DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This action is in reply to Application 18/905,877 filed on 3 October 2024.
Claims 1-20 are currently pending, and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, representative method claim 1 is directed towards facilitating the processing and analysis of unstructured data items related to protection products (e.g., insurance). Claim 1 is directed to the abstract idea of using rules and/or instructions to implement an economic/commercial activity (e.g., insurance management) comprising the field-of-use steps of receiving data associated with a product, monitoring the data source (data feed), processing the data (converting/extracting contextual, attribute information), applying the data (inputting, outputting a result), grouped under the certain methods of organizing human activity – fundamental economic principles, practices or concepts; sales activity; following set of instructions; commercial or legal interactions (agreements in the form of contracts; business relations); managing interactions between people (including social activities, teachings, following rules or instructions) as well as mathematical concepts – mathematical relationships, inasmuch as the claimed method as a whole is directed towards facilitating utilizing a mathematical process to perform calculations utilizing an algorithm, but for the recitation of computer-related components.
Other than the mere nominal recitation of a computer-related device – nothing in the claim element precludes the steps from the organizing human interactions and mathematical concepts groupings grouping, in prong one of step 2A. Accordingly, for these reasons, the claim recites an abstract idea.
Claim 1 recites: “receiving, …, a plurality of unstructured data items associated with a plurality of protection records, the plurality of protection records corresponding to at least one protection product;
monitoring, … and using at least one data feed, multimedia unstructured data items associated with at least one protection record of the plurality of protection records, the at least one protection record corresponding to the at least one protection product;
converting, …, the multimedia unstructured data items into unstructured data items of the plurality of unstructured data items;
generating, …, a prompt based upon the plurality of unstructured data items …, wherein generating the prompt comprises extracting one or more associations corresponding to contextual information and attribute information of the plurality of unstructured data items;
applying …, the plurality of unstructured data items and the prompt as input to … generate an output regarding at least one of an occurrence prediction or a pattern identification within the plurality of unstructured data items, wherein the output comprises an update to at least one protection parameter of the at least one protection product; and
determining, …, at least one action to apply the update to the at least one protection parameter of the at least one protection product, wherein the at least one action comprises a response to the plurality of unstructured data items and the prompt, the response causing a tuning of a probability metric of the occurrence prediction or a tuning of a frequency metric of the pattern identification”.
Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (See 2019 Revised Patent Subject Matter Eligibility Guidance), the additional elements of the claim such as a “processor”, “computer readable-storage media”, “artificial intelligence (AI) model”, represent the use of a computer as a tool (intermediary) to perform an abstract idea and/or does no more than apply the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e. automate) implement the acts of using rules and/or instructions to implement an economic/commercial activity (e.g., insurance management) comprising the field-of-use steps of receiving data associated with a product, monitoring the data source (data feed), processing the data (converting/extracting contextual, attribute information), applying the data (inputting, outputting a result).
When analyzed under step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claims merely describe the concept of using rules and/or instructions to implement an economic/commercial activity (e.g., insurance management) comprising the field-of-use steps of receiving data associated with a product, monitoring the data source (data feed), processing the data (converting/extracting contextual, attribute information), applying the data (inputting, outputting a result) using computer technology. Therefore, the use of these additional elements does no more than employ a computer as a tool to automate and/or implement the abstract idea, which cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Hence, claim 1 is not patent eligible.
Independent claim 1 recites substantially the same limitations as claim 19 above and is ineligible for the same reasons. The subject matter of claim 1 corresponds to the subject matter of claim 19 in terms of a system (e.g., machine). Therefore the reasoning provided for claim 19 applies to claim 1 accordingly.
Independent claim 13 recites substantially the same limitations as claim 19 above and is ineligible for the same reasons. The subject matter of claim 13 corresponds to the subject matter of claim 19 in terms of a system (e.g., machine). Therefore the reasoning provided for claim 19 applies to claim 13 accordingly.
