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 .
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).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12190416. Although the claims at issue are not identical, they are not patentably distinct from each other because they are obvious variants of the same invention. Furthermore, the claims of the patent are narrower in scope and anticipate the claims of the application.
Claims of application Claims of the patent
1. A non-transitory processor-readable medium storing code representing instructions to be executed by one or more processors, the instructions comprising code to cause the one or more processors to:
receive a representation of a first natural language prompt;
send a representation of the first natural language prompt to a generative artificial intelligence (AI) model configured to identify a first pattern based on the first natural language prompt;
determine, based on a graph, a first inlet associated with the first pattern and at the graph, and a first outlet associated with the first pattern and at the graph;
determine a first modification based on the first inlet and the first outlet; and
modify the graph based on the first modification without using a template to generate a first modified graph.
2. The non-transitory processor-readable medium of claim 1, wherein the first natural language prompt is received from a compute device, the instructions further comprise code to cause the one or more processors to: cause the first modified graph to be displayed via at least one of a no-code user interface at the compute device or a low-code user interface at the compute device.
3. The non-transitory processor-readable medium of claim 1, wherein the instructions further comprise code to cause the one or more processors to: receive a representation of a second natural language prompt; send a representation of the second natural language prompt to the generative AI model configured to identify a second pattern based on the second natural language prompt; determine based on the first modified graph and not based on the graph, a second inlet associated with the second pattern and at the first modified graph, and a second outlet associated with the second pattern and at the first modified graph; determine a second modification based on the second inlet and the second outlet; and modify, via the processor, the first modified graph and not the graph based on the second modification to generate a second modified graph.
4. The non-transitory processor-readable medium of claim 1, wherein the generative AI model is further configured to identify a second pattern based on the first natural language prompt, the instructions further comprise code to cause the one or more processors to: determine, using the first modified graph, a second inlet associated with the second pattern and at the first modified graph, and a second outlet associated with the second pattern and at the first modified graph; determine a second modification based on the second inlet and the second outlet; and modify the first modified graph based on the second modification to generate a second modified graph.
5. The non-transitory processor-readable medium of claim 1, wherein the generative AI model is further configured to identify a second pattern based on the first natural language prompt, the instructions further comprise code to cause the one or more processors to: determine, using the graph, a second inlet associated with the second pattern and at the graph, and a second outlet associated with the second pattern and at the graph; and determine a second modification based on the second inlet and the second outlet, , the code to modify the graph to generate the first modified graph is further based on the second modification.
6. The non-transitory processor-readable medium of claim 1, wherein the graph includes a plurality of nodes, the instructions further comprise code to cause the one or more processors to: receive an indication of a set of nodes from the plurality of nodes selected by a user, the first modification further determined based on the set of nodes.
7. The non-transitory processor-readable medium of claim 1, the instructions further comprise code to cause the one or more processors to: receive a representation of a second natural language prompt; send a representation of the second natural language prompt to the generative AI model, the generative AI model configured to generate an output indicating that the second natural language prompt does not include any patterns; and refrain, in response to generating the output indicating that the second natural language prompt does not include any patterns, from modifying at least one of the graph or the first modified graph.
8. A method, comprising: receiving serially each natural language prompt from a plurality of natural language prompts; as each natural language prompt from the plurality of natural language prompts is received and without waiting for remaining natural language prompts from the plurality of natural language prompts to be received, identifying at least one pattern based on that natural language prompt by inputting that natural language prompt into a generative artificial intelligence (AI) model, a plurality of patterns resulting from the plurality of natural language prompts being input into the generative AI model; as each natural language prompt from the plurality of natural language prompts is received and without waiting for remaining natural language prompts from the plurality of natural language prompts to be received, updating a graph based on the at least one pattern associated with that natural language prompt and without using a template, a modified graph resulting from the graph being iteratively updated based on the plurality of patterns; and reformatting the modified graph to generate a reformatted graph, the reformatted graph displayed via a user interface that is at least one of a no-code user interface or a low-code user interface and that receives a set of user inputs and generates a set of responses to the set of user inputs generated based on the reformatted graph.
9. The method of claim 8, wherein at least one natural language prompt from the plurality of natural language prompts is received without receiving an indication of at least one node selected by a user and included in the graph.
10. The method of claim 8, wherein the modified graph includes a first number of edge crossings and the reformatted graph includes a second number of edge crossings that is less than the first number of edge crossings.
11. The method of claim 8, wherein the plurality of natural language prompts is received from a remote compute device, the method further comprising: causing the reformatted graph to be displayed at the remote compute device.
12. An apparatus comprising: a memory; and a processor operatively coupled to the memory, the processor configured to: receive a first natural language prompt; input the first natural language prompt into a large language model (LLM) to identify a first pattern; generate a graph based the first pattern and at a no-code user interface; receive a second natural language prompt that includes a representation of a desired modification; input the second natural language prompt into the LLM to identify a second pattern; and update the graph, at the no-code user interface, based the second pattern and without generating a new graph, to generate a modified graph that incorporates the desired modification.
