DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/22/2026 has been entered.
Status of Claims
This action is in response to the RCE received 1/22/2026.
Claim 9 and 17 were amended 1/22/2026.
Claims 9-12, 14-20 and 22 are currently pending and have been examined.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 9 and 17 and therefore their dependent claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Independent claims 9 and 17 recite “wherein the ML model is an Atificial Intelligence (“AI”) algorithm, trained with sample data, that continuously improves task performance and is associated with all of computational statistics, mathematical optimization, data mining, and predictive analytics”. However, in the specification paragraph 68 it is the analyst (user) uses the displayed result to approve or deny the extension of the recommendation and it is this recommendation that is used to improve the machine learning model (STD model). The Artificial Intelligence model is not continuously improved using its computational statistics, mathematical optimization, data mining and predictive analytics. It is an analyst that is manually approving or denying recommendations that improves the model, not a continuous process provided by the algorithm itself (paragraph 68). Further, the machine learning model might be associated with computational statistics, mathematical optimization datamining, and/or predictive analytics (paragraph 48), however the specification is silent to a machine learning algorithm using these specific algorithm functions to continuously improve itself. The specification is silent on an artificial intelligence algorithm continuously improving task performance and points to an analyst manually performing the task.
Claim Rejections - 35 USC § 112(b)
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 9 and 17 and therefore their dependent claims 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. Independent claims 9 and 17 recite “wherein the ML model is an Atificial Intelligence (“AI”) algorithm, trained with sample data, that continuously improves task performance and is associated with all of computational statistics, mathematical optimization, data mining, and predictive analytics”. However, in the specification paragraph 68 it is the analyst (user) uses the displayed result to approve or deny the extension of the recommendation and it is this recommendation that is used to improve the machine learning model (STD model). The Artificial Intelligence model is not continuously improved using its computational statistics, mathematical optimization, data mining and predictive analytics. It is an analyst that is manually approving or denying recommendations that improves the model, not a continuous process provided by the algorithm itself. Further, the machine learning model might be associated with computational statistics, mathematical optimization datamining, and/or predictive analytics (paragraph 48), however the specification is silent to a machine learning algorithm using these specific algorithm functions to continuously improve itself. It is not clear how the artificial intelligence algorithm is working or how it is continuously improving itself as the specification indicates that an analyst is manually performing the task.
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 9-12, 14-20 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 9-12, 14-20 and 22 are drawn to a system and a non-transitory computer readable medium which are statutory categories of invention (Step 1: YES).
Independent claims 9 and 17 recite encrypted electronic records that represent a plurality of Short Term Disability (“STD”) claims between the insurer and a plurality of entities, wherein each electronic record includes an electronic record identifier and a set of resource allocation values associated with risk attributes that have been collected from the entities and service providers; a text factory that identifies and flags text including: information associated with STD daily non-cancelled claims, specific types of claims to be dropped, old claims, and claims that have been migrated; and programmed to (i) receive an extension request for a STD claim from an entity, (ii) if it is determined that service provider records are not required for the STD claim: including duration likelihood scores and confidence thresholds for each of a range of durations, if the highest likelihood score has a confidence threshold below a pre- determined value, generating a null model recommendation, otherwise, using the shortest duration in the range of durations with the highest likelihood score as a model recommendation, and combining the model recommendation with provider and guideline values to generate a final recommendation and reduce the number of messages that need to be transmitted and (iii) if it is determined that service provider records are required for the STD claim: using the service provider records and a set of rules to automatically generate the final recommendation and (iv) transmitting information about the final recommendation directly, a workflow application, and a calendar application to facilitate insurance claim processing and (v) automatically generating a customized approval letter, including an indication of the final recommendation, for the entity that submitted the STD claim extension request and transmitting information about the letter directly.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that “The system may include a resource allocation data store that contains electronic records representing requested resource allocations between the enterprise and a plurality of entities (collected from the entities and service providers).” (see: specification paragraph 3). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “automat[ing] medical approval judgements, motivat[ing] and influenc[ing] STD claimant behavior, reduce a number of electronic records that need to be transmitted via communication networks” (see: specification paragraph 80). This problem is addressed “Elements of the system 100 might help, for example, an insurance claim handler or ability analyst quickly determine key claim information about an injured worker, insured, and/or treatment provider along with an appropriate workflow that can be used to help process the claim after a full and fair investigation and thorough review of the claim.” (see: specification paragraph 45). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Further, the recited limitations, as drafted, under the broadest reasonable interpretation, cover mathematical relationships by calculating a highest likelihood score based on a set of rules to generate a recommendation. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships or mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea (Step 2A Prong One: YES).
