# US 2026/0093545 A1 · Task planning using machine learning models > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0093545 A1 - Document ID: US20260093545A1 - Status: Published application - Kind: A1 - Application number: 19/345,015 - Filed: 2025-09-30 - Published: 2026-04-02 - Earliest claimed: 2024-10-01 - Assignees: GDM Holding LLC - Applicants: GDM Holding LLC - Inventors: Xinran Zhao; Hanie Sedghi; Bernd Bohnet; Azade Nova - Web page: https://patentgrep.com/patent/US20260093545A1 ## CPC - G06F 9/5027: {the resource being a machine, e.g. CPUs, Servers, Terminals} - G06N 3/045: Combinations of networks - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0475: Generative networks - G06N 3/08: Learning methods - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/09: Supervised learning - G06N 3/094: Adversarial learning - G06N 3/096: Transfer learning - G06N 20/00: Machine learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a plan for an input task. The method involves receiving a query for the input task and obtaining candidate task-plan examples. An initial plan, comprising an initial action sequence, is obtained for the input task. For each candidate example, an action sequence similarity score is computed, measuring similarity between the initial action sequence and the action sequence in the candidate example. A set of task-plan examples is selected from the candidates using these similarity scores. Finally, a generative machine learning model processes the query and prompt inputs generated from the selected set of examples to generate an output plan. This approach improves plan generation by selecting relevant examples based on procedural similarity rather than superficial task description similarity, enhancing the quality and accuracy of the output plan. ## Claims ### Claim 1 A computer-implemented method, comprising: receiving a query comprising a request to generate a plan for an input task; obtaining candidate example data defining a plurality of candidate task-plan examples, wherein each candidate task-plan example comprises (i) a respective example task and (ii) a respective example plan for achieving the respective example task; obtaining data defining an initial plan for the input task, wherein the initial plan comprises an initial action sequence that includes a sequence of a plurality of actions; computing, for each of the plurality of candidate task-plan examples, a respective action sequence similarity score measuring a similarity between (i) the initial action sequence and (ii) a respective action sequence included in the respective example plan in the candidate task-plan examples; selecting, using the action sequence similarity scores, a set of task-plan examples from the plurality of candidate task-plan examples; and processing, using a generative machine learning model, (i) the query and (ii) one or more prompt inputs generated using the selected set of task-plan examples, to generate an output plan. ### Claim 2 (depends on 1) The method of claim 1, wherein obtaining the data defining the initial plan for the input task comprises: selecting an initial set of task-plan examples from the candidate task-plan examples; and processing, using the generative machine learning model, (i) the query and (ii) one or more prompt inputs generated using the selected initial set of task-plan examples to generate the initial plan. ### Claim 3 (depends on 2) The method of claim 2, wherein the initial set of task-plan examples is randomly selected from the candidate task-plan examples. ### Claim 4 (depends on 2) The method of claim 2, further comprising: determining a validity of the initial plan for the input task; and in response to determining that the initial plan is invalid, generating a revised initial plan. ### Claim 5 (depends on 4) The method of claim 4, wherein generating the revised initial plan comprises: selecting a new initial set of task-plan examples from the candidate task-plan examples; and generating the revised initial plan for the input task using the new initial set of task-plan examples from the candidate task-plan examples. ### Claim 6 (depends on 2) The method of claim 2, wherein obtaining the data defining the initial plan for the input task comprises selecting the initial set of task-plan examples at each of a plurality of iterations, wherein selecting the initial set of task-plan examples comprises, at each iteration after the first iteration: computing updated action sequence similarity scores between (i) an action sequence included in the output plan generated by the generative machine learning model in a preceding iteration and (ii) the respective action sequences included the respective example plans in the candidate task-plan examples; selecting, using the updated action sequence similarity scores, an updated set of task-plan examples from the plurality of candidate task-plan examples; and processing, using the generative machine learning model, (i) the query and (ii) one or more prompt inputs generated using the updated set of task-plan examples, to generate the output plan for the iteration. ### Claim 7 (depends on 1) The method of claim 1, wherein selecting, using the action sequence similarity scores, the set of task-plan examples from the plurality of candidate task-plan examples comprises: selecting a first subset of candidate task-plan examples with respective action sequence similarity scores above a first threshold score; and including the first subset of candidate task-plan examples in the selected set of task-plan examples. ### Claim 8 (depends on 7) The method of claim 7, wherein determining the first threshold score comprises: obtaining distribution data characterizing a distribution of the respective action sequence similarity scores of the set of candidate task-plan examples; and determining the