1DETAILED 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 .
Response to Amendment
Claims 1-20 were previously pending and subject to non-final action filed on 02/09/2026. In the response filed 06/15/2026, claims 1 and 11 were amended. Therefore, claims 1-20 are currently pending and subject to the final action below.
Response to Arguments
Applicant's arguments filed 06/15/2026, see pages 7-11, with respect to claims 1-20 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant’s Argument: Hayward, Megill, and Florance, whether alone or in combination, fail to teach, suggest, or motivate a web crawler configured to identify a relevancy for each of the plurality of data sets by generating a relevancy score for each of the plurality of data sets. Applicant directs the Office to the instant Specification which states "In some embodiments, web crawler 110 may be configured to determine the relevancy of a data pattern. Relevancy may be determined by a relevancy score.
The Office states "Hayward teaches a chat bot/robo-advisor (Col- 8, II. 5) but does not explicitly teach: generate a web crawler." (Office Action, pg. 9). To fil this gap, the Office then introduces Florance which describes a location based search engine 140 that may include a web crawler 141. (Office Action, pg. 9). Florance teaches a web crawler 141 that can scan computers associated with a computer network in order to identify resources that may include data associated with one or more services.
However, Florance's disclosure is limited to a resource identifying web crawler, a crawler that merely locates and retrieves available resources. Florance does not teach, suggest, or motivate a crawler that performs machine learning based relevancy scoring. Florance's crawler does not determine the relevancy of data patterns, does not generate relevancy scores, and does not receive or apply outputs from a machine learning model. A crawler that simply identifies resources is fundamentally different in purpose, structure, and operation from a crawler configured to evaluate data patterns using machine learning generated relevancy scores. Nothing in Florance bridges this gap. Nothing in Hayward suggests modifying its chat bot/robo-advisor to incorporate such a crawler.
Megill fails to cure the deficiencies of Hayward and Florance. Megill is silent as to a web crawler. The Office states "Online resources 140 is a web crawler." (Office Action, pg. 10). Applicant respectfully traverses this incorrect characterization. A hosting resource does not teach, suggest, or motivate a web crawler, which is a specialized agent to traverse networks, fetch documents, and discover new content. The Office's mischaracterization of a server that stores web pages with a crawler that navigates and retrieves web pages is unsupported by Megill's disclosure and is inconsistent with the ordinary meaning of these terms. Accordingly, Megill fails to cure the deficiencies of Hayward and Florance because it does not teach, suggest or motivate the missing limitation, namely a web crawler, let alone one configured to perform machine-learning based relevancy determinations. Applicant submits that a resource that hosts or serves web pages does not teach or suggest a web crawler.
Examiner Response: After careful consideration and review of the prior art, the examiner respectfully disagrees. During examination, the claims must be interpreted as broadly as their terms reasonably allow. In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 U.S.P.Q.2d 1827, 1834 (Fed. Cir. 2004).
Hayward teaches: receive a plurality of data sets from one or more data sources; (Hayward − [Col. 11 ll. 60-67] database 210 may be adapted to store data related to the operation of the real property monitoring system 100. Such data might include, for example, telematics data collected by the intelligent monitoring system controller 106 from the intelligent building products 110, 112, 114, 116, 118 pertaining to the real property monitoring system 100 such as sensor data, power usage data, control data, input data, other data pertaining to the usage of the intelligent building products, user profiles and preferences, and/or other types of data. [Col. 29 ll. 43-60] input data 402 may include data that has been entered and stored by a user (e.g., via a mobile computing device or other client device), and/or may include telematics data generated by one or more buildings or other types of real property that is automatically received by the system 400, e.g., from one or more real property monitoring systems 100, and stored. )
to search and detect one or more data patterns related to the plurality of data sets (Hayward − [Col. 30 ll. 39-54] Claim analysis unit 420 may also include text analysis unit 426, which may include pattern matching unit 428 and natural language processing (NLP) unit 430. In some embodiments, pattern matching unit 428 may search textual claim data loaded into AI platform 404 for specific strings or keywords in text (e.g., “dryer vent blocked”) which may be indicative of particular types of risk.)
generate natural language training data from the plurality of data sets (Hayward − [Col. 40 ll. 1-15] In one embodiment, input analysis application 560 may process the data from client 502, such as by matching patterns, converting raw text to structured text via natural language processing, by extracting content from images, by converting speech to text, and so on. Data from client 502 is plurality of data sets; structured text via natural langue processing is the generate natural language training data)
and the detected one or more patterns related to the plurality of data sets, (Hayward − [Col. 30 ll. 39-54] Claim analysis unit 420 may also include text analysis unit 426, which may include pattern matching unit 428 and natural language processing (NLP) unit 430. In some embodiments, pattern matching unit 428 may search textual claim data loaded into AI platform 404 for specific strings or keywords in text (e.g., “dryer vent blocked”) which may be indicative of particular types of risk.)
