DETAILED ACTION
Comments
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.
Claims 1-25 are pending and examined in the instant Office action.
Information Disclosure Statement
The IDS of 2/10/2026 has been considered.
Withdrawn Rejections
The 35 U.S.C 112 rejections are withdrawn in view of amendments filed to the instant claims on 4 December 2025.
The 35 U.S.C. 101 rejections are withdrawn in view of arguments on pages 10-17 of the Remarks. Specifically, while the claims recite judicial exceptions in the form of mental steps and mathematical equations, the mathematical algorithms recites in the claims have the practical application of a more computationally efficient estimation of state-action value functions for a Fitted Q-iteration than conventional algorithms with analogous objectives.
The rejections of claims 4-6, 11-13, 18-20 under 35 U.S.C. 103 are withdrawn in view of amendments filed to the instant claims on 4 December 2025.
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 following rejection is reiterated:
35 U.S.C. 103 Rejection #1:
Claim(s) 1-2, 8-9, 15-16, and 22-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gaeta et al. [Applied Mathematical Modelling, volume 40, 2016, pages 9183-9196; on IDS] in view of Minato [NTT LSO Laboratory, 30th ACM/IEEE Design Automation Conference, 1993, pages 272-277].
The independent claims are drawn to an algorithm for estimating a state-action value function for a Fitted Q-iteration. The algorithm comprises obtaining a set of tuples D and a discount factor, wherein each of the set of tuples includes a state, an action, a reward, and a resulting state. The algorithm comprises constructing a ZDD or BDD of feature vectors for each of the resulting states of the set of tuples. A feature vector is a sparse bit vector and there is a set of actions applicable to a state. The technique comprises updating parameters of a parameterized state-action value function and repeating the updating step a predetermined number of times of increasing t.
Each of claims 2, 9, 16, 23, and 25 recites equations for updating the parameters.
The document of Gaeta et al. studies fitted Q-iteration by functional networks for control problems [title]. Paragraph 2 of Section 2.1 of Gaeta et al. recites the use of parametric tuples to analyze Q-functions. Section 3 on pages 9185-9186 of Gaeta et al. acquiring the equivalent of the recited tuple data, and updating the parameters using equations that are obvious variants of the recited equations. Paragraph 3 of the introduction of Gaeta et al. teaches using sparse sets of tuples. Paragraph 1 of Section 3.2 of Gaeta et al. teaches expressing tuples as vectors. Figure 9 on page 9191 of Gaeta et al. illustrates the results of updating the data a predetermined number of times within a given time frame.
Gaeta et al. does not teach a BDD or ZDD.
The document of Minato studies zero-suppressed BDDs for set manipulation in combinatorial problems [title]. The figures of Minato illustrate BDDs and ZDDs.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the fitted Q-iteration calculations of Gaeta et al. by use of the BDDs and ZDDs of Minato wherein the motivation would have been that the diagrams of Minato are additional mathematical techniques that facilitate the fitted Q-iteration calculations of Gaeta et al. [Figures of Minato].
Response to arguments:
Applicant's arguments filed 4 December 2025 have been fully considered but they are not persuasive.
Applicant argues that the prior art does not teach or suggest constructing ZDD for feature vectors. The prior art of Gaeta et al. teaches feature vectors. The prior art of Minato teaches ZDD.
Applicant argues that the prior art does not teach or suggest sparse bit vectors. This argument is not persuasive because paragraph 3 of the introduction of Gaeta et al teaches sparse sets of tuples, and paragraph 1 of Section 3.2 of Gaeta et al. teaches expressing tuples as vectors.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the motivation for combining Gaeta et al. and Minato would have been that the diagrams of Minato are additional mathematical techniques that facilitate the fitted Q-iteration calculations of Gaeta et al. [Figures of Minato]. There would have been a reasonable expectation of success in combining Gaeta et al. and Minato because both studies are analogously applicable to using specific mathematical techniques to analyze state functions.
