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 .
Claims 1-20 are presented for examination.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character not mentioned in the description: 225. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because “Presented herein” is used and should be avoided. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because of the following informalities:
Paragraph [0035] line 1 recites “different clusters 119,’ in which “119” appears to be a typographical error for “110,” the number referring to the sequence of clusters in the drawings (see, for example, the same paragraph [0035] line 5 reciting “two clusters 110, three clusters 110,” and paragraph [0036] line 2 and paragraph [0037] lines 4 and 5)
Appropriate correction is required.
Claim Objections
Claim 5 and 15 are objected to because of the following informalities:
Claims 5 and 15 recite ‘the plurality application instances’; however, it should recite - - the plurality of application instances - -.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 4, in lines 6-8, it is not clearly understood what is meant by “assigning the first data structure to the first application instance associated with at least one of the first location or second location”. It is uncertain what is the relationship of the second location to the first application instance, the second location to the environmental event, or the second location to the first policy. For the purpose of the examiner, the examiner will interpret the limitation as “second location” associated environmental event as the first location, where the first data structure is assigned to the first application instance based on the criteria of an indicated first data structure of the first policy and an environmental event associated with the second location.
Regarding claim 14, in lines 5 and 6, it is not clearly understood what is meant by “assign the first data structure to the first application instance associated with at least one of the first location or second location”. It is uncertain what is the relationship of the second location to the first application instance, the second location to the environmental event, or the second location to the first policy. For the purpose of the examiner, the examiner will interpret the limitation as “second location” associated environmental event as the first location, where the first data structure is assigned to the first application instance based on the criteria of an indicated first data structure of the first policy and an environmental event associated with the second location.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1
Step 1: The claim recites “A method of assigning policies across application instances using machine learning (ML) models, comprising”; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
identifying… a first data structure of a first policy: These limitations recite a mentally performable process to identify a first data structure of a first policy.
applying… a ML model to the first data structure and the first plurality of attributes: These limitations recite a mentally performable process of using judgement to apply the selection of a ML model to the first data structure and the first plurality of attributes.
assigning… from applying the ML model, the first data structure of the first policy to a first application instance from the plurality of application instances of the policy administration system: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to assign… from applying the ML model, the first data structure of the first policy to a first application instance from the plurality of application instances of the policy administration system.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
by the one or more processors of a policy administration system… by the one or more processors: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
obtaining… a first plurality of attributes associated with the first policy: These additional elements are recited at a high level of generality and merely recite insignificant extra-solution activity of obtaining… a first plurality of attributes associated with the first policy. See MPEP 2106.05(g).
wherein the ML model is trained using a plurality of instance assignments, each of the plurality of instance assignments identifying (i) a second data structure of a second policy, (ii) a second plurality of attributes, and (iii) a respective application instance selected from a plurality of application instances based on the second data structure and the second plurality of attributes: These additional elements generally link the performing of abstract idea to a field of use and/or technological environment wherein the ML model is trained using a plurality of instance assignments, each of the plurality of instance assignments identifying (i) a second data structure of a second policy, (ii) a second plurality of attributes, and (iii) a respective application instance selected from a plurality of application instances based on the second data structure and the second plurality of attributes. See MPEP 2106.05(h).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of obtaining… a first plurality of attributes associated with the first policy which is well-understood, routine and conventional activity similar to presenting offers and gathering statistics. See MPEP 2106.05(d)(II). Further additional elements include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Further additional elements include invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 2
Step 1: a process, as in claim 1.
determining… a performance metric of the first application instance based on a volume of data structures assigned to the first application instance: These limitations recite a mentally performable process of observation to determine… a performance metric of the first application instance based on a volume of data structures assigned to the first application instance.
allocating… based on the performance metric, hardware resources to the first application instance to process the first data structure: These limitations recite a mentally performable process with the aid of pen and paper of using judgement of evaluation to allocate… based on the performance metric, hardware resources to the first application instance to process the first data structure.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
by the one or more processors: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of invoking a computer or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 3
Step 1: a process, as in claim 1.
