DETAILED ACTION
Election/Restrictions
Applicant’s election without traverse of Claims 1-7, 12-15, 17, and 19 in the reply filed on March 24, 2026 is acknowledged.
Claims 8-11, 16, 18, and 20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on March 24, 2026.
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 .
Priority
Applicant’s claim for the benefit to CN202411046153 filed July 31, 2024 is acknowledged. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The instant abstract is written in claim form and uses legal phraseology. It is suggested that the abstract be amended to be written in a narrative format.
Claim Rejections - 35 USC § 112
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 1-7, 12-15, 17, 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.
With regard to claims 1 and 12, the claim recites “descriptive information” multiple times, apparently referring to distinct claim elements. Claim 12 appears to recite substantially similar limitations as claim 1 and is rejected based upon the same reasoning and rational. This claim limitation lacks antecedent basis. Each unique claim element is expected to have a unique claim label. The use of the same label for distinct elements raises an antecedent basis issue. For examination purposes this claim limitation has been construed to mean --preset descriptive information--, wherein the preset descriptive information is of the preset intent, and --first descriptive information--, wherein the first descriptive information is of the first candidate intent.
With regard to claims 3 and 14, claim 3 recites:
“wherein the historical interaction information comprises at least one of:
[3.a] a historical query statement corresponding to the user identifier;
[3.b] a second candidate intent corresponding to a historical query statement;
[3.c] descriptive information corresponding to the second candidate intent;
[3.e] a second target intent corresponding to the historical query statement; or
[3.f] a response statement corresponding to the historical query statement.”
(Note claim limitation labels added to facilitate discussion)
Claim 14 appears to recite substantially similar language and is rejected based upon the same reasoning and rational. There is insufficient antecedent basis for the ‘historical query statement’ in the claim.
Limitations [3.a] and [3.b] both recite “a historical query statement”. It is unclear if applicant is attempting to recite a new claim element or if applicant is referring to the previously recited claim element. The descriptions of the historical query statements are distinct, the first corresponding to the user identifier and the second corresponding to the second candidate intent. It is unclear if this is intended to refer to two distinct historical query statements, or if the claim is intended to recite a single historical query statement which corresponds to both the user identifier and the second candidate intent.
Limitations [3.e] and [3.f] references to “the historical query statement”, but it is unclear which historical query statement is being referenced to: the historical query statement corresponding to the user identifier (e.g. [3.a]) or the historical query statement corresponding to the second candidate intent (e.g. [3.b]).
It is suggested that clam labels be used consistently within the claims, and that the term ‘a’ only be used at the first recitation and the term ‘the’ be used when referring to a previously recited claim element.
The claim only requires at least one of the recited limitations (see the claim preamble), which are further recited in the alternative (see the ‘or’). Yet the limitations of the claim appear to be logically dependent on each other for antecedent basis (see the historical query statement references in [3.e] and [3.f] which refer to the elements first recited in [3.a] and [3.b]). It is suggested that the claim be amended to adjust the structure of the claim so that the historical query statement is not within the alternative limitations (see the construction for examination purposes for an example).
The ’descriptive information’ in limitation [3.c] lacks antecedent basis as claim 1 already recites ‘descriptive information’. The use of the same claim label to refer to distinct claim elements lacks antecedent basis. The descriptive information in claim 1 appears to be related to the query statement recited in claim 1, while the descriptive information in claim 3 appears to be related to the historical query statement. It is suggested that the claim be amended to give clearly distinct claim labels for distinct claim elements.
For examination purposes this claim limitation has been construed to mean:
--wherein the historical interaction information comprises [3.a] a historical query statement corresponding to the user identifier; and at least one of:
[3.b] i. a second candidate intent corresponding to the historical query statement, and [3.c] second descriptive information corresponding to the second candidate intent;
[3.e] ii. a second target intent corresponding to the historical query statement; or
[3.f] iii. a response statement corresponding to the historical query statement.--
With regard to claims 4 and 15, claim 4 recites “determining a second intent corresponding to the query statement from another first candidate intent except for the first intent by inputting the second prompt information into the LLM, wherein a processing operation corresponding to the second intent in the query statement is executed after a processing operation corresponding to the first intent in the query statement;” This claim limitation lacks antecedent basis. Claim 15 appears to recite substantially similar language and is rejected based upon the same reasoning and rational.
