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 .
Response to Amendment
Claims 6 and 17 are canceled. Claims 1 and 11 are amended. As such, claims 1-5, 7-16, and 18-20 are presented for examination.
Response to Arguments
Rejection under 35 U.S.C. 101
Applicant's arguments have been fully considered but they are not persuasive. Applicant argues “the claimed subject matter provides for an improvement in the manner in which LLMs can operate by using batched prompts and prompt-engineering inputs, ‘wherein the one or more prompt-engineering inputs identifies the batched prompt as including queries from a plurality of users.’ Applicant submits that the recited features go beyond merely performing an abstract idea using a computer. Accordingly, Applicant respectfully submits that the claims are directed to a display that goes beyond the mental process. In addition, the ability to aid in stent planning constitutes an improvement to the treatment that is being provided to the patient, which is also patentable subject matter.”
However, in order for the claims to provide an improvement to LLMs, the improvement must be provided by one or more additional elements and not a judicial exception alone. Specifically, MPEP 2106.05(a) states, “the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements… the improvement can be provided by the additional element(s) in combination with the recited judicial exception.” In this case, the provided prompt-engineering input represents a statement containing a set of instructions related to a batched prompt that is input to a generic LLM using a computer. This prompt-engineering input can be determined mentally, and the claim merely recites using a computer as a tool to provide the prompt-engineering input to the LLM. Thus, the claim does not integrate the judicial exception (mental process) into a practical application.
Further, neither the claims nor the specification mention a specific display or interface used to facilitate the generation of batched prompts. Also, there is no mention of stent planning in the claims nor specification.
Rejection under 35 U.S.C. 103
Applicant's arguments have been fully considered but they are not persuasive. Applicant argues “paragraph [0031] merely indicates that a nodes of the system ‘service a plurality of different prompts from different users in parallel.’ Applicant submits that this does not constitute a teaching in which a batched prompt is provided along with prompt-engineering inputs that identify the batched prompt as including queries from a plurality of users.”
However, Radmilac further teaches a batching criterion accompanied with the batched request, in which the criterion can be a maximum number of processing requests (user queries). Specifically, paragraph [0178] of Radmilac states, “Systems also identify at least one batching criterion (e.g., batching criteria 726) for a particular node (e.g., node f5) of the different processing nodes (act 1120). Some examples of batching criteria include waiting to transmit a batch of processing requests until a minimum number of processing requests are received, based on a maximum number of processing requests that a particular node can process, waiting for a predetermined amount of time between receiving batching requests, or other batching criterion.” This batching criterion can determine a number of processing requests for a batched prompt, and these requests can be from a plurality of different users in parallel. In combination with the Lin reference, the batching criterion described in Radmilac can be part of a prompt-engineering input that identifies that a batched prompt includes queries from multiple users.
Claim Objections
Claim 18 objected to because of the following informalities: claim 18 is dependent on canceled claim 17. Appropriate correction is required.
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, 7-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, the claim recites "(a) identify a plurality of LLM queries from a plurality of users for inclusion as part of a batched prompt", "(b) generate the batched prompt from the plurality of LLM queries and one or more prompt- engineering inputs", "(c) receive an output from the LLM that is based on the batched prompt", "(d) generate, from the output, a plurality of responses corresponding to the plurality of users", and "(e) provide a corresponding response, from the plurality of responses, to each of the plurality of users." Limitations (a) - (e) recite mental processes that may be practically performed in the mind using pen and paper. For example, limitation (a) can be done someone receiving and determining multiple queries from users. Limitation (b) can be done by someone determining a batched prompt using the user queries and additional instructions. Limitation (c) can be done by someone receiving an output from an LLM. Limitation (d) can be done by someone determining multiple responses for multiple users using the LLM output. Limitation (e) can be done by someone writing down corresponding responses to show to users. Under its broadest reasonable interpretation when read in light of the specification, the actions to "identify," "generate," "receive," and "provide" encompass mental processes practically performed in the human mind by evaluation and judgement using pen and paper. Accordingly, the claim recites an abstract idea (Step 2A, Prong One).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of "(f) provide the one or more prompt-engineering inputs and the batched prompt to an LLM, wherein the one or more prompt-engineering inputs identifies the batched prompt as including queries from a plurality of users." Limitations (a) - (f) are recited as being performed by a computer. In limitations (a) - (e), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. The limitation (f) provides nothing more than mere instructions to implement an abstract idea on a generic computer. The large language model recited in limitation (f) is used to perform limitation (c) without placing any limits on how the model function. Rather, this model only recites the outcomes and does not include any details on how the outcomes are accomplished. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to an abstract idea (Step 2A: YES).