Step 2A Prong 1: the claim recites, inter alia:
identifying… for each of the plurality of application instances, a first transaction log of activity over a first time period: These limitations recite a mentally performable process of observation to identify… for each of the plurality of application instances, a first transaction log of activity over a first time period.
applying the first transaction log for each of the plurality of application instances: These limitations recite a mentally performable process of using judgement to apply the selection of the first transaction log for each of the plurality of application instances.
wherein assigning the first data structure further comprises selecting the first application instance from the plurality of application instances based on a performance metric determined for the first application instance: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to assign the first data structure further comprising selecting the first application instance from the plurality of application instances based on a performance metric determined for the first application instance.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein applying the ML model further comprises… by the one or more processors: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
wherein each of the plurality of instance assignments identifies, for each corresponding application instance of the plurality of application instances, (i) a respective second transaction log over a second time period and (ii) a respective performance metric identifying a volume of activity subsequent to the second time period: These additional elements generally link the performing abstract idea to a field of use and/or technological environment wherein each of the plurality of instance assignments identifies, for each corresponding application instance of the plurality of application instances, (i) a respective second transaction log over a second time period and (ii) a respective performance metric identifying a volume of activity subsequent to the second time period. See MPEP 2106.05(h).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of invoking a computer or other machinery to apply the underlying judicial exception. Further additional elements include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 4
Step 1: a process, as in claim 1.
Step 2A Prong 1: the claim recites, inter alia:
assigning the first data structure to the first application instance associated with at least one of the first location or a second location: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to assign the first data structure to the first application instance associated with at least one of the first location or a second location.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein assigning the first data structure further comprises: These limitations generally link the abstract idea to a technological environment wherein assigning the first data structure further comprises. See MPEP 2106.05(h).
by the one or more processors from a data source: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
receiving… an indication of an environmental event associated with a first location indicated in the first data structure of the first policy: These additional elements are recited at a high level of generality and merely recite insignificant extra-solution activity of receiving… an indication of an environmental event associated with a first location indicated in the first data structure of the first policy. See MPEP 2106.05(g).
wherein applying the ML model further comprises applying the ML model to the indication of the environmental event: These additional elements are recited at a high level of generality and merely recite applying the ML model to the indication of the environmental event without details of how it is to be accomplished, e.g. no description of inventive steps of how applying the ML model to the indication of the environmental event in the controller works other than only that it happens, and is equivalent of “apply it” 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of receiving… an indication of an environmental event associated with a first location indicated in the first data structure of the first policy which is well-understood, routine and conventional activity similar to presenting offers and gathering statistics. See MPEP 2106.05(d)(II). Further additional elements include a recitation of the words “apply it” (or an equivalent). Further additional elements include a recitation of invoking computers or other machinery to apply the underlying judicial exception. Further additional elements include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 5
Step 1: a process, as in claim 1.
Step 2A Prong 1: the claim recites, inter alia:
identifying the first data structure assigned to a second application instance of the plurality application instances, responsive to an indication to change assignment: These limitations recite a mentally performable process to identify the first data structure assigned to a second application instance of the plurality application instances, responsive to an indication to change assignment.
reassigning the first data structure of the first policy from the second application instance to the first application instance.
These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to reassign the first data structure of the first policy from the second application instance to the first application instance.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein identifying the first data structure further comprises: These limitations generally link the abstract idea to a technological environment wherein identifying the first data structure further comprises. See MPEP 2106.05(h).
wherein assigning the first data structure further comprises: These limitations generally link the abstract idea to a technological environment wherein assigning the first data structure further comprises. See MPEP 2106.05(h).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 6
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
retraining, by the one or more processors, the ML model using a second plurality of instance assignments, wherein the second plurality of instance assignments identifies at least one reassignment of a third data structure of a third policy from a second application instance to a third application instance of the plurality of application instances: These additional elements recite the idea or outcome of retraining the ML model using a second plurality of instance assignment that identifies at least one reassignment of a third data structure of a third policy from a second application instance to a third application instance of the plurality of application instances without any inventive concepts or details of how this is achieved, e.g. no specific ML model and retraining algorithm. Thus, these additional elements are equivalent of “apply it” limitations to be added to the performance of abstract ideas and cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of the words “apply it” (or an equivalent). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 7
Step 1: a process, as in claim 1.
