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 .
Claim Objections
Claims 2 and 3 are objected to because of the following informalities: In claim 2, line 4, the language “demonstrations from expert by machine-learning algorithm” is grammatically incorrect. The term “expert” should recite “an expert” or “experts”. The term “machine-learning algorithm” should recite “a machine-learning algorithm”.
In claim 3, line 2, “Acrylic” should recite “Acyclic”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 1, the language in lines 6-8 renders the claim indefinite because it includes typographical errors which makes the limitation unclear, and it is unclear if these lines includes two different limitations. Examiner treats claim 1, line 7 as if there were a semicolon after “states” and as if “encode the causal graph by updating state variable embeddings;” were a separate step on the next line.
Claims 2-5 are rejected for failing to cure the deficiencies of claim 1.
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 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1-5 recite a system, which is one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1: Generate a causal graph indicating relationships between the current states based on the current states is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. Specification paragraph 27 discloses that Fig. 1 depicts a causal graph in the top right section of the figure. A person can draw the causal graph on a piece of paper.
Encode the causal graph by updating state variable embeddings is a mathematical calculation. Specification paragraphs [0053]-[0055] and [0062]-[0065] include equations (4) and (8) which encode a trajectory of states and generates an embedding of state variables.
Predict an action for the target to be taken with respect to the states based on the state variable embeddings is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2: An action prediction system comprising: at least one memory storing instructions, and at least one processor configured to access the at least one memory and execute the instructions amounts to generic computer components for apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Obtain current states of a target task amounts to insignificant pre-solution activity under MPEP 2106.05(g).
Output the predicted action amounts to insignificant post-solution activity under MPEP 2106.05(g).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: An action prediction system comprising: at least one memory storing instructions, and at least one processor configured to access the at least one memory and execute the instructions amounts to generic computer components for apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Obtain current states of a target task is analogous to retrieving information from memory, which is a well-understood, routine, convention activity recognized by the courts under MPEP 2106.05(d)(II).
Output the predicted action is analogous to presenting offers and gathering statistics, which is a well-understood, routine, convention activity recognized by the courts under MPEP 2106.05(d)(II).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 2 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The model is adversarially trained with a discrimination model to discriminate between predicted actions and demonstrations from expert by machine-learning algorithm is a mathematical calculation. Specification paragraphs [0070]-[0073] discloses an adversarial learning objective function for a policy.
Step 2A Prong 2 and Step 2B: The action is predicted by using a model and the updated state variable embeddings amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. In the context of claim 1, generating a causal graph that is a Directed Acrylic Graph indicating relationships between the current states is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. Specification paragraph 27 discloses that Fig. 1 depicts a directed acyclic causal graph in the top right section of the figure. A person can draw the causal graph on a piece of paper.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The causal graph is generated by optimizing the causal graph based on constraints amounts to mathematical calculations. [0048]-[0052] disclose equations for a sparsity constraint and acyclicity constraint.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The action is a treatment for a patient by a doctor based on health states of the patient amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 3 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Haan et al. (“Causal Confusion in Imitation Learning”).
Regarding claim 1, Haan teaches: An action prediction system comprising: at least one memory storing instructions; and at least one processor configured to access the at least one memory and execute the instructions to: (Page 2, § 3 to page 3, line 15 and Algorithm 1 on page 6 teaches computing causal graphs. This computing environment inherently requires a memory storing instructions and processor for executing the instructions.)
obtain current states of a target task; (Page 2, § 3, from lines 1 to page 3, line 12 and page 5, § 4.1, lines 2-3.)
generate a causal graph indicating relationships between the current states based on the current states; (Page 5, § 4.1, lines 2-7 discloses generating a number of possible graphs.)
encode the causal graph by updating state variable embeddings; (Page 5, § 4.1, lines 2-8 and equation (1) discloses performing element-wise multiplication between state variable vector Xi and a binary parameterization of graph G. This encodes the causal graph and updates state variable vector Xi into an embedding.)
predict an action for the target to be taken with respect to the states based on the state variable embeddings; and (Page 5, § 4.1, lines 1-16, where the output of a policy corresponding to each candidate causal graph is an action.)
output the predicted action. (Page 5, § 4.1, lines 7-16, where evaluating fϕ in equation (1) outputs a predicted action.)
