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 .
Claim Rejections - 35 USC § 112: Indefiniteness
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5-6, 12-13, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 5, the claim recites the processed dataset output by the large language model. There is insufficient antecedent basis for this limitation in the claim because the processed dataset was not output by the large language model in claim 1. Instead, the processed dataset was provided to the large language model in claim 1. For the purposes of examination, the enriched dataset is interpreted as being output by the large language model.
Regarding claim 6, the claim recites wherein the textual description includes a short description and a long description. The terms “short description” and “long description” are relative terms which renders the claim indefinite. The terms “short description” and “long descirption” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For the purposes of examination, the terms short description and long descriptions are interpreted as two different descriptions.
Regarding claims 12-13, the claims are similar to claims 5-6 and are rejected under the same rationales.
Regarding claim 19, the claim is similar to claim 5 and is rejected under the same rationales.
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.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A method for generating an automated chatbot training dataset, the method comprising:. The claim recites a method. A method is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
cleaning…the at least one initial chatbot dataset; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like removing samples from a dataset, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
generating…at least one processed dataset, wherein the at least one processed dataset is based on the at least one cleaned initial chatbot dataset; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like formatting a dataset, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
and generating…at least one enriched dataset incorporating the dataset property. (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like adding samples to a dataset based on a criteria, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
collecting…at least one initial chatbot dataset; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
…via a computer… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
providing…the at least one processed dataset to a large language model; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
requesting…a dataset property from the large language model, wherein the dataset property is based on a query submitted to the large language model; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
receiving…the dataset property from the large language model; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation(s) (IV and VI-VIII), under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitation (V), under the broadest reasonable interpretation, merely recite steps that apply generic computer components as a tool to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 2 recites wherein the initial chatbot dataset comprises one or more of a bot dataset, an intent dataset, or a dialog dataset. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 2 does not solve the deficiencies of claim 1.
Regarding claim 3, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 3 recites wherein cleaning further comprises identifying data types in the initial chatbot dataset for removal or alteration. Under the broadest reasonable interpretation, the limitations recite identifying samples for removal which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 3 does not solve the deficiencies of claim 1.
Regarding claim 4, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 4 recites wherein the large language model is a proprietary large language model. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic proprietary LLM, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 4 does not solve the deficiencies of claim 1.
Regarding claim 5, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 5 recites wherein the at least one enriched dataset includes a textual description of the processed dataset output by the large language model. Under the broadest reasonable interpretation, the limitations recite steps of mere data outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 5 does not solve the deficiencies of claim 1.
Regarding claim 6, it is dependent upon claim 5 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 6 recites wherein the textual description includes a short description and a long description. Under the broadest reasonable interpretation, the limitations recite steps of mere data outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 6 does not solve the deficiencies of claim 5.
Regarding claim 7, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 7 recites further comprising training, via the computer, a machine learning model using the enriched dataset. Under the broadest reasonable interpretation, the limitations merely recite steps that apply generic training using a dataset, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 7 does not solve the deficiencies of claim 1.
Regarding claim 8, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A system for generating an automated chatbot training dataset, the system comprising: a non-transitory computer readable medium configured to store processor-readable instructions; and a processor operatively connected to the non-transitory computer readable medium, and configured to execute the instructions to perform operations comprising:. The claim recites system with hardware components which is interpreted as a machine. A machine is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 8 is similar to claim 1 it is rejected under the same rationales as claim 1.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
A system for generating an automated chatbot training dataset, the system comprising: a non-transitory computer readable medium configured to store processor-readable instructions; and a processor operatively connected to the non-transitory computer readable medium, and configured to execute the instructions to perform operations comprising: (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claims 9-14, the claims are similar to claims 2-7 and are rejected under the same rationales.
Regarding claim 15, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A non-transitory computer readable medium. The claim recites a non-transitory computer readable medium which is interpreted as an article of manufacture. An article of manufacture is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 15 is similar to claim 1 it is rejected under the same rationales as claim 1.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
A non-transitory computer readable medium configured to store processor-readable instructions, wherein when executed by a processor, the instructions perform operations comprising: (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claims 16-19, the claims are similar to claims 2-5 and are rejected under the same rationales.
Regarding claim 20, the claim is similar to claim 7 and is rejected under the same rationales.
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 1-2, 7-9, 14-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jandaghi, et al., Non-Patent Literature “Faithful Persona-based Conversational Dataset Generation with Large Language Models” (“Jandaghi”) in view of Frasier, US Pre-Grant Publication US20250245550A1 (“Frasier”).