Step 2A Prong 1: the claim recites, inter alia:
identifying the first plurality of attributes including an agent assigned to handle the first policy defined by the first data structure: These limitations recite a mentally performable process to identify the first plurality of attributes including an agent assigned to handle the first policy defined by the first data structure.
wherein assigning the first data structure further comprises: These limitations generally link the abstract idea to a technological environment wherein assigning the first data structure further comprises.
assigning the first data structure of the first policy to the first application instance associated with the agent: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to assign the first data structure of the first policy to the first application instance associated with the agent.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein identifying the first plurality of attributes further comprises: These limitations generally link the abstract idea to a technological environment wherein identifying the first plurality of attributes further comprises. See MPEP 2106.05(h).
wherein assigning the first data structure further comprises: These limitations generally link the abstract idea to a technological environment wherein assigning the first data structure further comprises. See MPEP 2106.05(h).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 8
Step 1: a process, as in claim 1.
Step 2A Prong 1: the claim recites, inter alia:
assigning… the computing device to the first application instance based on the request: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to assign… the computing device to the first application instance based on the request.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
by the one or more processors: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
receiving… from a computing device associated with an agent, a request to access at least one of the plurality of application instances: These additional elements are recited at a high level of generality and merely recite insignificant extra-solution activity of receiving… from a computing device associated with an agent, a request to access at least one of the plurality of application instances. See MPEP 2106.05(g).
providing, by the one or more processors via an interface of the first application instance, information associated with the first policy defined by the first data structure: These additional elements are recited at a high level of generality and merely recite insignificant extra solution activity of generic output by defined input. See MPEP 2106.05(g).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of receiving… from a computing device associated with an agent, a request to access at least one of the plurality of application instances and providing, by the one or more processors via an interface of the first application instance, information associated with the first policy defined by the first data structure which are well-understood, routine and conventional activity similar to presenting offers and gathering statistics. See MPEP 2106.05(d)(II). Further additional elements include a recitation of invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 9
Step 1: a process, as in claim 1.
Step 2A Prong 1: the claim recites, inter alia:
process data from a holder associated with the first policy, in accordance with the first policy: These limitations recite a mentally performable process for evaluation of data from a holder associated with the first policy, in accordance with the observed first policy.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the first application instance is configured to: These additional elements are recited at a high level of generality and merely recite computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of invoking a computer or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 10
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein each of the plurality of application instances is supported by at least one of a respective on-premises system or a respective cloud service: These elements amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claims 11-17
Step 1: These claims are directed to “A system for assigning policies across application instances using machine learning (ML) models, comprising: one or more processors coupled with memory, configured to”; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: Claims 11-17 recite substantially the same abstract ideas as in claims 1-7, respectively.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application.
Step 2B: These claims do not contain significantly more than the judicial exception. The analysis at this step is substantially the same as that of claims 1-7, respectively.
Claims 18-20
Step 1: These claims are directed to “A non-transitory computer readable medium storing instructions, which when executed by at least one processor, cause the at least one processor to”; therefore, these claims are directed to the statutory category of an article of manufacture.
Step 2A Prong 1: Claims 18-20 recite substantially the same abstract ideas as in claims 1-3. respectively.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application.
Step 2B: These claims do not contain significantly more than the judicial exception. The analysis at this step is substantially the same as that of claims 1-3, respectively.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, 7, 10, 11, 12, 14, 17, 18, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mulligan et al. (US 20220188949 A1, filed 12/16/2020), hereinafter Mulligan.
