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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. The applicant's submission, the Amendment filed on 07 October 2025, has been entered.
Status of the Claims
The pending claims in the present application are claims 1-3, 5-10, 12-17, 19, and 20 of the Amendment.
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 1-3, 5-10, 12-17, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “system” of claims 1-3 and 5-7 constitutes a machine under 35 USC 101, the “method” of claims 8-10 and 12-14 constitutes a process under the statute, and the “non-transitory computer readable medium” of claims 15-17, 19, and 20 constitutes a manufacture under the statute. Accordingly, claims 1-3, 5-10, 12-17, 19, and 20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“... receiving ... a prescription purchasing recommendation request from ... a user ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... obtaining current policy coverage information of a plurality of insurance policies issued by different insurance providers to the user, wherein the insurance policies include a medical insurance policy and at least one of a dental insurance policy, an accident insurance policy, a disability insurance policy, a critical illness insurance policy, an auto insurance policy, or a hospital indemnity insurance policy that provide different prescription coverages; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... obtaining current user prescription information of prescriptions that have been prescribed to the user; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... obtaining, from one or more pharmacy benefits managers, current prescription cost information based on the different prescription coverages for the prescriptions from a plurality of pharmacies; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... applying one or more ... techniques to respectively map the current prescription cost information into a standard prescription cost schema, the current policy coverage information into a standard policy coverage schema, and the current user prescription information into a standard prescription schema, wherein the standard prescription cost schema, the standard policy coverage schema, and the standard prescription schema form a standard schema; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... obtaining a prescription cost information prediction ... based on the current prescription cost information, the current policy coverage information, and the current user prescription information as mapped ... to output the prescription cost information prediction, wherein the prescription cost information prediction identifies and ranks a subset of pharmacies from the plurality of pharmacies and respective cost saving information from using the subset of pharmacies; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... present the subset of pharmacies and the respective cost saving information ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: fundamental economic principles or practices, including pricing goods; commercial interactions, including advertising, marketing, or sales activities or behaviors, associated with providing patients with prescription drugs; and managing personal behavior or relationships or interactions between people, including prescription drug suppliers and consumer, which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “receiving” and “obtaining” limitations), and evaluation, judgment, and/or opinion (e.g., the recited “applying” and “causing” limitations), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use pending claim 1 as an example, the claim recites the following additional element limitations:
“A data processing system comprising: a processor; and a machine-readable storage medium storing executable instructions which, when executed by the processor, cause the processor alone or in combination with other processors to perform operations of: ...” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “receiving” is “via an insurance portal” and is “from a computing device” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
“... wherein the insurance portal is configured on a virtual machine that is configured on at least one physical server and supports an authentication pipeline with the computing device for access control, wherein the authentication pipeline provides two-factor authentication, and the at least one physical server connects with the computing device via a network; ...” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “techniques” include “fuzzy matching techniques” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “obtaining” is performed “in substantially real-time” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “mapped” is performed using “a machine learning, the machine learning model being trained using training data formatted according to the standard schema” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “present” is performed by “causing a user interface of the computing device” and happens “in substantially real-time” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, mere automation of manual processes, instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, which courts have found to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome (see MPEP 2106.05(f)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, and selecting a particular data source or type of data to be manipulated in the form of selecting information, based on types of information and availability of information in an environment, for collection, analysis, and display, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. Further, claim 1 also fails to meet the criteria of Step 2B because at least some of the additional elements are analogous to: receiving or transmitting data over a network, e.g., using the Internet to gather data, performing repetitive calculations, electronic recordkeeping, and storing and retrieving information in memory, which courts have recognized as well-understood, routine, conventional activity, and as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding pending claims 2, 3, and 5-7, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “further displays a map of locations of the subset of pharmacies” of claim 2, the “wherein the respective cost saving information provides guidance for switching the prescriptions to a selected pharmacy from the subset of pharmacies” of claim 3, the “obtaining location information indicative of a location associated with the user, wherein ... further analyzes the location information to obtain the prescription cost information prediction” of claim 5, the “converting past claim information made by the user against the plurality of insurance policies into the format associated with the standard schema; and analyzing the past claim information and the current policy coverage information as converted, as well as and user demographic information associated with the location ... to recommend a comprehensive insurance plan for the user that includes a bundle of insurance policies” of claim 6, the “refining ... based on user feedback data” of claim 7). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “data processing system” of claims 2, 3, and 5-7, the “user interface” of claim 2, the “wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of” claims 5-7, the “machine learning model” of claim 5, the “using the machine learning model or another machine learning model” of claim 6, and the “machine learning model” of claim 7). Accordingly, claims 2, 3, and 5-7 also are rejected as ineligible under 35 USC 101.
