processing model is a rate order calculator (ROC) (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0034).
In regards to claim 4, modified Hendy discloses the computer-implemented method of claim 1, and further discloses wherein the model parameters are received in one or more files (source code files (product definition), fig. 1), the one or more files associating input values in the information regarding the requestor with attributes of the precompiled data processing model (values of the lookup table are contained in, for example, a "liability base rates.csv" file stored alongside the source code, para. 0034).
In regards to claim 5, modified Hendy discloses the computer-implemented method of claim 1, and discloses further comprising precompiling the data processing model (compiler verifies the dependencies of the directed graph, para. 0033).
In regards to claim 6, modified Hendy discloses the computer-implemented method of claim 1, and further discloses wherein compiling the data processing model using the parameters (compiler 12 executes a series of transformations on the source code, the compiler constructs an intermediate graph object representation of the relevant source code that can then be executed by the execution engine 14 via the web-based runtime environment 16, para. 0027) and the dependency (it is noted that when the compiler 12 compiles the source code, the compiler verifies the dependencies of the directed graph, para. 0033) comprises calculating values for attributes of the data processing model such that no uncalculated attribute of the data processing model depends on any other uncalculated attribute of the data processing model (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0035), but fails to disclose the dependency tree.
Pudiyapura, In the related field of compiler architecture, teaches a dependency tree (for each of the functions in the list 3002, the cloud mapper 622 determines all of the possible signal paths 30 3102 from the inputs and output of the selected function to primary inputs (for the inputs) and final outputs (for the output) using any one or combination of the other functions in the list 3002, which together form a dependency tree 3104 for that function, col. 50, lines 28-49). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the method of Hendy with the ability to use a dependency tree as taught by the method of Pudiyapura. The motivation for doing so would have been to determine the restrictions and input/output dependencies to map the functions (Pudiyapura, col. 49, lines 59-67).
In regards to claim 8, modified Hendy discloses the computer-implemented method of claim 1, and disclose further comprising: receiving updated parameters for the data processing model (when business rules in extensions are updated, the updated source code in the extension are compiled and the extension graph object artifacts 22 and/or 23 for one or more iterations of those base insurance products are deployed, para. 0064); asynchronously updating the data processing model using the updated parameters (infrastructure allows insurance product specifications (i.e., product+extensions artifacts) to be updated and will perform a rolling update of any currently running instances, para. 0075); receiving a second request for a second personalized parameter for the product or service; calculating the second personalized parameter for the product or service using the updated data processing model (infrastructure allows insurance product specifications (i.e., product+extensions artifacts) to be updated and will perform a rolling update of any currently running instances, para. 0075); and providing the second personalized parameter for display to the requestor (exemplary meta-model 30 can be used to model all insurance product domains, such as commercial and personal insurance product lines, para. 0029).
In regards to claim 9, modified Hendy discloses the computer-implemented method of claim 1 and further discloses, wherein the data processing model calculates the personalized parameter by a process comprising: parsing the information regarding the requestor into one or more internal types (compiler 12 parses the source code grammar into an abstract syntax tree (AST), populates a symbol table and performs a code generation transformation on the parsed source code, para. 0035); querying a database to identify a single scenario with constraints that match the parsed information regarding the requestor (built-in rate view is querying metadata and the execution model 60 for all nodes which are of rate computation type, requesting their value, which in-turn get the required inputs from the populated execution engine instance environment 70, para. 0067); resolving one or more formulas for the single scenario without performing additional database queries (rate view computation node 64a executes its associated expression which recursively executes computation nodes 64b-64d, which in turn requests input data from input nodes 62a and 62b previously populated in the execution engine instance environment 70, para. 0066); and determining the personalized parameter based on the resolved formulas (computation nodes 64a-64f are executed to provide the rating or pricing result, para. 0066).
In regards to claim 10, modified Hendy discloses the computer-implemented method of claim 1, and further discloses wherein the product or service is insurance and the personalized parameter is a premium for the insurance (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0035).
