CTNF 18/459,258 CTNF 94985 DETAILED ACTION Claims 1-30 are pending and have been examined. -- Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statements (IDS) submitted on 08/31/2023 and 03/18/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Claim Objections 07-29-01 AIA Claim s 6, 14, 22, 30 are objected to because of the following informalities: In claims 6, 14, 22, 30, “minimizing a second loss the inference model” should be “minimizing a second loss for the inference model.” Appropriate correction is required. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Claim 9 recites “means for observing an environment via one or more sensors associated with a robotic device; means for generating, via an inference model, a belief of the environment based on data associated with prior actions of the robotic device in the environment; and means for controlling the robotic device to perform an action in the environment based on generating the belief” that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f). The specification identified the corresponding structure for all the “means” elements in [0009] “Some other aspects of the present disclosure are directed to an apparatus having one or more processors, and one or more memories coupled with the one or more processors and storing instructions operable, when executed by the one or more processors, to cause the apparatus to observe an environment...” Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 7, 15 and 23 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, 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 pre-AIA the applicant regards as the invention. Claim 7, 15 and 23 recites the limitation “the device.” There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner has interpreted “the device” to be “the robotic device.” Claim Rejections - 35 USC § 101 07-04 AIA 07-04-01 t35 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 1-30 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more Step 1 : Claims 1-8 recite a method. Claims 9-16 and 17-24 recite an apparatus. Claims 25-30 recite a non-transitory medium. Therefore, claims 1-8 are directed to a process, claims 9-16 and 17-24 are directed to a machine, and claims 25-30 are directed to a manufacture. With respect to claims 1, 9, 17 and 25: 2A Prong 1: The claim recites a judicial exception. generating… a belief of the environment based on data associated with prior actions of the robotic device in the environment (mental process – evaluation or judgement,--- a human can manually a belief based in data) 2A Prong 2: The judicial exception is not integrated into a practical application. (claim 17) one or more processors; and one or more memories coupled with the one or more processors and storing instructions operable, when executed by the one or more processors, to cause the apparatus to (claim 25) having program code recorded thereon, the program code executed by one or more processors and comprising (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components ) observing an environment via one or more sensors associated with a robotic device (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting; using sensors to receive observation data ) via an inference model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of using a model to generate a belief ) controlling the robotic device to perform an action in the environment based on generating the belief (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of controlling the robot to perform an action ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. (claim 17) one or more processors; and one or more memories coupled with the one or more processors and storing instructions operable, when executed by the one or more processors, to cause the apparatus to (claim 25) having program code recorded thereon, the program code executed by one or more processors and comprising (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components ) observing an environment via one or more sensors associated with a robotic device (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i)); using sensors to receive observation data ) via an inference model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of using a model to generate a belief ) controlling the robotic device to perform an action in the environment based on generating the belief (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of controlling the robot to perform an action ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 2, 10, 18 and 26: 2A Prong 2: The judicial exception is not integrated into a practical application. further comprising training the inference model based on the data associated with prior actions of an expert in the environment (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of training the model based on the data ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. further comprising training the inference model based on the data associated with prior actions of an expert in the environment (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of training the model based on the data ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 3, 11, 19 and 27: 2A Prong 2: The judicial exception is not integrated into a practical application. wherein a dynamics model trains the inference