the natural language training data comprising industry verbiages; (Hayward − [Col. 34 ll. 12-20] The training process may be performed in parallel, and training unit 452 may analyze all or a subset of claims 410-1 through 410-n. Specifically, training unit 452 may train a neural network (or other machine learning model, module, algorithm, or program) to identify claim risk factors in claim records 410-1 through 410-n. other machine learning algorithm such as natural language processing. [Col. 40 ll. 1-15] Fig. 5, neural network training application 564 may correspond to neural network unit 450 of environment 400 of FIG. 4. In one embodiment, input analysis application 560 may process the data from client 502, such as by matching patterns, converting raw text to structured text via natural language processing, by extracting content from images, by converting speech to text, and so on.)
train, using the natural language training data, a natural language processing module; (Hayward − [Col. 30,31; ll. 66-67, 1-4] In some embodiments, text analysis unit 426 may comprise text processing algorithms (e.g., lexers and parsers, regular expressions, etc.) and may emit structured text in a format which may be consumed by other components. [Col. 36 ll. 35-55] The output of natural language processing unit 430 may be provided to neural network unit 450;) text analysis unit 426 is the NLP module convert raw text to structured text; that includes pattern matching unit, NLP unit. The NLP module is trained
extract, using the trained natural language processing module, key data points from the plurality of data sets; (Hayward − [Col. 36 ll. 35-55] Matched data may be provided to natural language processing unit 430, which may further process the matched data to determine parts of speech such as verbs and objects, as well as relationships between the objects. The output of natural language processing unit 430 may be provided to neural network unit 450; For example, if natural language processing unit 452 indicates a theft of electronics or other personal property, then the neural network (or other machine learning model, module, algorithm, or program) may generate a label of THEFT, indicating that the input data 402 may indicate a personal property or personal articles insurance policy.)
classify the plurality of data sets into one or more data point groups as a function of the key data points; (Hayward − [Col. 36 ll. 56-67] That is, the neural network model (or other machine learning model, module, algorithm, or program) may generate multiple labels corresponding to an indication by pattern matching unit 428 and/or natural language processing unit 430 that both types of insurance coverage are indicated. The multiple labels are classifying a the data sets into groups. Theft
generate an adaptive data structure as a function of the one or more data point groups; (Hayward − [Col. 31 ll. 27-32] Labels may be saved to and/or retrieved from an electronic database, such as risk indication data 442, and claim labels may be generated from already-existing labels, and/or dynamically created labels. Generated labels by AI saved)
MEGILL teaches: generate an interactive user interface displaying the adaptive data structure, (MEGILL − [0010] the generation of graphical user interfaces (GUIs) that facilitate data visualization of normalized and integrated real estate data. [0014] generating a data collection website comprising a collection graphical user interface (GUI) displaying the client ID, a reporting period, and an input element. [0054] methods may create tailored GUIs for each user that take into account, their role, pending tasks)
wherein the interactive user interface comprises one or more event handlers, (MEGILL − [0015] generate a data collection website comprising a collection graphical user interface (GUI) displaying the client ID, a reporting period, and an input element. [0052] dynamically produce a multi-source hybrid webpages that display both real estate functions and marketing functions. [0054] methods may generate GUIs in which icons get automatically organized on the GUI based on most used icons and/or more urgent tasks.)
wherein the one or more event handlers are configured to receive a user input through a user input field; (MEGILL − [0054] For example, some embodiments of the disclosed methods may generate GUIs in which icons get automatically organized on the GUI based on most used icons and/or more urgent tasks. For example, the disclosed systems and methods may provide means for rearranging icons on a GUI based on requirements of the integrated real estate data.)
update the adaptive data structure as a function of the user input. (MEGILL − [0169] Although not shown in FIG. 12, GUI 1200 may have input icons that allow updating images, PDFs, word documents, and excel files.)
