Detailed Action
This communication is in response to the Arguments and Amendments filed on 2/23/2026.
Claims 1-20 are pending and have been examined.
Claims 1-20 are rejected. Hence, this action has been made Final.
Claims 1, 11 and 17 are independent method and system and storage medium claims respectively.
Apparent priority: 3/22/2024.
Any previous objection/rejection not mentioned in this Office Action has been withdrawn by the Examiner.
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 .
Arguments and Amendments
Applicant has amended the independent claims to include “1. (Currently amended) A method comprising: accessing an initial meta prompt, wherein the initial meta prompt is a prompt for a generative artificial intelligence (AI) model to enhance a task prompt; executing a first generative AI model to generate a first set of variant meta prompts based on the initial meta prompt; executing a second generative model to generate a first set of enhanced baseline task prompts corresponding to a set of baseline task prompts using a second generative AI model based on the first set of variant meta prompts; evaluating the first set of variant meta prompts [[by]] comprising comparing a first set of enhanced baseline outputs corresponding to the first set of enhanced baseline task prompts and a set of baseline outputs corresponding to the set of baseline task prompts to obtain a first set of evaluation data; selecting a first variant meta prompt as a first optimized meta prompt based on the first set of evaluation data; and providing the first optimized meta prompt to a third generative AI model for task prompt enhancement.”
Regarding the Rejections under 35 U.S.C. 101 Applicant notes
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is allegedly directed to an abstract idea without significantly more.
According to the USPTO October 2019 Update: Subject Matter Eligibility, "Examiners should identify at least one abstract idea grouping [a claim limitation(s) is determined to fall within], ..., and proceed with the analysis in Step 2A Prong Two." The abstract idea groups enumerated in the 2019 PEG by the USPTO include Mathematical Concepts, Certain Methods of Organizing Human Activity, and Mental Processes. However, the Office Action does not identify any abstract idea group that the claim limitations of the present application allegedly fall within. Applicant asserts that the claims at issue are not directed to any of the enumerated abstract idea groups.
Specifically, the claims are not directed to mental processes because they recite features that cannot practically be performed in the human mind. The amended independent claims recite limitations that cannot practically be performed by the human mind, such as "executing a first generative Al model to generate a first set of variant meta prompts based on the initial meta prompt" and "executing a second generative model to generate a first set of enhanced baseline task prompts corresponding to a set of baseline task prompts based on the first set of variant meta prompts." Further, to the extent that an abstract idea is recited in the claims, the claims integrate the abstract idea into a practical application because the claims provide the first optimized meta prompt to a third generative AI model for enhancing a task prompt, which is more instructive and of better quality for an LLM to follow so that the LLM can generate an output with more details and better quality. Therefore, withdrawal of the rejections under 35 U.S.C. § 101 is respectfully requested for at least the following reasons.
Examiner notes the Memo’s reminder that the mental process grouping has limits is acknowledged, but it does not preclude application of the mental process grouping here because several claim limitations recite high level cognitive/data manipulation concepts (e.g., accessing prompts executing models generating prompts evaluating prompts comparing outputs selecting prompts) that can be characterized as mental/data processing concepts. The fact that a artificial intelligence performs them in practice does not automatically remove them from the judicial exception analysis without claim language or specification evidence showing a concrete technological improvement to computer functionality.
Applicant notes A. The claims do not recite an abstract idea
The Office action alleges that the claim limitations of claims 1, 11, and 17 relate to the human performing similar tasks. Office Action, pages 3-4. However, a person or the human mind cannot possibly perform certain steps recited in claims 1, 11, and 17.
A claim with limitations that cannot practically be performed in the human mind does not recite a mental process. M.P.E.P. §2106.04(a)(2).III.A. "Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." Id., citing SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019). Further, the USPTO Memo "Reminders on evaluation subject matter eligibility of claims under 35 U.S.C. 101" on August 4, 2025 clearly states that "Claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping [of mental processes]."
Examiner notes absent claim detail tying the operations to specific technical mechanisms that go beyond mere data processing, these limitations are susceptible to classification as mental/data manipulation concepts.
Applicant argues the recited AI components (Models) cannot be performed in the human mind and therefore cannot be mental processes. That point is acknowledged: the literal operation of a deep neural network with millions/billions of parameters cannot be executed mentally.
o However, the statutory exception analysis asks whether the claim is “directed to” an abstract idea — and courts and the USPTO have recognized that claims which at a high level recite the performance of cognitive or mental tasks (e.g., accessing executing generating evaluating comparing and selecting) may be placed in the mental process grouping even if implemented by machines (see examples in the August 2025 Memo and cases like SAP, Electric Power Group).
o The claim language here is largely high level and functional: “generating a phone feature,” “generating a word embedding vector sequence,” “accessing executing generating evaluating comparing and selecting — these can be reasonably characterized as information processing/mental like steps (conceptual transformations of data prompt information). Absent claim detail tying the operations to specific technical mechanisms that go beyond mere data processing, these limitations are susceptible to classification as mental/data manipulation concepts under Step 2A Prong One.
o One way to analyze would be to ask whether the limitation uses an AI model in practice does not alone place it outside the mental process grouping; what matters for Step 2A prong one is whether the claim language, read as a whole, is directed to a judicial exception (here, data processing/mathematical/mental concepts). The August 2025 Memo narrows the mental process group but does not establish a per se rule that any claim that names a neural network component is removed from the judicial exception analysis.
Applicant notes The independent claims recite multiple steps that encompass AI in a way that could not be practically performed in the human mind. For example, the human mind could not practically execute a first generative AI model to generate a first set of variant meta prompts on the initial meta prompt. The human mind could not practically execute a second generative model to generate a first set of enhanced baseline task prompts corresponding to a set of baseline task prompts based on the first set of variant meta prompts either.