In regards to claim 11, Hendry discloses a non-transitory computer-readable medium comprising computer program code (execution engine has an execution model derived from the graph object artifact that is loaded into memory and an application program interface that provides an interface to the web-based runtime environment, para. 0007) that, when executed, causes a computing system to perform operations (fig. 9 process of execution engine handling a user request input via a service running on an insurance hosting system, para. 0017, fig. 9) including: receiving a request for a personalized parameter of a product or service (exemplary user request contains data associated with a particular insurance policy or submission for a future policy based on a submitted application, para. 0066), the request including information regarding the requestor (exemplary user request contains data associated with a particular insurance policy or submission for a future policy based on a submitted application, para. 0066); applying the requestor information as input to a data processing model (FIG. 9 is exemplary process of the execution engine handling a user request input via a service running on an insurance hosting system, para. 0017, fig. 9), wherein the data processing model was precompiled (system 10 includes compiler 12 that translates source code defining an insurance product as a directed graph into a collection of nodes representing either a computation or an input, para. 0027) by a process comprising: receiving model parameters (for each insurance product the compiler 12 executes a series of transformations on the source code, and if the source code is syntactically and semantically valid, the compiler constructs an intermediate graph object representation of the relevant source code that can then be executed by the execution engine 14 via the web-based runtime environment 16, para. 0027); building a dependency using the model parameters (each insurance product is defined using the meta-model of FIG. 2, which results in a structure that is a directed graph made up of a set of nodes, points or vertices connected by directed edges, para. 0033), wherein the dependency represents dependencies between model parameters (compiler verifies the dependencies of the directed graph, para. 0033) of the data processing model (reason defining an insurance product as a directed graph is that relationships between meta-model elements, including products 32a, lines of business 32b, entities 32c, limits 32i, deductibles 32i, features 32d, and computations 34a, are preserved which maintains the semantics described in the source code in the compiled graph object artifact, para. 0033); and compiling the data processing model using the parameters (compiler 12 executes a series of transformations on the source code, the compiler constructs an intermediate graph object representation of the relevant source code that can then be executed by the execution engine 14 via the web-based runtime environment 16, para. 0027) and the dependency (it is noted that when the compiler 12 compiles the source code, the compiler verifies the dependencies of the directed graph, para. 0033); receiving the personalized parameter as output from the data processing model; and providing the personalized parameter for display (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0035). Hendy fails to disclose a dependency tree.
Pudiyapura, In the related field of compiler architecture, teaches a dependency tree (for each of the functions in the list 3002, the cloud mapper 622 determines all of the possible signal paths 30 3102 from the inputs and output of the selected function to primary inputs (for the inputs) and final outputs (for the output) using any one or combination of the other functions in the list 3002, which together form a dependency tree 3104 for that function, col. 50, lines 28-49). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the method of Hendy with the ability to use a dependency tree as taught by the method of Pudiyapura. The motivation for doing so would have been to determine the restrictions and input/output dependencies to map the functions (Pudiyapura, col. 49, lines 59-67).
In regards to claim 12, modified Hendy discloses the non-transitory computer-readable medium of claim 11, and further discloses wherein the information about the requestor includes one or more of: a geographic location of the requestor (structure of the data model instance associated with an insurance product defines what input data the insurance carrier will need to collect from a customer in order to service that customer's insurance policy, para. 0030), demographic information of the requestor, a discount the requestor is entitled to, or a parameter for the product or service selected by the requestor (exemplary user request contains data associated with a particular insurance policy or submission for a future policy based on a submitted application, para. 0066).
In regards to claim 13, modified Hendy discloses the non-transitory computer-readable medium of claim 11, and further discloses wherein the data processing model is a rate order calculator (ROC) (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0034).
In regards to claim 14, modified Hendy discloses the non-transitory computer-readable medium of claim 11, and further discloses wherein the model parameters are received in one or more files (source code files (product definition), fig. 1), the one or more files associating input values in the information regarding the requestor with attributes of the precompiled data processing model (values of the lookup table are contained in, for example, a "liability base rates.csv" file stored alongside the source code, para. 0034).
In regards to claim 15, modified Hendy discloses the non-transitory computer-readable medium of claim 11, and further discloses wherein compiling the data processing model using the parameters (compiler 12 executes a series of transformations on the source code, the compiler constructs an intermediate graph object representation of the relevant source code that can then be executed by the execution engine 14 via the web-based runtime environment 16, para. 0027) and the dependency (it is noted that when the compiler 12 compiles the source code, the compiler verifies the dependencies of the directed graph, para. 0033) comprises calculating values for attributes of the data processing model such that no uncalculated attribute of the data processing model depends on any other uncalculated attribute of the data processing model (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0035), but fails to disclose the dependency tree.