Regarding claim 1, Mulligan teaches: A method of assigning policies across application instances using machine learning (ML) models, comprising: (Mulligan; [0041], Briefly described, relevant contextual data can be defined by an entity, including an ML model, that implements policy comparison system using historical usage pattern data like historical policy holder data; [0079], Briefly described, a medical insurance domain which may contain different medical insurance policies based on one or more application instances like age, location of residence, pre-existing condition, and/or another feature of a certain entity like an ML model).
identifying, by one or more processors of a policy administration system, a first data
structure of a first policy: (Mulligan; [0020], Briefly described, one or more processors; [0027], Briefly described, identifying first data in the first policy data; [0032], Briefly described, first and/or second data can include, but is not limited to, structured data; [0041]. Briefly described, relevant contextual data can be defined by an entity that implements policy comparison system using historical usage pattern data like historical policy holder data):
obtaining, by the one or more processors, a first plurality of attributes associated with
the first policy: (Mulligan; [0020], Briefly described, one or more processors; [0028], Briefly described, first data policy data of a first policy associated with a feature like a characteristic or an attribute, and this first policy can include an insurance policy):
applying, by the one or more processors, a ML model to the first data structure and the
first plurality of attributes: (Mulligan; [0020], Briefly described, one or more processors; [0014], Briefly described, an entity that can comprise a machine learning (ML) model; [0032], Briefly described, first and/or second data can include, but is not limited to, structured data; [0028], Briefly described, first policy data of a first policy against those of a second policy data of a second policy associated with features like characteristics or attributes, and the first and/or second policy can include an insurance policy):
wherein the ML model is trained using a plurality of instance assignments: (Mulligan;
[0014], Briefly described, an entity that can comprise a machine learning (ML) model; [0029], Briefly described, a comparison component that can employ one or more machine learning (ML) models, and the first policy data of a first policy is compared against the second policy data of a second policy based on an instance assignment through eligibility criteria):
each of the plurality of instance assignments identifying (i) a second data structure of a
second policy: (Mulligan; [0029], Briefly described, the first policy data of a first policy is compared against the second policy data of a second policy based on an instance assignment through eligibility criteria):
(ii) a second plurality of attributes: (Mulligan; [0028], Briefly described a second policy
data of a second policy associated with features like characteristics or attributes, and the first and/or second policy can include an insurance policy):
(iii) a respective application instance selected from a plurality of application instances
based on the second data structure and the second plurality of attributes: [0028], Briefly described, first policy data of a first policy against those of a second policy data of a second policy associated with application instances like characteristics or attributes, and the first and/or second policy can include an insurance policy; [0029], Briefly described, a comparison component that can employ one or more machine learning (ML) models, and the first policy data of a first policy is compared against the second policy data of a second policy based on an instance assignment through eligibility criteria; [0032], Briefly described, first and/or second data can include, but is not limited to, structured data; [0041], Briefly described, relevant contextual data can be defined by an entity that implements policy comparison system using historical usage pattern data like historical policy holder data):
assigning, by the one or more processors, from applying the ML model, the first data
structures of the first policy to a first application instance from the plurality of application instances of the policy administration system: (Mulligan; [0020], Briefly described, one or more processors; [0032], Briefly described, employed or assigned ML models, and first and/or second data can include, but is not limited to, structured data; [0041], Briefly described, relevant contextual data can be defined by an entity that implements policy comparison system using historical usage pattern data like historical policy holder data; [0079], Briefly described, a medical insurance domain which may contain different medical insurance policies based on one or more features like age, location of residence, pre-existing condition, and/or another feature of a certain entity like an ML model).