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “system”, “back-end application computer server”, “resource allocation data store”, “a bill review system to update a machine learning model”, “computer processor”, “computer memory”, “running a Machine Learning (“ML”) model to generate raw model output”, “wherein the ML model is an Artificial Intelligence (“AI”) algorithm, trained with sample data, that continuously improves task performance and is associated with all of computational statistics, mathematical optimization, data mining, and predictive analytics”, “email server”, “network”, “postal mail server” and “non-transitory computer readable medium”, “distributed communication network”, which are recited at a high level of generality (e.g., that the calculating and generating is performed using generic computer components with instructions are executed to perform the claimed limitations, and that the machine learning model is a generic model that inputs/outputs processed data and is trained generically). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
The claims recite the additional elements of “via a security feature component” and “wherein information associated with the final recommendation is used in a feedback loop to analyze performance and continuously adjust operation achieving better performance”, which are nominal or tangential addition to the abstract idea and amount to insignificant post-solution activity concerning an insignificant application. The better performance is directed towards the algorithm model, not the computing device, by inputting/outputting data in a feedback loop (specification paragraph 68). The addition of an insignificant extra-solution activity limitation does not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. In the claimed context, these claimed additional elements are incidental to the performance of encoding healthcare data as outlined in the recitations above. See: MPEP 2106.05(g).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 2, Figure 18 and
Paragraph 42, where “The back-end application computer server 150 and/or the other elements of the system 100 might be, for example, associated with a Personal Computer ("PC"), laptop computer, smartphone, an enterprise server, a server farm, and/or a database or similar storage devices. According to some embodiments, an "automated" back-end application computer server 150 (and/or other elements of the system 100) may facilitate updates of electronic records in the resource allocation data store 110. As used herein, the term "automated" may refer to, for example, actions that can be performed with little ( or no) intervention by a human.”
Paragraph 46, where “According to some embodiments, the elements of the system 100 automatically transmit information associated with an interactive user interface display over a distributed communication network. FIG. 2 illustrates a method 200 that might be performed by some or all of the elements of the system 100 described with respect to FIG. 1, or any other system, according to some embodiments of the present invention…Note that any of the methods described herein may be performed by hardware, software, or any combination of these approaches. For example, a computer-readable storage medium may store thereon instructions that when executed by a machine result in performance according to any of the embodiments described herein.”
Paragraph 48, where “At S230, the system may execute a machine learning trained data science model to determine an appropriate workflow for the selected requested resource allocation. As used herein, the phrase "machine learning" may refer to various artificial intelligence techniques including algorithms and mathematical models that computer systems use to continuously improve performance associated with a specific task. Machine learning algorithms may comprise a mathematical model of sample data (e.g., "training data" associated with prior insurance claims) to make predictions. The machine learning algorithms and models might be associated with computational statistics, mathematical optimization, data mining, and/or predictive analytics”
Paragraph 59, where “The back-end application computer server 1850 may also retrieve information from a machine learning process 1820, provider data 1830 (e.g., from medical providers or police reports), and/or predictive models 1840 in connection with an insurance claim analysis tool 1855. The back-end application computer server 1850 may also exchange information with a claim handler device 1860 (e.g., via communication port 1865 that might include a firewall) to enable a manual review of a STD insurance claim The back-end application computer server 1850 might also transmit information directly to an email server (or postal mail server), a workflow application, and/or a calendar application 1870 to facilitate STD insurance claim processing.”
