DETAILED ACTION
This Office action is in reply to application no. 18/679,840, filed 31 May 2024. Claims 1-20 are pending and are considered below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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) 1-10 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mifflin et al. (U.S. Publication No. 2023/0222601) in view of Dmitriev et al. (U.S. Publication No. 2021/0383289).
In-line citations are to Mifflin.
With regard to Claim 1:
Mifflin teaches: A computer-implemented method [abstract; “computer-implemented method”] comprising:
deriving, by one or more processors, a model linking one or more environments with each of a plurality of conditions associated with the one or more environments; [Sheet 3, Fig. 3; various conditions and rules are maintained within the computer; per the applicant’s own specification, e.g. ¶ 9, the computer environment itself is within the applicant’s use of “environment”]
receiving, by the one or more processors, a resource associated with a condition from the plurality of conditions requested by a user; [0006; the system receives “event notifications” associated with a “respective event payload”; abstract; this may be related to an insurance claim]
receiving, by the one or more processors, environment context data of the user… [0177; user input reads on environmental context data of the user; claim 8; a user modification of a claim further reads on this]
determining, by the one or more processors, a transferability of the resource requested; [0073; that a notification of an electronic funds transfer is sent reads on having determined its transferability] and
based on the determining, changing, by the one or more processors, a status of the resource requested, wherein the changing of the status initiates a transfer evaluation of the resource requested. [0077; a claim payment is made]
Mifflin does not explicitly teach applying, by the one or more processors, the model to the environment context data of the user and the condition to identify a link of at least one environment, determined from the environment context data of the user, to the condition, or performing a step based on the identified link, though the association cited above reads on the link itself, but it is known in the art. Dmitriev teaches an optimized workflow system [title] in which a “machine learning model” uses “context attributes” to determine a “transferability score”. [0050] Feedback may be used to update the model. [0057] Dmitriev and Mifflin are analogous art as each is directed to electronic means for linking attribute data to contextual data.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Dmitriev with that of Mifflin in order to optimize a quantity, as taught by Dmitriev; further, it is simply a substitution of one known part for another with predictable results, simply determining a quantity in the manner of Dmitriev in place of that of Mifflin; the substitution produces no new and unexpected result.
With regard to Claim 2:
The computer-implemented method of claim 1, wherein the model is a mapping table, [Sheet 3, Fig. 3 as cited above in regard to claim 1] and deriving the model comprises:
generating the mapping table based on known data for a plurality of resources requested by a plurality of users, [abstract; the system receives an “evaluation request from an application”; 0016; a plurality of requests may be received; 0082; the request may come from a user; 0028; the user may be one of a plurality of “customers/policyholders”] the known data for each of the plurality of resources including: one or more of the plurality of conditions associated with the respective resource, environment context data of the respective user, and a transfer outcome of the respective resource. [See above in regard to claim 1; the various data components read on these items]
That the data includes these three items is considered but given no patentable weight. First, it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretations of data but which impart neither structure nor functionality to the claimed method. Second, as the data only “includes” these, it can include other information, and any further processing can be based entirely on the other information. The reference is provided for the purpose of compact prosecution.
With regard to Claim 3:
The computer-implemented method of claim 2, further comprising:
receiving, by the one or more processors, a transfer outcome of the resource as feedback from the transfer evaluation; and
updating, by the one or more processors, the mapping table based on the feedback. [0073; 0077 as cited above in regard to claim 1; a transfer is made and notification provided; 0028; database records are updated based on transactions]
With regard to Claim 4:
The computer-implemented method of claim 1, wherein the model is a machine learning model trained to learn the linking of the one or more environments with each of the plurality of conditions associated with the one or more environments based on a plurality of training data sets associated with a plurality of users, each training data set of the plurality of training data sets including: a known resource associated with one or more of the plurality of conditions requested by a respective user, a known environment context data of the respective user, and a known transfer outcome of the known resource. [Dmitriev, as cited above in regard to claim 1; machine learning is used for such determinations]
As the step of training the model is not positively recited as being within the scope of the claim, the manner in which the training is conducted refers to a step outside the scope of the claimed process and so is considered but given no patentable weight.