Regarding claim 2, Mulligan teaches the method of claim 1 wherein determining, by the one or more processors, a performance metric of the first application instance based on a volume of data structure assigned to the first application instance: (Mulligan; [0020], Briefly described, one or more processors; [0026], Briefly described, a policy comparison system using a processor to facilitate a performance of operations; [0027], Briefly described, a performance metric, a similarity score, calculated or determined based on a feature or attribute of an application instance of at least one entity or ML model; [0028], Briefly described, attributes of an entity being compared based on the operation of the similarity score, and the volume of data of these attributes determining the outcome of the similarity score):
allocating, by the one or more processors, based on the performance metric, hardware
resources to the first application instance to process the first data structure: (Mulligan; [0020], Briefly described, one or more processors; [0085], Briefly described, hardware resources like a classical processor, quantum hardware, and/or other classical computing devices or quantum software, being employed by the policy comparison system to execute the ML model; [0107], Briefly described, processors allocating resources of the computer using an operating system; [0027] Briefly described, the policy comparison system utilizing an allocated processor to extract the first data from the first policy data and the second data from the second policy data, where both the first and second data correspond to a feature or attribute of a first application instance of the entity or the ML model, and this ML model providing the feature or attribute of a first application instance based on a performance metric of a similarity score).
Regarding claim 4, Mulligan teaches the method of claim 1 wherein receiving, by the one or more processors from a data source, an indication of an environmental event associated with a first location indicated in the first data structure of the first policy: (Mulligan; [0084], Briefly described, a policy comparison system in which a first data structure with a first policy is located is associated with a cloud computing environment where a first location can be detected in an environmental event in an area):
wherein applying the ML model further comprises applying the ML model to the
indication of the environmental event: (Mulligan; [0085], Briefly described, a processor to run the policy comparison system being executed by an ML model, and a cloud computing environment consisting of the processor in order to apply the ML model to the first data structure with the first policy in the located associated cloud computing environment where a location can be detected in an environmental event in an area):
wherein assigning the first data structure further comprises assigning the first data
structure to the first application instance associated with at least one of the first location or a second location: (Mulligan; [0032], Briefly described, defined ML models, and first and/or second data can include, but is not limited to, structured data; [0041], Briefly described, relevant contextual data can be defined by an entity that implements policy comparison system using historical usage pattern data like historical policy holder data; [0079], Briefly described, Briefly described, a medical insurance domain which may contain different medical insurance policies based on one or more features like age, location of residence, pre-existing condition, and/or another feature; [0091], assigned first data structure to the first application instance has resources pooled to a location like a country, state, or datacenter based on the demand of the location the user is in).
Regarding claim 7, Mulligan teaches the method of claim 1 wherein identifying the first plurality of attributes further comprises identifying the first plurality of attributes including an agent assigned to handle the first policy defined by the first data structure: (Mulligan; [0014], Briefly described, an entity being comprised of an agent that implements a policy comparison system that can handle the first policy defined by the first data structure; [0046], Briefly described, an identification performed by a similarity component, where the first similarity score identifies and is calculated based on first features or first attributes of an entity (or an agent) that is employed or assigned as a semantic similarity model to handle the first policy defined by the first data structure):
wherein assigning the first data structure further comprises assigning the first data
structure of the first policy to the first application instance associated with the agent: (Mulligan; [0014]. Briefly described, an entity being comprised of an agent that implements a policy comparison system that can handle the first policy defined by the first data structure; [0045], Briefly described, domain knowledge being defined as cost of services covered by policies, census data, taxonomies, etc. corresponding with the first policy data and the first policy, and an employed or assigned domain knowledge of a first data consisting of the first policy during extraction; [0042], Briefly described, the extraction component consisting of a first data corresponding to features or attributes of an entity (or an agent); [0079], Briefly described, a medical insurance domain which may contain different medical insurance policies based on one or more features like age, location of residence, pre-existing condition, and/or another first features or first attributes of the first application instances of a certain entity like an agent).
Regarding claim 10, Mulligan teaches the method of claim 1 wherein each of the plurality of application instances is supported by at least one of a respective on-premises system or a respective cloud system: (Mulligan; [0076], Briefly described, the policy comparison system is associated with cloud computing technologies).