Regarding pending claims 8-10 and 12-14, while the claims are of different scope relative to claims 1-3 and 5-7, the claims recite limitations similar to the limitations of claims 1-3 and 5-7. As such, the rejection rationales applied to reject claims 1-3 and 5-7 also apply for purposes of rejecting claims 8-10 and 12-14. Claims 8-10 and 12-14 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding pending claims 15-17, 19, and 20, while the claims are of different scope relative to claims 1-3, 5, and 6 and to claims 8-10, 12, and 13, the claims recite limitations similar to the limitations of claims 1-3, 5, 6, 8-10, 12, and 13. As such, the rejection rationales applied to reject claims 1-3, 5, 6, 8-10, 12, and 13 also apply for purposes of rejecting claims 15-17, 19, and 20. Claims 15-17, 19, and 20 are, therefore, also rejected as ineligible under 35 USC 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or no obviousness.
Claims 1-3, 5, 7-10, 12, 14-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. App. Pub. No. 2021/0074401 A1 to Bezdek et al. (hereinafter referred to as “Bezdek”), in view of U.S. Pat. App. Pub. No. 2015/0012300 A1 to Smith (hereinafter referred to as “Smith”), further in view of U.S. Pat. App. Pub. No. 2018/0268488 A1 to Stevenot (hereinafter referred to as “Stevenot”), further in view of U.S. Pat. App. Pub. No. 2016/0055313 A1 to Sellars (hereinafter referred to as “Sellars”), and further in view of U.S. Pat. App. Pub. No. 2021/0210185 A1 to Allred et al. (hereinafter referred to as “Allred”).
Regarding claim 1, Bezdek discloses the following limitations:
“A data processing system comprising: a processor; and a machine-readable storage medium storing executable instructions which, when executed by the processor, cause the processor alone or in combination with other processors to perform operations of: ...” - Bezdek discloses, a “SYSTEM 100” (FIG. 1), and “the method is implemented by the one or more hardware processors configured to execute specific instructions stored in the memory” (para. [0018]). The system including hardware processors executing instructions stored in memory to implement the method, in Bezdek, reads on the recited limitation.
“... receiving, via an insurance portal, a prescription purchasing recommendation request from a computing device associated with a user ...” - Bezdek discloses, “At block 502, the system 400 provides a user interface 210 for providing drug pricing information from multiple PBMs 290A, 290B and from the insurance pricing system 440. Users can request pricing information about a specific drug, supply their insurance information, and supply user identifying information (user name, mailing address, email address, password etc.) via the user interface 210. For example, the user can enter a user name, mailing address, and email address, password, the name of the drug, and the insurance information in the user interface 210, and send a request for pricing information to the system 400” (para. [0163]), and “The user interface 640 can be provided on a computing device or a display associated with the computing device. The computing device can be, for example, the mobile phone 111, the computer 112, the tablet 113, the laptop 114, etc. as shown in FIG. 1” (para. [0189]). The system receiving the request for pricing information from the computing device of the user, in Bezdek, reads on the recited limitation.
“... obtaining current policy coverage information ... issued by ... insurance providers to the user, wherein the insurance policies include a medical insurance policy ...” - See the aspects of Bezdek that have been cited above. Bezdek also discloses, “health insurance coverage” (para. [0004]). The users supplying their health insurance information to the system, in Bezdek, reads on the recited limitation.