The claim does not include additional elements that are sufficient to amount to more than the judicial exception. As discussed above, the recitation of a computer to perform limitations (a) - (f) amount to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, this additional element represents mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept (Step 2B).
Regarding claim 11, the claim is rejected with similar analysis to claim 1.
Similarly, dependent claims 2-5, 7-10, 12-16, and 18-20 include additional steps that are considered abstract ideas because they fail to provide meaningful significance that goes beyond generally linking the use of an abstract idea to a particular technological environment and using the computer to perform an abstract idea.
Claims 2 and 12 reads on someone determining parameters for LLM queries.
Claims 3-5 and 13-15 recite using specific parameters and associated parameter characteristics/metadata among the LLM queries.
Claim 7 reads on someone determining segmentation instructions to input to an LLM. output.
Claims 8 and 18 read on someone determining user identifiers to apply to the LLM
Claims 9 and 19 read on someone determining a query threshold and combining LLM queries when that threshold is reached.
Claims 10 and 20 read on someone determining the number of tokens for multiple inputs and determining similarities between terms in the inputs.
Claim 16 reads on someone determining that a prompt contains queries from multiple users.
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.
Claims 1-4, 9-14, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (US 20250077844 A1; hereinafter referred to as Lin) in view of Radmilac et al. (US 20240412040 A1; hereinafter referred to as Radmilac).
Regarding claim 1, Lin discloses: one or more processors having access to one or more memories ([0070] The computer system 500 also includes memory 503 in electronic communication with the processor 501), wherein the one or more processors are configured to... generate the batched prompt from the plurality of LLM queries ([0023] multiple input prompts are included or otherwise combined within a batch prompt. In one or more embodiments described herein, the batch prompt is a combination of a task (or multiple similar tasks) with multiple data inputs) and one or more prompt- engineering inputs ([0022] an input prompt refers to a task (e.g., a task specification) and an associated data input (e.g., a data specification) including data to be processed or labeled by the LLM in accordance with a context that takes into account a knowledge base, the task, and any other data provided to the LLM in connection with the input query);
provide the one or more prompt-engineering inputs and the batched prompt to an LLM… ([0039] a plurality of batch permutations 134a-c (e.g., including the batch permutations 134a-c and/or the original batch prompt 124 as shown in FIG. 2A) maybe provided as inputs to the LLM 108. The LLM 108 may analyze the batch data set 130a-c of each of the batch permutations 134 with respect to the task 126);
receive an output from the LLM that is based on the batched prompt ([0049] The output generator 118 may consider the outputs for each of the data inputs from across the plurality permutation output sets 140a-c to determine a batched output set 150 (or batch prompt output) including a set of final outputs 146. As mentioned above, the batch output set 150 may be a set of outputs determined to be responsive to the batch prompt 124 and/or responsive to the plurality of input prompts 122a-c used in generating the batch prompt 124);
generate, from the output, a plurality of responses corresponding to the plurality of users ([0042] the LLM 108mayevaluateeachofthe data inputs 128with respect to the task 126 and may determine a corresponding answer, result, response, or other output as the output 144 for that data input 128);
and provide a corresponding response, from the plurality of responses, to each of the plurality of users ([0051] the output generator 118 may generate the batch output set 150 corresponding with the answers, results, responses, or other outputs for the task 126 as applied to each of the input data instances represented within the batch prompt 124, which, as discussed above, may include a final output 146 for each of the input prompts 122a-c that are used in generating the batch prompt 124).
Lin does not explicitly, but Radmilac teaches: identify a plurality of LLM queries from a plurality of users for inclusion as part of a batched prompt… ([0181] a batch corresponds to one or more requests received from a plurality of users);
wherein the one or more prompt-engineering inputs identifies the batched prompt as including queries from a plurality of users… ([0178] Systems also identify at least one batching criterion (e.g., batching criteria 726) for a particular node (e.g., node f5) of the different processing nodes (act 1120). Some examples of batching criteria include waiting to transmit a batch of processing requests until a minimum number of processing requests are received, based on a maximum number of processing requests that a particular node can process, waiting for a predetermined amount of time between receiving batching requests, or other batching criterion. The batching criterion can determine that a batched prompt has multiple processing requests, and therefore multiple user queries.);
Lin and Radmilac are considered analogous in the field of large language models. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lin to combine the teachings of Radmilac because doing so would allow for multiple users to provide queries in order to create a batch prompt for input to an LLM, which would improve user experience and LLM efficiency (Radmilac [0140] By implementing systems according to such embodiments, the user experience is improved by allowing a user to submit data prompts and retrieve moderated outputs in a streamlined manner since all of the underlying models and data dependencies between models are abstracted away from the user interface).