Florance teaches: generate a web crawler configured to search: (Florance − [0049-0051] [0049] Location based search engine 140 may be configured to receive and execute search queries that are associated with a location component. [0050] Location based search engine 140 may include a web crawler 141, an indexer 142, and a query processor 143. [0051] Web crawler 141 may be configured to traverse computers connected to a computer network such as, for example, the Internet, to scan and identify data associated with particular properties.)
and identify a relevancy for each of the plurality of data sets by generating a relevancy score for each of the plurality of data sets; the relevancy score for each of the plurality of data sets and the relevancy score for each of the plurality of data sets; and the relevancy score for each of the plurality of data sets; (Florance − [0060] As described in more detail below in the discussion of FIG. 3B, the ranking engine 160 may assign rank resources in such a manner by determining the availability of the one or more properties associated with each resource. [0076] [0080] ranking engine 160 assigning a ranking score to resource and search results responsive to a search query. The ranking scores in a manner that favors resources that include information that has been recently updated over resources that include information that has less recently been updated. In this way, users may be more quickly connected with relevant listing information, and may spend less time browsing or following up on outdated property listings. [0080] May further be based on property availability such that resources associated with properties available within a relevant timeframe are favored.)
Applicant argues that Florance merely teaches a resource-identifying web crawler and does not teach a web crawler configured to perform machine-learning based relevancy scoring. However, the claim does not require that the relevancy score be generated using machine learning. Rather, the claim recites identifying a relevancy for each of the plurality of data sets by generating a relevancy score.
Further, Applicant’s argument focusses only on Florance’s web crawler 141. Florance teaches location-based search engine 140 including web crawler 141 configured to identify data associated with resources (see [0049-0050]), and ranking engine 160 configured to assign ranking scores to identified resources/search results. Florance further teaches that the ranking scores favor recently updated information to provide relevant listing information and may favor properties available within a relevant timeframe (see [0076], [0080]). Thus, Florance teaches identifying relevancy of the identified resources using corresponding ranking scores.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hayward (US PAT: 10497250 B1, Filed Date: Sep. 20, 2018) in view of MEGILL (US PGPUB: 20210349955 A1, Filed Date: May 11, 2021) in view of Florance (US 20170236224 A1, Filed Date: Feb. 12, 2016).
Regarding independent claim 1, Hayward teaches An apparatus for adaptive data structure generation, the apparatus comprising: (Hayward − [Col. 2,3 ll. 60-67, 1-5] a real property monitoring system; a data storage entity communicatively connected to the one or more processors, and storing dynamic characteristic data;)
at least a processor; (Hayward − [Col. 10 ll. 49-67] FIG. 2 illustrates a more detailed block diagram of the exemplary intelligent monitoring system controller 106 of FIG. 1. a processor 206;)
and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: (Hayward − [Col. 10 ll. 49-67] FIG. 2 the controller 202 may include a program memory 204, a processor 206 (may be called a microcontroller or a microprocessor), a random-access memory (RAM) 208, and an input/output (I/O) circuit 214, all of which may be interconnected via an address/data bus 216.)
receive a plurality of data sets from one or more data sources; (Hayward − [Col. 11 ll. 60-67] database 210 may be adapted to store data related to the operation of the real property monitoring system 100. Such data might include, for example, telematics data collected by the intelligent monitoring system controller 106 from the intelligent building products 110, 112, 114, 116, 118 pertaining to the real property monitoring system 100 such as sensor data, power usage data, control data, input data, other data pertaining to the usage of the intelligent building products, user profiles and preferences, and/or other types of data. [Col. 29 ll. 43-60] input data 402 may include data that has been entered and stored by a user (e.g., via a mobile computing device or other client device), and/or may include telematics data generated by one or more buildings or other types of real property that is automatically received by the system 400, e.g., from one or more real property monitoring systems 100, and stored. )
to search and detect one or more data patterns related to the plurality of data sets (Hayward − [Col. 30 ll. 39-54] Claim analysis unit 420 may also include text analysis unit 426, which may include pattern matching unit 428 and natural language processing (NLP) unit 430. In some embodiments, pattern matching unit 428 may search textual claim data loaded into AI platform 404 for specific strings or keywords in text (e.g., “dryer vent blocked”) which may be indicative of particular types of risk.)
generate natural language training data from the plurality of data sets (Hayward − [Col. 40 ll. 1-15] In one embodiment, input analysis application 560 may process the data from client 502, such as by matching patterns, converting raw text to structured text via natural language processing, by extracting content from images, by converting speech to text, and so on. Data from client 502 is plurality of data sets; structured text via natural langue processing is the generate natural language training data)
and the detected one or more patterns related to the plurality of data sets, (Hayward − [Col. 30 ll. 39-54] Claim analysis unit 420 may also include text analysis unit 426, which may include pattern matching unit 428 and natural language processing (NLP) unit 430. In some embodiments, pattern matching unit 428 may search textual claim data loaded into AI platform 404 for specific strings or keywords in text (e.g., “dryer vent blocked”) which may be indicative of particular types of risk.)