Regarding claim 3 Haan teaches: The action prediction system according to claim 1, wherein the causal graph is a Directed Acrylic Graph indicating relationships between the current states. (Page 2, § 3, from lines 1 to page 3, line 12 and Fig. 2 discloses a DAG)
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Haan et al. (“Causal Confusion in Imitation Learning”) in view of Wang et al. (“Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes”).
Regarding claim 2, Haan teaches: The action prediction system according to claim 1, wherein the action is predicted by using a model and the updated state variable embeddings wherein the model is
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, Equation (1) discloses training via machine learning, and “demonstrations from experts” includes expert response A in expert query mode.)
However, Haan does not explicitly teach (underlines indicate limitations not taught): wherein the model is adversarially trained with a discrimination model to discriminate between predicted actions and demonstrations from expert
But Wang teaches: wherein the model is adversarially trained with a discrimination model to discriminate between predicted actions and demonstrations from expert by machine-learning algorithm. (Page 1785, all of Abstract; and page 1788, § 3.1, lines 1-13 and 17-21 discloses a dynamic treatment regime policy πθ is trained with an adversarial discriminator Da, which discriminates between treatments predicted by the policy and real-world treatments. The “model” is the policy πθ, “demonstrations from expert” are real-world treatments prescribed by doctors, and a “machine-learning algorithm” is the training/updating of the policy πθ.)
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 incorporated Wang’s adversarial discriminator into Haan, and to have updated the model from Haan’s policy based on feedback from Wang’s adversarial discriminator. A motivation for the combination is to train the model to mimic successful treatments. (Page 1788, col. 1, § 3.1)
Regarding claim 5, Haan teaches: The action prediction system according to claim 1,
However, Haan does not explicitly teach: wherein the action is a treatment for a patient by a doctor based on health states of the patient.
But Wang teaches: wherein the action is a treatment for a patient by a doctor based on health states of the patient. (Page 1785, col. 2, § 1, lines 1-7 and page 1788, § 3.1, lines 1-5)
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 applied Haan’s causal graph to Wang’s dynamic treatment regime. A motivation for the combination is that a variety of factors may contribute to the outcome of a patient, including medical history, laboratory measurements, and demographics. (Wang, page 1785, col. 2, § 1, lines 1-7)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Haan et al. (“Causal Confusion in Imitation Learning”) in view of Zhang et al. (US 20170235626 A1).
Regarding claim 4, Haan teaches: The action prediction system according to claim 1,
However, Haan does not explicitly teach: wherein the causal graph is generated by optimizing the causal graph based on constraints.
But Zhang teaches: wherein the causal graph is generated by optimizing the causal graph based on constraints. (Zhang’s claim 4 on page 4, the title “Uncover Causal Relations via Non-Negative Sparse Regression” above paragraph [0021], and paragraphs [0021]-[0023], where an optimization is the regression in Equation (4) and a constraint is a sparsity constraint.)
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 optimized Haan’s causal graph based on a sparsity constraint. A motivation for the combination is to capture the dependence relationships among variables in a time series. (Zhang, [0002], lines 11-15)
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 3 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of copending Application No. 17/877,081 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other.
Regarding claim 1, the limitations “An action prediction system comprising: at least one memory storing instructions; and at least one processor configured to access the at least one memory and execute the instructions to:” are anticipated by reference claim 15 limitation “A system for learning a self-explainable imitator by discovering causal relationships between states and actions, the system comprising: a memory; and one or more processors in communication with the memory configured to”. Communication between the processor and memory and the processor being configured to perform steps indicates the processor executes instructions stored in the memory.
The limitations “obtain current states of a target task” and “generate a causal graph indicating relationships between the current states based on the current states” are anticipated by reference claim 15 limitation “generate dynamic causal graphs for each environment state in the learned policy, wherein each dynamic causal graph is a continuous directed acyclic graph over state variables and actions and is generated prior to action prediction”. Current states are environment states in the learned policy. Generating dynamic causal graphs that are based on environment states indicates obtaining the environment states before generating the dynamic causal graphs.
The limitation “encode the causal graph by updating state variable embeddings” is anticipated by reference claim 15 limitation “encode discovered causal relationships by updating the state variable embeddings”
The limitations “predict an action for the target to be taken with respect to the states based on the state variable embeddings” and “output the predicted action” are both anticipated by reference claim 15 limitation “output the learned policy including trajectories similar to the demonstrations from the experts, wherein the action prediction is computed from the state variable embeddings updated according to the dynamic causal graph.” An action prediction being computed indicates predicting an action and outputting the predicted action.