Regarding claim 1, Jandaghi discloses:
A method for generating an automated chatbot training dataset, the method comprising: collecting,…at least one initial chatbot dataset; (Jandaghi, pg. 1 col 2, “In this paper, we propose a novel framework for generating large, dynamic, persona-based conversational datasets [A method for generating an automated chatbot training dataset,]”, and Jandaghi, pg. 3 col 1, “We create such conversations with minimum human input, starting from an initial dataset. [the method comprising: collecting,…at least one initial chatbot dataset;]”).
cleaning,…the at least one initial chatbot dataset; (Jandaghi, pg. 5 col 2, “The Generator outputs conversations for pairs of users (U1,U2) by prompting an LLM (Brown et al., 2020b; Wei et al., 2023). At each iteration, it randomly selects 5 samples from an initial set of conversations, each containing a pair of user profiles and a dialogue among them. [cleaning,…the at least one initial chatbot dataset;]”).
generating,…at least one processed dataset, wherein the at least one processed dataset is based on the at least one cleaned initial chatbot dataset; (Jandaghi, pg. 5 col 2 and Table 6, “It feeds these samples to a template that instructs the LLM to generate a series of candidate conversations for the given user pair [generating,…at least one processed dataset, wherein the at least one processed dataset is based on the at least one cleaned initial chatbot dataset;].”).
providing,…the at least one processed dataset to a large language model; (Jandaghi, pg. 5 col 2, “It feeds these samples to a template that instructs the LLM to generate a series of candidate conversations for the given user pair [providing,…the at least one processed dataset to a large language model;].”).
requesting,…a dataset property from the large language model, wherein the dataset property is based on a query submitted to the large language model; (Jandaghi, pg. 13 col 1, “In our framework, the critic is implemented by prompting an LLM [wherein the dataset property is based on a query submitted to the large language model;]. We included a mixture of experts approach in the critic, where each expert prompts the LLM to assess a specific policy in the candidate conversations. Our framework includes a set of experts to control the general conversation quality.”, and Jandaghi, pg. 13 col 2, “We also included a toxicity expert and a persona faithfulness expert in the critic [requesting…a dataset property from the large language model,].”).
receiving,…the dataset property from the large language model; (Jandaghi, pg. 5 col 2, “The Critic evaluates the candidate conversations based on the predetermined policies [receiving,…the dataset property from the large language model;]”).
and generating,…at least one enriched dataset incorporating the dataset property. (Jandaghi pg. 5 col 2, “The best candidate conversations are added to the dataset for the next iteration [and generating,…at least one enriched dataset incorporating the dataset property.]”).
While Jandaghi teaches a method for generating enriched chatbot datasets, Jandaghi does not explicitly teach:
…via a computer…
Frasier teaches …via a computer… (Frasier, ⁋78, “In addition, embodiments of the disclosure may be practiced within a general purpose computer or in any other circuits or systems. [via a computer,]”).
Jandaghi and Frasier are both in the same field of endeavor (i.e. machine learning). jandaghi teaches a base method for using machine learning for data augmentation. Frasier teaches a known technique of using a computer to perform machine learning functions. It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Jandaghi and Frasier to teach the above limitation(s). The motivation for doing so is that applying Frasier’s known technique of using a computer to perform machine learning to Jandaghi’s base system of using machine learning for data augmentation would yield predictable results.
Regarding claim 2, Jandaghi in view of Fraiser teaches the method of claim 1. Jandaghi further teaches wherein the initial chatbot dataset comprises one of a bot dataset, an intent dataset, or a dialog dataset. (Jandaghi, pg. 6 col 1, “We evaluate our persona expansion module on two seed datasets: Wikipedia, and Persona-Chat.”, Jandaghi, pg. 7 col 2, “We randomly select 200 conversations from PC, together with their corresponding user pairs, and use our method to generate conversations among the same users [wherein the initial chatbot dataset comprises one or more of a bot dataset, an intent dataset, or a dialog dataset.].”).
Regarding claim 7, Jandaghi in view of Frasier teaches the method of claim 1. Jandaghi further teaches further comprising training, via a computer, a machine learning model using the enriched dataset. (Jandaghi, pg. 1 col 1, “The Generator is an LLM prompted to output conversations.”, and Jandaghi, pg. 5 col 2, “The Critic selects the best generated conversations to fine-tune the Generator [further comprising training, via a computer, a machine learning model using the enriched dataset.].”).