the natural language training data comprising industry verbiages; (Hayward − [Col. 34 ll. 12-20] The training process may be performed in parallel, and training unit 452 may analyze all or a subset of claims 410-1 through 410-n. Specifically, training unit 452 may train a neural network (or other machine learning model, module, algorithm, or program) to identify claim risk factors in claim records 410-1 through 410-n. other machine learning algorithm such as natural language processing. [Col. 40 ll. 1-15] Fig. 5, neural network training application 564 may correspond to neural network unit 450 of environment 400 of FIG. 4. In one embodiment, input analysis application 560 may process the data from client 502, such as by matching patterns, converting raw text to structured text via natural language processing, by extracting content from images, by converting speech to text, and so on.)
train, using the natural language training data, a natural language processing module; (Hayward − [Col. 30,31; ll. 66-67, 1-4] In some embodiments, text analysis unit 426 may comprise text processing algorithms (e.g., lexers and parsers, regular expressions, etc.) and may emit structured text in a format which may be consumed by other components. [Col. 36 ll. 35-55] The output of natural language processing unit 430 may be provided to neural network unit 450;) text analysis unit 426 is the NLP module convert raw text to structured text; that includes pattern matching unit, NLP unit. The NLP module is trained
extract, using the trained natural language processing module, key data points from the plurality of data sets; (Hayward − [Col. 36 ll. 35-55] Matched data may be provided to natural language processing unit 430, which may further process the matched data to determine parts of speech such as verbs and objects, as well as relationships between the objects. The output of natural language processing unit 430 may be provided to neural network unit 450; For example, if natural language processing unit 452 indicates a theft of electronics or other personal property, then the neural network (or other machine learning model, module, algorithm, or program) may generate a label of THEFT, indicating that the input data 402 may indicate a personal property or personal articles insurance policy.)
classify the plurality of data sets into one or more data point groups as a function of the key data points; (Hayward − [Col. 36 ll. 56-67] That is, the neural network model (or other machine learning model, module, algorithm, or program) may generate multiple labels corresponding to an indication by pattern matching unit 428 and/or natural language processing unit 430 that both types of insurance coverage are indicated. The multiple labels are classifying a the data sets into groups. Theft
generate an adaptive data structure as a function of the one or more data point groups; (Hayward − [Col. 31 ll. 27-32] Labels may be saved to and/or retrieved from an electronic database, such as risk indication data 442, and claim labels may be generated from already-existing labels, and/or dynamically created labels. Generated labels by AI saved)
Hayward does not explicitly teach: generate an interactive user interface displaying the adaptive data structure,
However, MEGILL teaches: generate an interactive user interface displaying the adaptive data structure, (MEGILL − [0010] the generation of graphical user interfaces (GUIs) that facilitate data visualization of normalized and integrated real estate data. [0014] generating a data collection website comprising a collection graphical user interface (GUI) displaying the client ID, a reporting period, and an input element. [0054] methods may create tailored GUIs for each user that take into account, their role, pending tasks)
wherein the interactive user interface comprises one or more event handlers, (MEGILL − [0015] generate a data collection website comprising a collection graphical user interface (GUI) displaying the client ID, a reporting period, and an input element. [0052] dynamically produce a multi-source hybrid webpages that display both real estate functions and marketing functions. [0054] methods may generate GUIs in which icons get automatically organized on the GUI based on most used icons and/or more urgent tasks.)
wherein the one or more event handlers are configured to receive a user input through a user input field; (MEGILL − [0054] For example, some embodiments of the disclosed methods may generate GUIs in which icons get automatically organized on the GUI based on most used icons and/or more urgent tasks. For example, the disclosed systems and methods may provide means for rearranging icons on a GUI based on requirements of the integrated real estate data.)
update the adaptive data structure as a function of the user input. (MEGILL − [0169] Although not shown in FIG. 12, GUI 1200 may have input icons that allow updating images, PDFs, word documents, and excel files.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, and MEGILL as each of the inventions are related to incorporating digital functionalities in web pages. Adding the teaching of MEGILL provides Hayward with dynamic/adaptive data and graphical interface. Therefore, providing the benefit of improving the technical field of data collection, analysis and generating of personalized graphical interface for the collection of user data.