“... obtaining current user prescription information of prescriptions that have been prescribed to the user; ...” - See the aspects of Bezdek that have been cited above. Bezdek also discloses, “A user enters information about a prescription such as the name of the drug (generic or brand-name), the form and the dosage” (para. [0009]). Receiving information about the prescription via entry of the information by the users, in Bezdek, reads on the recited limitation.
“... obtaining, from one or more pharmacy benefits managers, current prescription cost information based on the different prescription coverages for the prescriptions from a plurality of pharmacies; ...” - Bezdek discloses, “The user interface 1100 may also display prices 1120 for the drug available from one or more pharmacies. The prices 1120 may be for the most common or user selected package for the drug. In one embodiment, the user interface 1100 displays a default number of prices, such as portion of the drug pricing information returned from the PBMs. In the example of FIG. 11, each price 1120 is associated with one pharmacy” (para. [0500]), “The user interface 1100 may also display prices 1170A, 1170B for the drug through the user's insurance company. Typically, the user interface 1100 displays prices 1170A or price 1170B. Prices 1170A indicate the insurance price of the drug as negotiated by each of the various pharmacies” (para. [0502]), and “Price 1170B indicates the amount of the user's co-pay when the user's insurance covers the cost of prescriptions less the co-pay amount” (para. [0503]). The system obtaining drug pricing information from the PBMs, based on insurance prices of the drugs as negotiated by the pharmacies, in Bezdek, reads on the recited limitation.
“... applying one or more ... techniques to respectively map the current prescription cost information into a standard prescription cost schema, the current policy coverage information into a standard policy coverage schema, and the current user prescription information into a standard prescription schema, wherein the standard prescription cost schema, the standard policy coverage schema, and the standard prescription schema form a standard schema; ...” - See the aspects of Bezdek that have been cited above. Bezdek also discloses, “A user presents a prescription to be filled and the unique set of identifiers to the pharmacy system 610. For example, the unique set of identifiers may be presented online via the user interface 640, via email and/or text, or by presenting a physical discount card at the pharmacy counter” (para. [0199]), “The pharmacy system 610 collects pertinent information, such as name, dosage, form, quantity, number of refills, etc., about the drug from the prescription. The pharmacy system 610 also collects the user's unique set of identifiers assigned by system 600, including at least the BIN associated with the system 600” (para. [0200]), “The pharmacy system 610 formats a data packet with the collected information to send to the system 600 for drug pricing information, in the same manner that the pharmacy system 600 would format a data packet to send to a PBM for drug pricing information” (para. [0201]), “An example of the syntax of a transmission request and response may be as follows” (para. [0202]), “Product Name” (para. [0235]), “Other Coverage Code” (para. [0341]), and “Usual and Customary Charge” (para. [0427]). The data packet of information having the format with the syntax including the charge, the coverage, and the product, in Bezdek, reads on the recited limitation.
“... obtaining a prescription cost information prediction in substantially real-time based on the current prescription cost information, the current policy coverage information, and the current user prescription information as mapped ... to output the prescription cost information prediction, wherein the prescription cost information prediction identifies and ranks a subset of pharmacies from the plurality of pharmacies and respective cost saving information from using the subset of pharmacies; and - See the aspects of Bezdek that have been cited above. Bezdek also discloses, “Cut a higher-dosage pill in half to save 50% or more,” “$14.64 with discount,” “$15.00 co-pay,” and “$25.14 with insurance” (FIG. 11), and “The system 200 may rank the drug prices from multiple PBMs prior to displaying drug prices in the user interface 210. The system 200 may display only some of the prices from the ranking process” (para. [0116]). The system receiving and displaying pricing information for the prescribed drug with respect to the user (with discount, with insurance, and with co-pay), based on the price of the drug, the insurance coverage of the user, and the user’s prescription, wherein the pricing information numerically specifies which prices of pharmacies are higher or lower than those of others, in Bezdek, reads on the recited limitation.
“... causing a user interface of the computing device to present the subset of pharmacies and the respective cost saving information in substantially real-time.” - See the aspects of Bezdek that have been cited above. Causing displaying of the interface shown in FIG. 11 of Bezdek, including multiple pharmacies, cost information indicating differences in cost, and tips for saving money, in Bezdek, reads on the recited limitation.