Applicant notes B. The claims recite elements which integrate a practical application
Even assuming arguendo, the claims did recite an abstract idea grouping of mental processes, the independent claims 1, 11, and 17 integrate the alleged abstract idea into a practical application.
Applicant notes The amended claims integrate the alleged abstract idea into a practical application by optimizing a meta prompt which is used to enhance task prompts for an AI model to generate high- quality outputs from the AI model. Specification [0015] and [0018]. The specification of the instant application explicitly describes the improvement provided by the claimed invention to the technologies of generative AI, or more specifically task prompt enhancement for an AI model:
[0014] A prompt can provide instructions to an artificial intelligence (AI)
or machine learning (ML) model for a certain task. The AI or ML model, for example a large language model (LLM), can generate an output based on an
input and a prompt; however, the quality of the prompt can affect the quality of the output. Not every user is trained to write sophisticated prompts for AI or ML
models for performing various tasks and so they may not be able to take full
advantage of the AI or ML model's capabilities.
[0015] To obtain high-quality outputs from an AI/ML model for
generative tasks, it is desirable to enhance a task prompt before it is provided to
the AI/ML model. For example, a communication platform may provide a meta prompt to facilitate the enhancement of the task prompts. The meta prompt can be considered as a prompt about a task prompt. In other words, a meta prompt
can instruct an AI/ML model to generate an enhanced prompt based on an initial
task prompt as input.
[0016] A meta prompt rephrasing module on the communication platform
can generate multiple variant meta prompts based on an initial meta prompt
using an LLM trained for rephrasing. The initial meta prompt is a pre-defined meta prompt that can be applied to a task prompt to enhance the task prompt.
However, the meta prompt itself may be improved by generating variant meta
prompts and testing their effectiveness at improving a task prompt. Certain
parameters of the trained LLM can be adjusted, for example the temperature
parameter can be adjusted higher to diversify the variant meta prompts generated
from the initial meta prompt. The meta prompt rephrasing module can also use evaluation data associated with previously generated variant meta prompts as feedback to refine or fine-tune the trained LLM when generating variant meta
prompts.
[0018] A prompt evaluation module on the communication platform can
evaluate the multiple variant meta prompts. For example, the prompt evaluation
module applies an enhanced baseline task prompt to a baseline input to obtain an
enhanced baseline output using a trained LLM. The prompt evaluation module
can evaluate the enhanced baseline output corresponding to the enhanced baseline task prompt and the baseline output corresponding to the original
baseline task prompt, to determine whether the enhanced output is better than the
baseline output and provide evaluation data for the enhanced baseline output corresponding to the enhanced baseline task prompt. The evaluation data can also represent the quality of the corresponding enhanced baseline task prompt,
and in turn the quality of a corresponding variant meta prompt. This way, the prompt evaluation module can evaluate the multiple variant meta prompts. The
evaluation data can include evaluation scores and reasoning data corresponding to the multiple variant meta prompts. The variant meta prompt with the highest
evaluation score can be provided as an optimized meta prompt to a trained LLM for task prompt enhancement. When a user provides a task prompt to the trained LLMfor task prompt enhancement, unseen by the task prompt enhancing module,
the trained LLM can generate an enhanced task prompt. The enhanced task
prompt can then be provided to an LLM to generate an output based on an input
and the enhanced task prompt.
Specification [0014], [0015], [0016], and [0018].
Integrating the recited judicial exception into a practical application involves "an improvement in the functioning of a computer, or an improvement to other technology or technical field." M.P.E.P. § 2106.04(d).I. In the independent claims 1, 11, and 17, the recited claim limitations provide an improvement to at least the technology of generative AI, or more specifically prompt enhancement for generative AI, which is a consideration in M.P.E.P. § 2106.05(a). The additional elements include "executing a first generative AI model to generate a first set of variant meta prompts based on the initial meta prompt;" "evaluating the first set of variant meta prompts ... to obtain a first set of evaluation data;" and "selecting a first variant meta prompt as afirst optimized meta prompt based on the first set of evaluation data." As described in paragraphs [0013] and [0017], such additional elements improve the meta prompt by generating variant meta prompts and testing their effectiveness at improving a task prompt. Filed Specification, paragraph [0016].
Examiner notes On the present claim wording, the limitations are largely functional and outcome oriented (accessing executing generating evaluating comparing and selecting) without concrete computational detail or a recitation of how the arrangements materially improve the functioning of the computer system itself (e.g., speed/latency reductions, memory or computational efficiency, novel data representations that reduce error by a measurable metric, or specific unconventional network architectures constrained in a way that produces the improvement).
Applicant notes The fact that the claims integrate the alleged abstract idea into a practical application at Step 2A prong two is further bolstered by a recent USPTO Appeals Review Panel (ARP) decision issued on September 26, 2025, regarding Exparte Desjardins. In Exparte Desjardins, the Appeals Review Panel reversed the Patent Trial and Appeal Board's new ground of rejection under § 101. The ARP decision pointed to the Federal Circuit's Enfish decision, which recognized that "[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes." Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016). The application in Ex parte Desjardins relates to training machine learning models, and reflects improvements in artificial intelligence (AI) technology that "effectively learn new tasks in succession whilst protecting knowledge about previous tasks," citing paragraph [0021] of the associated specification. Ex parte Desjardins, p. 9. Claim 1 in Ex parte Desjardins also reflects the improvement by including a feature of "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task."
Applicant notes Similarly, claim 1 at issue provides improvements in AI technology, specifically prompt enhancement, where "the meta prompt itself may be improved by generating variant meta prompts and testing their effectiveness at improving a task prompt." Filed Specification, paragraph [0016]. At least claim elements "executing afirst generative AI model to generate a first set of variant meta prompts based on the initial meta prompt;" "evaluating the first set of variant meta prompts ... to obtain a first set of evaluation data;" "selecting a first variant meta prompt as afirst optimized meta prompt based on the first set of evaluation data;" and "providing the first optimized meta prompt to a third generative Ai model for task prompt enhancement" in claim 1 reflects such an improvement.