With regard to Claim 5:
The computer-implemented method of claim 4, further comprising: receiving, by the one or more processors, a transfer outcome of the resource as feedback from the transfer evaluation, wherein the machine learning model is updated based on the feedback. [Dmitriev, 0057 as cited above in regard to claim 1]
With regard to Claim 6:
The computer-implemented method of claim 1, wherein the condition is indicated by a code included in a request for the resource, and the method further comprising:
querying, by the one or more processors and using the code, the model or a separate data store configured to store a plurality of codes for each of the plurality of conditions associated with the one or more environments, to determine the resource is associated with the condition. [0035; a code may be used to represent a type of event; abstract as cited above; the request from an application reads on a query; the “claims database” reads on the separate data store; any database can store codes]
With regard to Claim 7:
The computer-implemented method of claim 6, further comprising:
receiving, by the one or more processors, transfer outcome data for transfer evaluated resources as feedback; and
updating, by the one or more processors, the model or the separate data store based on the feedback to eliminate one or more codes from the plurality of codes. [0077; 0028 as cited above in regard to claims 1 and 3]
With regard to Claim 8:
The computer-implemented method of claim 1, wherein receiving the environment context data of the user comprises:
generating an API call to request the environment context data of the user to one or more external resources; and
receiving the environment context data of the user from the one or more external resources responsive to the API call. [0006; API may be used for the interprocess communications]
With regard to Claim 9:
The computer-implemented method of claim 1, wherein determining the at least one environment from the environment context data of the user comprises:
identifying one or more entities from the environment context data of the user; [0017; a performer for correspondence is identified; 0019; this may be in the context of an insurance claim] and
determining an association between the one or more entities and the at least one environment. [0024; the correspondence may be sent to a policy holder]
With regard to Claim 10:
The computer-implemented method of claim 1, further comprising:
generating a summary [0028; reports may be generated when claim events occur] including the condition, the at least one environment, and the identified link.
The content of information which is, at most, merely displayed or transmitted, such as “the condition, the at least one environment, and the identified link”, consist entirely of nonfunctional printed matter which bear no functional relation to the substrate and are therefore considered but given no patentable weight.
With regard to Claim 12:
Mifflin teaches: A system comprising:
one or more processors; [0006; “one or more processors”] and
at least one memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations [0008; a “non-transitory computer readable medium storing instructions” for the processor(s) to execute] including:
deriving a model linking one or more environments with each of a plurality of conditions associated with the one or more environments; [Sheet 3, Fig. 3; various conditions and rules are maintained within the computer; per the applicant’s own specification, e.g. ¶ 9, the computer environment itself is within the applicant’s use of “environment”]
receiving a resource associated with a condition from the plurality of conditions requested by a user; [0006; the system receives “event notifications” associated with a “respective event payload”; abstract; this may be related to an insurance claim]
receiving environment context data of the user… [0177; user input reads on environmental context data of the user; claim 8; a user modification of a claim further reads on this]
determining a transferability of the resource requested; [0073; that a notification of an electronic funds transfer is sent reads on having determined its transferability] and
based on the determining, changing a status of the resource requested, wherein the changing of the status initiates a transfer evaluation of the resource requested. [0077; a claim payment is made]
Mifflin does not explicitly teach applying the model to the environment context data of the user and the condition to identify a link of at least one environment, determined from the environment context data of the user, to the condition, or performing a step based on the identified link, though the association cited above reads on the link itself, but it is known in the art. Dmitriev teaches an optimized workflow system [title] in which a “machine learning model” uses “context attributes” to determine a “transferability score”. [0050] Feedback may be used to update the model. [0057] Dmitriev and Mifflin are analogous art as each is directed to electronic means for linking attribute data to contextual data.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Dmitriev with that of Mifflin in order to optimize a quantity, as taught by Dmitriev; further, it is simply a substitution of one known part for another with predictable results, simply determining a quantity in the manner of Dmitriev in place of that of Mifflin; the substitution produces no new and unexpected result.
With regard to Claim 13:
The system of claim 12, wherein the model is a mapping table, [Sheet 3, Fig. 3 as cited above in regard to claim 12] and deriving the model comprises:
generating the mapping table based on known data for a plurality of resources requested by a plurality of users, [abstract; the system receives an “evaluation request from an application”; 0016; a plurality of requests may be received; 0082; the request may come from a user; 0028; the user may be one of a plurality of “customers/policyholders”] the known data for each of the plurality of resources including: one or more of the plurality of conditions associated with the respective resource, environment context data of the respective user, and a transfer outcome of the respective resource. [See above in regard to claim 12; the various data components read on these items]
That the data includes these three items is considered but given no patentable weight. First, it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretations of data but which impart neither structure nor functionality to the claimed system. Second, as the data only “includes” these, it can include other information, and any further processing can be based entirely on the other information. The reference is provided for the purpose of compact prosecution.
With regard to Claim 14:
The system of claim 13, further comprising:
receiving a transfer outcome of the resource as feedback from the transfer evaluation; and
updating the mapping table based on the feedback. [0073; 0077 as cited above in regard to claim 12; a transfer is made and notification provided; 0028; database records are updated based on transactions]
With regard to Claim 15:
The system of claim 12, wherein the model is a machine learning model trained to learn the linking of the one or more environments with each of the plurality of conditions associated with the one or more environments based on a plurality of training data sets associated with a plurality of users, each training data set of the plurality of training data sets including: a known resource associated with one or more of the plurality of conditions requested by a respective user, a known environment context data of the respective user, and a known transfer outcome of the known resource. [Dmitriev, as cited above in regard to claim 12; machine learning is used for such determinations]
As the step of training the model is not positively recited as being within the scope of the claim, the manner in which the training is conducted refers to a step outside the scope of the claimed process and so is considered but given no patentable weight.