Pudiyapura, In the related field of compiler architecture, teaches a dependency tree (for each of the functions in the list 3002, the cloud mapper 622 determines all of the possible signal paths 30 3102 from the inputs and output of the selected function to primary inputs (for the inputs) and final outputs (for the output) using any one or combination of the other functions in the list 3002, which together form a dependency tree 3104 for that function, col. 50, lines 28-49). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the method of Hendy with the ability to use a dependency tree as taught by the method of Pudiyapura. The motivation for doing so would have been to determine the restrictions and input/output dependencies to map the functions (Pudiyapura, col. 49, lines 59-67).
In regards to claim 17, modified Hendy discloses the non-transitory computer-readable medium of claim 11, and further discloses wherein the operations further include: receiving updated parameters for the data processing model receiving updated parameters for the data processing model (when business rules in extensions are updated, the updated source code in the extension are compiled and the extension graph object artifacts 22 and/or 23 for one or more iterations of those base insurance products are deployed, para. 0064); asynchronously updating the data processing model using the updated parameters (infrastructure allows insurance product specifications (i.e., product+extensions artifacts) to be updated and will perform a rolling update of any currently running instances, para. 0075); receiving a second request for a second personalized parameter for the product or service; calculating the second personalized parameter for the product or service using the updated data processing model (infrastructure allows insurance product specifications (i.e., product+extensions artifacts) to be updated and will perform a rolling update of any currently running instances, para. 0075); and providing the second personalized parameter for display to the requestor (exemplary meta-model 30 can be used to model all insurance product domains, such as commercial and personal insurance product lines, para. 0029).
In regards to claim 18, modified Hendy discloses the non-transitory computer-readable medium of claim 11, and further discloses wherein the data processing model calculates the personalized parameter by a process comprising: parsing the information regarding the requestor into one or more internal types (compiler 12 parses the source code grammar into an abstract syntax tree (AST), populates a symbol table and performs a code generation transformation on the parsed source code, para. 0035); querying a database to identify a single scenario with constraints that match the parsed information regarding the requestor (built-in rate view is querying metadata and the execution model 60 for all nodes which are of rate computation type, requesting their value, which in-turn get the required inputs from the populated execution engine instance environment 70, para. 0067); resolving one or more formulas for the single scenario without performing additional database queries (rate view computation node 64a executes its associated expression which recursively executes computation nodes 64b-64d, which in turn requests input data from input nodes 62a and 62b previously populated in the execution engine instance environment 70, para. 0066); and determining the personalized parameter based on the resolved formulas (computation nodes 64a-64f are executed to provide the rating or pricing result, para. 0066).
In regards to claim 19, modified Hendy discloses the non-transitory computer-readable medium of claim 11, and further discloses wherein the product or service is insurance and the personalized parameter is a premium for the insurance (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0035).