Applicant argues that the prior art does not teach parameterized state functions. This argument is not persuasive because paragraph 2 of Section 2.1 of Gaeta et al. teaches parametric analysis of Q functions.
The following rejection is reiterated:
35 U.S.C. 103 Rejection #2:
Claim(s) 3, 7, 10, 14, 17, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gaeta et al. in view of Minato as applied to claims 1-2, 8-9, 15-16, and 22-25 above, in further view of Gottipati et al. [arXiv:2001.08116v1, 20 May 2020; on IDS].
Gaeta et al. and Minato make obvious conducting fitted Q-iteration calculations with the assistance of BDDs and ZDDs, as discussed above.
Gaeta et al. and Minato do not apply their analyses to chemicals.
The document of Gottipati et al. studies learning to navigate the synthetically accessible chemical space using reinforcement learning [title]. Figure 1 of Gottipati et al. teaches using machine learning to generate new molecular structures satisfying target properties using chemical reactions. Figure 1 of Gottipati et al. illustrates a plurality of candidates. Page 2 of Gottipati et al. suggests that the product with the lowest synthetic accessibility score is selected.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the fitted Q-iteration calculations of Gaeta et al. and the BDDs and ZDDs of Minato by use of the application of machine learning to chemical reactions using reinforcement learning of Gottipati et al. wherein the motivation would have been that Gottipati et al. gives a real-world biological/chemical application of the mathematical calculations of Gaeta et al. and Minato [abstract and Figure 1 of Gottipati et al.].
Response to arguments:
Applicant's arguments filed 4 December 2025 have been fully considered but they are not persuasive.
Applicant argues that Gottipati et al. does not overcome the alleged deficiencies of the initial obviousness prior art rejection. This argument is not persuasive because the initial obviousness prior art rejection is not deficient.
The following rejection is necessitated by amendment:
35 U.S.C. 103 Rejection #3:
Claim(s) 4-6, 11-13, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gaeta et al. in view of Minato in view of Gottipati et al. as applied to claims 1-3, 7-10, 14-17, and 21-25 above, in further view of Takeda et al. [US PGPUB 2019/0286791 A1; on attached 892 form].
The claims recite that each feature vector in a Morgan fingerprint.
Gaeta et al. and Minato make obvious conducting fitted Q-iteration calculations with the assistance of BDDs and ZDDs, as discussed above.
Gaeta et al. and Minato do not apply their analyses to chemicals.
The document of Gottipati et al. studies learning to navigate the synthetically accessible chemical space using reinforcement learning [title]. Figure 1 of Gottipati et al. teaches using machine learning to generate new molecular structures satisfying target properties using chemical reactions. Figure 1 of Gottipati et al. illustrates a plurality of candidates. Page 2 of Gottipati et al. suggests that the product with the lowest synthetic accessibility score is selected.
Gaeta et al. and Minato do not teach that each feature vector in a Morgan fingerprint.
The document of Takeda et al. studies creation of new chemical compounds having desired properties using accumulated chemical data to construct a new chemical structure for synthesis [title]. The abstract of Takeda et al. teaches using feature vectors to create a regression model. Paragraph 24 of Takeda et al. teaches the use of Morgan fingerprints.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the fitted Q-iteration calculations of Gaeta et al., the BDDs and ZDDs of Minato, and the application of machine learning to chemical reactions using reinforcement learning of Gottipati et al. by use of the Morgan fingerprints of Takeda et al. wherein the motivation would have Morgan fingerprints are an additional mathematical tool to facilitate the analysis of feature vectors [abstract and paragraph 24 of Takeda et al.].
Response to arguments:
This rejection is newly applied and necessitated by amendment.
E-mail Communications Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
No claim is allowed.
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 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 Russell Negin, whose telephone number is (571) 272-1083. This Examiner can normally be reached from Monday through Thursday from 8 am to 3 pm and variable hours on Fridays.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Larry Riggs, Supervisory Patent Examiner, can be reached at (571) 270-3062.
/RUSSELL S NEGIN/ Primary Examiner, Art Unit 1686 26 February 2026