model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of using a dynamics model training the inference model ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. wherein a dynamics model trains the inference model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of using a dynamics model training the inference model ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 4, 12, 20 and 28: 2A Prong 2: The judicial exception is not integrated into a practical application. wherein the data associated with the prior actions is deconfounded from the inference model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of using an inference mode to deconfound the data associated with the prior actions; in light of spec [0051] ‘perform actions based on the expert data that is deconfounded with the inference model.’) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. wherein the data associated with the prior actions is deconfounded from the inference model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; high level recitation of using an inference mode to deconfound the data associated with the prior actions ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 5, 13, 21 and 29: 2A Prong 2: The judicial exception is not integrated into a practical application. wherein the inference model is a component of a variational encoder-decoder (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; claim 2 recites “training the inference model”; high level recitation of training a VAE model ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. wherein the inference model is a component of a variational encoder-decoder (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; claim 2 recites “training the inference model”; high level recitation of training a VAE model ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 6, 14, 22 and 30: 2A Prong 1: The claim recites a judicial exception. wherein training the inference model includes minimizing a first loss for the dynamic model and minimizing a second loss the inference model (mathematical concept - mathematical equation, in light of spec [0056]) With respect to claims 7, 15 and 23: 2A Prong 2: The judicial exception is not integrated into a practical application. further comprising observing the prior actions of the agent in the environment via one or more sensors of the device (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting; using sensors to receive observation data including prior actions ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. further comprising observing the prior actions of the agent in the environment via one or more sensors of the device (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i)); using sensors to receive observation data including prior actions ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 8, 16 and 24: 2A Prong 2: The judicial exception is not integrated into a practical application. wherein the expert is a human or another robotic device (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; claim 2 recites “training the inference model based on the data associated with prior actions of an expert,” which is mere instructions to apply an exception, specifying more details about the data does not cause the limitation to integrate the exception into a practical application ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. wherein the expert is a human or another robotic device (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; claim 2 recites “training the inference model based on the data associated with prior actions of an expert,” which is mere instructions to apply an exception, specifying more details about the data does not cause the limitation to be significantly more than the judicial exception ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-2, 7-10, 15-18 and 23-26 rejected under 35 U.S.C. 103 as being unpatentable over Yavuz ("Design of a String Encoder-and-IMU-Based 6D Pose Measurement System for a Teaching Tool and Its Application in Teleoperation of a Robot Manipulator" 20210616) in view of Pertsch ("Accelerating Reinforcement Learning with Learned Skill Priors" 20201022) In regard to claims 1, 9, 17 and 25, Yavuz teaches: A processor-implemented method comprising: observing an environment via one or more sensors associated with a robotic device; (Yavuz, p. 3, 2.2. Sensors and Infrastructure Used " The overall system [a robotic device] setup includes a robot, an IMU, six string encoder sensors , two designed system controller boards, one designed system program, and one personal computer (PC)."; p. 2, 1. Introduction "In this study, a sensor system that consists of an IMU and six string encoder position sensors [sensors] to measure and record the 6D pose of a teaching tool which is controlled by an operator during a teaching process is proposed... six string encoder position sensors are used to locate the 3D position of the tip of the teaching tool and to find the yaw angle of the tool (rotation around the z-axis) by using the law of cosines.") Yavuz does not teach, but Pertsch teaches: generating, via an inference model, a belief of the environment based on data associated with prior actions of the robotic device in the environment; and (Pertsch, . 