Applicant notes Accordingly, even if the claims did recite an abstract idea (a premise with which the Applicant does not agree), each of independent claims 1, 11, and 17 as a whole integrates the judicial exception into a practical application and thus the claims are not directed to a judicial exception. For similar reasons, the respective dependent claims of claims 1, 11, and 17 are also patent-eligible. Withdrawal of the rejections under 35 U.S.C. § 101 is respectfully requested.
Examiner notes Assuming arguendo that some claim limitations recite an abstract idea, the additional elements must supply an “inventive concept.” The claim recites known functional components (AI Models) and high level data transformations. Without claim specificity tying those components to particular unconventional architectures, constrained parameterizations, training/regimen steps, or demonstrable improvements, the recited elements appear to be routine, conventional uses of neural networks and generic software components, and therefore fail to supply an inventive concept (see Alice; Berkheimer — factual showing may rebut this with evidence).
Naming AI Model components without structure or constraints does not automatically transform an abstract data processing claim into a patent eligible technological improvement.
As written, the claim’s high level functional language does not make the improvement evident on the face of the claim; therefore the rejection stands absent amendment or evidentiary support.
• The § 101 analysis is highly fact sensitive. If Applicant can show (via claim amendment and/or evidence) that the recited components and their combination are unconventional and that the claimed steps produce a technical improvement in TTS systems (and that such improvement is reflected in the claim language), the analysis may change. As acknowledged above, the August 2025 Memo narrows the mental process group and asks examiners to avoid overbroad application; that instruction is applied here but does not, on the present claim language, lead to allowance.
Regarding the Rejections under 35 U.S.C. § 103 Applicant notes
The Office Action alleges that the "multiple variants of an original query" in Alakuijala is equivalent to "a first set of variant meta prompts," as specified in claim 1. Office Action, p. 10. This misreads claim 1 at issue and Alakuijala. Alakuijala is directed to generating query variants for a submitted query, which may be considered as a task prompt in claim 1 at issue. However, Alakuijala does not disclose or make any meta prompt. A meta prompt is a prompt for a generative model to enhance a task prompt, as specified in claim 1. In other words, a meta prompt can be considered as a prompt about a task prompt. Filed Specification, paragraph [0015]. Since Alakuijala does not disclose or make obvious of any meta prompt, it is impossible for Alakuijala to generate "a first set of variant meta prompts."
Examiner notes Alakuijala discloses metaprompts which optimize the original prompt. “The improved efficiency can lie in the speed with which relevant results can be obtained, as it is not necessary to require a user to re-submit a modified query in the event that an initial query does not generate any relevant results. The disclosed implementations enable a plurality of query variants to be tested automatically. Convergence of results can also be ensured via the training of the model used to generate the variants, such that improved efficiency is achieved not simply through simultaneous processing of multiple queries, but through targeted query variant generation.” “The controller engine 114 can generate output over the control model based on the applied input, and utilize the output to determine whether to instruct the variant engine 112 to generate a further variant or to instead cease variant generation.”
The Office Action concedes that Alakuijala does not disclose "selecting a first variant metaPage prompt as a first optimized meta prompt based on the first set of evaluation data; and providing the first optimized meta prompt to a third generative model for task prompt enhancement," but alleges that Hu provides the missing disclosure. Office Action, p. 12. This is incorrect. Similar to Alakuijala, Hu is also directed to processing an original query and generating a rewritten query corresponding to the original query. Hu, Abstract. It does not disclose or make obvious any meta prompt or variant meta prompt, which are as specified in claim 1. Paragraph [0027] in Hu cited by the Office Action describes using historical queries and historical search results to train a preliminary query processing model. The original queries, rewritten queries, or historical queries in Hu may be equivalent to a task prompt in claim 1, but not the meta prompt, which is a prompt for a generative model to enhance a task prompt, as specified in claim 1. Since Hu does not disclose or make obvious any meta prompt, it is impossible for Hu to generate variant meta prompts or selecting a first variant meta prompt as a first optimized meta prompt.
Examiner notes Hu teaches [0020] the at least one processor may be further directed to: rewrite the query based on a query processing model” and “and training a preliminary query processing model based on the plurality of third segmented historical search records to generate the query processing model.” Examiner further notes applicants claim describes meta prompt is a prompt for a generative artificial intelligence (AI) model to enhance a task prompt. Hu enhances the task query using the query processing model.
Examiner notes the Applicants arguments and amendments do not overcome the previously applied prior art. Updated mappings to reflect Applicants amendments are below.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The independent Claims are directed to statutory categories:
Claim 1 is a Method claim and directed to the machine or manufacture category of patentable subject matter.
Claim 11 is a System claim and is directed to the machine or manufacture category of patentable subject matter.
Claim 17 is a Storage Medium claim and is directed to the machine or manufacture category of patentable subject matter.
Independent claim 1 recites,
“1. A method comprising: accessing an initial meta prompt, wherein the initial meta prompt is a prompt for a generative artificial intelligence (AI) model to enhance a task prompt; (This relates to a human accessing a prompt by visual means.)
executing a first generative AI model to generate a first set of variant meta prompts based on the initial meta prompt; (This relates to a human generating meta prompts using natural language understanding and logic and reasoning.)
executing a second generative model to generate a first set of enhanced baseline task prompts corresponding to a set of baseline task prompts using a second generative AI model based on the first set of variant meta prompts; (This relates to a human generating enhanced task prompts using natural language understanding and logic and reasoning.)
evaluating the first set of variant meta prompts comprising comparing a first set of enhanced baseline outputs corresponding to the set of enhanced baseline task prompts and a set of baseline outputs corresponding to the first set of baseline task prompts to obtain a first set of evaluation data; (This relates to a human evaluating variant meta prompts in the human mind by comparing enhanced baseline outputs to the baseline outputs.)
selecting a first variant meta prompt as a first optimized meta prompt based on the first set of evaluation data; (This relates to a human selecting using vision or pen and paper a variant meta prompt from evaluation data.)
and providing the first optimized meta prompt to a third generative AI model for task prompt enhancement. (This relates to a human providing an optimized meta prompt using pen and paper.)