Paragraph 64, where “sending information from a text factory 2310 (e.g., that identifies and flags text in, for example, bill data in a bill review system) to an STD model 2320. This might include, for example, information associated with STD daily noncancelled claims, specific types of claims to be dropped (e.g., maternity, particular ICD codes, etc.), old claims (e.g., more than one year from the DOD), claims that have been migrated, etc. This information may be used to filter data received from a STD claim database 2330 at (2). The STD model 2320 can then generate raw model output 2340 at (3), such as the information described in connection with Tables 1 and 2.”
Paragraph 43, where “As used herein, devices, including those associated with the back-end application computer server 150 and any other device described herein may exchange information via any communication network which may be one or more of a Local Area Network ("LAN"), a Metropolitan Area Network ("MAN"), a Wide Area Network ("WAN"), a proprietary network, a Public Switched Telephone Network ("PSTN"), a Wireless Application Protocol ("W AP") network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol ("IP") network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.”
Paragraph 68, “The real-time IT processing 2420 may involve an analyst filling out a user interface extension screen and selecting a "Get Recommendation" icon. The system may then pull the final analytical recommendation and execute a rules engine to display a result to the analyst and/or have the analyst manually approve or deny the extension at (7). Information may be saved back into the STD claim database 2330 and be used in a feedback loop at (8) to analyze performance of final analytical recommendation and improve the STD model 2320. In this way, the system may continuously adjust operation to achieve better performance. As a result, the system may provide more satisfactory customer service by quickly and accurately processing extension requests.”
The claims recite additional elements for extra-solution activity, as recited above, each of which amounts to mere post-solution activity concerning an insignificant application. The specification (e.g., as excerpted above) does not indicate that the additional element(s) provide anything other than well‐understood, routine, and conventional functions when claimed in a merely generic manner (as they are here). See: MPEP 2106.05(g).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 10-12, 14-16, 18-20 and 22 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Response to Arguments
The arguments filed 1/22/2026 have been fully considered.
Regarding the arguments pertaining to the 101 rejection, these arguments are not persuasive. Applicant argues that the amendments including of the Machine Learning (“ML”) model is run to generate raw model output including duration likelihood scores and confidence thresholds for each of a range of durations and the ML model is an Artificial Intelligence ("AI") algorithm, trained with sample data, that continuously improves task performance and is associated with all of computational statistics, mathematical optimization, data mining, and predictive analytics provide a practical application. Examiner respectfully disagrees. The machine learning model and the artificial intelligence algorithm are recited generically in the specification (paragraph 48). Specifically, the machine learning uses various artificial intelligence techniques and can include several algorithms and mathematical models and is silent to a specific technological improvement. Implementing algorithmic rules onto a generic machine learning model using generic artificial intelligence techniques on a generic computing device does not provide significantly more to the abstract idea. Applicant argues that the training of the machine learning model using the artificial intelligence algorithm improves task performance. A dataset that is output does not improve the performance of the computing device, the machine learning algorithm, or the artificial intelligence algorithm. The specification is silent to how the computing system or the machine learning model itself is being improved and points to the task of customer service (i.e., certain methods of organizing human activity) being improved (paragraph 68 of the specification). Merely stating that the machine learning model is improved by using trained data does not provide significantly more to the abstract idea. The specification is referencing that the improved performance is related to task performance implemented by a user. The specification is silent on the improved performance being on the technology itself.
Generic machine learning models create improved data outputs using generic feedback loops which is what the current claimed invention is accomplishing using various generic machine learning techniques. An improved data output to enhance user task performance using generic computing components and generic machine learning components is part of the abstract idea and does not provide significantly more to overcome the abstract.
The use of the feedback loop to analyze performance and continuously adjust operation achieving better performance is directed to the algorithm’s performance, not the performance of the computing device (specification paragraph 68). The improvement of an algorithm run on a generic computing device does not provide a technological improvement that solves a technical problem and the specification does not recite that the computer system performance is improved, merely that the algorithmic model is improved.
The arguments regarding Desjardins have been considered. No training has been received at this time.
The 101 rejection is maintained. The dependent claims rely on the arguments of the independent claims and are rejected upon the reasons stated above.
Conclusion
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/KIMBERLY A. SASS/Examiner, Art Unit 3686