Regarding claim 2, Lin in view of Radmilac teaches: the system of claim 1. Lin further teaches: wherein the one or more processors are further configured to determine that the plurality of LLM queries share one or more batching parameters ([0033] The batch prompt generation system 110 may group the input prompts 122a-n within respective groupings. For example, the batch manager112 may receive a plurality of input prompts 122a-n and categorize them based on the tasks 126a-n that are included. For instance, the batch manager 112 may categorize the input prompts 122a-n within a first grouping according to a (e.g., same or similar) first task and a second grouping according to a (e.g., identical or similar) second task. The batch manager 112maygrouptheinputprompts 122a-n in accordance with any of a variety of criteria or characteristics of the input prompts 122a-n).
Regarding claim 3, Lin in view of Radmilac teaches: the system of claim 2. Radmilac further teaches: wherein the one or more batching parameters comprise at least one of a temporal parameter, a spatial parameter, and a subject-matter parameter ([0178] Some examples of batching criteria include waiting to transmit a batch of processing requests until a minimum number of processing requests are received, based on a maximum number of processing requests that a particular node can process, waiting for a predetermined amount of time between receiving batching requests, or other batching criterion).
Lin and Radmilac are considered analogous in the field of large language models. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lin and Radmilac to further combine the teachings of Radmilac because doing so would improve the processing of batch prompts by utilizing batching parameters to increase efficiency and user experience (Radmilac [0124-0125] system identifies one or more batching criteria associated with node f5. For example, as shown in FIG. 20, batching criteria 726 includes a node maximum 728, time elapsed since last request 730, or other 732 batching criteria… Larger batches are hardware-efficient but result in increased latency. Thus, setting a maximum batch size ensures that batches are not so large as to increase the latency to an unacceptable level such that it degrades the user experience, particularly in streaming applications).
Regarding claim 4, Lin in view of Radmilac teaches: the system of claim 3. Radmilac further teaches: wherein the temporal parameter comprises the plurality of LLM queries ([0121] each model graph instance is generated fora different data processing request from one or more different users or in response to receiving different data prompts) having timestamps that are within a predefined amount of time from one another ([0182] the batching criteria are based on a minimum or maximum number of data processing requests and/or based on a maximum wait time between received data processing requests. In some instances, the maximum wait time is less than a millisecond, or less than a few milliseconds, depending on the model processing specifications).
Regarding claim 9, Lin in view of Radmilac teaches: the system of claim 1. Radmilac further teaches: wherein the one or more processors are configured to identify the plurality of LLM queries to be combined based on a determination that an LLM- query threshold has been reached ([0125] The node maximum 728 indicates the maximum number of data processing requests that a node can process as a single input. This is the threshold maximum queue size of requests for the batch. In particular, the node maximum 728 dictates a maximum number of data processing requests that can be included in a batch queue or cache assigned to a particular node before the enqueued requests are dispatched to the node for processing).
Regarding claim 10, Lin in view of Radmilac teaches: the system of claim 1. Lin further teaches: wherein the one or more processors are further configured to provide the batched prompt as tokenized input ([0019] To address the quality issue while maintaining high token resource utilization, implementations of the batch prompt generation system described herein introduce batch permutation and ensembling as well as filtering or removing certain datapoints between subsequent permutations of a batch input), and wherein the tokenized input identifies overlap between one or more terms within the plurality of LLM queries ([0015] the batch prompt generation system batches input prompts in a manner that reduces the number of tokens expended in processing a series of related prompts. Indeed, by batching input prompts in accordance with one or more embodiments, reduces the number of tokens used as a result of removing multiple instances of a task statement (e.g., a task specification) in processing a series of related prompts. The batch prompt generation system identifies overlap in task statements.).