Dependent claims 2-12, 14-18, and 20 add further details and contain limitations that narrow the scope of the invention. However, these details do not result in significantly more than the abstract idea itself. As explained in the December 16, 2014 Interim Eligibility Guidance from the USPTO (in reference to the BuySAFE, Inc. v. Google, Inc. decision), further narrowing the details of an abstract idea does not change the § 101 analysis since a more narrow abstract idea does not make it any less abstract.
Viewed individually and in combination, these additional elements do not provide meaningful limitations to transform the abstract idea such that the claims amount to significantly more than the abstraction itself.
Accordingly, the present pending claims are not patent eligible and are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Batra et al., US 2023/0315996 A1 (“Batra”), in view of Mueller, US 2018/0114142 A1 (“Mueller”), further in view of Arriaga, US 2025/0054068 A1 (“Arriaga”).
Re Claim 1: Batra discloses a modeling system for modeling unstructured data items using artificial intelligence (AI) models, comprising:
one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising: (¶[0022]: “Model building system 110 may comprise at least one computing device in the form of a computer or processor, or a set of computers/processors, although other types of computing units or systems may be used such as, for example, a server, web server, pooled servers, or the like.”)
receiving a plurality of unstructured data items associated with a plurality of protection records, the plurality of protection records corresponding to at least one protection product; (¶[0003]: “Systems, methods, and articles of manufacture (collectively, the "system") for the identifying data of interest are disclosed. The system may retrieve unstructured data from an internet data source, wherein the unstructured data is retrieved directly or from a web link hosting the unstructured data.”)
Regarding the following limitation feature(s), Mueller discloses:
generating a prompt based upon the plurality of unstructured data items for one or more AI models, wherein generating the prompt comprises extracting one or more associations corresponding to contextual information and attribute information of the plurality of unstructured data items; (¶[0002]: “The present invention relates to automated or semiautomated systems for extracting, accessing, manipulating and/or classifying and/or labeling data from structured, semi-structured and unstructured documents. In particular, it relates to the field of automated, autarkic labeling and classifying of large underwriting and claim documents in the field of risk-transfer, with typically inconsistent data sets, and thereby automatically generating training data sets for machine learning classifiers.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Mueller with the invention of Batra as disclosed above for the motivation of facilitating the identification and extraction of data of interests to assist in decision-making.
Regarding the following limitation feature(s), Arriaga discloses:
applying the plurality of unstructured data items and the prompt as input to the one or more AI models to cause the one or more AI models to generate an output regarding at least one of an occurrence prediction or a pattern identification within the plurality of unstructured data items, wherein the output comprises an update to at least one protection parameter of the at least one protection product;
(¶[0045]: “In some cases, classifier machine learning model may be trained through user feedback wherein a user may indicate whether a particular element corresponds to a particular
class. In some cases, classifier machine learning model may be trained using inputs and outputs based on previous iterations. In some cases, a user may input previous dataset
120 and corresponding target categorizations wherein classifier machine learning model may be trained based on the input.”; ¶[0101]: “Training sets may be drawn from diverse sets of data such as, as non-limiting examples, … unstructured data …”; ¶[0118]: “Inputs may additionally include unstructured data …”; ¶[0154]: “As a further nonlimiting example, a machine-learning model 424 may be generated by creating an artificial neural network …”; ¶[0166]: “… neural network 500 also known as an artificial neural network, is a network of "nodes," or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arriaga with the invention of Batra as disclosed above for the motivation of determining a validity of the plurality of target data within a dataset
of interests for purposes of decision-making.
