DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This Office action is in response to Applicant’s communication filed on August 26, 2025. The replacement drawings filed on August 26, 2025 are accepted and entered. Hence, the objections to the drawings are withdrawn. Amendments to claims 31, and 43 and cancellation of claims 32, 33, 35-37, 42, and 44-47 have been entered. Claims 31, 34, 38-41, 43, and 48-50 are pending and have been examined. The statement of reasons for the indication of allowable subject matter over prior art was already discussed in the Office action mailed on May 29, 2025 and hence not repeated here. The rejections and response to arguments are stated below.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 31, 34, 38-41, 43, and 48-50 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) a method for optimizing a benchmark score associated with a metric of a user” which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements as discussed below. This judicial exception is not integrated into a practical application as discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Analysis
Step 1: In the instant case, exemplary claim 31 is directed to a method.
Step 2A – Prong One: The limitations of “A method for optimizing a benchmark score associated with a metric of a user, comprising:
receiving data associated with the metric of the user, using one or more processors, wherein the benchmark score is based on a comparison of the received data and a benchmark associated with the metric;
obtaining demographic information of i) the user, ii) a person associated with the user, or iii) both, using the one or more processors;
updating, via an artificial intelligence (AI), a collection of historical data in real-time based on i) the received data of the user and the obtained demographic information, and ii) a plurality of data received associated with the metric from a plurality of different users, and iii) associated demographic information of the plurality of different users for the metric, wherein the AI comprises a machine-learning algorithm executable by the one or more processors;
wherein the demographic information of the plurality of different users comprises demographic information of i) a member of the plurality of different users, ii) a person associated with a member of the plurality of different users or iii) both associated with the metric,
training the AI so as to identify trends and patterns for the benchmark score based on the collection of historical data and new data received by the one or more processors in real-time, wherein the collection of historical data and new data received include i) the received data and the obtained demographic information, ii) the plurality of data associated with the metric and associated demographic information of the plurality of different users, iii) data associated with a plurality of different metrics for a) the user, and b) the plurality of different users, and iv) associated demographic information for the plurality of different metrics;
applying the AI to determine a course of action for the user, so as to optimize the benchmark score, wherein determining the course of action is based on the received data, the benchmark associated with the metric, the obtained demographic information, and the identified trends and patterns,
the course of action comprising i) calibrating the benchmark, ii) determining an activity to receive future data associated with the metric of the user that is different from the received data, or iii) both; and
repeating the training of the AI in real-time to adjust the identified trends and patterns. wherein repeating the training of the AI is based on the new data being received continuously, such that the AI is configured to adjust the course of action in real-time for optimizing the benchmark score” as drafted, when considered collectively as an ordered combination without the italicized portions, is a process that, under the broadest reasonable interpretation, covers the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements.
Optimizing a benchmark score is a fundamental economic practice.
The steps of “receiving data associated with the metric of the user, using one or more processors, wherein the benchmark score is based on a comparison of the received data and a benchmark associated with the metric;
obtaining demographic information of i) the user, ii) a person associated with the user, or iii) both, using the one or more processors;
updating, via an artificial intelligence (AI), a collection of historical data in real-time based on i) the received data of the user and the obtained demographic information, and ii) a plurality of data received associated with the metric from a plurality of different users, and iii) associated demographic information of the plurality of different users for the metric, wherein the Al comprises a machine-learning algorithm executable by the one or more processors;
wherein the demographic information of the plurality of different users comprises demographic information of i) a member of the plurality of different users, ii) a person associated with a member of the plurality of different users or iii) both associated with the metric,
training the AI so as to identify trends and patterns for the benchmark score based on the collection of historical data and new data received by the one or more processors in real-time, wherein the collection of historical data and new data received include i) the received data and the obtained demographic information, ii) the plurality of data associated with the metric and associated demographic information of the plurality of different users, iii) data associated with a plurality of different metrics for a) the user, and b) the plurality of different users, and iv) associated demographic information for the plurality of different metrics;
applying the AI to determine a course of action for the user, so as to optimize the benchmark score, wherein determining the course of action is based on the received data, the benchmark associated with the metric, the obtained demographic information, and the identified trends and patterns,
the course of action comprising i) calibrating the benchmark, ii) determining an activity to receive future data associated with the metric of the user that is different from the received data, or iii) both” considered collectively is fulfilling agreements between the parties concerned. Hence, the steps of the claim, under the broadest reasonable interpretation, considered collectively as an ordered combination without the italicized portions, covers the abstract category of “Certain Methods of organizing human activity”.
That is, other than, one or more processors and the artificial intelligence (AI) comprising a machine-learning algorithm executable by the one or more processors, nothing in the claim precludes the steps from being performed as a method of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of one or more processors and the artificial intelligence (AI) comprising a machine-learning algorithm executable by the one or more processors to perform all the steps. A plain reading of Figures 41-46 and descriptions in at least paragraphs [0088] – [0099] reveals that the one or more processors may be generic processors suitably programmed to perform the associated functions. The AI comprising a machine-learning algorithm is broadly interpreted to correspond to Mathematical models and generic computer components suitably programmed to perform the associated functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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. Hence, claim 31 is directed to an abstract idea.