Hayward teaches a chat bot/robo-advisor (Col – 8, ll. 5) but does not explicitly teach: generate a web crawler; and identify a relevancy for each of the plurality of data sets by generating a relevancy score for each of the plurality of data sets;
However, Florance teaches: generate a web crawler configured to search: (Florance − [0049-0051] [0049] Location based search engine 140 may be configured to receive and execute search queries that are associated with a location component. [0050] Location based search engine 140 may include a web crawler 141, an indexer 142, and a query processor 143. [0051] Web crawler 141 may be configured to traverse computers connected to a computer network such as, for example, the Internet, to scan and identify data associated with particular properties.)
and identify a relevancy for each of the plurality of data sets by generating a relevancy score for each of the plurality of data sets; the relevancy score for each of the plurality of data sets and the relevancy score for each of the plurality of data sets; and the relevancy score for each of the plurality of data sets; (Florance − [0060] As described in more detail below in the discussion of FIG. 3B, the ranking engine 160 may assign rank resources in such a manner by determining the availability of the one or more properties associated with each resource. [0076] [0080] ranking engine 160 assigning a ranking score to resource and search results responsive to a search query. The ranking scores in a manner that favors resources that include information that has been recently updated over resources that include information that has less recently been updated. In this way, users may be more quickly connected with relevant listing information, and may spend less time browsing or following up on outdated property listings. [0080] May further be based on property availability such that resources associated with properties available within a relevant timeframe are favored.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 2, depends on claim 1, Hayward does not explicitly teach: web crawler
However, MEGILL teaches: wherein receiving the plurality of data sets comprises receiving the plurality of data sets using a web crawler, wherein the web crawler is configured to retrieve the plurality of data sets from one or more web sources as a function of data retrieval rules. (MEGILL − [0057] Online resources 140 may include one or more servers or storage services provided by an entity such as a provider of website hosting, networking, cloud, or backup services. In some embodiments, online resources 140 may be associated with hosting services or servers that store web pages of real estate services and/or vendors with web pages containing information about properties. In other embodiments, online resources 140 may be associated with a cloud computing service such as Microsoft Azure™ or Amazon Web Services™. [0087] Rules for compiler and classifier 236, or similar tools to retrieve information from databases 180 or online resources 140 and may employ keywords or trends. Online resources 140 is web crawler )
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 3, depends on claim 2, Hayward teaches: updating the data retrieval rules as a function of the user input, wherein the user input comprises data feedback; (Hayward – [Col. 54 ll 48-54] a processing element may be provided with example inputs and their associated outputs, and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output. [Col. 52 64-63] The insurance policy and/or the insurance rate may be electronically transmitted to an owner of the undamaged insurable asset for review and/or approval, which may be provided by the owner electronically, if desired.)
Hayward does not explicitly teach: web crawler
However, MEGILL teaches: updating the data retrieval rules as a function of the user input, wherein the user input comprises data feedback; (MEGILL – Fig. 12, survey review, additional information.)
and retrieving the plurality of data sets using the web crawler as a function of the updated data retrieval rules. (MEGILL − [0057] Online resources 140 may include one or more servers or storage services provided by an entity such as a provider of website hosting, networking, cloud, or backup services. In some embodiments, online resources 140 may be associated with hosting services or servers that store web pages of real estate services and/or vendors with web pages containing information about properties. In other embodiments, online resources 140 may be associated with a cloud computing service such as Microsoft Azure™ or Amazon Web Services™. [0087] Rules for compiler and classifier 236, or similar tools to retrieve information from databases 180 or online resources 140 and may employ keywords or trends. Online resources 140 is web crawler )
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 4, depends on claim 1, Hayward does not explicitly teach: identifying a discrepancy datum in the plurality of data sets; and generating a notification datum as a function of the discrepancy datum.
