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 .
Receipt of Applicant’s Amendment filed January 23, 2026, is acknowledged.
Response to Amendment
Claims 1, 8, and 14 have been amended. Claims 1, 3, 9, and 15 have been canceled. Claims 2, 4-8, 10-14, 16, and 17 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant's arguments filed January 23, 2026, have been fully considered but they are not persuasive. A response is provided below in bold where appropriate.
Applicant argues Double Patenting, pg. 8 of Remarks:
Double Patenting
Per page 6 of the Office Action, Claims 2, 4 - 8, 10 - 14, 16, and 17 each stand rejected on the ground of non-statutory double patenting as being unpatentable over Claims 1 - 16 of U.S. Patent No. 11,545,267.
Applicant wishes to remind the Office that a timely filed terminal disclaimer has already been submitted on December 20, 2024, disclaiming the terminal part of the statutory term of any patent granted on the instant application which would extend beyond the expiration of the full statutory term of U.S. Patent No. 11,545,267. Accordingly, this non-statutory double patenting should be withdrawn.
Noted and withdrawn.
Applicant cites claim amendments, pg. 8 of Remarks:
Claim Amendments
By this paper, Applicant has amended Claims 2, 8, and 14 to amend instances of 'for" with "of". While Applicant believes that either form is correct, this amendment has been added to clarify the Office's assertions made at page 4 of the Office Action. Support for Applicant's20 amendment to Claims 2, 8, and 14 may be found at, inter alia, page 22, lines 15 – 17 of Applicant’s specification as filed. Hence, no new matter has been entered by virtue of Applicant’s amendments to Claims 2, 8, and 14.
Noted.
Applicant argues 35 USC §101, starting pg. 8 of Remarks:
35 U.S.C. §101
Per page 7 of the Office Action, Claims 2, 4 - 8, 10 - 14, 16, and 17 each stand rejected under 35 U.S.C. §101 as the claimed invention is allegedly directed towards an abstract idea without significantly more. In response thereto, Applicant provides the following remarks:
Claims 2, 8, and 14 - Applicant respectfully disagrees with the Office's contention that Claims 2, 8, and 14 "under their broadest reasonable interpretation, cover performance of the limitation as mental processes. " For example, Claim 8 recites in pertinent part:
"... receiving feedback on the determined set of diagnostic assessments and the determined set of additional diagnostic assessments and using the received feedback on the determined set of diagnostic assessments and the determined set of addtional diagnostic assessments to update the policy and the state value function of the assessment RL agent; and
receiving feedback on the determined set of treatment plans and the determined additional set of treatment plans and using the received feedback on the determined set of treatment plans and the determined additional set of treatment plans to update the policy and the state value function of the plan RL agent independent from the update of the policy and the state value function of the assessment RL agent."{emphasis added}
Applicant again respectfully directs the Office's attention to Example 39 ("Methodfor15 Trainina Neural Network for Facial Detection") of the Subject Matter Eligibility Examples: Abstract Ideas that should be used in conjunction with the 2019 PEG. Moreover, Example 39 is explicitly referenced in the "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101," memo by Deputy Commissioner Charles Kim, USPTO (dated August 4, 2025) (hereinafter "Updated Guidance dated August 4, 2025"). Example 39 of the 2019 PEG claims a computer-implemented method of training a neural network for facial detection. As set forth in
Example 39:
"A neural network is a framework of machine learning algorithms that work together to classify inputs based on a previous training process. In facial detection, a neural network classifies images as either containing a human face or not, based upon the model being previously trained on a set of facial and non-facial images. However, these prior methods suffer from the inability to robustly detect human faces in images where there are shifts, distortions, and variations in scale and rotation of the face pattern in the image. Applicant's invention addresses this issue by using a combination of features to more robustly detect human faces. The first feature is the use of an expanded training set of facial images to train the neural network.... The neural networks are then trained with this expanded training set using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network. Unfortunately, the introduction of an expanded training set increases false positives when classifying non-facial images. Accordingly, the second feature of applicant's invention is the minimization of these false positives by performing an iterative training algorithm, in which the system is retrained with an updated training set containing the false positives produced after face detection has been performed on a set of non-facial images. This combination of features provides a robust face detection model that can detect faces in distorted images while limiting the number of false positives." {emphasis added}
Similar to Example 39, the recited claims establish a policy and state value function of an assessment RL agent using historical diagnostic assessment outcomes. Additionally, the recited claims establish a policy and state value function of a plan RL agent using historical diagnostic and treatment recommendations for a predetermined set of diagnostic assessments. The recited claims update the policy and state value function of the assessment RL agent and the plan RL agent over time based on received feedback. Moreover, the update of the policy and state value function of the plan RL agent is independent from the update of the policy and state value function of the assessment RL agent. Accordingly, Claims 2, 8, and 14 do not recite a judicial exception. Even though, for example, the "update of the policy and the state value function of the plan RL agent independent from the update of the policy and the state value function of the assessment RL agent" involves a broad array of techniques that may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols.