It is unclear how many processing operations are being claimed, or if applicant is referring to the previously recited processing operation. For examination purposes this claim limitation has been construed to mean -- determining a second intent corresponding to the query statement from another first candidate intent except for the first intent by inputting the second prompt information into the LLM, wherein a second processing operation corresponding to the second intent in the query statement is executed after a first processing operation corresponding to the first intent in the query statement;--.
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.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim appears to be directed to software per sae. Claim 19 is directed to a computer program product comprising computer instructions. One of ordinary skill in the art may reasonably read the computer instructions, and as such the computer program product as being software per sae. The ‘processor’ is not recited as being part of the ‘computer program product’ itself, to which the claim is directed. It is suggested that the claims be amended to recite that the computer program product comprises circuitry to ensure that the claims are not directed to software per sae (e.g. that the computer program product comprises the processor).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4, 7, 12, 15, 17, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by RodriguezGarcia [2025/0307564].
With regard to claim 1 RodriguezGarcia teaches A method for intent recognition based on a large language model (LLM) (RodriguezGarcia, ¶30 “In some implementations, both neural networks 106 and 124 may be large language models (LLMs), which have undergone extensive training on vast and diverse text corpora encompassing a wide range of domains and context.”), comprising:
obtaining a query statement as obtaining an utterance (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”), a preset intent as the corresponding label of known intent (id; ¶24 “a list of known intents pertinent to the domain. These known intents may be retrieved from a database. Additionally, each prompt may include selected examples ( e.g., utterances) from a training dataset to provide context to the prompt”), and descriptive information as the task description (RodriguezGarcia, ¶25 “a prompt comprising a task description and an input label pair”; ¶52 “the instructions 302 may be a task description that instructs a prompt generator (e.g., the prompt generator 106 described in FIG. 1) to generate a prompt for use by another LLM (e.g., the intent predictor 124 described in FIG. 1).”) of the preset intent as the known intents (RodriguezGarcia, ¶41);
obtaining a first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; Figure 1, 126; Figure 2, 220) corresponding to the query statement as for the utterance (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) by matching (RodriguezGarcia, ¶39 “based on a similarity measure (e.g., cosine distance) between the one or more examples and their respective test-batch utterance.”; ¶94 “In some implementations, utterances corresponding to the list of known intents are semantically similar to utterances in the list of test examples. In some implementations, an utterance in the few-shot example is semantically similar to an utterance in at least one of the list of test examples.”) the query statement as the utterance in the test examples (Id) with the preset intent as the list of known intents (Id) and the descriptive information (RodriguezGarcia, ¶52 “the instructions 302 may be a task description that instructs a prompt generator (e.g., the prompt generator 106 described in FIG. 1) to generate a prompt for use by another LLM (e.g., the intent predictor 124 described in FIG. 1).”) of the preset intent as the known intents (RodriguezGarcia, ¶41);
generating first prompt information as prompt 208 (RodriguezGarcia, ¶50 “the one or more software components augment the prompt 208 generated by the process 200A with samples from few-shot samples 212 and intents from known intents 214 to create an augmented prompt 216.”) based on the query statement as the utterance in the test examples (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”), the first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; Figure 1, 126; Figure 2, 220), and descriptive information as the task description (RodriguezGarcia, ¶25; ¶52; Please note this claim limitation has been construed to mean --first descriptive information--). of the first candidate intent as the discovered intent (RodriguezGarcia, ¶53; Figure 1, 126; Figure 2, 220); and
determining a first target intent as a new discovered intent, discovered during iteration (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; ¶24 “Identified intents that are not in the list of known intents are used to update the database. Through multiple iterations with varying test data, the database progressively expands its repository of known intents related to each domain.”) corresponding to the query statement as the utterance in the test examples (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”) from the first candidate intent as the discovered intent which becomes a known intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; ¶24 “These known intents may be retrieved from a database. Additionally, each prompt may include selected examples ( e.g., utterances) from a training dataset to provide context to the prompt.”; Figure 1, 126; Figure 2, 220) by inputting the first prompt information as prompt 208 (RodriguezGarcia, ¶50 “the one or more software components augment the prompt 208 generated by the process 200A with samples from few-shot samples 212 and intents from known intents 214 to create an augmented prompt 216.”) into the LLM (RodriguezGarcia, ¶30; Figure 2, 206; 218).