3, 3.1 Problem Formulation "We assume access to a dataset D of pre-recorded agent experience [prior actions] in the form of state-action trajectories τi = {(s0, a0),...,(sTi, aTi)}."; p. 4, Figure 2 "Deep latent variable model for joint learning of skill embedding and skill prior. Given a state-action trajectory from the dataset, the skill encoder [an inference model] maps the action sequence [data associated with prior actions] to a posterior distribution q( z |ai) over latent skill embeddings . [skill embedding z: a belief of the environment] The action trajectory gets reconstructed by passing a sample from the posterior through the skill decoder.") PNG media_image1.png 566 788 media_image1.png Greyscale controlling the robotic device to perform an action in the environment based on generating the belief. (Pertsch, p. 4, 3.3 Skill Prior Regularized Reinforcement Learning "we learn a policy π (z|st) that outputs skill embeddings, which we decode into action sequences using the learned skill decoder {a...} ~ p(ai|z). We execute these actions for H steps ..."; p. 5, Algorithm 1 SPiRL: Skill-Prior RL "5: zt ~ pi(zt|st) -> sample skill from policy 6: st'~ p(st+H|st, zt ) -> execute skill in environment [controlling the robotic device to perform an action based on z (the belief)] ") It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Yavuz to incorporate the teachings of Pertsch by applying the recorded pose/data (generated by an operator during a teaching process) to Pertsch's a deep latent variable model with RL learning. Doing so would allow effective skill transfer from rich datasets. (Pertsch, p. 1, Abstract "In this work, we propose to implement this intuition by learning a prior over skills. We propose a deep latent variable model that jointly learns an embedding space of skills and the skill prior from offline agent experience... We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets.") Claims 9, 17 and 25 recite substantially the same limitation as claim 1, therefore the rejection applied to claim 1 also apply to claims 9, 17 and 25. In addition, Pertsch teaches: (claim 17) one or more processors; and one or more memories coupled with the one or more processors and storing instructions operable, when executed by the one or more processors, to cause the apparatus to (claim 25) having program code recorded thereon, the program code executed by one or more processors and comprising (Pertsch, p. 12, B Implementation Details "Training on a single high-end NVIDIA GPU takes approximately 8 hours"; using a GPU inherently teaches all the general computer components ) The rationale for combining the teachings of Yavuz and Pertsch is the same as set forth in the rejection of claim 1. In regard to claims 2, 10, 18 and 26, Yavuz does not teach, but Pertsch teaches: further comprising training the inference model based on the data associated with prior actions of an expert in the environment. (Pertsch, p. 3, "We define a skill ai as a sequence of actions {ai...} [based on the data associated with prior actions of an expert] with fixed horizon H... We randomly sample H-step trajectories from the training sequences and maximize the following evidence lower bound (ELBO): ... (log p(ai|z) reconstruction - ... (log q(z|ai)) ... regularization) (1) "; p. 4, Figure 2 "Colorful arrows indicate the propagation of gradients from reconstruction (orange arrow), regularization (blue arrows) and prior training objectives."; gradients from reconstruction (orange arrow), regularization (blue arrows) are used to update the skill encoder [training the inference model] ) The rationale for combining the teachings of Yavuz and Pertsch is the same as set forth in the rejection of claim 1. In regard to claims 7, 15 and 23, Yavuz teaches: further comprising observing the prior actions of the agent in the environment via one or more sensors of the device. (Yavuz, p. 3, 2.2. Sensors and Infrastructure Used " The overall system [the robotic device] setup includes a robot, an IMU, six string encoder sensors, two designed system controller boards, one designed system program, and one personal computer (PC)."; p. 2, 1. Introduction "In this study, a sensor system that consists of an IMU and six string encoder position sensors [sensors of the device] to measure and record the 6D pose of a teaching tool which is controlled by an operator [prior actions of the agent/a human] during a teaching process is proposed... six string encoder position sensors are used to locate the 3D position of the tip of the teaching tool and to find the yaw angle of the tool (rotation around the z-axis) by using the law of cosines. ") In regard to claims 8, 16 and 24, Yavuz does not teach, but Pertsch teaches: wherein the expert is a human or another robotic device. (Pertsch, p. 3, 3.1 Problem Formulation "We assume access to a dataset D of pre-recorded agent experience in the form of state-action trajectories τi = {(s0, a0),...,(sTi, aTi)}. This data can be collected using previously trained agents across a diverse set of tasks [33, 34], through agents autonomously exploring their environment [11, 12], via human teleoperation [a human] [35, 27, 36, 10] or any combination of these.") The rationale for combining the teachings of Yavuz and Pertsch is the same as set forth in the rejection of claim 1 . 