The rejection is not based on mathematical concepts, rather mental concepts and certain methods of organizing human activity. For example, a person in their mind with pen and paper can establish a policy and a state value function using historical diagnostic assessment outcomes. Also, for example, diagnosing outcomes is teaching, which falls under certain methods of organizing human activity. Example 39 was directed to facial detection.
Although Applicant had previously referenced Example 39 in Applicant's response dated December 20, 2024, in the reply contained in the Office Action dated March 11, 2025, the Office erroneously implied that Example 39 was no longer valid guidance as "[t]o the extent that earlier guidance from the Office, including certain sections of the MPEP (R-07.2022), is inconsistent with 2024 AI SME Update, Office personnel are to follow the 2024 AI SME update." Applicant again notes that the Updated Guidance dated August 4, 2025, explicitly references Example 39. Similar to Example 39, as a judicial exception is not recited in Claims 2, 8, and 14, the inquiry should end at Step 2A, Prong 1 of the eligibility analysis.
Noted. However, the Memorandum dated August 4, 2025, points out that Example 39 did not recite a judicial exception, such as a mathematical concept.
The July 2024 SME was looking for improvement to artificial intelligence technology, and this was reinforced by USPTO Memorandum dated December 5, 2025, “Advance notice of change to the MPEP in light of Ex Parte Desjardins,” where improvement to AI technology is a consideration. There is no indication the claims are improving a machine learning or AI technology.
Accordingly, Claims 2, 8, and 14 as presented herein are eligible because these claims do not recite any of the judicial exceptions enumerated in the 2019 PEG at Step 2A - Prong 1 of the subject matter eligibility analysis. Applicant respectfully requests withdrawal of the Office's 35 U.S.C. §101 rejection of the pending claims.
Respectfully, the claims recite abstract concepts.
However, assuming arguendo that the inquiry does not end at Step 2A, Prong 1 (a point that Applicant expressly disagrees with), the Office's conclusion that the claims merely recite "a combination of abstract steps [and accordingly are] ... still abstract" is respectfully erroneous. The claims do not merely recite only the idea of a solution or outcome, rather the claims cover a particular solution to a problem or a particular way to achieve the desired outcome. Rather than simply providing for a description of reinforcement learning generally, the recited claims provide for an architecture that allows for the independent optimization of the policy and state value functions of two (2) distinct RL agents, namely the assessment RL agent and the plan RL agent. While these two RL agents operate in tandem with one another, this independent optimization is advantageous as it simplifies the development and optimization of the respective policy and state value functions for these RL agents. See also, for example, page 21, lines 23 - 28 of Applicant's specification as filed.
The claim limitations appear to be using policy and state value function of a RL agent. This is recited at a high level of generality and with no indication of an improvement to such technology.
Per the Updated Guidance dated August 4, 2025, the Office is "reminded that if it is a 'close call' as to whether a claim is eligible, they should only make a rejection when it is more10 likely an not (i.e., more than 50%) that the claim is ineligible under 35 U.S.C. 101." At least given the fact that Example 39 is a valid example, and that Claims 2, 8, and 14 are similar to Example 39, the Office should refrain from maintaining that the pending claims are ineligible under 35 U.S.C. §101.
Noted. However, using reinforcement learning at a high level of generality is not improving machine learning technology itself. Based on the above response, the rejection is respectfully maintained but modified for the claim amendments.
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, 4-8, 10-14, 16, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 2, 4-8, 10-14, 16, and 17 are directed to a product, method, or system, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 8 as the claim that represents the claimed invention for analysis and is similar to product Claim 1 and system Claim 14.