Batra further discloses:
determining at least one action to apply the update to the at least one protection parameter of the at least one protection product, wherein the at least one action comprises a response to the plurality of unstructured data items and the prompt, the response causing a tuning of a probability metric of the occurrence prediction or a tuning of a frequency metric of the pattern identification. (¶[0003]: “Systems, methods, and articles of manufacture (collectively, the "system") for the identifying data of interest are disclosed. The system may retrieve unstructured data from an internet data source, wherein the unstructured data is retrieved directly or from a web link hosting the unstructured data. The system may input the unstructured data into a first machine learning model, a second machine learning model, a named entity recognition (NER) model, and a semantic role labeling (SRL) model. The system may calculate a sentiment score by inputting the unstructured data into a sentiment scoring algorithm. The system may identify the unstructured data to be of interest in response to an output of at least one of the first machine learning model, the second machine learning model, the NER model, the SRL model, or the sentiment score indicating that the unstructured data has a probability of being of interest.”; ¶[0004]: “In various embodiments, the system inputs the output of at least one of the first machine learning model, the second machine learning model, the NER model, the SRL model, or the sentiment score into a gradient boosted regression tree (GBRT) machine learning model. The system may identify the unstructured data to be of interest based on a final output from the GBRT machine learning model.”)
Re Claim 2: Batra in view of Mueller discloses the modeling system of claim 1. Regarding the following limitation feature(s), Mueller discloses:
wherein the operations further comprising: monitoring, using at least one data feed, multimedia data of at least one protection record of the plurality of protection records, the at least one protection record corresponding to the at least one protection product; and
converting the multimedia data into the plurality of unstructured data items of the at least one protection record.
(¶[0013]: “The selected unclassified data sets can e.g. be converted into a composition of graphic and text data forming a compound data set to be classified, wherein the unclassified data sets can be pre-processed by optical character recognition converting images of typed, handwritten or printed text into machine-encoded text. The selected unclassified data sets can e.g. be converted into a composition of graphic and text data forming a compound data set to be classified, wherein the graphic data are stored as raster graphics images in tagged image file format; and the text data are stored in plain text format or rich text format.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Mueller with the invention of Batra as disclosed above for the motivation of facilitating the identification and extraction of data of interests to assist in decision-making.
Re Claim 3: Batra in view of Mueller discloses the modeling system of claim 1. Batra further discloses:
wherein the plurality of unstructured data items corresponds to non-relational data generated by a plurality of sources, and wherein applying the plurality of unstructured data items and the prompt as the input to the one or more AI models comprises:
transforming the plurality of unstructured data items into a plurality of feature vectors; normalizing the plurality of feature vectors to a scale;
inputting the normalized plurality of feature vectors into the one or more AI models to perform predictive and pattern recognition to cause the one or more AI models to generate the output.
(¶[0033]: “Model training engine 240 may be in electronic communication with training dataset identification engine 230 and/or model database 107 … The machine learning algorithm may comprise any suitable machine learning model or algorithm capable of identifying documents of interest. For example, the machine learning algorithm may comprise a Naïve Bayes algorithm. In various embodiments, and as a further example, the machine learning algorithm may comprise support vector machines …”)
Re Claim 4: Batra in view of Mueller discloses the modeling system of claim 1. Regarding the following limitation feature(s), Arriaga discloses:
wherein the one or more AI models comprise a generative AI model, and wherein the generative AI model comprise at least one of (i) a supervised learning model trained on labeled protection records of the plurality of protection records or (ii) an unsupervised learning model trained on unlabeled protection records of the plurality of protection records.
(¶[0087]:”aspects of "generative artificial intelligence (AI)," a type of AI that uses machine learning algorithms to create, establish, or otherwise generate data such as, without limitation, … the like in any data structure as described herein … In an embodiment, machine learning
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arriaga with the invention of Batra as disclosed above for the motivation of determining a validity of the plurality of target data within a dataset
of interests for purposes of decision-making.
Re Claim 5: Batra in view of Mueller discloses the modeling system of claim 4. Regarding the following limitation feature(s), Mueller discloses:
wherein the supervised learning model and the unsupervised learning model comprise at least one of (i) an association detector to assign the probability metric of the occurrence prediction, or (ii) a pattern tracker to assign the frequency metric of the pattern identification.