With regard to claims 4 and 15 RodriguezGarcia further teaches wherein determining the first target intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”) corresponding to the query statement as for the utterance (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) from the first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”) by inputting the first prompt information as prompt 208 (RodriguezGarcia, ¶50) into the LLM (RodriguezGarcia, ¶30; Figure 2, 206; 218) comprises:
obtaining a first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”) corresponding to the query statement as for the utterance (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) from the first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”) by inputting the first prompt information as prompt 208 (RodriguezGarcia, ¶50) into the LLM (RodriguezGarcia, ¶30; Figure 2, 206; 218);
generating second prompt information as augmented prompt 216 (RodriguezGarcia, Figure 2B, 216) based on the first intent as a new discovered intent, discovered during iteration (RodriguezGarcia, ¶53) and the first prompt information as prompt 208 (RodriguezGarcia, ¶50 “the one or more software components augment the prompt 208 generated by the process 200A with samples from few-shot samples 212 and intents from known intents 214 to create an augmented prompt 216.”);
determining a second intent as a second discovered intent (RodriguezGarcia, Figure 2B, 220) corresponding to the query statement as the utterance in the test examples (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”) from another first candidate intent as the list of known intents selected (RodriguezGarcia, ¶70 “In operation 606, the processing logic modifies the prompt to include one or more of: a list of known intents, a few-shot example, or a list of test examples, as shown in FIG. 2B. The list of known intents includes one or more intents from a training dataset and one or more intents discovered by the second large language model in one or more previous iterations. The few-shot example may be selected from a few-shot pool that was prepopulated with utterance-intent pairs extracted from the training dataset.”) except for the first intent as the intents not selected for the few-shot pool (Id) by inputting the second prompt information as augmented prompt 216 (RodriguezGarcia, Figure 2B, 216) into the LLM (RodriguezGarcia, ¶30; Figure 2, 206; 218), wherein a processing operation as update with Novel Intents 222 for the second intent in a second iteration (RodriguezGarcia, Figure 2B; ¶6 “the list of known intents includes at one intent from a training dataset and at least one intent discovered by the second large language model in a previous iteration.”; ¶46 “These expanded known intents may be used as contextual information by the intent predictor 124 to discover (generate) intents on utterances in subsequent iterations.”) corresponding to the second intent as a second discovered intent (RodriguezGarcia, Figure 2B, 220) in the query statement as the utterance in the test examples (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”) is executed after a processing operation as update with Novel Intents 222 for the first intent in the first iteration (RodriguezGarcia, Figure 2B; ¶6; ¶46; Please see the 112b above, this claim limitation has been construed to man –a second processing operation--) corresponding to the first intent as the first discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; Figure 1, 126; Figure 2, 220) in the query statement as for the utterance (RodriguezGarcia, ¶39);
generating new second prompt information as updating the known intents with the newly discovered intent (RodriguezGarcia, ¶46 “the intent discovery system 102 may update the known intents 112 with the newly discovered intents, which are intent predictions 126, to expand the known intents.”) based on the second intent as a second discovered intent (RodriguezGarcia, Figure 2B, 220) and the second prompt information as augmented prompt 216 (RodriguezGarcia, Figure 2B, 216), and returning to perform an operation as iterate (RodriguezGarcia, ¶70) of obtaining the second intent as discovering intents (RodriguezGarcia, Figure 2B, 220) until the LLM (RodriguezGarcia, ¶30; Figure 2, 206; 218) outputs termination indication information as when the LLM has completed the task, and has updated the list of known intents with a new intent (RodriguezGarcia, Figure 6, 612; Figure 7 710); and
determining the first intent and the second intent as the first target intent as a new discovered intent, discovered during iteration which are stored (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; ¶24 “Identified intents that are not in the list of known intents are used to update the database. Through multiple iterations with varying test data, the database progressively expands its repository of known intents related to each domain.”).