Regarding claims 11, 12, 14, and 17, they are apparatus claims that correspond to method claims 1, 2, 4, and 7. Therefore, they are rejected for the same reason as claims 1, 2, 4, and 7 above.
Regarding claims 18 and 19, they are computer-readable storage medium claims that correspond to claims 1 and 2. Therefore, they are rejected for the same reason as claims 1 and 2 above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3, 5, 8, 9, 13, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mulligan et al. (US 20220188949 A1, filed 12/16/2020), hereinafter Mulligan, as applied in claims 1, 11, and 20 above, and in view of Paul et al. (US 11748151 B1, filed 07/31/2020, hereinafter Paul.
Regarding claim 3, Mulligan teaches the invention of claim 1 wherein assigning the first data structure further comprises selecting the first application instance from the plurality of application instances based on a performance metric determined for the first application instance: (Mulligan; [0026], Briefly described, a policy comparison system using a processor to facilitate a performance of operations like the extraction component and the similarity component; [0027], Briefly described, the selecting or extracting of a first data containing a first feature or attribute of an application instance based on a performance metric, a similarity score, being used to compare the similarity between the first and second data on a first feature or attribute of a first application instance from the first data; [0028], Briefly described, attributes of an entity being compared based on the operation of the similarity score, and the volume of data of these attributes determining the outcome of the similarity score; [0079], Briefly described, a medical insurance domain which may contain different medical insurance policies based on one or more features like age, location of residence, pre-existing condition, and/or another features, serving a role as application instances, of a certain entity like an ML model):
However, Mulligan fails to expressly teach – identifying, by the one or more processors, for each of the plurality of application instances, a first transaction log of activity over a first time period; and – wherein applying the ML model further comprises applying the first transaction log for each of the plurality of application instances, wherein each of the plurality of instance assignments identifies, for each corresponding application instance of plurality of application instances, (i) a respective second transaction log over a second time period and (ii) a respective performance metric identifying a volume of activity subsequent to the second time period.
In the same field of endeavor, Paul teaches:
identifying, by the one or more processors, for each of the plurality of application
instances, a first transaction log of activity over a first time period: (Paul; BRIEF SUMMARY OF THE DISCLOSURE (10), Briefly described, an activity log; [Abstract] the activity of a bot controller application instance for which the activity log is used for each of the plurality of bot controller application instances to be displayed through a graphical user interface; (10), Briefly described, displaying the activity log through a graphical user interface; (114), Briefly described, one or more processors; (68), Briefly described, an activity log for a bot controller application instance that includes a time stamp for indicating or identifying when activity occurred during a first time period;
wherein applying the ML model further comprises applying the first transaction log for
each of the plurality of application instances, wherein each of the plurality of instance assignments identifies, for each corresponding application instance of plurality of application instances, (i) a respective second transaction log over a second time period and (ii) a respective performance metric identifying a volume of activity subsequent to the second time period: (Paul; [Abstract] the activity of a bot controller application instance for which the activity log is applied for each of the plurality of bot controller application instances to be displayed through a graphical user interface; (10), Briefly described, displaying the activity log through a graphical user interface; (63), Briefly described, a second bot controller application instance for which an activity log is used; (68), Briefly described, an activity log for a bot controller application instance that includes a time stamp for indicating when activity occurred during a second time period; (48), Briefly described, a performance metric where the bot controller application instances are monitored for task performance which can be based on a second time period of the second bot controller application instance, (25), Briefly described, artificial intelligence or machine learning capabilities of applying and automating the tasks of the bot controller application instances).