The Dependent Claims do not include additional limitations that could incorporate the abstract idea into a practical application or cause the Claim as a whole to amount to significantly more than the underlying abstract idea.
As to Independent Claim 11, Claim 11 is An apparatus Claim with limitations similar to that of Claim 1 and is rejected under the same rationale.
As to Independent Claim 17, Claim 17 is a Storage Medium Claim with limitations similar to that of Claim 1 and is rejected under the same rationale.
This judicial exception is not integrated into a practical application. In particular, claim 11 recites additional elements of “processor”. For example, in [0113] The example computing device 900 includes a processor 910 which is in communication with the memory 920 and other components of the computing device 900 using one or more communications buses 902. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitation in the claims noted above are directed towards insignificant solution activity. The claims are not patent eligible.
Dependent claim 2 recites,
“2. The method of claim 1, further comprising:
receiving a task prompt from a user device; (This relates to a human using visual or auditory system to receive a prompt). A user device is noted as an additional element.)
and executing the third generative AI model to generate an enhanced task prompt based on the task prompt and the first optimized meta prompt. (This relates to a human using natural language understanding and speech or pen and paper to generate an enhanced task prompt).
Dependent claim 3 recites,
“3. The method of claim 2, further comprising:
receiving input data for a generative task from a user device; (This relates to a human receiving input through the auditory or visual systems.)
executing a fourth generative AI model to generate an output based on the input data and the enhanced task prompt; (This relates to a human using pen and paper to provide output.)
and providing the output for the generative task to the user device.
(This relates to a human using pen and paper to provide output. A user device is noted as an additional element.)
Dependent claim 4 recites,
“4. The method of claim 1, wherein evaluating the first set of variant meta prompts by comparing a first set of enhanced baseline outputs corresponding to the first set of enhanced baseline task prompts and a set of baseline outputs corresponding to the set of baseline task prompts comprises:
applying an enhanced baseline task prompt generated based on a variant meta prompt of the first set of variant meta prompts to a baseline input to obtain an enhanced baseline output; (This relates to a human using pen and paper and logic and reasoning to apply a task prompt and a meta prompt to a base input for evaluation.)
evaluating the enhanced baseline task prompt by comparing the enhanced baseline output corresponding to the enhanced baseline task prompt and a baseline output corresponding to a baseline task prompt associated with the enhanced baseline task prompt to obtain evaluation data associated with the enhanced baseline task prompt; (This relates to a human using logic and reasoning to evaluate a task prompt by comparison)
generating a subset of evaluation data for the variant meta prompt based on a subset of evaluation data associated with a subset of the first set of enhanced baseline task prompts corresponding to the set of baseline task prompts enhanced by the variant meta prompt; (This relates to a human using pen and paper to generate evaluation data.)
and aggregating subsets of evaluation data for the first set of variant meta prompts to obtain the first set of evaluation data. (This relates to a human using pen and paper to aggregate the data.)
Dependent claim 5 recites,
“5. The method of claim 1, wherein the first set of evaluation data comprises multiple evaluation scores corresponding to the first set of variant meta prompts, wherein selecting the first variant meta prompt as the first optimized meta prompt based on the first set of evaluation data comprises:
determining whether a highest evaluation score of the multiple evaluation scores satisfies a predetermined threshold; (This relates to a human using logic and reasoning to determine whether a highest evaluation score of the multiple evaluation scores satisfies a predetermined threshold.)
and in response to determining the highest evaluation score of the multiple evaluation scores satisfies a predetermined threshold, selecting the variant meta prompt corresponding to the highest evaluation score as the first optimized meta prompt. (This relates to a human using pen and paper to select a variant meta prompt.)
Dependent claim 6 recites,
“6. The method of claim 5, further comprising:
in response to determining the highest evaluation score of the multiple evaluation scores does not satisfy the predetermined threshold,
generating a second set of variant meta prompts using the first generative model based on the initial meta prompt; (This relates to a human using pen and paper to generate variant meta prompts.)
generating a second set of enhanced baseline task prompts corresponding to the set of baseline task prompts using the second generative model based on the second set of variant meta prompts; (This relates to a human using pen and paper to generate enhanced baseline task prompts.)
evaluating the second set of variant meta prompts comparing a second set of enhanced baseline outputs corresponding to the second set of enhanced baseline task prompts and the set of baseline outputs corresponding to the set of baseline task prompts to provide a second set of evaluation data; (This relates to a human using logic and reasoning and natural language understanding to evaluate prompts by comparing outputs.)
selecting a second variant meta prompt from the second set of variant meta prompts as a second optimized meta prompt based on the second set of evaluation data; (This relates to a human logic and reasoning to select a prompt.)
and providing the second optimized meta prompt to the third generative model for task prompt enhancement. (This relates to a human using pen and paper to provide a prompt.)
Dependent claim 7 recites,
“7. The method of claim 1, wherein the first set of evaluation data comprises analytics data associated with the first set of variant meta prompts, wherein the method further comprises: extracting a set of common points from the analytics data associated with the first set of variant meta prompts; (This relates to a human using logic and reasoning to extract common points from data.)
and provide the set of common points as feedback input to the first generative model for variant meta prompt generation. (This relates to a human using pen and paper to provide points as feedback input.)