Regarding claim 8, the claim is similar to claim 1 and is rejected under the same rationales. Frasier teaches the additional limitations the system comprising: a non-transitory computer readable medium configured to store processor-readable instructions; and a processor operatively connected to the non-transitory computer readable medium, and configured to execute the instructions to perform operations comprising: (Frasier, claim 1, “A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising:”, and Frasier, ⁋80, “The term computer readable media as used herein may include computer storage media…Any such computer storage media may be part of the computing device 500. Computer storage media does not include a carrier wave or other propagated or modulated data signal [the system comprising: a non-transitory computer readable medium configured to store processor-readable instructions; and a processor operatively connected to the non-transitory computer readable medium, and configured to execute the instructions to perform operations comprising:].”).
Jandaghi and Frasier are both in the same field of endeavor (i.e. machine learning). jandaghi teaches a base method for using machine learning for data augmentation. Frasier teaches a known technique of using a computer to perform machine learning functions. It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Jandaghi and Frasier to teach the above limitation(s). The motivation for doing so is that applying Frasier’s known technique of using a computer to perform machine learning to Jandaghi’s base system of using machine learning for data augmentation would yield predictable results.
Regarding claim 9, the claim is similar to claim 2 and is rejected under the same rationales.
Regarding claim 14, the claim is similar to claim 7 and is rejected under the same rationales.
Regarding claim 15, the claim is similar to claim 1 and is rejected under the same rationales. Frasier teaches the additional limitations A non-transitory computer readable medium configured to store processor-readable instructions, wherein when executed by a processor, the instructions perform operations comprising: (Frasier, claim 1, “A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising:”, and Frasier, ⁋80, “The term computer readable media as used herein may include computer storage media…Any such computer storage media may be part of the computing device 500. Computer storage media does not include a carrier wave or other propagated or modulated data signal [A non-transitory computer readable medium configured to store processor-readable instructions, wherein when executed by a processor, the instructions perform operations comprising:].”).
Jandaghi and Frasier are both in the same field of endeavor (i.e. machine learning). jandaghi teaches a base method for using machine learning for data augmentation. Frasier teaches a known technique of using a computer to perform machine learning functions. It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Jandaghi and Frasier to teach the above limitation(s). The motivation for doing so is that applying Frasier’s known technique of using a computer to perform machine learning to Jandaghi’s base system of using machine learning for data augmentation would yield predictable results.
Regarding claim 16, the claim is similar to claim 2 and is rejected under the same rationales.
Regarding claim 20, the claim is similar to claim 7 and is rejected under the same rationales.
Claim 3, 10, and 17 are rejected under 35 U.S.C 103 as being unpatentable over Jandaghi, et al., Non-Patent Literature “Faithful Persona-based Conversational Dataset Generation with Large Language Models” (“Jandaghi”) in view of Frasier, US Pre-Grant Publication US20250245550A1 (“Frasier”) and further in view of Oviedo, et al., Non-Patent Literature “Four Data Cleaning Techniques to Improve Large Language Model (LLM) Performance” (“Oviedo”).
Regarding claim 3, Jandaghi in view of Frasier teaches the method of claim 1. While the combination teaches cleaning datasets, the combination does not explicitly teach wherein cleaning further comprises identifying data types in the initial chatbot dataset for removal or alteration.
Oviedo further teaches wherein cleaning further comprises identifying data types in the initial chatbot dataset for removal or alteration. (Oviedo, pg. 4, “Step 1: Data Cleaning and Noise Reduction We’ll start by removing symbols or characters that don’t provide meaning, such as HTML tags (in the case of scraping), XML parses, JSON, emojis, and hashtags [wherein cleaning further comprises identifying data types in the initial chatbot dataset for removal or alteration.].”).
Jandaghi, in view of Frasier, and Oviedo are both in the same field of endeavor (i.e. data processing). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Jandaghi, in view of Frasier, and Oviedo to teach the above limitation(s). The motivation for doing so is that removing unnecessary types of data from datasets improves the performance of language models (cf. Oviedo, pg. 4, “Unnecessary characters often confuse the model, and increase the number of context tokens and therefore the computational cost.”).
Regarding claim 10, the claim is similar to claim 3 and is rejected under the same rationales.
Regarding claim 17, the claim is similar to claim 3 and is rejected under the same rationales.
Claim 4, 11, and 18 are rejected under 35 U.S.C 103 as being unpatentable over Jandaghi, et al., Non-Patent Literature “Faithful Persona-based Conversational Dataset Generation with Large Language Models” (“Jandaghi”) in view of Frasier, US Pre-Grant Publication US20250245550A1 (“Frasier”) and further in view of Törnberg, Non-Patent Literature “Best Practices for Text Annotation with Large Language Models” (“Törnberg”).