It would have been obvious to one of ordinary skill in the art before the filing data of the invention to have incorporated – identifying, by the one or more processors, for each of the plurality of application instances, a first transaction log of activity over a first time period; and – wherein applying the ML model further comprises applying the first transaction log for each of the plurality of application instances, wherein each of the plurality of instance assignments identifies, for each corresponding application instance of plurality of application instances, (i) a respective second transaction log over a second time period and (ii) a respective performance metric identifying a volume of activity subsequent to the second time period as suggested by Mulligan and Paul. Doing so would be desirable because the machine learning (ML) models assigning polices across application instances could utilize the activity logs to comprise of information about the assigning of data structures based on the application instances of the plurality of application instances of the policy administration system. The first and second transaction log can indicate the activity between the first and second data and policies assigned based on a first application instance from the combination of Mulligan and Paul. This provides a metric of monitoring the performance of the memory usage and response time.
Regarding claim 5, Mulligan teaches the invention of claim 1 wherein identifying the first data structure further comprises identifying the first data structure assigned to a second application instance of the plurality application instances, responsive to an indication to change assignment: (Mulligan; [0032], Briefly described, defined ML models, and first and/or second data can include, but is not limited to, structured data; [0027], Briefly described, identifying a first data; [0041], Briefly described, relevant contextual data can be defined by an entity that implements policy comparison system using historical usage pattern data like historical policy holder data; [0042], Briefly described, a first data and a second data corresponding to a feature, indicating change in an assignment being made when compared against each other in the second application instance of the plurality of application instances):
However, Mulligan fails to expressly teach – wherein assigning the first data structure further comprises reassigning the first data structure of the first policy from the second application instance to the first application instance.
In the same field of endeavor, Paul teaches:
wherein assigning the first data structure further comprises reassigning the first data
structure of the first policy from the second application instance to the first application instance: (Paul; (26), Briefly described, policy information being designated a task for a bot controller application instance to follow; (107), Briefly described, tasks like that of policy information being reassigned from one bot controller application instance to another bot controller application instance; (63), Briefly described, a second bot controller application instance to reassign the policy information from the second bot controller application instance to the first bot controller application instance).
It would have been obvious to one of ordinary skill in the art before the filing data of the invention to have incorporated - wherein assigning the first data structure further comprises reassigning the first data structure of the first policy from the second application instance to the first application instance as suggested by Mulligan and Paul. Doing so would be desirable because the data of the first policy could utilize the capabilities of the bot controller application instances to reassign the policy data from the second application instance bot to the first application instance bot. The reassignment step can be completed following the assignment step as taught from the combination of Mulligan and Paul. This allows the policies to be assigned and reassigned between the first and second application instances.
Regarding claim 6, Mulligan teaches the invention of claim 1.
However, Mulligan fails to expressly teach – retraining, by the one or more processors, the ML model using a second plurality of instance assignments, wherein the second plurality of instance assignments identifies at least one reassignment of a third data structure of a third policy from a second application instance to a third application instance of the plurality of application instances.
In the same field of endeavor, Paul teaches:
retraining, by the one or more processors, the ML model using a second plurality of
instance assignments, wherein the second plurality of instance assignments identifies at least one reassignment of a third data structure of a third policy from a second application instance to a third application instance of the plurality of application instances: (Paul; (114), Briefly described, one or more processors; (25), Briefly described, artificial intelligence or machine learning capabilities of applying and automating the tasks of the bot controller application instances; (26), Briefly described, policy information being designated a task for a bot controller application instance to follow; (107), Briefly described, tasks like that of policy information being reassigned from one bot controller application instance to another bot controller application instance; (63), Briefly described, a second bot controller application instance and a third bot controller application instance to reassign the policy information of a third policy and data from the second bot controller application instance to the third bot controller application instance through retraining from the machine learning capabilities of automating tasks of the bot controller application instances).
It would have been obvious to one of ordinary skill in the art before the filing data of the invention to have incorporated - retraining, by the one or more processors, the ML model using a second plurality of instance assignments, wherein the second plurality of instance assignments identifies at least one reassignment of a third data structure of a third policy from a second application instance to a third application instance of the plurality of application instances as suggested by Mulligan and Paul. Doing so would be desirable because a third bot controller application instance allows for existing first and second bot controller application instances to transfer data through further training and retraining of their respect ML models. This reassignment step can be performed by these bot controller application instances from the combination of Mulligan and Paul.