The combination of Bezdek and Smith (hereinafter referred to as “Bezdek/Smith”) teaches limitations below of claim 1 that do not appear to be disclosed in their entirety by Bezdek:
“... wherein the insurance portal is configured on a virtual machine that is configured on at least one physical server and supports an authentication pipeline with the computing device for access control, wherein the authentication pipeline provides two-factor authentication, and the at least one physical server connects with the computing device via a network; ...” - Smith teaches, “A private cloud computing network containing secure authentication and authorization modules can provide a secure, interactive method of data exchange, notification, and electronic ordering and decision making between patients and medical providers” (para. [0022]), “The "Triple Factor Authentication Compiler" (FIG. 4m) obtains information from the referring physician's profile that contains the identification information required for authentication (FIG. 4n) and performs authentication using the "Authentication as a Service" Module (FIG. 4o). The "Authentication Approval and Security Token Generator" (FIG. 4p) confirms the referring physician's authentication and generates a security token to verify successful authentication and approves physician access to the electronic ordering response processor (FIG. 4o)” (para. [0053]), “FIG. 7a demonstrates one embodiment of an "Interactive Health Information Smart Forms Processor Device" which resides on a windows based Virtual Machine. FIG. 7b demonstrates the "Referring Physician Response Questionnaire/Order Compiler" operating on a Windows based Virtual Machine on a IBM Server” and “The "Triple Factor Authentication Compiler" resides on a Linx based Virtual Machine (FIG. 7M). The "Authentication as a System Module" resides on a Windows based Virtual machine (FIG. 7o). The "Authentication and Approval and Security Token Generator" resides on a Windows based Virtual Machine (FIG. 7p)” (para. [0057]). Use of the virtual machine on the IBM server to implement the triple factor authentication compiler within the private cloud computing network, of Smith, in the context of the system in Bezdek, reads on the recited limitation.
Smith teaches “collecting medical information from patients and other sources and distributing information as necessary” (abstract), similar to the claimed invention, and to Bezdek. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of computing devices, of Bezdek, to include the virtual machine, server, and authentication features, of Smith, for security, as taught by Smith (see para. [0022]).
The combination of Bezdek, Smith, and Stevenot (hereinafter referred to as “Bezdek/Smith/Stevenot”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Bezdek/Smith:
The claimed “insurance polices” also include “at least one of a dental insurance policy, an accident insurance policy, a disability insurance policy, a critical illness insurance policy, an auto insurance policy, or a hospital indemnity insurance policy that provide different prescription coverages ...” - Stevenot discloses, “The computationally implemented method may include insurance plans like but not limited to: health insurance, dental insurance, vision insurance, auto insurance, home insurance, renters' insurance, life insurance, liability insurance and the service/care providers” (para. [0006]), “The computationally implemented method may also include a search feature as well as a list of optional plans in addition to a primary insurance plan, such as dental, hearing, clinical surgical, or psych” (para. [0014]), and “Step. 104 of FIG. 1 illustrates selecting at least one detail view from a plurality of viewing options of the policy(s) owned by the at least one subject which the at least one user requires to render appropriate service and treatment. The viewing options available to the user may be limited by the software license purchased from the provider of the computational method and its permissions. The viewing options each containing detailed information about at least one aspect of an insurance policy, such as types of coverage (dental, vision, medical, etc.) and items and/or treatments that qualify for insurance coverage, such as surgical treatments, specialist treatments, prescription drugs etc.” (para. [0018]). The combination of health insurance and the other types of insurance, in Stevenot, reads on the recited limitation.
Stevenot discloses, “to solve the complex problems related to accessing accurate, organized and current insurance policy information of a subject or client by a service provider or user” (Abstract), similar to the claimed invention and to Bezdek/Smith. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the health insurance information, of Bezdek/Smith, to be considered in combination with other insurance information, as in Stevenot, for a more comprehensive understanding of an individuals insurance coverage, as taught by Stevenot (see paras. [0002]-[0004]).