With regard to claim 7 RodriguezGarcia further teaches in response to the descriptive information as the task description (RodriguezGarcia, ¶25 “a prompt comprising a task description and an input label pair”; ¶52 “the instructions 302 may be a task description that instructs a prompt generator (e.g., the prompt generator 106 described in FIG. 1) to generate a prompt for use by another LLM (e.g., the intent predictor 124 described in FIG. 1).”) comprising a parameter type as instructions regarding classifications (RodriguezGarcia, ¶61) corresponding to the preset intent as the corresponding label of known intent (RodriguezGarcia, ¶3; ¶24 and definition information as the predefined contents and format template (RodriguezGarcia, ¶68 “The first large language model generates the prompt based on a pre-determined template that defines contents and format of the prompt.”) corresponding to the parameter type as the template containing the classification instructions (RodriguezGarcia, ¶61), generating third prompt information as a second augmented prompt 216 during iteration (RodriguezGarcia, Figure 2B, 216; ¶70) based on the query statement as for the utterance (RodriguezGarcia, ¶39), the first candidate intent as the first discovered intent (RodriguezGarcia, ¶53), and the descriptive information as the first task description (RodriguezGarcia, ¶25; ¶52) corresponding to the first candidate intent as the first discovered intent (RodriguezGarcia, ¶53), wherein the third prompt information as a second augmented prompt 216 during iteration (RodriguezGarcia, Figure 2B, 216; ¶70) is configured to prompt the LLM (RodriguezGarcia, ¶30; Figure 2, 206; 218) to determine the first target intent as a new discovered intent, discovered during iteration (RodriguezGarcia, ¶53; ¶24) corresponding to the query statement as the utterance in the test examples (RodriguezGarcia, ¶3) from the first candidate intent as the discovered intent which becomes a known intent (RodriguezGarcia, ¶53; ¶24) and extract a target parameter as instructions regarding classifications (RodriguezGarcia, ¶61) corresponding to the first target intent as a new discovered intent, discovered during iteration (RodriguezGarcia, ¶53; ¶24) from the query statement as the utterance in the test examples (RodriguezGarcia, ¶3);
obtaining the first target intent as a new discovered intent, discovered during iteration (RodriguezGarcia, ¶53; ¶24)corresponding to the query statement as the utterance in the test examples (RodriguezGarcia, ¶3) and the target parameter as instructions regarding classifications (RodriguezGarcia, ¶61) associated with the first target intent as a new discovered intent, discovered during iteration (RodriguezGarcia, ¶53; ¶24) in the query statement s the utterance in the test examples (RodriguezGarcia, ¶3) by inputting the third prompt information as a second augmented prompt 216 during iteration (RodriguezGarcia, Figure 2B, 216; ¶70) into the LLM(RodriguezGarcia, ¶30; Figure 2, 206; 218); and
determining a response statement (RodriguezGarcia, ¶64 “a response format instruction 518 that instructs to the LLM to respond in a particular format. For example, the response format instruction 518 may state: "RESPONSE FORMAT: ID: <i>, Utterance: <content>, Intent: <intent> Use the same ID in the test example."”) corresponding to the query statement as the utterance in the test examples (RodriguezGarcia, ¶3) based on the first target intent as a new discovered intent, discovered during iteration (RodriguezGarcia, ¶53; ¶24) and the target parameter as instructions regarding classifications (RodriguezGarcia, ¶61).