13. The apparatus of claim 12, wherein the graph is a first graph and the processor is further configured to: receive a third natural language prompt; input the third natural language prompt into the LLM to identify a third pattern; and generate a second graph that is new and not a modified version of the first graph based the third pattern.
14. The apparatus of claim 13, wherein the third natural language prompt is received without receiving an indication of any nodes included in the modified graph that have been selected by a user.
15. The apparatus of claim 12, wherein the processor is further configured to: receive an indication of at least one node included in the graph and selected by a user, the modified graph further generated based on the at least one node.
16. The apparatus of claim 12, wherein the processor is further configured to: determine a main inlet, a success outlet, and an error outlet at the graph, the modified graph further generated based on the main inlet, the success outlet, and the error outlet.
17. The apparatus of claim 12, wherein the processor is further configured to: receive a third natural language prompt; input the third natural language prompt into the LLM to generate an output that indicates the third natural language prompt does not include any patterns; and in response to generating the output, refrain from updating at least one of the graph or the modified graph.
18. The apparatus of claim 12, wherein the processor is further configured to: establish a starting point at the graph, the modified graph further generated based on the starting point.
19. The apparatus of claim 12, wherein the graph is generated and updated at the no-code user interface without writing code and without using a template.
20. The apparatus of claim 12, wherein the first pattern is associated with a pattern context definition indicating at least one parameter the first pattern requires.
1. A method, comprising:
receiving, via a processor, a representation of a first natural language prompt;
sending, via the processor, a representation of the first natural language prompt to a large language model (LLM) configured to identify a first pattern based on the first natural language prompt;
determining, via the processor and based on a graph, a first main inlet associated with the first pattern at the graph, a first success outlet associated with the first pattern at the graph, and a first error outlet associated with the first pattern at the graph;
determining a first modification based on the first main inlet, the first success outlet, and the first error outlet; and
modifying the graph based on the first modification to generate a first modified graph.
2. The method of claim 1, wherein the first natural language prompt is received from a compute device, the method further comprising: causing the first modified graph to be displayed via at least one of a no-code user interface at the compute device or a low-code user interface at the compute device.
3. The method of claim 1, further comprising: receiving, via the processor, a representation of a second natural language prompt; sending, via the processor, a representation of the second natural language prompt to the LLM configured to identify a second pattern based on the second natural language prompt; determining, via the processor, based on the first modified graph and not based on the graph, a second main inlet associated with the second pattern at the first modified graph, a second success outlet associated with the second pattern at the first modified graph, and a second error outlet associated with the second pattern at the first modified graph; determining, via the processor, a second modification based on the second main inlet, the second success outlet, and the second error outlet; and modifying, via the processor, the first modified graph and not the graph based on the second modification to generate a second modified graph.
4. The method of claim 1, wherein the LLM is further configured to identify a second pattern based on the first natural language prompt, the method further comprising: determining, using the first modified graph, a second main inlet associated with the second pattern at the first modified graph, a second success outlet associated with the second pattern at the first modified graph, and a second error outlet associated with the second pattern at the first modified graph; determining a second modification based on the second main inlet, the second success outlet, and the second error outlet; and modifying the first modified graph based on the second modification to generate a second modified graph.
5. The method of claim 1, wherein the LLM is further configured to identify a second pattern based on the first natural language prompt, the method further comprising: determining, via the processor and using the graph, a second main inlet associated with the second pattern at the graph, a second success outlet associated with the second pattern at the graph, and a second error outlet associated with the second pattern at the graph; and determining, via the processor, a second modification based on the second main inlet, the second success outlet, and the second error outlet, the modifying of the graph to generate the first modified graph further based on the second modification.
6. The method of claim 1, wherein the graph includes a plurality of nodes, the method further comprising: receiving an indication of a set of nodes from the plurality of nodes selected by a user, the first modification further determined based on the set of nodes.
7. The method of claim 1, further comprising: receiving, via the processor, a representation of a second natural language prompt; sending, via the processor, a representation of the second natural language prompt to the LLM, the LLM configured to generate an output indicating that the second natural language prompt does not include any patterns; and refraining, via the processor and in response to generating the output indicating that the second natural language prompt does not include any patterns, from modifying at least one of the graph or the first modified graph.
8. A non-transitory processor-readable medium storing code representing instructions to be executed by one or more processors, the instructions comprising code to cause the one or more processors to: receive serially each natural language prompt from a plurality of natural language prompts; as each natural language prompt from the plurality of natural language prompts is received and without waiting for remaining natural language prompts from the plurality of natural language prompts to be received, identify at least one pattern based on that natural language prompt by inputting that natural language prompt into a large language model (LLM), a plurality of patterns resulting from the plurality of natural language prompts being input into the LLM; as each natural language prompt from the plurality of natural language prompts is received and without waiting for remaining natural language prompts from the plurality of natural language prompts to be received, update a graph based on the at least one pattern associated with that natural language prompt, a modified graph resulting from the graph being iteratively updated based on the plurality of patterns; and reformat the modified graph to generate a reformatted graph, the reformatted graph displayed via a no-code user interface that receives a set of user inputs and generates a set of responses to the set of user inputs generated based on the reformatted graph.