The combination of Bezdek, Smith, Stevenot, and Sellars (hereinafter referred to as “Bezdek/Smith/Stevenot/Sellars”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Bezdek/Smith/Stevenot:
The claimed “techniques” include “fuzzy matching” - Sellars discloses, “the data normalizer 504 may identify new entries from the candidate prescription strings and generate a fuzzy map to match new entries” (para. [0059]), and “A third function 706 is to generate a fuzzy map to characterize relations among the new entries and/or between the new entries and the existing prescription strings. Each relation may be associated with a confidence score representing a confidence about the relation. A fourth function 708 is to confirm fuzzy relations generated by the third function 706 and manually match the new entries based on the fuzzy map” (para. [0068]). Use of the fuzzy mapping, in Sellars, reads on the recited limitation.
Sellars discloses, “the present teaching relates to methods, systems, and programming for computer aided prescription” (para. [0001]), similar to the claimed invention and to Bezdek/Smith/Stevenot. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the handling of the raw prescription data, of the combination of Bezdek/Smith/Stevenot, to include use of fuzzy mapping to determine relationships between data, as in Sellars, for normalizing data, as taught by Sellars (see paras. [0059] and [0060]).
The combination of Bezdek, Smith, Stevenot, Sellars, and Allred (hereinafter referred to as “Bezdek/Smith/Stevenot/Sellars/Allred”) teaches limitations below of claim 1 that do not appear to be taught in their entirety by Bezdek/Smith/Stevenot/Sellers:
The claimed “mapped” involves “using a machine learning model, the machine learning model being trained using training data formatted according to the standard schema ... - Allred discloses, “the prescription processing system and/or other components of the network may be an artificial intelligence (AI) system which develops foundational knowledge based on historical data sets. Historical data sets (e.g., historical price data from previously processed medical prescriptions) may be used as inputs to a machine learning model such as, for example, a recurrent neural network (RNN) or other form of artificial neural network. As is generally understood in the art, an artificial neural network functions similar to a biologic neural network and is comprised of a series of nodes and connections. The machine learning model is trained to predict one or more values based on the input data. In some embodiments, the AI system may be trained to predict pricing for a prescribed medication” (para. [0078]). Use of the AI and machine learning to predict pricing of prescribed medication, wherein the AI and machine learning is trained on historical data sets, in Allred, when applied in the context of performing drug pricing, in Bezdek/Smith/Stevenot/Sellars, reads on the recited limitation.
Allred teaches “processing medical prescriptions” (Abstract), similar to the claimed invention and to Bezdek/Smith/Stevenot/Sellars. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the drug price determining processes, of Bezdek/Smith/Stevenot/Sellars, to include use of AI and machine learning, of Allred, for more accurate pricing through the use of trained neural networks, as taught by Allred (see para. [0078]).
Regarding claim 2, Bezdek/Smith/Stevenot/Sellars/Allred teaches the following limitations:
“The data processing system of claim 1, wherein the user interface further displays a map of locations of the subset of pharmacies.” - Bezdek discloses, “The user interface 1100 may also provide a map 1145 showing a region that includes the pharmacy locations for the prices 1120 displayed in the user interface 1100” (para. [0504]).
Regarding claim 3, Bezdek/Smith/Stevenot/Sellars/Allred teaches the following limitations:
“The data processing system of claim 1, wherein the respective cost saving information provides guidance for switching the prescriptions to a selected pharmacy from the subset of pharmacies.” - See the aspects of Bezdek that have been cited above. The differences between listed prices in FIG. 11 direct users to switch prescriptions to pharmacies to benefit from lower costs.