However, MEGILL teaches: identifying a discrepancy datum in the plurality of data sets; and generating a notification datum as a function of the discrepancy datum. (MEGILL − [0047] Further, the generated service platform may also include communications features, alert and reporting systems, and user role determination features. Fig 26B [0229] In step 2408, integration system 105 may determine whether the user input is acceptable. If integration system 105 determines that the user input is not acceptable (step 2408: No), integration system 105 may continue to step 2410 and generate error or alert message.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 5, depends on claim 1, Hayward teaches: wherein extracting the key data points comprises extracting an image-based data point of the key data points from image data of the plurality of data sets using a machine vision module. (Hayward – [Col. 30 ll. 20-25] image analysis or image processing unit 424 which may comprise, respectively, algorithms for converting human speech into text and analyzing images (e.g., extracting information from hotel and rental receipts). [Col. 14 ll. 20-21] The sensor 112 may be a camera’ image processing unit 424/camera is machine vision module. [Col. 54 ll 40-43]image or object recognition, optical character recognition)
Regarding dependent claim 6, depends on claim 1, Hayward teaches: wherein extracting the key data points comprises extracting a text-based data point of the key data points from image data of the plurality of data sets using an optical character recognition. (Hayward – [Col. 30 ll. 20-25] image analysis or image processing unit 424 which may comprise, respectively, algorithms for converting human speech into text and analyzing images (e.g., extracting information from hotel and rental receipts). [Col. 14 ll. 20-21] The sensor 112 may be a camera’ image processing unit 424/camera is machine vision module. [Col. 54 ll 40-43]image or object recognition, optical character recognition)
Regarding dependent claim 7, depends on claim 1, Hayward teaches: wherein classifying the plurality of data sets into the one or more data point groups comprises: generating classification training data comprising exemplary data sets correlated to exemplary data point groups; training a group classifier using the classification training data; and classifying the plurality of data sets using the trained group classifier. (Hayward − [Col. 7,8 ll. 65-67, 1-2] Systems and methods may include natural language processing of free-form notes/text, or free-form speech/audio, recorded by call center and/or claim adjustor, photos, and/or other evidence. Industry verbiages received from database and input data; [Col. 36 ll. 56-67] That is, the neural network model (or other machine learning model, module, algorithm, or program) may generate multiple labels corresponding to an indication by pattern matching unit 428 and/or natural language processing unit 430 that both types of insurance coverage are indicated. The multiple labels are classifying a the data sets into groups. Theft and Comprehensive data point groups classified from the plurality of data sets)
Regarding dependent claim 8, depends on claim 1, Hayward does not explicitly teach: authenticating a user credential of a user; and updating the adaptive data structure as a function of the user credential by prioritizing at least a portion of the key data points in the adaptive data structure.
However, MEGILL teaches: authenticating a user credential of a user; and updating the adaptive data structure as a function of the user credential by prioritizing at least a portion of the key data points in the adaptive data structure. (MEGILL − [0015] generate a data collection website comprising a collection graphical user interface (GUI) displaying the client ID, a reporting period, and an input element. [0052] dynamically produce a multi-source hybrid webpages that display both real estate functions and marketing functions. [0054] methods may generate GUIs in which icons get automatically organized on the GUI based on most used icons and/or more urgent tasks. [0124] The authentication request may include credentials of a user, such as name and password. Alternatively, or additionally, the authentication request may include information of client devices 150, like their location, associated MAC address, or other digital footprints.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 9, depends on claim 8, Hayward does not explicitly teach: wherein generating the adaptive data structure comprises generating a read-only section and a writable section of the adaptive data structure as a function of the user credential.
However, MEGILL teaches: wherein generating the adaptive data structure comprises generating a read-only section and a writable section of the adaptive data structure as a function of the user credential. (MEGILL − [0130] If in step 730 integration system 105 determines that the role is not manager (step 730: No), integration system 105 may continue to step 734 and display a read-only site. [0162] When the user's role is property manager or property manager assistant, GUI 1100 may be configured to display read-only options. Under such role, GUI 1100 may be configured to allow access to billing information sections, communications sections, document and data management sections, lease information sections, marketing materials sections, and tenant reporting sections. GUI 1100 may also prevent access to certain sections of the platform under this role. For example, GUI 1100 may deny access to admin sections.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 10, depends on claim 1, Hayward teaches: wherein updating the adaptive data structure comprises: generating structure training data, wherein the structure training data comprises exemplary key data points training a structure machine-learning model using the structure training data; and generating the adaptive data structure using the trained structure machine-learning model. (Hayward − [Col. 7,8 ll. 65-67, 1-2] Systems and methods may include natural language processing of free-form notes/text, or free-form speech/audio, recorded by call center and/or claim adjustor, photos, and/or other evidence. Industry verbiages received from database and input data; [Col. 31 ll. 27-32] Labels may be saved to and/or retrieved from an electronic database, such as risk indication data 442, and claim labels may be generated from already-existing labels, and/or dynamically created labels. Generated labels by AI saved)
Hayward does not explicitly teach: and exemplary user credentials correlated to exemplary adaptive data structures;
However, MEGILL teaches: (MEGILL − [0130] If in step 730 integration system 105 determines that the role is not manager (step 730: No), integration system 105 may continue to step 734 and display a read-only site. [0162] When the user's role is property manager or property manager assistant, GUI 1100 may be configured to display read-only options. Under such role, GUI 1100 may be configured to allow access to billing information sections, communications sections, document and data management sections, lease information sections, marketing materials sections, and tenant reporting sections. GUI 1100 may also prevent access to certain sections of the platform under this role. For example, GUI 1100 may deny access to admin sections.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding independent claim 11, is directed to a method. Claim 11 have similar/same technical features/limitation as claim 1 and the claims are rejected under the same rationale.