Dependent claim 8 recites,
“8. The method of claim 1, wherein the set of baseline task prompts comprise prompts for a set of tasks, wherein the set of tasks comprise summarization, paraphrasing, evaluation, question-answer generation, audio generation, or video generation. (This relates to a human using pen and paper to set task prompts.)
Dependent claim 9 recites,
“9. The method of claim 1, wherein generating a first set of variant meta prompts using a first generative model based on the initial meta prompt comprising:
diversifying the initial meta prompt by setting a temperature parameter of the first generative model above a predetermined value to obtain the first set of variant meta prompts. (This relates to a human using pen and paper to set a temperature parameter.)
Dependent claim 9 recites,
“10. The method of claim 1, wherein at least the second generative model and the third generative model are the same generative model. (This relates to a human using pen and paper to make two identical models)
As to Dependent claim 12, Claim 12 is a system claim with limitations similar to Claims 2 and 3, and is rejected under the same rationale.
As to Dependent claim 13, Claim 13 is a system claim with limitations similar to Claim 4, and is rejected under the same rationale.
As to Dependent claim 14, Claim 14 is a system claim with limitations similar to Claim 5, and is rejected under the same rationale.
As to Dependent claim 15, Claim 15 is a system claim with limitations similar to Claim 6, and is rejected under the same rationale.
As to Dependent claim 16, Claim 16 is a CRM claim with limitations similar to Claim 7, and is rejected under the same rationale.
As to Dependent claim 18, Claim 18 is a CRM claim with limitations similar to Claims 2 and 3, and is rejected under the same rationale.
As to Dependent claim 19, Claim 19 is a CRM claim with limitations similar to Claim 7, and is rejected under the same rationale.
As to Dependent claim 20, Claim 20 is a CRM claim with limitations similar to Claim 4, and is rejected under the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 1-8, and 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alakuijala (U.S. Patent Number US 20200142888 A1) in view of Hu (U.S. Patent Number US 20210089531 A1).
Regarding Claim 1, Alakuijala teaches
Alakuijala teaches 1. A method comprising: accessing an initial meta prompt, wherein the initial meta prompt is a prompt for a generative artificial intelligence (AI) model to enhance a task prompt;
executing a first generative (AI) model to generate a first set of variant meta prompts based on the initial meta prompt; (see Alakuijala, [0008] “In some implementations, multiple variants of an original query are generated utilizing the generative model, each of the multiple variants are submitted to a search system, and corresponding response(s) received for each of the multiple variants. An output can be generated based on one or more of the responses, and the output provided in response to the original query. For example, the output can include the “best” response (e.g., as indicated by response scores provided by the search system), multiple of the “best” responses, and/or a variant and corresponding response(s) (e.g., when the variant is of a follow-up type). In this and other manners, response(s) to variant(s) of an original query can be utilized to provide output, in response to the original query, where the output directly answers the original query. Further, response(s) to variant(s) of an original query can be utilized to substantiate/corroborate response(s) to the original query and/or response(s) to other variant(s). For example, the accuracy of an “answer” to an original query can be determined based on whether affirmative answers are provided for variants of the original query. For instance, based on whether other affirmative answers are provided for variant(s) of a follow-up type and/or based on whether affirmative similar answers (similar to the answer of the original query) are available to variant(s) of equivalent, generalization, and/or language translation type(s). In this and other manners, unsubstantiated/uncorroborated response(s) can be determined and not utilized in provided output, and/or flagged as uncorroborated if utilized in provided output (e.g., flagged as “potentially fake”).”) executing a second generative model to generate a first set of enhanced baseline task prompts corresponding to a set of baseline task prompts using a second generative AI model based on the first set of variant meta prompts; (see Alakuijala, [0110] “10. The method of claim 6, further comprising: generating a training instance that includes training instance input and training instance output, the training instance input including: first query tokens of a first query, and a task attribute, the training instance output including: second query tokens of a second query; wherein the training instance is generated with the task attribute as training instance input based on determining that a past submission of the first query, followed by a past submission of the second query, is associated with the predicted task; and training the generative model based on the generated training instance. 11. The method of claim 6, further comprising: selecting a trained generative model, from a plurality of trained generative models, based on the trained generative model being trained based on past query submissions associated with the predicted task”) evaluating the first set of variant meta prompts comprising comparing a first set of enhanced baseline outputs corresponding to the first set of enhanced baseline task prompts and a set of baseline outputs corresponding to the set of baseline task prompts to obtain a first set of evaluation data; (see Alakuijala, [0047] “In some implementations, the controller engine 114 determines, for a submitted query, whether any variants are to be generated by the variant engine 112 for the submitted query. For example, the controller engine 114 can make such a determination based on the submitted query itself and/or based on response(s) (if any) from the search system 140 for the submitted query. For instance, the controller engine 114 can determine to generate variants only if an answer response is not returned by the search system 140 or if any returned answer response is of insufficient quality (e.g., has a search system provided score that fails to satisfy a threshold). In some of those implementations, the controller engine 114 applies tokens of the submitted query and/or features of response(s) to the submitted query to one of the control models 154, and generates output over the control models 154 that indicates whether variants are to be generated. In some additional or alternative implementations, the controller engine 114 applies tokens of the submitted query and/or features of response(s) to one of the control models 154, and generates output over the control models 154 that is provided to the variant engine 112 for application as input to a generative model in generating a variant (thereby influencing the variant generation).”)
Alakuijala does not specifically teach selecting a first variant meta prompt as a first optimized meta prompt based on the first set of evaluation data; and providing the first optimized meta prompt to a third generative AI model for task prompt enhancement. However, Hu does teach this limitation (see Hu [0027] In some embodiments, wherein the performing a search based on the one or more phrases to obtain a search result associated with the query, the method may further include: rewriting the query based on a query processing model, wherein the query processing model may be provided by: obtaining a plurality of third historical search records, wherein each of the plurality of third historical search records may include a third historical query of a third historical user and a third historical search result selected by the third historical user corresponding to the third historical query; segmenting each of the plurality of third historical search records; and training a preliminary query processing model based on the plurality of third segmented historical search records to generate the query processing model.”)
