Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The following is a FINAL Office action in reply to the Amendments and Arguments received on February 12, 2026.
Status of Claims
Claims 2, 12, and 20 have been amended.
Claims 3, 4, 13 and 14 have been cancelled.
Claims 2, 5-12 and 15-21 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 2, 5-12 and 15-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 12, 15-21 are drawn to methods while claim(s) 2, 5-12 is/are drawn to an apparatus. As such, claims 2, 5-12 and 15-21 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
Claim 12 (representative of independent claim(s) 2) recites the following steps:
A method for recommending employee benefits using derived population preferences, the method comprising:
automatically categorizing, a plurality of employees of a subject employer into a profile summary comprising a plurality of census categories, each respective census category of the plurality of census categories comprising a set of divisions, wherein employee demographic information and current plan information of each respective employee of the plurality of employees is used to add the respective employee to a count of employees grouped into a respective census division of the set of divisions of each category of the plurality of census categories, wherein the plurality of census categories comprises at least one of a current plan, a coverage tier, or a plan subgroup, and two or more of age or age range indication, gender, salary or salary range indication, type of employee, regional location, or family size classification;
automatically generating, from a plurality of benefit attribute options, a plurality of offering scenarios, wherein each respective offering scenario comprises at least one of i) a different combination of attribute options of the plurality of benefit attribute options, or ii) a different combination of values allocated to a respective combination of attribute options of the plurality of benefit attribute options,
automatically generating comprises i) using a current product offering offered by the subject employer to the plurality of employees, generating a plurality of modified status quo offering scenarios of the plurality of offering scenarios by modifying, for each offering scenario of the plurality of modified status quo offering scenarios, one or more respective values of one or more attribute options of the current product offering within a predetermined threshold amount and by a predefined increment, and ii) generating a plurality of standard offering scenarios of the plurality of offering scenarios by adding, to each respective attribute value combination of a standard set of attribute value combinations representing one or more attribute options, one or more additional attribute options generated by random selection from a portion of the plurality of benefit attribute options not including the one or more attribute options of the respective attribute value combination;
training, a plurality of algorithms to compute a probability of selection, by a given person according to demographic attributes and benefits attributes of the given person, a given offering scenario comprising a set of benefit attribute options of the plurality of benefit attribute options, wherein the plurality of benefit attribute options comprises one or more of a premium amount, an employee contribution amount, an employer subsidy amount, an individual deductible, a family deductible, an individual out-of-pocket maximum, a family out-of-pocket maximum, a copay amount, or a coinsurance amount,
a first one or more machine learning algorithms of the plurality of machine learning algorithms are trained using training data including actual benefit attributes held by a plurality of individuals of a population, wherein each individual of the plurality of individuals is associated with a respective employer of a plurality of employers different than the subject employer, and the first one or more machine learning algorithms apply an inertia model trained to determine the probability of selection of each of a first set of offering scenarios similar to a status quo benefits plan currently held the given person
a second one or more machine learning algorithms of the plurality of machine learning algorithms are trained using sets of survey data submitted by at least a first portion of the plurality of individuals, wherein the second one or more algorithms apply a free choice model trained to determine the probability of selection of each of a second set of offering scenarios dissimilar from the status quo benefits plan
applying the profile summary and the plurality of offering scenarios to the plurality of machine learning algorithms comprises applying the plurality of modified status quo offering scenarios to the first one or more machine learning algorithms to obtain a first portion of the plurality of sets of selection probabilities, and applying the plurality of standard offering scenarios to the second one or more machine learning algorithms to obtain a second portion of the plurality of sets of selection probabilities
applying the profile summary and the plurality of offering scenarios to the plurality of algorithms comprises
applying the plurality of modified status quo offering scenarios to the first one or more algorithms, to obtain a first portion of the plurality of sets selection probabilities and
applying the plurality of standard offering scenarios to the second one or more algorithms to obtain second portion of the plurality of sets of selection probabilities;
determining, using the plurality of sets of selection probabilities, a plurality of employee perception scores comprising, for each respective offering scenario of the plurality of offering scenarios, a respective employee perception score
selecting, based at least in part on the employee perception scores corresponding to the plurality of offering scenarios, at least one recommended scenario of the plurality of offering scenarios; and
providing the at least one recommended scenario for review by an end user
These steps, under its broadest reasonable interpretation, encompass mathematical calculations or relationships (i.e. models, algorithms). These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
Alternatively, these steps, under its broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) recommending employee benefits using derived population preferences, (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the "mental processes" subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 12 recites an abstract idea (Step 2A - Prong One: YES).