With regard to claim 12 RodriguezGarcia teaches An electronic device, comprising:
at least one processor as a processor (RodriguezGarcia, ¶5” In some implementations, a system for intent discovery comprises one or more processors and memory including computer-executable instructions. The one or more processors, when executing computer-executable instructions, cause the system to perform operations that comprises”); and
a memory as a memory (Id) communicatively coupled to the at least one processor (Id),
wherein the processor (Id) is configured to:
obtain a query statement as obtaining an utterance (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”), a preset intent as the corresponding label of known intent (id; ¶24 “a list of known intents pertinent to the domain. These known intents may be retrieved from a database. Additionally, each prompt may include selected examples ( e.g., utterances) from a training dataset to provide context to the prompt”), and descriptive information as the task description (RodriguezGarcia, ¶25 “a prompt comprising a task description and an input label pair”; ¶52 “the instructions 302 may be a task description that instructs a prompt generator (e.g., the prompt generator 106 described in FIG. 1) to generate a prompt for use by another LLM (e.g., the intent predictor 124 described in FIG. 1).”) of the preset intent as the known intents (RodriguezGarcia, ¶41);
obtain a first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; Figure 1, 126; Figure 2, 220) corresponding to the query statement as for the utterance (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) by matching (RodriguezGarcia, ¶39 “based on a similarity measure (e.g., cosine distance) between the one or more examples and their respective test-batch utterance.”; ¶94 “In some implementations, utterances corresponding to the list of known intents are semantically similar to utterances in the list of test examples. In some implementations, an utterance in the few-shot example is semantically similar to an utterance in at least one of the list of test examples.”) the query statement as the utterance in the test examples (Id) with the preset intent as the list of known intents (Id) and the descriptive information (RodriguezGarcia, ¶52 “the instructions 302 may be a task description that instructs a prompt generator (e.g., the prompt generator 106 described in FIG. 1) to generate a prompt for use by another LLM (e.g., the intent predictor 124 described in FIG. 1).”) of the preset intent as the known intents (RodriguezGarcia, ¶41);
generate first prompt information as prompt 208 (RodriguezGarcia, ¶50 “the one or more software components augment the prompt 208 generated by the process 200A with samples from few-shot samples 212 and intents from known intents 214 to create an augmented prompt 216.”) based on the query statement as the utterance in the test examples (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”), the first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; Figure 1, 126; Figure 2, 220), and descriptive information as the task description (RodriguezGarcia, ¶25; ¶52; Please note this claim limitation has been construed to mean --first descriptive information--). of the first candidate intent as the discovered intent (RodriguezGarcia, ¶53; Figure 1, 126; Figure 2, 220); and
determine a first target intent as a new discovered intent, discovered during iteration (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; ¶24 “Identified intents that are not in the list of known intents are used to update the database. Through multiple iterations with varying test data, the database progressively expands its repository of known intents related to each domain.”) corresponding to the query statement as the utterance in the test examples (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”) from the first candidate intent as the discovered intent which becomes a known intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; ¶24 “These known intents may be retrieved from a database. Additionally, each prompt may include selected examples ( e.g., utterances) from a training dataset to provide context to the prompt.”; Figure 1, 126; Figure 2, 220) by inputting the first prompt information as prompt 208 (RodriguezGarcia, ¶50 “the one or more software components augment the prompt 208 generated by the process 200A with samples from few-shot samples 212 and intents from known intents 214 to create an augmented prompt 216.”) into the LLM (RodriguezGarcia, ¶30; Figure 2, 206; 218).
With regard to claim 17 RodriguezGarcia further teaches A non-transitory computer readable storage medium storing computer instructions, wherein the computer instructions (RodriguezGarcia, ¶5” In some implementations, a system for intent discovery comprises one or more processors and memory including computer-executable instructions. The one or more processors, when executing computer-executable instructions, cause the system to perform operations that comprises”) are configured to cause a computer to implement the method of claim 1 (Please see the mapping for claim 1).