Alakuijala and Hu are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Method of Alakuijala to incorporate the teachings of Hu to include selecting a first variant meta prompt as a first optimized meta prompt based on the first set of evaluation data; and providing the first optimized meta prompt to a third generative model for task prompt enhancement. This allows for processing a query to determine an optimal search result associated with the query as recognized by Hu in [0003].
Regarding independent Claim 11, claim 11 is a Device claim with limitations similar to that of Claim 1 and is rejected under the same rationale. Additionally, Alakuijala teaches 11. A system comprising: a communications interface; a non-transitory computer-readable medium; and one or more processors communicatively coupled to the communications interface and the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non-transitory computer-readable medium to: (see Alakuijala [0024] “Various implementations disclosed herein may include one or more non-transitory computer readable storage media storing instructions executable by a processor (e.g., a central processing unit (CPU), graphics processing unit (GPU), and/or Tensor Processing Unit (TPU)) to perform a method such as one or more of the methods described herein. Yet other various implementations may include a system of one or more computers that include one or more processors operable to execute stored instructions to perform a method such as one or more of the methods described herein.”)
Regarding independent Claim 17, claim 17 is a CRM claim with limitations similar to that of Claim 1 and is rejected under the same rationale. Additionally, Alakuijala teaches 17. A non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors to: (see Alakuijala [0024] “Various implementations disclosed herein may include one or more non-transitory computer readable storage media storing instructions executable by a processor (e.g., a central processing unit (CPU), graphics processing unit (GPU), and/or Tensor Processing Unit (TPU)) to perform a method such as one or more of the methods described herein. Yet other various implementations may include a system of one or more computers that include one or more processors operable to execute stored instructions to perform a method such as one or more of the methods described herein.”)
As to Dependent Claim 2, Alakuijala in view of Hu teaches 2. The method of claim 1,
Furthermore, Alakuijala teaches 2. The method of claim 1, further comprising: receiving a task prompt from a user device; and executing the third generative AI model to generate an enhanced task prompt based on the task prompt and the first optimized meta prompt. (see Alakuijala [0099] “Turning now to FIG. 8A and FIG. 8B, example graphical user interfaces 800A and 800B are illustrated for providing output that is based variant(s) generated according to implementations disclosed herein. The graphical user interfaces 800A and 800B may be presented at client device 106 (e.g., in a browser executing at client device 106 and/or in another application executing at client device 106).”)
As to Dependent Claim 3, Alakuijala in view of Hu teaches 3. The method of claim 2,
Furthermore, Alakuijala teaches 3. The method of claim 2, further comprising: receiving input data for a generative task from a user device; executing a fourth generative AI model to generate an output based on the input data and the enhanced task prompt; and providing the output for the generative task to the user device. (See Alakuijala [0099] Turning now to FIG. 8A and FIG. 8B, example graphical user interfaces 800A and 800B are illustrated for providing output that is based variant(s) generated according to implementations disclosed herein. The graphical user interfaces 800A and 800B may be presented at client device 106 (e.g., in a browser executing at client device 106 and/or in another application executing at client device 106).”)
As to Dependent Claim 4, Alakuijala in view of Hu teaches 4. The method of claim 1,
Furthermore, Alakuijala teaches wherein evaluating the first set of variant meta prompts by comparing a first set of enhanced baseline outputs corresponding to the set of enhanced baseline task prompts generated based on a set of baseline outputs corresponding to the set of baseline task prompts comprises: applying an enhanced baseline task prompt and a variant meta prompt of the first set of variant meta prompts to a baseline input to obtain an enhanced baseline output; evaluating the enhanced baseline task prompt by comparing the enhanced baseline output corresponding to the enhanced baseline task prompt and a baseline output corresponding to a baseline task prompt associated with the enhanced baseline task prompt to obtain evaluation data associated with the enhanced baseline task prompt; (see Alakuijala [0044] In some implementations, multiple generative models 152 are accessible to the variant engine 112 and the variant engine 112 selects a subset of one or more of the multiple generative models 152 for generating variant(s) for a submitted query based on one or more parameters. For example, multiple generative models 152 can be provided, with each of the generative models being trained based on training data that is based on past query submissions of a unique group of users. For example, a first generative model can be generated based on training data that is based on past query submissions of users having attributes A and B. A second generative model can be generated based on training data that is based on past query submissions of users having attributes B and C. For a submitted query of a user having attributes B and C (but not A), the variant engine 112 can select the second generative model (without also selecting the first generative model) in generating variants for that query—as the user attributes B and C match those utilized in training the second generative model.”) (see Alakuijala [0110] “15. The method of claim 1, further comprising: selecting a trained generative model, from a plurality of trained generative models, based on the trained generative model being trained based on past query submissions of a group of users having one or more attributes in common with the user, and applying tokens of the original query as input to the selected trained generative model.”) generating a subset of evaluation data for the variant meta prompt based on a subset of evaluation data associated with a subset of the first set of enhanced baseline task prompts corresponding to the set of baseline task prompts enhanced by the variant meta prompt; and aggregating subsets of evaluation data for the first set of variant meta prompts to obtain the first set of evaluation data. (see Alakuijala [0057] “In some implementations, the variant engine 112 transmits, to the client device 106, the variants as output to be provided based on the original query. In some implementations, the variant engine 112 additionally or alternatively provides one or more of the variants to search system 140, which determines one or more response(s) (e.g., a single answer search result, or multiple search results) for the variant(s), and transmits the response(s) to the client device as output to be provided based on the original query.”)