Claim 8 recites the limitations of:
A method for the application of reinforcement learning (RL) to the treatment of animals, the method comprising:
establishing a policy and state value function for an assessment RL agent using historical diagnostic assessment outcomes as an input for the establishing of the policy and the state value function of the assessment RL agent;
establishing a policy and state value function for a plan RL agent using historical diagnostic and treatment recommendations for a predetermined set of diagnostic assessments as an input for the establishing of the policy and the state value function of the plan RL agent;
determining a set of diagnostic assessments, using the policy and the state value function of the assessment RL agent, using a set of observations of an animal;
determining a set of treatment plans based on the determined set of diagnostic assessments using the policy and the state value function of the plan RL agent, the set of treatment plans comprising additional diagnostic tests to be performed;
receiving additional observations subsequent to performance of the additional diagnostic tests;
determining an additional set of diagnostic assessments, using the policy and the state value function of the assessment RL agent, using the additional observations;
determining an additional set of treatment plans based on the determined additional set of diagnostic assessments using the policy and the state value function of the plan RL agent;
storing the determined set of diagnostic assessments, the determined set of additional diagnostic assessments, the determined set of treatment plans, and the determined additional set of treatment plans in a historical log storage device;
receiving feedback on the determined set of diagnostic assessments and the determined additional diagnostic assessments and using the received feedback on the determined set of diagnostic assessments and the determined set of additional diagnostic assessments to update the policy and the state value function of the assessment RL agent; and
receiving feedback on the determined set of treatment plans and the determined additional set of treatment plans and using the received feedback on the determined set of treatment plans and the determined additional set of treatment plans to update the policy and the state value function of the plan RL agent independent from the update of the policy and the state value function of the assessment RL agent.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. The claim recites elements, in non-bold above, which covers performance of the limitation that can be concepts performed in the mind of a person or with pen and paper. For example, a person can mentally or with pen and paper establish a policy and state value function for an assessment and plan reinforcement learning (RL) agent, determine a set of diagnostic assessments using a set of observations of an animal, determine a set of treatment plans based on the determined diagnostic assessment, receive additional observations, determine additional set of diagnostic assessments using additional observations, determine additional set of treatment plans based on the determined set of diagnostic assessments, store (write down) the determined set of diagnostic assessments and treatment plans, receive feedback on the determined set of diagnostic assessments and determined set of treatment plans. Further, MPEP 2106.04(a)(2) III C indicates that judicial exceptions can be performed by a generic computer (i.e., using a generic computer to perform a mental process is still abstract as a mental process). It is also noted that the claimed steps do not require a computer to perform the steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a mental process, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 1 and 14 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as the claim is determining a set of diagnostic assessments using a set of observations of an animal (human), determining a set of treatment plans based on the diagnostic assessments, receiving additional observations, determining additional diagnostic assessments using additional observations, receiving feedback on the determined set of diagnostic assessments and additional diagnostic assessments, and receiving feedback on the determined set of treatment plans and additional treatment plans, the claims are also abstract as managing personal behavior, therefore abstract as certain methods of organizing human activity (see pg. 2, lines 2-7 where humans are animals). Also, using a policy and state value function of an assessment RL agent is following rules and instructions (managing personal behavior). Determining a set of treatment plans using the policy and state value function of the plan RL agent is teaching (managing personal behavior).
This judicial exception is not integrated into a practical application. In particular, the claims only recite: non-transitory computer-readable storage apparatus, processor, historical log storage device (Claim 2); historical log storage device (Claim 8); subjective biological storage device, objective biological data storage device, historical log storage device (Claim 14). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. See Applicant’s specification (pg. 2, lines 3-7 and 10-24) about use of or application of RL technology to different fields of use, and pg. 10, lines 21-28 about implantation using various computer devices and MPEP 2106.05(f), where applying a computer as a tool is not indicative of significantly more. The RL agents and engines appear to be software claimed at a high level of generality, and where steps are performed “of” the RL agents not “by” the RL agents. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 8, and 14 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as receiving and storing are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 8, and 14 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 4-7, 10-13, 16, and 17 further define the abstract idea that is present in their respective independent claims 2, 8, and 14 and thus correspond to Mental Processes and Mathematical Concepts and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Claims 4-6, 10-12, and 17 also recite storage devices which are generic computer components recited at a high level of generality. Claims 5, 7, 11, 13, 16, and 17 further recite RL agent, which is at a high level of generality and also itself could be abstract as Mathematical Concepts. Therefore, the claims 4-7, 10-13, 16, and 17 are directed to an abstract idea. Thus, the claims 2, 4-8, 10-14, 16, and 17 are not patent-eligible.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Prior Art Search
A prior art search was conducted but does not result in a prior art rejection at this time. Prior art of Pub. No. US 2021/0133509 to Wall et al. teaches both an assessment and prediction (plan) model (Fig. 2, ref. 110 and 120). However, they fail to teach a policy. The prior art of Pub. No. US 2021/0027878 to He et al. teaches RL agent and policy (e.g., reward in para. [0027]), however they fail to teach two different reinforcement learning. Also, the Examiner incorporates by reference the notice of allowance from 16/985106 as further reasons for overcoming the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following prior art teaches reinforcement learning agents:
US-20200337648-A1
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 KENNETH BARTLEY whose telephone number is (571)272-5230. The examiner can normally be reached Mon-Fri: 7:30 - 4:00 EST.
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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