Regarding claim 8, Mulligan teaches the invention of claim 1.
However, Mulligan fails to expressly teach – receiving, by the one or more processors, from a computing device associated with an agent, a request to access at least one of the plurality of application instances; and – assigning, by the one or more processors, the computing device to the first application instance based on the request; and – providing, by the one or more processors via an interface of the first application instance, information associated with the first policy defined by the first data structure.
In the same field of endeavor, Paul teaches:
receiving, by the one or more processors, from a computing device associated with an
agent, a request to access at least one of the plurality of application instances: (Paul; (114), Briefly described, one or more processors; (23), Briefly described, a bot host referring to a program that is deployed on a networked or computing device, and a bot host computing device may be associated with an agent like an unmanned remote machine; BRIEF SUMMARY OF DISCLOSURE (3), Briefly described, a bot host computing device receiving information from a bot controller application instance; (73). Briefly described, a request to access from a bot host computing device a bot controller application instance):
assigning, by the one or more processors, the computing device to the first application
instance based on the request: (Paul, (114), Briefly described, one or more processors; (23), Briefly described, a bot host referring to a program that is deployed on a networked or computing device; (73), Briefly described, a first bot controller application instance being determined to have a bot host computing device be assigned based on a request):
providing, by the one or more processors via an interface of the first application
instance, information associated with the first policy defined by the first data structure: (Paul; (114), Briefly described, one or more processors; BRIEF SUMMARY OF DISCLOSURE (8), Briefly described, receiving data using a graphical user interface; (26), Briefly described, information associated with a first policy like a title company or the policy holder’s information to be used as data).
It would have been obvious to one of ordinary skill in the art before the filing data of the invention to have incorporated – receiving, by the one or more processors, from a computing device associated with an agent, a request to access at least one of the plurality of application instances; and – assigning, by the one or more processors, the computing device to the first application instance based on the request; and – providing, by the one or more processors via an interface of the first application instance, information associated with the first policy defined by the first data structure as suggested by Mulligan and Paul. Doing so would be desirable because the graphical user interface provides user intractability to provide the user with information via an interface for the first application instance. Bot controller application instances also allow for access following a request to the plurality of application instances in the policy administration system from the combination of Mulligan and Paul.
Regarding claim 9, Mulligan teaches the invention of claim 1.
However, Mulligan fails to expressly teach – wherein the first application instance is configured to process data from a holder associated with the first policy, in accordance with the first policy.
In the same field of endeavor, Paul teaches:
wherein the first application instance is configured to process data from a holder
associated with the first policy, in accordance with the first policy: (Paul; (26), Briefly described, policy holder information being a part of a collection of data in an enterprise task being processed and associated with a first policy; (26), Briefly described, policy information being designated a task for a first bot controller application instance to process and follow).
It would have been obvious to one of ordinary skill in the art before the filing data of the invention to have incorporated - wherein the first application instance is configured to process data from a holder associated with the first policy, in accordance with the first policy as suggested by Mulligan and Paul. Doing so would be desirable because the policy holder information can be assigned to a first application instance of the policy administration system. The holder information can be processed to stay intact with the first policy once it is assigned from the combination of Mulligan and Paul.
Regarding claims 13, 16, and 15, They are apparatus claims that correspond to method claims 3, 5, and 6. Therefore, they are rejected for the same reasons as claims 3, 6, and 6 above.
Regarding claim 20, it is a computer-readable storage medium claim that corresponds to claim 3. Therefore, it is rejected for the same reason as claim 3 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Nushi et al. (US 20230162096 A1) teaches in Paragraph 84 a third level of application instances that can be performed using a machine learning system.
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/ROLANDO PATRICK VIRREIRA/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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