Regarding claim 5, Bezdek/Smith/Stevenot/Sellars/Allred teaches the following limitations:
“The data processing system of claim 1, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: obtaining location information indicative of a location associated with the user, wherein the machine learning model further analyzes the location information to obtain the prescription cost information prediction.” - See the aspects of Bezdek that have been cited above. The system including hardware processors executing instructions stored in memory to implement the method, in Bezdek, reads on the recited “wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of” limitation. Also see the aspects of Allred that have been cited above. Allred also teaches, “A pharmacy preference may depend on a variety of factors, including but not limited to distance, travel time, convenience, accessibility, a patient's comfort, a patient's familiarity, and/or a patient's trust associated with a particular location. Thus, in some embodiments, the pharmacy preference data 215 may include input from a patient indicating one or more pharmacies (i.e., one or more specific locations) as preferred pharmacies. In some embodiments, the pharmacy preference data 215 may alternatively or additionally include one or more geographical locations and/or a proximity to one or more geographical locations. For example, the pharmacy preference data 215 may include one or more locations such as a recent (e.g., current) geographical location of the patient, a home address, a work address, previously visited pharmacy locations, a currently selected pharmacy, and/or additional locations of interest for the patient. The pharmacy preference data 215 may also include a proximity threshold, such as a maximum distance and/or travel time in conjunction with the one or more locations. As such, even without explicit indication by the patient of one or more preferred pharmacies, the system 105 may use the available data to deduce likely preferential pharmacy locations” (para. [0042]), and “any functions of the system and/or steps of the methods as described herein may be further automated or optimized by the AI system” (para. [0078]). Using current (and/or preferred) geographical location information about users to predict prices for prescribed medications, in Allred, reads on the recited “obtaining location information indicative of a location associated with the user, wherein the machine learning model further analyzes the location information to obtain the prescription cost information prediction” limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 5.
Regarding claim 7, Bezdek/Smith/Stevenot/Sellars/Allred teaches the following limitations:
“The data processing system of claim 1, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: refining the machine learning model based on user feedback data.” - See the aspects of Bezdek that have been cited above. The system including hardware processors executing instructions stored in memory to implement the method, in Bezdek, reads on the recited “wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of” limitation. Also see the aspects of Allred that have been cited above. Allred also teaches, “the AI system may be trained to interpret benefits contracts and/or denial letters in order to identify patient costs for medications, covered and uncovered equivalent medications” (para. [0078]). Operation of the RNN, in Allred, reads on the recited “refining the machine learning model based on user feedback data” limitation. Rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 7.
Regarding claims 8-10, 12, and 14, while the claims are of different scope relative to claims 1-3, 5, and 7, the claims recite limitations similar to those recited by claims 1-3, 5, and 7. As such, the rationales used to reject claims 1-3, 5, and 7 also apply for purposes of rejecting claims 8-10, 12, and 14. Claims 8-10, 12, and 14 are, therefore, also rejected under 35 USC 103 as obvious in view of Bezdek/Smith/Stevenot/Sellars/Allred.
Regarding claims 15-17 and 19, while the claims are of different scope relative to claims 1-3 and 5 and to claims 8-10 and 12, the claims recite limitations similar to those recited by claims 1-3, 5, 8-10, and 12. As such, the rationales used to reject claims 1-3, 5, 8-10, and 12 also apply for purposes of rejecting claims 15-17 and 19. Claims 15-17 and 19 are, therefore, also rejected under 35 USC 103 as obvious in view of Bezdek/Smith/Stevenot/Sellars/Allred.
Examiner Comments
Claims 6, 13, and 20 have not been rejected in view of the cited references above. Each of the claims recites limitations that do not appear to be taught or suggested by the cited references, whether the reference are viewed alone or in combination. Using claim 6 as an example, the claim recites, “analyzing the past claim information and the current policy coverage information as converted, as well as and user demographic information associated with the location using the machine learning model or another machine learning model to recommend a comprehensive insurance plan for the user that includes a bundle of insurance policies.” While the cited references perform various analyses on past claims involving prescriptions, utilize raw data about consumers, and perform machine learning processes, such steps are focused on drug pricing, not recommending insurance plans that bundle insurance policies. As such, claim 6 distinguishes over the cited references. Claims 13 and 20, while of different scope, recite limitations similar to those of claim 6. As such, claims 13 and 20 receive similar treatment.