In regards to claim 20, Hendy discloses a computer system comprising: one or more processors (fig. 9 process of execution engine handling a user request input via a service running on an insurance hosting system, para. 0017, fig. 9); and one or more non-transitory computer-readable media storing computer program code (execution engine has an execution model derived from the graph object artifact that is loaded into memory and an application program interface that provides an interface to the web-based runtime environment, para. 0007) that, when executed by at least some of the one or more processors, causes the computer system to perform operations (fig. 9 process of execution engine handling a user request input via a service running on an insurance hosting system, para. 0017, fig. 9) including: receiving a request for a personalized parameter of a product or service (exemplary user request contains data associated with a particular insurance policy or submission for a future policy based on a submitted application, para. 0066), the request including information regarding the requestor (exemplary user request contains data associated with a particular insurance policy or submission for a future policy based on a submitted application, para. 0066); applying the requestor information as input to a data processing model (FIG. 9 is exemplary process of the execution engine handling a user request input via a service running on an insurance hosting system, para. 0017, fig. 9), wherein the data processing model was precompiled (system 10 includes compiler 12 that translates source code defining an insurance product as a directed graph into a collection of nodes representing either a computation or an input, para. 0027) by a process comprising: receiving model parameters (for each insurance product the compiler 12 executes a series of transformations on the source code, and if the source code is syntactically and semantically valid, the compiler constructs an intermediate graph object representation of the relevant source code that can then be executed by the execution engine 14 via the web-based runtime environment 16, para. 0027); building a dependency using the model parameters (each insurance product is defined using the meta-model of FIG. 2, which results in a structure that is a directed graph made up of a set of nodes, points or vertices connected by directed edges, para. 0033), wherein the dependency represents dependencies between model parameters (compiler verifies the dependencies of the directed graph, para. 0033) of the data processing model (reason defining an insurance product as a directed graph is that relationships between meta-model elements, including products 32a, lines of business 32b, entities 32c, limits 32i, deductibles 32i, features 32d, and computations 34a, are preserved which maintains the semantics described in the source code in the compiled graph object artifact, para. 0033); and compiling the data processing model using the parameters (compiler 12 executes a series of transformations on the source code, the compiler constructs an intermediate graph object representation of the relevant source code that can then be executed by the execution engine 14 via the web-based runtime environment 16, para. 0027) and the dependency (it is noted that when the compiler 12 compiles the source code, the compiler verifies the dependencies of the directed graph, para. 0033); receiving the personalized parameter as output from the data processing model; and providing the personalized parameter for display (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0035). Hendy fails to disclose a dependency tree.
Pudiyapura, In the related field of compiler architecture, teaches a dependency tree (for each of the functions in the list 3002, the cloud mapper 622 determines all of the possible signal paths 30 3102 from the inputs and output of the selected function to primary inputs (for the inputs) and final outputs (for the output) using any one or combination of the other functions in the list 3002, which together form a dependency tree 3104 for that function, col. 50, lines 28-49). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the method of Hendy with the ability to use a dependency tree as taught by the method of Pudiyapura. The motivation for doing so would have been to determine the restrictions and input/output dependencies to map the functions (Pudiyapura, col. 49, lines 59-67).
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Hendry, in view of Pudiyapora, and further in view of US 10452992 to Lee et al. (hereinafter referred to as Lee).
In regards to claim 7, modified Hendy discloses the computer-implemented method of claim 1, and further discloses wherein the personalized parameter is calculated by the data processing model (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0035), but fails to disclose calculated in no more than one second.
Lee, in the related field of machine learning services, teaches calculated in no more than one second (In some embodiments, goals may be specified in absolute terms (e.g. that the model execution time must be less than X seconds) or in terms of distributions or percentiles (e.g., that 90% of the model execution times must be less than x seconds), col. 0074, lines 1-44). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the method of Hendy with the ability to calculate in less than a second as taught by the method of Lee. The motivation for doing so would have been to prioritize run time goals (Lee, col. 0074, lines 1-44).
In regards to claim 16, modified Hendy discloses the non-transitory computer-readable medium of claim 11, and further discloses wherein the personalized parameter is calculated by the data processing model (calculation of the insurance premium component is defined in the source code as taking the limit and deductible, performing the look-up using the data file referenced in the look-up definition, and returning the result, i.e., the insurance premium amount, para. 0035), but fails to disclose calculated in no more than one second.
Lee, in the related field of machine learning services, teaches calculated in no more than one second (In some embodiments, goals may be specified in absolute terms (e.g. that the model execution time must be less than X seconds) or in terms of distributions or percentiles (e.g., that 90% of the model execution times must be less than x seconds), col. 0074, lines 1-44). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the method of Hendy with the ability to calculate in less than a second as taught by the method of Lee. The motivation for doing so would have been to prioritize run time goals (Lee, col. 0074, lines 1-44).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Amos et al. (US 2013/0332449) teaches a computer-implemented code generation system that generates data processing code from a directed acyclic graph (DAG).
Tuteja et al. (US 12411873) teaches aggregating data ingested from disparate sources for processing using machine learning models.
Thattai et al. (US 10275221) teaches systems and methods for generating data visualization applications.
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/PAUL S SCHWARZENBERG/Primary Examiner, Art Unit 3695 4/24/2026
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