With regard to Claim 16:
The system of claim 15, further comprising:
receiving a transfer outcome of the resource as feedback from the transfer evaluation, wherein the machine learning model is updated based on the feedback. [Dmitriev, 0057 as cited above in regard to claim 12]
With regard to Claim 17:
The system of claim 12, wherein the condition is indicated by a code included in a request for the resource, and the operations further including:
querying, using the code, the model or a separate data store configured to store a plurality of codes for each of the plurality of conditions associated with the one or more environments, to determine the resource is associated with the condition. [0035; a code may be used to represent a type of event; abstract as cited above; the request from an application reads on a query; the “claims database” reads on the separate data store; any database can store codes]
With regard to Claim 18:
The system of claim 12, wherein receiving the environment context data of the user comprises:
generating an API call to request the environment context data of the user to one or more external resources; and
receiving the environment context data of the user from the one or more external resources responsive to the API call. [0006; API may be used for the interprocess communications]
With regard to Claim 19:
The system of claim 12, wherein determining the at least one environment from the environment context data of the user comprises:
identifying one or more entities from the environment context data of the user; [0017; a performer for correspondence is identified; 0019; this may be in the context of an insurance claim] and
determining an association between the one or more entities and the at least one environment. [0024; the correspondence may be sent to a policy holder]
With regard to Claim 20:
Mifflin teaches: A non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations [0006; “one or more processors”; 0008; a “non-transitory computer readable medium storing instructions” for the processor(s) to execute] comprising:
deriving a model linking one or more environments with each of a plurality of conditions associated with the one or more environments; [Sheet 3, Fig. 3; various conditions and rules are maintained within the computer; per the applicant’s own specification, e.g. ¶ 9, the computer environment itself is within the applicant’s use of “environment”]
receiving a resource associated with a condition from the plurality of conditions requested by a user; [0006; the system receives “event notifications” associated with a “respective event payload”; abstract; this may be related to an insurance claim]
receiving environment context data of the user… [0177; user input reads on environmental context data of the user; claim 8; a user modification of a claim further reads on this]
determining a transferability of the resource requested; [0073; that a notification of an electronic funds transfer is sent reads on having determined its transferability] and
based on the determining, changing a status of the resource requested, wherein the changing of the status initiates a transfer evaluation of the resource requested. [0077; a claim payment is made]
Mifflin does not explicitly teach applying the model to the environment context data of the user and the condition to identify a link of at least one environment, determined from the environment context data of the user, to the condition, or performing a step based on the identified link, though the association cited above reads on the link itself, but it is known in the art. Dmitriev teaches an optimized workflow system [title] in which a “machine learning model” uses “context attributes” to determine a “transferability score”. [0050] Feedback may be used to update the model. [0057] Dmitriev and Mifflin are analogous art as each is directed to electronic means for linking attribute data to contextual data.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Dmitriev with that of Mifflin in order to optimize a quantity, as taught by Dmitriev; further, it is simply a substitution of one known part for another with predictable results, simply determining a quantity in the manner of Dmitriev in place of that of Mifflin; the substitution produces no new and unexpected result.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Mifflin et al. in view of Dmitriev et al. further in view of Syed et al. (U.S. Publication No. 2023/0025371).
With regard to Claim 11:
The computer-implemented method of claim 10, wherein the initiation of the transfer evaluation comprises:
automatically opening and assigning a case associated with the resource requested, wherein the summary is stored in association with the case.
Mifflin and Dmitriev teach the method of claim 10 but do not explicitly teach this particular step in the claims processing work-flow, but it is known in the art. Syed teaches a system for using AI [title] in which an “analyst is assigned a plurality of projects”, [0014] and the interface for managing a case is generated by an AI interface tool. [0048] The information may be related to insurance [0060] and may include “claim and/or loss data”. [0021] A summary is generated for each project. [0015] Syed and Mifflin are analogous art as each is directed to electronic means for managing insurance claim data.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Syed with that of Mifflin and Dmitriev in order to remedy the deficiencies of previous AI models, as taught by Syed; [0015] further, it is simply a substitution of one known part for another with predictable results, simply creating records in the manner of Syed in place of, or in addition to, that of Mifflin; the substitution produces no new and unexpected result.
Conclusion
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/SCOTT C ANDERSON/Primary Examiner, Art Unit 3694
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