(¶[0008]: “… it is known in the art to apply supervised learning structures for automated classifier systems with the combined use of single classifier structures, as e.g. the aforementioned MeTA system, Mallet system or Torch system. Supervised machine learning tasks involve mapping input data to the appropriate outputs. In a classification learning task, each output is one or more classes to which the input belongs … Further, also known in the prior art are automated, so called meta classifier systems. In this context, data mining denotes a process that provides for automatically, or semiautomatically processing and analyzing a large database to find specified patterns and data …”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Mueller with the invention of Batra as disclosed above for the motivation of facilitating the identification and extraction of data of interests to assist in decision-making.
Re Claim 6: Batra in view of Mueller discloses the modeling system of claim 5. Regarding the following limitation feature(s), Arriaga discloses:
wherein the generative AI model implements reinforcement learning, wherein the reinforcement learning comprises updating the generative AI model based upon receiving feedback on the output and the at least one action from a reward signal generated from performance metrics of the plurality of protection records, the feedback corresponding to at least one user interaction with a user interface.
(¶[0087]:”aspects of "generative artificial intelligence (AI)," a type of AI that uses machine learning algorithms to create, establish, or otherwise generate data such as, without limitation, … the like in any data structure as described herein … In an embodiment, machine learning
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arriaga with the invention of Batra as disclosed above for the motivation of determining a validity of the plurality of target data within a dataset
of interests for purposes of decision-making.
Re Claim 7: Batra in view of Mueller discloses the modeling system of claim 1. Batra further discloses:
wherein the update to the at least one protection parameter is at least one of updating a deductible parameter for at least one future protection product, updating a coverage parameter for the at least one future protection product, updating a processing protocol parameter for the at least one future protection product, or generating a new query for the at least one future protection product.
(¶[0041]: “… For example, and in accordance with various embodiments, data alert 109 may be manually reviewed. Based on the manual review, feedback may be provided to model building system 110 to update future training datasets in response to identifying false positives …”)
Re Claim 8: Batra in view of Mueller discloses the modeling system of claim 1. Batra further discloses:
wherein the response causing the tuning of the probability metric comprises reducing the probability metric corresponding with reducing a probability of a future protection record, and wherein the response causing the tuning of the frequency metric of the pattern identification comprises updating the frequency metric corresponding with a persistence probability of a subset of the plurality of protection records.
(¶[0039]: “In various embodiments, sentiment scoring engine 380 may be in electronic communication with machine learning system 370. Sentiment scoring engine 380 may be configured to generate a sentiment score to increase the accuracy in determining whether data is of interest.”)
Re Claim 9: Batra in view of Mueller discloses the modeling system of claim 1. Batra further discloses:
wherein the one or more associations extracted from the plurality of unstructured data items comprises identifying correlations between the plurality of unstructured data items of the plurality of protection records, and wherein the contextual information corresponds to circumstantial factors and environmental factors of each of the plurality of protection records, and the attribute information corresponds to attributes and properties of each of the plurality of protection records.
(¶[0068]: “As used herein, "satisfy," "meet," "match," "associated with", or similar phrases may include an identical match, a partial match, meeting certain criteria, matching a subset of data, a correlation, satisfying certain criteria, a correspondence, an association, an algorithmic relationship and/or the like.”)
Re Claim 10: Batra in view of Mueller discloses the modeling system of claim 1. Batra further discloses:
wherein the operations further comprising: in response to generating the output regarding at least one of the occurrence prediction or the pattern identification within the plurality of protection records, determining additional data to apply as the input to the one or more AI models; and requesting or accessing, from at least one data feed, additional data corresponding to the plurality of protection records.
(¶[0049]: “… data may be preprocessed to increase the ability of the system to successfully and accurate identify data of interest, as discussed further herein. Data retrieval and processing engine 360 transmits the preprocessed data to machine learning system 370.”)
Re Claim 11: Batra in view of Mueller discloses the modeling system of claim 1. Batra further discloses:
wherein the operations further comprising: causing a user interface to display the output comprising a prompt element for receiving user input.