Regarding claim 11, it recites similar limitations as claim 1 and therefore is rejected similarly.
Regarding claim 12, it recites similar limitations as claim 2 and therefore is rejected similarly.
Regarding claim 13, it recites similar limitations as claim 3 and therefore is rejected similarly.
Regarding claim 14, it recites similar limitations as claim 4 and therefore is rejected similarly.
Regarding claim 16, Lin in view of Radmilac teaches: the method of claim 11. Radmilac further teaches: wherein the one or more prompt- engineering inputs identifies the batched prompt as including the plurality of LLM queries ([0178] Systems also identify at least one batching criterion (e.g., batching criteria 726) for a particular node (e.g., node f5) of the different processing nodes (act 1120). Some examples of batching criteria include waiting to transmit a batch of processing requests until a minimum number of processing requests are received, based on a maximum number of processing requests that a particular node can process, waiting for a predetermined amount of time between receiving batching requests, or other batching criterion. The batching criterion can determine that a batched prompt has multiple processing requests, and therefore multiple user queries.).
Regarding claim 19, it recites similar limitations as claim 9 and therefore is rejected similarly.
Regarding claim 20, it recites similar limitations as claim 10 and therefore is rejected similarly.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Radmilac, as applied to claims 1-4, 9-14, 16, and 19-20 above, and further in view of Lafon et al. (US 20250068741 A1; hereinafter referred to as Lafon).
Regarding claim 5, Lin in view of Radmilac teaches: the system of claim 3. Lin in view of Radmilac does not explicitly, but Lafon teaches: wherein the spatial parameter comprises the plurality of LLM queries ([0017] Prompts or user inputs or user queries which are intended to misguide generative Al models in the different ways described herein are usually made up of base prompt) having location metadata within a predefined region ([0039] the geographical location can be identified from the metadata of the user query 122).
Lin, Radmilac, and Lafon are considered analogous in the field of large language models. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lin and Radmilac to combine the teachings of Lafon because doing so would allow for output of an LLM to be modified based on the location associated with a user query, leading to more relevant responses for users (Lafon [0039] At 710, the content regulations for the geographical location are accessed from the regulator knowledgebase 188. At 712 it is determined if the model query response complies with the content regulation of the geographical location).
Regarding claim 15, it recites similar limitations as claim 5 and therefore is rejected similarly.
Claims 7-8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Radmilac, as applied to claims 1-4, 9-14, 16, and 19-20 above, and further in view of Bharadwaj et al. (US 20240273345 A1; hereinafter referred to as Bharadwaj).
Regarding claim 7, Lin in view of Radmilac teaches: the system of claim 1. Lin in view of Radmilac does not explicitly, but Bharadwaj teaches: wherein the one or more prompt engineering inputs comprise one or more segmentation-related instructions for the LLM to structure the output in a manner that allows for segmentation of the output in connection with each of the plurality of users ([0129] with regard to response module selection and/or construction the one or more responses to the first input query generated using the selected or constructed response modules may be tailored to a user segment, for instance, by using prompting optimized for that user segment, adapter models finetuned for generating outputs for that user segment, and so on).
Lin, Radmilac, and Bharadwaj are considered analogous in the field of large language models. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lin and Radmilac to combine the teachings of Bharadwaj because doing so would allow for specific outputs from the LLM to be directed to particular users, improving user experience (Bharadwaj [0198] identifying one or more population groups/user segments to serve the selected or constructed response modules at step 906, the method may proceed to step 908, wherein step 908 comprises serving the response modules to the identified population groups/user segments. After serving the response modules to the identified population groups/user segments at step 908, the method 900 may proceed to step 910, wherein step 910 comprises collecting metrics associated with the served modules).
Regarding claim 8, the combination of Lin, Radmilac, and Bharadwaj teaches: the system of claim 7. Bharadwaj further teaches: wherein the segmentation instructions ([0013] the plurality of machine learning models in each respective response module comprises each of a foundational language model) comprise instructions allow for portions of the output to be associated with one or more user identifiers from the plurality of users ([0098] one or more response modules maybe selected or constructed based on the input query characteristics, including the prompt, user/user identifier, data associated with the user/user identifier, and user preference metrics associated with the response modules. The selected or constructed one or more modules maybe used to generate one or more responses to the input query).
Regarding claim 18, it recites similar limitations as claim 8 and therefore is rejected similarly.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NATHAN TENGBUMROONG/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654