Regarding claim 4, Jandaghi in view of Frasier teaches the method of claim 1. While the combination teaches using a large language model, the combination does not explicitly teach wherein the large language model is a proprietary large language model.
Törnberg further teaches wherein the large language model is a proprietary large language model. (Törnberg, pg. 3, “Yet, most existing studies using LLMs for text annotation have employed one of OpenAI’s proprietary models – either GPT3.5 or GPT4 [wherein the large language model is a proprietary large language model.]”).
Jandaghi, in view of Frasier, and Törnberg are both in the same field of endeavor (i.e. textual analysis with LLMs). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Jandaghi, in view of Frasier, and Törnberg to teach the above limitation(s). The motivation for doing so is that proprietary LLMs are capable of more detailed analysis of datasets than open-source and/or local models (cf. Törnberg, pg. 4, “Challenging analysis tasks and long prompt instructions may require larger and more sophisticated models, such as GPT4.0, that are capable of higher levels of reasoning and performance on benchmark tasks.”).
Regarding claim 11, the claim is similar to claim 4 and is rejected under the same rationales.
Regarding claim 18, the claim is similar to claim 4 and is rejected under the same rationales.
Claims 5-6, 12-13, and 19 are rejected under 35 U.S.C 103 as being unpatentable over Jandaghi, et al., Non-Patent Literature “Faithful Persona-based Conversational Dataset Generation with Large Language Models” (“Jandaghi”) in view of Frasier, US Pre-Grant Publication US20250245550A1 (“Frasier”) and further in view of Manikandan, et al., Non-Patent Literature “Language models are weak learners” (“Manikandan”).
Regarding claim 5, Jandaghi in view of Frasier teaches the method of claim 1. While the combination teaches an enriched dataset, the combination does not explicitly teach wherein the at least one enriched dataset includes a textual description of the processed dataset output by the large language model.
Manikandan further teaches wherein the at least one enriched dataset includes a textual description of the processed dataset output by the large language model. (Manikandan, pg. 3, “We refer to the full method as Summary Boosting, as the core learning process is one that uses a language model to create a summary of (specifically chosen) samples from the dataset [wherein the at least one enriched dataset includes a textual description of the processed dataset output by the large language model.]”).
Jandaghi, in view of Frasier, and Manikandan are both in the same field of endeavor (i.e. data processing with LLMs). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Jandaghi, in view of Frasier, and Manikandan to teach the above limitation(s). The motivation for doing so is that including condensed summaries along with a dataset helps a machine learning model learn from data. (cf. Manikandan, pg. 3, “instead of storing knowledge in parameters, our approach concentrates on condensing knowledge into an intermediary representation referred to as "summary." This alternative strategy enhances interpretability and strictly learns through prompts, rendering it particularly suitable for small tabular data, where the prior knowledge in LLM can significantly benefit the learning process.”).
Regarding claim 6, Jandaghi in view of Frasier and Manikandan teaches the method of claim 5. Manikandan further teaches wherein the textual description includes a short description and a long description. (Manikandan, pg. 3, “Specifically, to ensure examples can serve as both training data and query inputs, we extract the descriptions of the features and concatenate them with the target label using a separator token”, and Manikandan, pg. 13, “Firstly, the data descriptions should not be too long or short, also be of comparable length. Excessively long descriptions limit the number of examples that can be fit inside the prompt and summarized…Then, we implement a resampling strategy, that generates descriptions until finding the one with a desired length ranging between 20 to 80 words” [wherein the textual description includes a short description and a long description.]”).
Jandaghi, in view of Frasier, and Manikandan are both in the same field of endeavor (i.e. data processing with LLMs). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Jandaghi, in view of Frasier, and Manikandan to teach the above limitation(s). The motivation for doing so is that changing a description length of a prompt improves model performance (cf. Manikandan, pg. 13, “The design of the prompt plays a pivotal role in our entire process. Specifying instructions precisely can create a significant difference, whether it comes to effectively describing tabular data, generating reliable summaries or inferring accurate predictions”).
Regarding claims 12-13, the claims are similar to claims 5-6 and are rejected under the same rationales.
Regarding claim 19, the claim is similar to claim 5 and is rejected under the same rationales.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tabacof, et al., US20250200332A1 discloses a system that uses a LLM to extract question and answer pairs from conversations between a user and a chatbot.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS S WU whose telephone number is (571)270-0939. The examiner can normally be reached Monday - Friday 8:00 am - 4:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at 571-431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.S.W./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
Prosecution Insights