(¶[0070]: “… a display device coupled to the processor and memory for displaying information derived from digital data processed by the processor …”)
Re Claim 12: Batra in view of Mueller discloses the modeling system of claim 11. Regarding the following limitation feature(s), Mueller discloses:
wherein the operations further comprising: generating, during an active session between a plurality of users, a query according to the at least one action, the active session comprises an active connection between a first computing system of a first user and a second computing system of a second user;
(¶[0008]: “… For example, an automated credit card system seeks to ensure that different users who defaulted on their credit card payments are separated from those users who did not default, respectively, based on other known information such as annual income. The goal would be to automatically capture and predict whether a new credit card applicant is likely or not likely to default on his credit card charges and thereby automatically deciding whether to approve or deny this applicant a new card.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Mueller with the invention of Batra as disclosed above for the motivation of facilitating the identification and extraction of data of interests to assist in decision-making.
Batra further discloses:
causing the user interface to display the query to at least the first computing system or the second computing system. (¶[0070]: “… a display device coupled to the processor and memory for displaying information derived from digital data processed by the processor …”)
Re Claim 13: Claim 13, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 1. Accordingly, claim 13 is rejected in the same or substantially the same manner as claim 1.
Re Claim 14: Claim 14, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 3. Accordingly, claim 14 is rejected in the same or substantially the same manner as claim 3.
Re Claim 15: Claim 15, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 4. Accordingly, claim 15 is rejected in the same or substantially the same manner as claim 4.
Re Claim 16: Claim 16, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 5. Accordingly, claim 16 is rejected in the same or substantially the same manner as claim 5.
Re Claim 17: Claim 17, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 6. Accordingly, claim 17 is rejected in the same or substantially the same manner as claim 6.
Re Claim 18: Claim 18, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 7. Accordingly, claim 18 is rejected in the same or substantially the same manner as claim 7.
Re Claim 19: Claim 19, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 1. Accordingly, claim 19 is rejected in the same or substantially the same manner as claim 1.
Re Claim 20: Claim 20, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 4. Accordingly, claim 20 is rejected in the same or substantially the same manner as claim 4.
Conclusion
The prior art(s) made of record and not relied upon is/are considered pertinent to applicant's disclosure.
Deshpande et al., (US 2016/0085852 A1) discloses supplementing structured information about entities with information from unstructured data sources. A method for supplementing structured information within a data system for entities based on unstructured data analyzes a document with unstructured data and extracts attribute values from the unstructured data for one or more entities of the data system. Entity records with structured information are retrieved from the data system based on the extracted attribute values. Entity references for corresponding entities of the data system are constructed based on a comparison of the retrieved entity records and the extracted attribute values. The entity references are linked to the corresponding entities within the data system, with the entity references including extracted attributes from the unstructured data for corresponding linked entities.
Sutrich et al., (US 2024/0037127 A1) discloses structured and unstructured data comparison with complex and variable natural language text. Described herein are techniques that may be implemented by a computerized system for parsing contracts using natural language understanding and/or textual analytics rules and determining whether a party to a contract is compliant with
insurance coverage identified by the contract. Some techniques described herein can create structured data from unstructured contract text describing insurance coverage that a party to a contract is required to hold. Some such techniques including comparing the structured data indicating required coverages (which was determined from the unstructured text) to other structured data indicating coverages held by a party, to determine whether the coverage held by the party satisfies the required coverages. In some embodiments, a system implementing these techniques output whether coverage is missing or otherwise insufficient, or whether the coverages held by a party satisfy required
coverages of the contract.
Claims 1-20 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Clifford Madamba whose telephone number is 571-270-1239. The examiner can normally be reached on Mon-Thu 7:30-5:00 EST Alternate Fridays.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon, can be reached at 571-272-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLIFFORD B MADAMBA/Primary Examiner, Art Unit 3692