Regarding dependent claim 12, depends on claim 11, Hayward does not explicitly teach: web crawler
However, MEGILL teaches: wherein receiving the plurality of data sets comprises receiving the plurality of data sets using a web crawler, wherein the web crawler is configured to retrieve the plurality of data sets from one or more web sources as a function of data retrieval rules. (MEGILL − [0057] Online resources 140 may include one or more servers or storage services provided by an entity such as a provider of website hosting, networking, cloud, or backup services. In some embodiments, online resources 140 may be associated with hosting services or servers that store web pages of real estate services and/or vendors with web pages containing information about properties. In other embodiments, online resources 140 may be associated with a cloud computing service such as Microsoft Azure™ or Amazon Web Services™. [0087] Rules for compiler and classifier 236, or similar tools to retrieve information from databases 180 or online resources 140 and may employ keywords or trends. Online resources 140 is web crawler )
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 13, depends on claim 12, Hayward teaches: updating the data retrieval rules as a function of the user input, wherein the user input comprises data feedback; (Hayward – [Col. 54 ll 48-54] a processing element may be provided with example inputs and their associated outputs, and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output. [Col. 52 64-63] The insurance policy and/or the insurance rate may be electronically transmitted to an owner of the undamaged insurable asset for review and/or approval, which may be provided by the owner electronically, if desired.)
Hayward does not explicitly teach: web crawler
However, MEGILL teaches: updating the data retrieval rules as a function of the user input, wherein the user input comprises data feedback; (MEGILL – Fig. 12, survey review, additional information.)
and retrieving the plurality of data sets using the web crawler as a function of the updated data retrieval rules. (MEGILL − [0057] Online resources 140 may include one or more servers or storage services provided by an entity such as a provider of website hosting, networking, cloud, or backup services. In some embodiments, online resources 140 may be associated with hosting services or servers that store web pages of real estate services and/or vendors with web pages containing information about properties. In other embodiments, online resources 140 may be associated with a cloud computing service such as Microsoft Azure™ or Amazon Web Services™. [0087] Rules for compiler and classifier 236, or similar tools to retrieve information from databases 180 or online resources 140 and may employ keywords or trends. Online resources 140 is web crawler )
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 14, depends on claim 11, Hayward does not explicitly teach: identifying a discrepancy datum in the plurality of data sets; and generating a notification datum as a function of the discrepancy datum.
However, MEGILL teaches: identifying a discrepancy datum in the plurality of data sets; and generating a notification datum as a function of the discrepancy datum. (MEGILL − [0047] Further, the generated service platform may also include communications features, alert and reporting systems, and user role determination features. Fig 26B [0229] In step 2408, integration system 105 may determine whether the user input is acceptable. If integration system 105 determines that the user input is not acceptable (step 2408: No), integration system 105 may continue to step 2410 and generate error or alert message.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 15, depends on claim 11, Hayward teaches: wherein extracting the key data points comprises extracting an image-based data point of the key data points from image data of the plurality of data sets using a machine vision module. (Hayward – [Col. 30 ll. 20-25] image analysis or image processing unit 424 which may comprise, respectively, algorithms for converting human speech into text and analyzing images (e.g., extracting information from hotel and rental receipts). [Col. 14 ll. 20-21] The sensor 112 may be a camera’ image processing unit 424/camera is machine vision module. [Col. 54 ll 40-43]image or object recognition, optical character recognition)
Regarding dependent claim 16, depends on claim 11, Hayward teaches: wherein extracting the key data points comprises extracting a text-based data point of the key data points from image data of the plurality of data sets using an optical character recognition. (Hayward – [Col. 30 ll. 20-25] image analysis or image processing unit 424 which may comprise, respectively, algorithms for converting human speech into text and analyzing images (e.g., extracting information from hotel and rental receipts). [Col. 14 ll. 20-21] The sensor 112 may be a camera’ image processing unit 424/camera is machine vision module. [Col. 54 ll 40-43]image or object recognition, optical character recognition)
Regarding dependent claim 17, depends on claim 1, Hayward teaches: wherein classifying the plurality of data sets into the one or more data point groups comprises: generating classification training data comprising exemplary data sets correlated to exemplary data point groups; training a group classifier using the classification training data; and classifying the plurality of data sets using the trained group classifier. (Hayward − [Col. 7,8 ll. 65-67, 1-2] Systems and methods may include natural language processing of free-form notes/text, or free-form speech/audio, recorded by call center and/or claim adjustor, photos, and/or other evidence. Industry verbiages received from database and input data; [Col. 36 ll. 56-67] That is, the neural network model (or other machine learning model, module, algorithm, or program) may generate multiple labels corresponding to an indication by pattern matching unit 428 and/or natural language processing unit 430 that both types of insurance coverage are indicated. The multiple labels are classifying a the data sets into groups. Theft and Comprehensive data point groups classified from the plurality of data sets)
Regarding dependent claim 18, depends on claim 11, Hayward does not explicitly teach: authenticating a user credential of a user; and updating the adaptive data structure as a function of the user credential by prioritizing at least a portion of the key data points in the adaptive data structure.