Independent claim(s) 2 is determined to recite an abstract idea under the same analysis.
Step 2A - Prong Two:
This judicial exception is not integrated into a practical application. The claim(s) recite the additional elements/limitations of:
system (Claim 2)
a non-volatile computer readable medium (Claim 2)
by the processing circuitry (Claim 2 and 12)
one or more machine learning algorithms (Claim 2 and 12)
a remote computing device (Claim 2 and 12)
The requirement to execute the claimed steps/functions listed above is equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. This/these limitation(s) do/does not impose any meaningful limits on producing the abstract idea and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A -Prong Two: NO).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above in "Step 2A - Prong 2", the requirement to execute the claimed steps/functions listed above is equivalent to adding the words "apply it" on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as "significantly more" (see MPEP 2106.05 (f)).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Regarding Dependent Claims:
Dependent claims 5-10, 15-18, fail to include any additional elements and are further part of the abstract idea as identified by the Examiner.
Dependent claims 11, and 19-21 include additional limitations that are part of the abstract idea except for:
remote computing device via an application programming interface
by the processing circuitry
The additional elements of the dependent claims are equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
Response to Arguments
Applicant's arguments with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Applicant Argues: Applicant respectfully submits that the Mathematical Concepts grouping of abstract ideas is not applicable to the currently pending claims.
Examiner respectfully disagrees. Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer,” and that “courts have found requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind” (see p. 8 of the October 2019 Update: Subject Matter Eligibility). The Examiner also notes that “both product claims (e.g., computer system, computer-readable medium, etc.) and process claims may recite mental processes (see p. 8 of the October 2019 Update: Subject Matter Eligibility). Absent of the computing functions cited, the steps could be performed using mathematical relationships, as well, as by mental process. The claims represent an abstract idea.
Applicant Argues: The Appeals Review Panel (ARP) decision in Ex parte Desjardins, Appeal 2024-000567, September 26, 2025 ("Desjardins") establishes that an improved function ("an improvement on how the machine learning model itself operates") represents patentable subject matter
Examiner agrees; however such an improvement can not be found in the instant claims. The claims recite the steps being performed by a high level recitation of process circuitry and algorithms and do not provide evidence of any improved function.
Applicant Argues: McRO, Inc v. Bandai Namco Games America Inc., 837 F. 3D 1299 (Fed. Cir. 2016) describes programming a generic processor to perform a different function. McRO simply recites "general-purpose computers" and "limited mathematical rules," yet the claims in McRO were found patent-eligible. See McRO, 837 F.3d at 1314-16. Thus, the Federal Circuit has maintained in recent decisions that the improvement leading to patent eligibility may be an improvement stemming from programming a generic processor to perform a different or improved function.
Examiner respectfully disagrees. Examiner notes that the concept in McRO is entirely different from that of the instant application, and thus cannot be relied upon as a prima facie basis for patent eligibility simply because Applicant purports their invention is similarly entrenched in an improvement stemming from
programming a generic processor to perform a different or improved function. While the Examiner appreciates Applicant's comparison to McRO, those "rules" are quite different than the alleged "rules" of the instant invention. The instant claims are certainly not like the complicated rules used in McRO. In McRO, the claims were drawn to a complex methodology of synchronizing audio files with lip movement and facial expressions in computer animation. In its ruling, the Federal Circuit noted that the claims did not simply disclose an automated task previously performed by humans (emphasis added), but instead recited a new computerized process that was not previously used by animators. In reaching this conclusion, the court looked to the pleadings to determine if the parties provided any evidence as to whether or not the process had been previously used in the animation.