Step 2B: The claim does 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 (identified above) to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, independent claim 31 is not patent eligible.
Dependent claims 34, 38-41, 43, and 48-50, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only refine the abstract idea further.
The steps in claims 34 and 38 of
“wherein the demographic information comprises i) age, ii) gender, iii) race, iv) experience level, v) highest level of education, vi) marital status, vii) family status, viii) household income, ix) geographic location, x) occupation, xi) origin, or xii) combinations thereof”,
“wherein obtaining data associated with the metric of the user comprises i) input from the user, ii) automatically gathering the data via the AI, or iii) combinations thereof” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
The steps in claims 39-41 of
“wherein the user is an advisor, and wherein the metric comprises the advisor's i) activity patterns, ii) productivity, iii) skill set, iv) responsiveness to notifications, v) need to implement corrective actions, vi) personal savings goals, vii) professional savings goals, viii) major purchase goals, ix) professional production goals, x) personal financial position, xi) professional financial position, or xii) combinations thereof”,
“wherein the user is a manager, and wherein the metric comprises i) a productivity of the manager, ii) activity patterns of the manager's one or more advisees, iii) common strengths of the manager's one or more advisees, iv) common weaknesses of the manager's one or more advisees, or v) combinations thereof” and
“wherein the user is an organization, and wherein the metric corresponds to collective metrics of advisors and managers that are a part of the organization”, under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the user and the metrics used in the intermediate steps of the underlying process.
The steps in claims 43, and 48-50 of
“wherein the AI is configured to identify when the trends are moving toward i) a better benchmark score, ii) a worse benchmark score, or iii) combinations thereof”,
“wherein determining the course of action comprises using data associated with one or more better benchmark scores to provide instruction to the user when the data of the user is associated with a worse benchmark score’,
“wherein the AI is configured to determine commonalities between users associated with better benchmark scores” and
“wherein determining the course of action comprises using the commonalities between users associated with better benchmark scores” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate/final steps of the underlying process.
In all the dependent claims, the judicial exception is not integrated into a practical application because the limitations are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; the claims do not affect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment. In addition, the dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Response to Arguments
4. In response to Applicants arguments on pages 1-8 of the Applicant’s remarks that the claims are patent-eligible under 35 USC 101 when considered under MPEP 2106, the Examiner respectfully disagrees.
The fact that the claims are Patent-Ineligible when considered under the MPEP 2106 has already been addressed in the rejection and hence not all the details of the rejection are repeated here.
Response to Applicants’ arguments regarding Step 2A – Prong one:
The claim(s) (exemplary claim 31) recite(s) a method for optimizing a benchmark score associated with a metric of a user” which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements as discussed in the rejection. The AI comprising a machine-learning algorithm is broadly interpreted to correspond to Mathematical models and generic computer components suitably programmed to perform the associated functions. The steps of “obtaining demographic information of i) the user, ii) a person associated with the user, or iii) both, using the one or more processors; updating, via an artificial intelligence (AI), a collection of historical data in real-time based on i) the received data of the user and the obtained demographic information, and ii) a plurality of data received associated with the metric from a plurality of different users, and iii) associated demographic information of the plurality of different users for the metric, wherein the Al comprises a machine-learning algorithm executable by the one or more processors; wherein the demographic information of the plurality of different users comprises demographic information of i) a member of the plurality of different users, ii) a person associated with a member of the plurality of different users or iii) both associated with the metric, training the AI so as to identify trends and patterns for the benchmark score based on the collection of historical data and new data received by the one or more processors in real-time, wherein the collection of historical data and new data received include i) the received data and the obtained demographic information, ii) the plurality of data associated with the metric and associated demographic information of the plurality of different users, iii) data associated with a plurality of different metrics for a) the user, and b) the plurality of different users, and iv) associated demographic information for the plurality of different metrics; applying the AI to determine a course of action for the user, so as to optimize the benchmark score, wherein determining the course of action is based on the received data, the benchmark associated with the metric, the obtained demographic information, and the identified trends and patterns, the course of action comprising i) calibrating the benchmark, ii) determining an activity to receive future data associated with the metric of the user that is different from the received data, or iii) both” is indeed a form of fulfilling agreements between the parties concerned. “Repeating the training of the AI in real-time to adjust the identified trends and patterns, wherein repeating the training of the AI is based on the new data being received continuously, such that the AI is configured to adjust the course of action in real-time for optimizing the benchmark score” only further improves the abstract idea of optimizing a benchmark score associated with a metric of a user. An improvement in abstract idea is still abstract (SAP America v. Investpic *2-3 (“We may assume that the techniques claimed are “groundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“A claim for a new abstract idea is still an abstract idea). The additional elements (identified in the rejection) are generic computer components used to apply the abstract idea. Hence, the claims recite an abstract idea. The Examiner does not see the parallel between the Applicant’s claims and the claim in Example 39 of the published USPTO examples. Therefore the Applicant’s arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2A – Prong two:
According to MPEP 2106, limitations that are indicative of integration into a practical application include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e).