However, MEGILL teaches: authenticating a user credential of a user; and updating the adaptive data structure as a function of the user credential by prioritizing at least a portion of the key data points in the adaptive data structure. (MEGILL − [0015] generate a data collection website comprising a collection graphical user interface (GUI) displaying the client ID, a reporting period, and an input element. [0052] dynamically produce a multi-source hybrid webpages that display both real estate functions and marketing functions. [0054] methods may generate GUIs in which icons get automatically organized on the GUI based on most used icons and/or more urgent tasks. [0124] The authentication request may include credentials of a user, such as name and password. Alternatively, or additionally, the authentication request may include information of client devices 150, like their location, associated MAC address, or other digital footprints.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 19, depends on claim 18, Hayward does not explicitly teach: wherein generating the adaptive data structure comprises generating a read-only section and a writable section of the adaptive data structure as a function of the user credential.
However, MEGILL teaches: wherein generating the adaptive data structure comprises generating a read-only section and a writable section of the adaptive data structure as a function of the user credential. (MEGILL − [0130] If in step 730 integration system 105 determines that the role is not manager (step 730: No), integration system 105 may continue to step 734 and display a read-only site. [0162] When the user's role is property manager or property manager assistant, GUI 1100 may be configured to display read-only options. Under such role, GUI 1100 may be configured to allow access to billing information sections, communications sections, document and data management sections, lease information sections, marketing materials sections, and tenant reporting sections. GUI 1100 may also prevent access to certain sections of the platform under this role. For example, GUI 1100 may deny access to admin sections.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Regarding dependent claim 20, depends on claim 11, Hayward teaches: wherein updating the adaptive data structure comprises: generating structure training data, wherein the structure training data comprises exemplary key data points training a structure machine-learning model using the structure training data; and generating the adaptive data structure using the trained structure machine-learning model. (Hayward − [Col. 7,8 ll. 65-67, 1-2] Systems and methods may include natural language processing of free-form notes/text, or free-form speech/audio, recorded by call center and/or claim adjustor, photos, and/or other evidence. Industry verbiages received from database and input data; [Col. 31 ll. 27-32] Labels may be saved to and/or retrieved from an electronic database, such as risk indication data 442, and claim labels may be generated from already-existing labels, and/or dynamically created labels. Generated labels by AI saved)
Hayward does not explicitly teach: and exemplary user credentials correlated to exemplary adaptive data structures;
However, MEGILL teaches: (MEGILL − [0130] If in step 730 integration system 105 determines that the role is not manager (step 730: No), integration system 105 may continue to step 734 and display a read-only site. [0162] When the user's role is property manager or property manager assistant, GUI 1100 may be configured to display read-only options. Under such role, GUI 1100 may be configured to allow access to billing information sections, communications sections, document and data management sections, lease information sections, marketing materials sections, and tenant reporting sections. GUI 1100 may also prevent access to certain sections of the platform under this role. For example, GUI 1100 may deny access to admin sections.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Hayward, MEGILL and Florance. Adding the teaching of Florance provides Hayward with a web crawler for identifying data associated with resources and ranking the identified resources based on characteristics of the resources. It would been obvious to utilize corresponding ranking scores of Florance in the data analysis process of Hayward, because the ranking scores identify and prioritize relevant data. Therefore, providing the benefit of identifying and prioritizing relevant data for use in generating training data, extracting key data points, and classifying the data set in the data analysis processes of Hayward.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARL E BARNES JR whose telephone number is (571)270-3395. The examiner can normally be reached Monday-Friday 9am-6pm.
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/CARL E BARNES JR/Examiner, Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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