Response to Arguments
On 9-16 of the Amendment, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. More specifically, regarding Step 2A, Prong One of the eligibility analysis specified in MPEP 2106, the applicant contends that the claims are not drawn to any of the judicial exceptions from the 2024 PEG. (Amendment, p. 9.) The applicant points to several recited limitations as support for the contention, while emphasizing technology and hardware limitations like the claimed “physical server.” (Amendment, pp. 9 and 10.) The applicant also emphasizes the claimed “fuzzy matching” and “standard schema,” while references MPEP 2106.04(a)(2)(III)(A) and the AI-SME Update (Amendment, p. 10.)
The examiner finds the arguments described above unpersuasive. The examiner generally agrees that the claim limitations emphasized by the applicant cannot be performed by humans alone or by the human mind. But the examiner asserts that the reasons they cannot be performed by humans or the human mind is because the claim limitations emphasize by the applicant include numerous additional elements. When the additional elements are properly set aside for later consideration (at Step 2A, Prong Two and Step 2B of the eligibility analysis), what remains can be performed by humans and using the human mind. Those remaining claim limitations are abstract idea elements falling under the enumerated groupings of MPEP 2106.04(a).
With respect to Step 2A, Prong Two and Step 2B of the eligibility analysis, the applicant contends that the eligibility rationales from Example 42 of the 2019 PEG support eligibility of the applicant’s claims. (Amendment, pp. 10-12.) The applicant also contends that paras. [0018], [0047], and [0049] specify eligibility-warranting improvements in line with MPEP 2106.04(d) and 2106.05(a). (Amendment, pp. 12 and 13.) The applicant also contends that the claims provide a first technical solution to convert current prescription coverages of various insurance policies into a standard insurance coverage schema, thereby providing technical benefits of comparing the prescription coverages in a consistent way. (Amendment, pp. 13-15.) The applicant also contends that the claims provide a second technical solution of applying machine learning to analyze prescription cost information, prescription coverage information, and prescription information, thereby providing technical benefits of obtaining prescription cost savings reports in real-time. (Amendment, p. 15.) The applicant also contends that the claims provide a third technical solution of training machine learning using data parsed and standardized into a standard schema. (Amendment, pp. 15 and 16.)
The examiner finds the arguments described above unpersuasive. Regarding Example 42, the examiner acknowledges that the applicant’s claims have wording that appears to overlap with wording of the eligible claim in Example 42. However, “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art.” (MPEP 2106.05(a).) The Background section of Example 42 provides context explaining why the claimed standardizing (and related limitations) are an improvement over prior art systems. The applicant’s disclosure, on the other hand, does not explain why a standard schema is an improvement. There is no indication of any technological problem in the applicant’s disclosure, that is solved by the claimed standard schema, in the way that Example 42 has a technological problem being solved by standardizing formats. Thus, the eligibility rationales from Example 42 do not apply to the applicant’s claims. Further, allowing prescription coverages to be compared in a consistent way is not a technical solution, but rather, is an abstract idea in the form of marketing or sales activities or behaviors (see MPEP 2106.04(a)). The examiner also asserts that applying machine learning is not a technical solution and does not warrant eligibility because it is mere instructions to apply an exception (see MPEP 2106.05(f)). The examiner also asserts that training machine learning using data parsed and standardized into a standard schema is not a technical solution and does not warrant eligibility because it is a step in generic, conventional machine learning, and because it is mere data gathering and/or selecting a particular data source or type of data to be manipulated, which all are forms of insignificant extra-solution activity (see MPEP 2106.05(g)). (Amendment, pp. 15 and 16.) Finally, the real-time aspect(s) of the applicant’s invention is no more than accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, which is a non-improvement per MPEP 2106.05(a).
On pp. 16-19 of the Amendment, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 103. The applicant contends that Sellars does not remedy the deficiencies of Bezdek. Or, in other words, Bezdek and Sellars do not teach or suggest certain claim limitations. The examiner finds the arguments unpersuasive. Sellars is cited primarily as a teaching of using fuzzy matching to process text data. Bezdek also discloses that text data that is processed and formatted into a syntax includes prescription information, prescription cost information, prescription coverages, and the like. One of ordinary skill would use the fuzzy matching in Sellars on all forms of text information already disclosed by Bezdek. The applicant’s contentions focus on Sellars without considering the disclosures of Bezdek. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624