In the instant case, the judicial exception is not integrated into a practical application, because none of the above criteria is met. The claim (exemplary claim 31) only recites the additional elements of one or more processors and the artificial intelligence (AI) comprising a machine-learning algorithm executable by the one or more processors to perform all the steps. A plain reading of Figures 41-46 and descriptions in at least paragraphs [0088] – [0099] reveals that the one or more processors may be generic processors suitably programmed to perform the associated functions. The AI comprising a machine-learning algorithm is broadly interpreted to correspond to Mathematical models and generic computer components suitably programmed to perform the associated functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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.
The claimed features and those recites on pages 4-7 such as “training the AI so as to identify trends and patterns for the benchmark score based on the collection of historical data and new data received in real-time... applying the AI to determine a course of action for the user, so as to optimize the benchmark score...and repeating the training of the AI in real-time to adjust the identified trends and patterns, wherein repeating the training of the AI is based on the new data being received continuously, such that the AI is configured to adjust the course of action in real- time for optimizing the benchmark score ,,,,, "training the AI," "applying the AI," and "repeating the training of the AI in real-time... such that the AI is configured adjust the course of action in real-time" may at best be characterized as an improvement in the abstract idea of optimizing a benchmark score associated with a metric of a user. An improvement in abstract idea is still abstract (SAP America v. Investpic *2-3 (“We may assume that the techniques claimed are “groundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). The additional elements (identified in the rejection) are generic computer components used to apply the abstract idea. Hence, the claims are directed to an abstract idea. It does not involve any improvements to another technology, technical field, or improvements to the functioning of the computer itself. The Examiner does not see the parallel between the Applicant’s claims and the claims in Diamond v. Diehr, 450 U.S. 175, 187, 188 (1981) (Diehr herein after). There is no similarity between the concept of optimizing a benchmark score associated with a metric of a user (in Applicant’s claims) and the concept of a computer-implemented process for curing rubber in Diehr. Therefore, Applicants’ arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2B:
As discussed in the rejection, 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 (identified in the rejection) to perform the claimed steps, amount to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claims are not patent eligible.
The alleged benefits such as “improvement to existing methods for optimizing the performance metrics for a user ….. timely providing tailored guidance for the user in improving said benchmark score ….. determining and updating a course of action in real-time” are due to improvements in the abstract idea of optimizing a benchmark score associated with a metric of a user, using the additional elements as tools in their ordinary capacity. Regarding applicant's arguments alleging the lack of prior art as evidence that the claims contain an improvement and therefore are significantly more, this argument-sounding in § 102 novelty-is beside the point for a §101 inquiry. See Amdocs (Isr.) Ltd. v. Openet Telecom, Inc., No. 1: 10cv910 (LMB/TRJ), 2014 WL 5430956, at *11 (E.D. Va. Oct. 24, 2014) ("The concern of § 101 is not novelty, but preemption."). The search for an inventive concept should not be confused with a novelty or non-obviousness determination. See, e.g., Synopses, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty.”). MPEP § 2106.05. Therefore, Applicants’ arguments are not persuasive.
For these reasons and those discussed in the rejection, the rejections under 35 USC § 101 are maintained.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(a) Feinschreiber; Steven Andrew et al. (US Pub. 2016/0063445 A1) discloses a method for optimizing healthcare and retirement benefits for an employee. The method includes receiving information about at least one medical insurance plan and at least one retirement savings account for which the employee is eligible and selecting for the employee a medical insurance plan from the at least one medical insurance plan. The method also includes determining a first amount for the employee to contribute on a pre-tax basis to at least one health care savings account. The first amount is at least equal to an estimated total of eligible expenses not covered by the medical insurance plan. The method further includes determining a second amount for the employee to contribute to the at least one retirement savings account. Selection of the medical insurance plan, the first amount and the second amount optimize the healthcare and retirement benefits for the employee.
(b) Wang, Jun-yi et al. (CN-115081790 A) discloses an investment manager performance assessment method and device, which can be used for financial field, wherein the method comprises: the evaluation data and market public data obtain the received combination; determining the combined yield of each examination period in the preset time interval and the value of each alternative performance assessment index; for each examination period in the preset time interval, according to the combined yield of the current examination period and the value of each alternative performance assessment index of the last examination period, determining the information coefficient of each candidate performance assessment index in each examination period; according to the information coefficient of each candidate performance assessment index in each examination period, determining the average information coefficient of each alternative performance assessment index in the preset time interval; screening the alternative performance assessment index according to the average information coefficient, determining the selection performance assessment index; according to the selected performance assessment index, performing investment management human performance assessment. The invention can carry out investment management human performance assessment, improve the evaluation efficiency and the accuracy of the assessment result.
6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Narayanswamy Subramanian whose telephone number is (571) 272-6751. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Abhishek Vyas can be reached at (571) 270-1836. The fax number for Formal or Official faxes and Draft to the Patent Office is (571) 273-8300.
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/Narayanswamy Subramanian/
Primary Examiner
Art Unit 3691
January 10, 2026