first threshold score based on the distribution data. ### Claim 9 (depends on 8) The method of claim 8, wherein determining the first threshold score comprises: obtaining a mean value and a standard deviation value of the distribution; and determining the first threshold score based on the mean value and the standard deviation value. ### Claim 10 (depends on 7) The method of claim 7, wherein selecting, using the action sequence similarity scores, the set of task-plan examples from the plurality of candidate task-plan examples further comprises: selecting a second subset of candidate task-plan examples with respective action sequence similarity scores between the first threshold score and a second threshold score, wherein the second threshold score is below the first threshold score; selecting a third subset of candidate task-plan examples from the second subset of candidate task-plan examples; and including the third subset of candidate task-plan examples in the selected set of task-plan examples. ### Claim 11 (depends on 10) The method of claim 10, wherein selecting the third subset of candidate task-plan examples comprises: performing a clustering operation on the second subset of candidate task-plan examples to group the candidate task-plan examples into clusters based on action sequence similarity scores computed for action sequences of each pair of the candidate task-plan examples in the second subset; and selecting one or more candidate task-plan examples from each cluster. ### Claim 12 (depends on 11) The method of claim 11, wherein selecting one or more candidate task-plan examples from each cluster comprises: selecting no more than a threshold number of candidate task-plan examples from each cluster. ### Claim 13 (depends on 1) The method of claim 1, wherein computing the respective action sequence similarity score between (i) the initial action sequence and (ii) the respective action sequence corresponding to the respective example plan comprises: identifying a longest common subsequence of actions between the initial action sequence and the respective action sequence; and computing the action sequence similarity score based on a ratio of the length of the longest common subsequence relative to the lengths of the initial action sequence and the respective action sequences. ### Claim 14 (depends on 1) The method of claim 1, wherein the generative machine learning model comprises a neural network comprising one or more attention layers. ### Claim 15 (depends on 1) The method of claim 1, wherein parameters of the generative machine learning model remain unchanged during generating the plan for the input task. ### Claim 16 (depends on 1) The method of claim 1, wherein the input task specifies a task in a real-world environment, and the output plan specifies actions or routes to be taken in the real-world environment. ### Claim 17 (depends on 1) The method of claim 1, further comprising: providing the plan to a computer-implemented control system to cause the computer-implemented control system to generate one or more control signals for executing the plan. ### Claim 18 (depends on 1) The method of claim 1, wherein the query comprises one or more of: a text sequence, an image, a video, an audio, or sensor data. ### Claim 19 A system comprising: one or more computers; and one or more storage devices storing instructions that when executed by the one or more computers, cause the one or more computers to perform the operations comprising: receiving a query comprising a request to generate a plan for an input task; obtaining candidate example data defining a plurality of candidate task-plan examples, wherein each candidate task-plan example comprises (i) a respective example task and (ii) a respective example plan for achieving the respective example task; obtaining data defining an initial plan for the input task, wherein the initial plan comprises an initial action sequence that includes a sequence of a plurality of actions; computing, for each of the plurality of candidate task-plan examples, a respective action sequence similarity score measuring a similarity between (i) the initial action sequence and (ii) a respective action sequence included in the respective example plan in the candidate task-plan examples; selecting, using the action sequence similarity scores, a set of task-plan examples from the plurality of candidate task-plan examples; and processing, using a generative machine learning model, (i) the query and (ii) one or more prompt inputs generated using the selected set of task-plan examples, to generate an output plan. ### Claim 20 One or more computer-readable storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform the operations of comprising: receiving a query comprising a request to generate a plan for an input task; obtaining candidate example data defining a plurality of candidate task-plan examples, wherein each candidate task-plan example comprises (i) a respective example task and (ii) a respective example plan for achieving the respective example task; obtaining data defining an initial plan for the input task, wherein the initial plan comprises an initial action sequence that includes a sequence of a plurality of actions; computing, for each of the plurality of candidate task-plan examples, a respective action sequence similarity score measuring a similarity between (i) the initial action sequence and (ii) a respective action sequence included in the respective example plan in the candidate task-plan examples; selecting, using the action sequence similarity scores, a set of task-plan examples from the plurality of candidate task-plan examples; and processing, using a generative machine learning model, (i) the query and (ii) one or more prompt inputs generated using the selected set of task-plan examples, to generate an output plan.