As to Dependent Claim 5, Alakuijala in view of Hu teaches 5. The method of claim 1,
Furthermore, Alakuijala teaches wherein the first set of evaluation data comprises multiple evaluation scores corresponding to the first set of variant meta prompts, wherein selecting the first variant meta prompt as the first optimized meta prompt based on the first set of evaluation data comprises: determining whether a highest evaluation score of the multiple evaluation scores satisfies a predetermined threshold; and in response to determining the highest evaluation score of the multiple evaluation scores satisfies a predetermined threshold, selecting the variant meta prompt corresponding to the highest evaluation score as the first optimized meta prompt. (see Alakuijala [0008] “In some implementations, multiple variants of an original query are generated utilizing the generative model, each of the multiple variants are submitted to a search system, and corresponding response(s) received for each of the multiple variants. An output can be generated based on one or more of the responses, and the output provided in response to the original query. For example, the output can include the “best” response (e.g., as indicated by response scores provided by the search system), multiple of the “best” responses, and/or a variant and corresponding response(s) (e.g., when the variant is of a follow-up type). In this and other manners, response(s) to variant(s) of an original query can be utilized to provide output, in response to the original query, where the output directly answers the original query. Further, response(s) to variant(s) of an original query can be utilized to substantiate/corroborate response(s) to the original query and/or response(s) to other variant(s). For example, the accuracy of an “answer” to an original query can be determined based on whether affirmative answers are provided for variants of the original query. For instance, based on whether other affirmative answers are provided for variant(s) of a follow-up type and/or based on whether affirmative similar answers (similar to the answer of the original query) are available to variant(s) of equivalent, generalization, and/or language translation type(s). In this and other manners, unsubstantiated/uncorroborated response(s) can be determined and not utilized in provided output, and/or flagged as uncorroborated if utilized in provided output (e.g., flagged as “potentially fake”).”)
As to Dependent Claim 6, Alakuijala in view of Hu teaches 6. The method of claim 5,
Furthermore, Alakuijala teaches further comprising: in response to determining the highest evaluation score of the multiple evaluation scores does not satisfy the predetermined threshold, generating a second set of variant meta prompts using the first generative model based on the initial meta prompt; generating a second set of enhanced baseline task prompts corresponding to the set of baseline task prompts using the second generative model based on the second set of variant meta prompts; evaluating the second set of variant meta prompts comparing a second set of enhanced baseline outputs corresponding to the second set of enhanced baseline task prompts and the set of baseline outputs corresponding to the set of baseline task prompts to provide a second set of evaluation data; selecting a second variant meta prompt from the second set of variant meta prompts as a second optimized meta prompt based on the second set of evaluation data; and providing the second optimized meta prompt to the third generative model for task prompt enhancement. (see Alakuijala [0047] “In some implementations, the controller engine 114 determines, for a submitted query, whether any variants are to be generated by the variant engine 112 for the submitted query. For example, the controller engine 114 can make such a determination based on the submitted query itself and/or based on response(s) (if any) from the search system 140 for the submitted query. For instance, the controller engine 114 can determine to generate variants only if an answer response is not returned by the search system 140 or if any returned answer response is of insufficient quality (e.g., has a search system provided score that fails to satisfy a threshold). In some of those implementations, the controller engine 114 applies tokens of the submitted query and/or features of response(s) to the submitted query to one of the control models 154, and generates output over the control models 154 that indicates whether variants are to be generated. In some additional or alternative implementations, the controller engine 114 applies tokens of the submitted query and/or features of response(s) to one of the control models 154, and generates output over the control models 154 that is provided to the variant engine 112 for application as input to a generative model in generating a variant (thereby influencing the variant generation).”)
As to Dependent Claim 7, Alakuijala in view of Hu teaches 7. The method of claim 1,
Furthermore, Alakuijala teaches wherein the first set of evaluation data comprises analytics data associated with the first set of variant meta prompts, wherein the method further comprises:
extracting a set of common points from the analytics data associated with the first set of variant meta prompts; and provide the set of common points as feedback input to the first generative model for variant meta prompt generation. (see Alakuijala [0044] In some implementations, multiple generative models 152 are accessible to the variant engine 112 and the variant engine 112 selects a subset of one or more of the multiple generative models 152 for generating variant(s) for a submitted query based on one or more parameters. For example, multiple generative models 152 can be provided, with each of the generative models being trained based on training data that is based on past query submissions of a unique group of users. For example, a first generative model can be generated based on training data that is based on past query submissions of users having attributes A and B. A second generative model can be generated based on training data that is based on past query submissions of users having attributes B and C. For a submitted query of a user having attributes B and C (but not A), the variant engine 112 can select the second generative model (without also selecting the first generative model) in generating variants for that query—as the user attributes B and C match those utilized in training the second generative model.”) (see Alakuijala [0110] “15. The method of claim 1, further comprising: selecting a trained generative model, from a plurality of trained generative models, based on the trained generative model being trained based on past query submissions of a group of users having one or more attributes in common with the user, and applying tokens of the original query as input to the selected trained generative model.”)