This is an anticipatory-type, provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Regarding claim 3, the limitation “the causal graph is a Directed Acrylic Graph indicating relationships between the current states” is anticipated by reference claim 15 limitation “each dynamic causal graph is a continuous directed acyclic graph over state variables and actions”.
This is an anticipatory-type, provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 2 and 5 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of copending Application No. 17/877,081 (hereinafter the “reference claim”) in view of Wang et al. (“Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes”).
Regarding claim 2, the limitations “wherein the action is predicted by using a model and the updated state variable embeddings” and “demonstrations from expert” are taught by reference claim 15 limitations “obtain demonstrations of a target task from experts for training a model to generate a learned policy; train the model the learning component computing actions to be taken with respect to states; … encode discovered causal relationships by updating the state variable embeddings. … and output the learned policy including trajectories similar to the demonstrations from the experts, wherein the action prediction is computed from the state variable embeddings updated according to the dynamic causal graph.” The “model” recited in claim 2 is a model that generates a learned policy for predicting actions.
However, reference claim 15 does not explicitly teach: wherein the model is adversarially trained with a discrimination model to discriminate between predicted actions and demonstrations from expert by machine-learning algorithm.
But Wang teaches: wherein the model is adversarially trained with a discrimination model to discriminate between predicted actions and demonstrations from expert by machine-learning algorithm. (Page 1785, all of Abstract; and page 1788, § 3.1, lines 1-13 and 17-21 discloses a dynamic treatment regime policy πθ is trained with an adversarial discriminator Da, which discriminates between treatments predicted by the policy and real-world treatments. The “model” is the policy πθ, “demonstrations from expert” are real-world treatments prescribed by doctors, and a “machine-learning algorithm” is the training/updating of the policy πθ.)
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 incorporated Wang’s adversarial discriminator into the system of reference claim 15, and to have updated the model from reference claim 15 based on feedback from Wang’s adversarial discriminator. A motivation for the combination is to train the model to mimic successful treatments. (Page 1788, col. 1, § 3.1)
This is an obviousness-type, provisional nonstatutory double patenting rejection.
Regarding claim 5, reference claim 15 teaches: The action prediction system according to claim 1.
However, reference claim 15 does not explicitly teach: wherein the action is a treatment for a patient by a doctor based on health states of the patient.
But Wang teaches: wherein the action is a treatment for a patient by a doctor based on health states of the patient. (Page 1785, col. 2, § 1, lines 1-7 and page 1788, § 3.1, lines 1-5)
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 applied the causal graph of reference claim 15 to Wang’s dynamic treatment regime. A motivation for the combination is that a variety of factors may contribute to the outcome of a patient, including medical history, laboratory measurements, and demographics. (Wang, page 1785, col. 2, § 1, lines 1-7)
This is an obviousness-type, provisional nonstatutory double patenting rejection.
Claim 4 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of copending Application No. 17/877,081 (hereinafter the “reference claim”) in view of Zhang et al. (US 20170235626 A1).
Regarding claim 4, reference claim 15 teaches: The action prediction system according to claim 1.
However, reference claim 15 does not explicitly teach: wherein the causal graph is generated by optimizing the causal graph based on constraints.
But Zhang teaches: wherein the causal graph is generated by optimizing the causal graph based on constraints. (Zhang’s claim 4 on page 4, the title “Uncover Causal Relations via Non-Negative Sparse Regression” above paragraph [0021], and paragraphs [0021]-[0023], where an optimization is the regression in Equation (4) and a constraint is a sparsity constraint.)
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 optimized the causal graph of reference claim 15 based on a sparsity constraint. A motivation for the combination is to capture the dependence relationships among variables in a time series. (Zhang, [0002], lines 11-15)
This is an obviousness-type, provisional nonstatutory double patenting rejection.
Claims 1 and 3 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/471,570 (hereinafter the “reference claim”) in view of Haan et al. (“Causal Confusion in Imitation Learning”).
Regarding claim 1, the limitations “An action prediction system comprising: at least one memory storing instructions; and at least one processor configured to access the at least one memory and execute the instructions to:” are taught by reference claim 1 limitations “A learning system comprising: at least one memory storing instructions; and at least one processor configured to access the at least one memory and execute the instructions to”.