In their answer, Defendants provided no evidence that the process previously used by animators was the same as the process required by the claims (McRO at 24). Conversely, the Plaintiffs substantiated their position by providing specific evidence that the method described in the claims was not previously used by animators, and that their process was distinguishable because it used "a combined order of specific rules that renders information into a specific format that is then used and applied to create desired results." (McRO at 25).
Turning to the instant invention, it is clear that the claims are merely drawn to a method of recommending employee benefits that is performed in a routine and conventional manner via generic computer technology. Therefore, notwithstanding Applicant's insistence to the contrary, the claims of the instant invention are not triggered by the holding in McRO, but rather disclose a number of abstract ideas (as explained above) that are associated with generic computer technology. As such, the claims remain ineligible under section 101.
Applicant Argues: In particular, the claimed features apply both "an inertia model" and "a free choice model" to "provide[] for accurate ... probability calculation for plans that are similar to those that are typically seen by clients as well as plans having designs that are not typically available in client offerings." Id. At ,I 56. As explained in the specification as filed, the division of sets of generated scenarios and application to differently trained machine learning algorithms "may improve current data fitting while simultaneously reacting correctly to pricing scenarios outside the range of trained data." Id.
Examiner respectfully disagrees. Using more than one training model does not represent the needed improvement. Referring to the Recentive Analytics v. Fox Corp decision, the U.S. Court of Appeals for the Federal Circuit affirmed the district court’s dismissal of a patent infringement lawsuit brought by Recentive Analytics against Fox Corporation, where it was determined that the machine learning models employed were conventional. The Federal Circuit reaffirmed that iteratively training a machine learning model on data does not transform an abstract idea into a patent-eligible invention. Similarly, confining the trained machine learning model to a particular technological field is insufficient unless the implementation introduces a specific, non-generic improvement to computing technology and describes how this improvement is accomplished. That improvement is not represented in the instant claims. It is important to note that most machine learning models are inherently trained on large, often complex datasets to generate recommendations. It is not apparent that such a non-generic improvement is reflective in the instant claims as the claims do not provide any detail that addresses any improvement to the broadly claimed training step. As such the rejection is maintained.
Applicant Argues: Applicant respectfully asserts that the pending claims differ from Recentive .4th Analytics, Inc. v Fox Corp., 134 F. 1205 (Fed. Cir. 2025), where the claim language failed to detail "steps through which the machine learning technology achieves an improvement." Recentive, 134 F.4th at 1212-13 ("Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system."). As described above in relation to the three primary sets of operations, the currently pending independent claims articulate implementation details representing "an inventive concept sufficient to transform the [alleged] abstract idea into a patent-eligible application." Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1364 (Fed. Cir. 2023) (quoting Alice Corporation v. CLS Bank International, 573 U.S. 208, 221 (2014)).
Examiner respectfully disagrees. See arguments for Recentive above. Similarly, the amended claims further describe the information being used to train the algorithm but does not detail steps through which the machine learning technology achieves an improvement. Similar to the recited Trinity case, Applicant claims the instant invention is directed to improvements to the functionality of a computer or network platform itself, but relies on generic computing terms such as “system (Claim 2); a non-volatile computer readable medium (Claim 2); by the processing circuitry (Claim 2 and 12); one or more machine learning algorithms (Claim 2 and 12); a remote computing device (Claim 2 and 12) which merely provides a generic environment in which to carry out the abstract idea.
Applicant Argues: As demonstrated above through the highlighted features of the independent claims, unlike Example 47 Claim 2, the pending independent claims recite "details about how the trained [ machine learning algorithms] operate[], "details that explain the analysis of [the two separate model types of machine learning algorithm]," and "limits on how the data is output" ( e.g., a particular format).
Examiner respectfully disagrees. The amended claims provide further detail on the information (profile summaries, benefits attributes, etc.) that is trained, not the how the machine learning algorithms operate. The rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHIDA R SHORTER whose telephone number is (571)272-9345. The examiner can normally be reached Monday- Friday from 9am- 530pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at (571) 270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RASHIDA R SHORTER/Primary Examiner, Art Unit 3626