9. The non-transitory processor-readable medium of claim 8, wherein at least one natural language prompt from the plurality of natural language prompts is received without receiving an indication of at least one node selected by a user and included in the graph.
10. The non-transitory processor-readable medium of claim 8, wherein the modified graph includes a first number of edge crossings and the reformatted graph includes a second number of edge crossings that is less than the first number of edge crossings.
11. The non-transitory processor-readable medium of claim 8, wherein the plurality of natural language prompts is received from a remote compute device and the instructions further comprise code to cause the one or more processors to: cause the reformatted graph to be displayed at the remote compute device.
12. An apparatus comprising: a memory; and a processor operatively coupled to the memory, the processor configured to: receive a first natural language prompt; input the first natural language prompt into a large language model (LLM) to identify a first pattern; generate a graph based the first pattern and at a no-code user interface, the graph representing a first conversation flow; receive a second natural language prompt that includes a representation of a desired modification to the first conversation flow; input the second natural language prompt into the LLM to identify a second pattern; and update the graph, at the no-code user interface, based the second pattern and without generating a new graph, to generate a modified graph that represents a second conversation flow that incorporates the desired modification to the first conversation flow.
13. The apparatus of claim 12, wherein the graph is a first graph and the processor is further configured to: receive a third natural language prompt; input the third natural language prompt into the LLM to identify a third pattern; and generate a second graph that is new and not a modified version of the first graph based the third pattern.
14. The apparatus of claim 13, wherein the third natural language prompt is received without receiving an indication of any nodes included in the modified graph that have been selected by a user.
15. The apparatus of claim 12, wherein the processor is further configured to: receive an indication of at least one node included in the graph and selected by a user, the modified graph further generated based on the at least one node.
16. The apparatus of claim 12, wherein the processor is further configured to: determine a main inlet, a success outlet, and an error outlet at the graph, the modified graph further generated based on the main inlet, the success outlet, and the error outlet.
17. The apparatus of claim 12, wherein the processor is further configured to: receive a third natural language prompt; input the third natural language prompt into the LLM to generate an output that indicates the third natural language prompt does not include any patterns; and in response to generating the output, refrain from updating at least one of the graph or the modified graph.
18. The apparatus of claim 12, wherein the processor is further configured to: establish a starting point at the graph, the modified graph further generated based on the starting point.
19. The apparatus of claim 12, wherein the graph is generated and updated at the no-code user interface without writing code and without using a template.
20. The apparatus of claim 12, wherein the first pattern is associated with a pattern context definition indicating at least one parameter the first pattern requires.
Potential Allowable Subject Matter
Claims 1-20 are potentially allowable over the prior art on record. The following is an examiner’s statement of reasons for allowance: Karaca et al. (USPG 2021/0124782) disclose a method of processing a natural language input to identify a graph pattern (abstract section); refining the graph database query utilizing the graph pattern (abstract section). Kale et al. (USPG 2018/0052884) teach a method of dynamically updating the knowledge graph using information extracted from a multi-turn interactive dialog (abstract section). Lu et al. (USPG 2023/0065468) teach a method for automatic generation and update of a knowledge graph utilizing information extracted from conversation. The prior art on record, individually or in combination, fail to explicitly disclose the combination of the limitations regarding “determine, based on a graph, a first inlet associated with the first pattern and at the graph, and a first outlet associated with the first pattern and at the graph; determine a first modification based on the first inlet and the first outlet; and modify the graph based on the first modification without using a template to generate a first modified graph” for claim 1, “as each natural language prompt from the plurality of natural language prompts is received and without waiting for remaining natural language prompts from the plurality of natural language prompts to be received, updating a graph based on the at least one pattern associated with that natural language prompt and without using a template, a modified graph resulting from the graph being iteratively updated based on the plurality of patterns; and reformatting the modified graph to generate a reformatted graph, the reformatted graph displayed via a user interface that is at least one of a no-code user interface or a low-code user interface and that receives a set of user inputs and generates a set of responses to the set of user inputs generated based on the reformatted graph” for claim 8, and “generate a graph based the first pattern and at a no-code user interface; receive a second natural language prompt that includes a representation of a desired modification; input the second natural language prompt into the LLM to identify a second pattern; and update the graph, at the no-code user interface, based the second pattern and without generating a new graph, to generate a modified graph that incorporates the desired modification.” for claim 12. The references fail to disclose the inventive concept of identifying patterns from the prompts sequentially for used to update and reformat graph to present to the user to receive user response. Furthermore, it would not have been obvious to one of ordinary skill in the art to modify the prior art in order to arrive at the claimed invention.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lutz (USPG 2024/0311348) teaches a method for guiding a generative model to create an interact with a data structure that is considered pertinent to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUYEN X VO whose telephone number is (571)272-7631. The examiner can normally be reached M-F, 8-4.
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/HUYEN X VO/Primary Examiner, Art Unit 2656
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