07-21-aia AIA Claim s 3-6, 11-14, 19-22 and 27-30 rejected under 35 U.S.C. 103 as being unpatentable over Yavuz in view of Pertsch as applied to claims 2, 10, 18 and 26, and in further view of Nair ("Visual Reinforcement Learning with Imagined Goals" 20181204) In regard to claims 3, 11, 19 and 27, Yavuz and Pertsch do not teach, but Nair teaches: wherein a dynamics model trains the inference model. (Nair, p. 6, Algorithm 1 RIG: Reinforcement learning with imagined goal "2: Train beta-VAE on D by optimizing (2)… 23: Fine-tune beta-VAE every K episodes on mixture of D and R [RL trains VAE model, a dynamics model (RL) trains the inference model (beta-VAE)] ") It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Yavuz and Pertsch to incorporate the teachings of Nair by including goal-conditioned policies with RL and VAE training algorithm. Doing so would be efficient to learn policies that operate on raw image observations and goals for a real-world robotic system. (Nair, p. 1, Abstract "In this paper, we propose an algorithm that acquires such general-purpose skills by combining unsupervised representation learning and reinforcement learning of goal-conditioned policies... Our off-policy algorithm is efficient enough to learn policies that operate on raw image observations and goals for a real-world robotic system, and substantially outperforms prior techniques.") In regard to claims 4, 12, 20 and 28, Yavuz does not teach, but Pertsch teaches: wherein the data associated with the prior actions is deconfounded from the inference model. (Pertsch, p. 4, Figure 2 "Deep latent variable model for joint learning of skill embedding and skill prior. Given a state-action trajectory from the dataset, the skill encoder [the inference model] maps the action sequence [the data associated with prior actions of the robotic device] to a posterior distribution q( z |ai) over latent skill embeddings . [deconfounded actions, hidden data] The action trajectory gets reconstructed by passing a sample from the posterior through the skill decoder."; in light of spec [0051] 'perform actions based on the expert data that is deconfounded with the inference model... the effect of hidden information on the expert and then account for the effect of the hidden information once the inference model has been trained' ) The rationale for combining the teachings of Yavuz and Pertsch is the same as set forth in the rejection of claim 1. In regard to claims 5, 13, 21 and 29, Yavuz does not teach, but Pertsch teaches: wherein the inference model is a component of a variational encoder-decoder. (Pertsch, p. 4, Figure 2 "Deep latent variable model for joint learning of skill embedding and skill prior. Given a state-action trajectory from the dataset, the skill encoder [the inference model is an encoder, a component of a variational encoder-decoder] maps the action sequence to a posterior distribution q(z|ai) over latent skill embeddings.") The rationale for combining the teachings of Yavuz and Pertsch is the same as set forth in the rejection of claim 1. In regard to claims 6, 14, 22 and 30, Yavuz and Pertsch do not teach, but Nair teaches: wherein training the inference model includes minimizing a first loss for the dynamic model and (Nair, p. 6, Algorithm 1 RIG: Reinforcement learning with imagined goal "15: Minimize (1) [minimizing a first loss for RL, for the dynamic model] using (z; a; z0; zg; r).") minimizing a second loss the inference model. (Nair, p. 6, Algorithm 1 RIG: Reinforcement learning with imagined goal "2: Train -VAE on D by optimizing (2) ."; p. 4 "The encoder and decoder parameters, φ and ψ respectively, are jointly trained to maximize… (2) … [minimizing a second loss the embedding model, the inference model] The use of beta values other than one is sometimes referred to as a beta-VAE [15]."; maximized a loss function is minimized its negative, i.e. loss function * -1 ) The rationale for combining the teachings of Yavuz, Pertsch and Nair is the same as set forth in the rejection of claim 3. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SU-TING CHUANG whose telephone number is (408)918-7519. The examiner can normally be reached Monday - Thursday 8-5 PT. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached at (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SU-TING CHUANG/Examiner, Art Unit 2146 Application/Control Number: 18/459,258 Page 2 Art Unit: 2146 Application/Control Number: 18/459,258 Page 3 Art Unit: 2146 Application/Control Number: 18/459,258 Page 4 Art Unit: 2146 Application/Control Number: 18/459,258 Page 5 Art Unit: 2146 Application/Control Number: 18/459,258 Page 6 Art Unit: 2146 Application/Control Number: 18/459,258 Page 7 Art Unit: 2146 Application/Control Number: 18/459,258 Page 8 Art Unit: 2146 Application/Control Number: 18/459,258 Page 9 Art Unit: 2146 Application/Control Number: 18/459,258 Page 10 Art Unit: 2146 Application/Control Number: 18/459,258 Page 11 Art Unit: 2146 Application/Control Number: 18/459,258 Page 12 Art Unit: 2146 Application/Control Number: 18/459,258 Page 13 Art Unit: 2146 Application/Control Number: 18/459,258 Page 14 Art Unit: 2146 Application/Control Number: 18/459,258 Page 15 Art Unit: 2146 Application/Control Number: 18/459,258 Page 16 Art Unit: 2146 Application/Control Number: 18/459,258 Page 17 Art Unit: 2146 Application/Control Number: 18/459,258 Page 18 Art Unit: 2146 Application/Control Number: 18/459,258 Page 19 Art Unit: 2146