The limitations “obtain current states of a target task; generate a causal graph indicating relationships between the current states based on the current states” are taught by reference claim 1 limitations “obtaining demonstrations of a target task from experts for training a model to generate a learned policy” and “generating dynamic causal graphs for each environment state”. Each environment state would be obtained in order to generate dynamic causal graphs.
The limitation “encode the causal graph by updating state variable embeddings” is taught by reference claim 1 limitation “encoding discovered causal relationships by updating state variable embeddings”.
The limitations “predict an action for the target to be taken with respect to the states
However, reference claim 1 does not explicitly teach: predict an action for the target to be taken with respect to the states based on the state variable embeddings;
But Haan teaches: predict an action for the target to be taken with respect to the states based on the state variable embeddings; (Page 5, § 4.1, lines 1-16 discloses performing element-wise multiplication between state variable vector Xi and a binary parameterization of graph G. The result of element-wise multiplication is a state variable embedding. An “action” is an output of a policy corresponding to each candidate causal graph.)
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 incorporated Haan’s causal graph-parameterized policy learning into reference claim 1. A motivation for the combination is that jointly learning policies corresponding to various causal graphs can lead to finding the true causal model of the expert’s actions. (Page 5, § 4, lines 1-4)
This is an obviousness-type, provisional nonstatutory double patenting rejection.
Regarding claim 3, the combination of reference claim 1 and Haan teaches: The action prediction system according to claim 1.
The limitation “wherein the causal graph is a Directed Acrylic Graph indicating relationships between the current states” is taught by reference claim 1 limitation “generating dynamic causal graphs for each environment state, wherein each of dynamic causal graphs is a Directed Acrylic Graph”.
This is an obviousness-type, provisional nonstatutory double patenting rejection.
Claim 2 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4 of copending Application No. 18/471,570 (hereinafter the “reference claim”) in view of Haan et al. (“Causal Confusion in Imitation Learning”). Reference claim 4 depends on reference claim 1 and it inherits every feature from reference claim 1.
Regarding claim 2, the combination of reference claim 1 and Haan teaches: The action prediction system according to claim 1.
The limitation “wherein the action is predicted by using a model
However, reference claim 4 does not explicitly teach: the action is predicted by using a model and the updated state variable embeddings
But Haan teaches: the action is predicted by using a model and the updated state variable embeddings (Page 5, § 4.1, lines 1-16, where a “model” is a single graph-parameterized policy
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, “the updated state variable embeddings” are results of the element-wise multiplication, and the policy outputs an action.)
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 incorporated Haan’s causal graph-parameterized policy learning into reference claim 4. A motivation for the combination is the same as the motivation given for claim 1.
This is an obviousness-type, provisional nonstatutory double patenting rejection.
Claim 4 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of copending Application No. 18/471,570 (hereinafter the “reference claim”) in view of Haan et al. (“Causal Confusion in Imitation Learning”). Reference claim 7 depends on reference claim 1 and it inherits every feature from reference claim 1.
Regarding claim 4, the combination of reference claim 1 and Haan teaches: The action prediction system according to claim 1.
The limitation “wherein the causal graph is generated by optimizing the causal graph based on constraints” is taught by reference claim 7 limitation “wherein the causal graph is generated by optimizing the causal graph based on constraints.”
This is an obviousness-type, provisional nonstatutory double patenting rejection.
Claim 5 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/471,570 (hereinafter the “reference claim”) in view of Haan et al. (“Causal Confusion in Imitation Learning”) and Wang et al. (“Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes”).
Regarding claim 5, the combination of reference claim 1 and Haan teaches: The action prediction system according to claim 1.
However, reference claim 1 and Haan do not explicitly teach: wherein the action is a treatment for a patient by a doctor based on health states of the patient.
But Wang teaches: wherein the action is a treatment for a patient by a doctor based on health states of the patient. (Page 1785, col. 2, § 1, lines 1-7 and page 1788, § 3.1, lines 1-5)
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 applied a dynamic causal graph of reference claim 1 to Wang’s dynamic treatment regime. A motivation for the combination is that a variety of factors may contribute to the outcome of a patient, including medical history, laboratory measurements, and demographics. (Wang, page 1785, col. 2, § 1, lines 1-7)
This is an obviousness-type, provisional nonstatutory double patenting rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.H.J./Examiner, Art Unit 2127 /ABDULLAH AL KAWSAR/ Supervisory Patent Examiner, Art Unit 2127
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