As to Dependent Claim 8, Alakuijala in view of Hu teaches 8. The method of claim 1,
Furthermore, Alakuijala teaches wherein the set of baseline task prompts comprise prompts for a set of tasks, wherein the set of tasks comprise summarization, paraphrasing, evaluation, question-answer generation, audio generation, or video generation. (see Alakuijala Figure 8A Element 892A shows summarization, Figure 8B element 895B shows paraphrasing) (see Alakuijala [0098] “At block 764, the system updates the current state based on the variant, and the response(s) to the variant. The system then proceeds back to block 754 and generates control output over the control model based on the current state that includes the updates of block 764. In this manner, in subsequent iterations of block 764, previously generated variant(s) and response(s) (i.e., generated in previous iterations of blocks 760 and 762) can be considered at the next iteration of block 754. The system then proceeds back to block 756 and determines, based on the control output, whether to generate another variant of the received query. When the system determines to generate another variant, it is noted that the reward signal and context provided at a next iteration of block 758 can likewise be conditioned on the previously generated variant(s) and response(s) (i.e., generated in previous iterations of blocks 760 and 762). In this manner, the variant generation of a next iteration of block 760 is resultantly influenced by the previously generated variant(s) and response(s).”) (see Alakuijala [0101] In FIG. 8B, a user has provided a query 891B of “did michelangelo paint the mona lisa”. In response, output is provided that includes a response 892B of “no”. Box 895B of FIG. 8B may optionally not be provided for display, but is presented as an example of variants that may be generated, according to techniques described herein, in order to generate the response 892B of “no”. Box 895B displays the original query (indicated by “O”) and includes a “Y” in parentheses to indicate that an answer response was generated by a search system in response to the original query. For example, the answer response could be that “yes, Michelangelo did paint the Mona Lisa”. However, instead of providing the answer response, multiple variants that are “follow-up” variants are generated in order to verify the accuracy of the response to the original query. In particular variants V1, V2, and V3 are generated. As indicated by the “N” in parentheses, “no answer” responses were generated by the search system in response to each of those follow-up variants. In view of no answer being available for those multiple follow-ups, a controller engine may determine that the “answer response” to the original query is incorrect (since follow-ups do not lead to any answers). As a result, the controller engine may provide the response 892B of “No”.”) (see Alakuijala [0100] “In FIG. 8A, a user has provided a query 891A of “did da vinci paint the mona lisa”. In response, output is provided that includes a response 892A and that also includes two variants 893A. The two variants 893A can be generated according to implementations disclosed herein. In some implementations, each of the variants is selectable and, in response to a selection, causes the corresponding variant to be submitted as a new query. In some implementations, the response 892A is also based on variant(s) generated according to implementations disclosed herein. For example, in some situations the response 892A may be the response for a variant of the query 891A (a variant that differs from variants 893A) and/or the response 892A may be for the query 891A, but verified based on response(s) to variant(s) of the query (e.g., by ensuring those variant's also generated affirmative responses). (see Alakuijala [0106] “User interface output devices 920 may include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices. The display subsystem may include a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a projection device, or some other mechanism for creating a regular image. The display subsystem may also provide non-visual display such as via audio output devices. In general, use of the term “output device” is intended to include all possible types of devices and ways to output information from computing device 910 to the user or to another machine or computing device.”)
As to Dependent Claim 10, Alakuijala in view of Hu teaches 10. The method of claim 1,
Furthermore, Alakuijala teaches wherein at least the second generative model and the third generative model are the same generative model. (See Alakuijala Figure 1, Element 152 “generative models”)
As to Dependent claim 12, Claim 12 is a system claim with limitations similar to Claims 2 and 3, and is rejected under the same rationale.
As to Dependent claim 13, Claim 13 is a system claim with limitations similar to Claim 4, and is rejected under the same rationale.
As to Dependent claim 14, Claim 14 is a system claim with limitations similar to Claim 5, and is rejected under the same rationale.
As to Dependent claim 15, Claim 15 is a system claim with limitations similar to Claim 6, and is rejected under the same rationale.
As to Dependent claim 16, Claim 16 is a CRM claim with limitations similar to Claim 7, and is rejected under the same rationale.
As to Dependent claim 18, Claim 18 is a CRM claim with limitations similar to Claims 2 and 3, and is rejected under the same rationale.
As to Dependent claim 19, Claim 19 is a CRM claim with limitations similar to Claim 7, and is rejected under the same rationale.
As to Dependent claim 20, Claim 20 is a CRM claim with limitations similar to Claim 4, and is rejected under the same rationale.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Alakuijala (U.S. Patent Number US 20200142888 A1) in view of Hu (U.S. Patent Number US 20210089531 A1), and further in view of Gardner (U.S. Patent Number US 7275053 B1).
As to Dependent Claim 9, Alakuijala in view of Hu teaches 9. The method of claim 1,
Alakuijala in view of Hu do not teach wherein generating a first set of variant meta prompts using a first generative model based on the initial meta prompt comprising: diversifying the initial meta prompt by setting a temperature parameter of the first generative model above a predetermined value to obtain the first set of variant meta prompts. However, Gardner does teach this limitation. (see Gardner (6:13-25) “(26) One Database Report 60 process is best illustrated and described with regard to FIG. 4. Beginning with Step 62, create a "ReportData" temp table (not shown) to contain all the surveillance data necessary to produce the requested report. At Step 64 query the MetaData tables for the relevant data and populate the ReportData table with same. Subsequent Step 66 provides for a loop through the ReportData table to again query and populate until all relevant data is collected. At Step 68 all MetaData queries are executed and the results are stored in the ReportData temp table. Step 70 provides for building the join fragments required for the temp tables. At Step 72 a final query is built using the temp table results and parameter calculations applied.”) (see Gardner (11:20-25) “(ii) query meta-data tables comprising telecommunication network data so as to determine location of information for data responsive to said call statement;”)
Alakuijala in view of Hu and Gardner are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of Alakuijala and Hu to incorporate Gardner to include diversifying the initial meta prompt by setting a temperature parameter of the first generative model above a predetermined value to obtain the first set of variant meta prompts. This allows for the combined data to be grouped by and filtered on the area of temperature as recognized by Gardner in (2(42-43).
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTEN MICHELLE MASTERS whose telephone number is (703)756-1274. The examiner can normally be reached M-F 8:30 AM - 5:00 PM.
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, Pierre Louis Desir can be reached at 571-272-7799. 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.
/KRISTEN MICHELLE MASTERS/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
Prosecution Insights