With regard to claim 19 RodriguezGarcia further teaches A computer program product comprising computer instructions, wherein when the computer instructions are executed by a processor (RodriguezGarcia, ¶5” In some implementations, a system for intent discovery comprises one or more processors and memory including computer-executable instructions. The one or more processors, when executing computer-executable instructions, cause the system to perform operations that comprises”), steps of the method of claim 1 are implemented (Please see the mapping for claim 1).
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.
Claim 2, 3, 5, 6, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over RodriguezGarcia in view of Kharbanda [20240362279].
With regard to claims 2 and 13 RodriguezGarcia further teaches wherein generating first prompt information as prompt 208 (RodriguezGarcia, ¶50 “the one or more software components augment the prompt 208 generated by the process 200A with samples from few-shot samples 212 and intents from known intents 214 to create an augmented prompt 216.”) based on the query statement as the utterance in the test examples (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”), the first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; Figure 1, 126; Figure 2, 220), and descriptive information as the task description (RodriguezGarcia, ¶25; ¶52; Please note this claim limitation has been construed to mean --first descriptive information--). of the first candidate intent as the discovered intent (RodriguezGarcia, ¶53; Figure 1, 126; Figure 2, 220) comprises:
obtaining a user as the user who makes the utterance (RodriguezGarcia, ¶24 “In some implementations, the test data may be real-world user utterances whose intents needs to be identified.”) [[ as the utterance being made (RodriguezGarcia, ¶24 “In some implementations, the test data may be real-world user utterances whose intents needs to be identified.”);
determining historical [[ as the known intents (RodriguezGarcia, ¶8 “In some implementations, the known intents are a subset of a plurality of known intents stored in a training dataset, where the training dataset includes a plurality of utterance-intent pairs of a particular domain.”) corresponding to the query statement as utterance (RodriguezGarcia, ¶8 “In some implementations, the known intents are a subset of a plurality of known intents stored in a training dataset, where the training dataset includes a plurality of utterance-intent pairs of a particular domain.”) based on the user [[ as the user who makes the utterance (RodriguezGarcia, ¶24); and
generating the first prompt information as prompt 208 (RodriguezGarcia, ¶50 “the one or more software components augment the prompt 208 generated by the process 200A with samples from few-shot samples 212 and intents from known intents 214 to create an augmented prompt 216.”) based on the query statement as the utterance in the test examples (RodriguezGarcia, ¶3 “method of intent discovery includes obtaining an utterance and a corresponding label representative of an intent of the utterance”), the first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”; Figure 1, 126; Figure 2, 220), the descriptive information of the first candidate intent as the discovered intent (RodriguezGarcia, ¶53; Figure 1, 126; Figure 2, 220), and the historical [[ as the known intents (RodriguezGarcia, ¶8).
RodriguezGarcia does not explicitly teach obtain a user identifier of the query statement; determining historical interaction information corresponding to the query statement based on the user identifier; and generating the first prompt information based on …and the historical interaction information.
Obtain a user identifier of the query statement;
Kharbanda teaches determining historical interaction information as the context which includes previous interaction data (¶130 “The context determination block 62 may identify and/or process metadata, user profile data (e.g., preferences, user search history, user browsing history, user purchase history, and/or user input data), previous interaction data”) corresponding to the query statement as the search is based on the context (¶39 “the system can provide more accurate search results by enhancing the query with additional signals that provide helpful context for the search.”) based on the user identifier as the user profile associated with a particular user (¶130 “The context determination block 62 may identify and/or process metadata, user profile data … associated with the user. The context can be associated… associated with the user and/or the retrieved or obtained data.”); and
generating the first prompt information as query refinement, which may include prompt generation (¶141 “For example, a search prompt, a purchase prompt, a generate prompt, a reservation prompt, a call prompt, a redirect prompt, and/or one or more other prompts may be determined to be associated with the output(s) of the sensor processing system 60”; ¶43 “In the context of search engines, query refinement can involve determining a user's intent by evaluating user input (e.g., image input 102, audio input 104) and refining the search query based on the user's intent.”) based on …and the historical interaction information as query refinement based on the context, e.g. the previous interactions (43 “In the context of search engines, query refinement can involve determining a user's intent by evaluating user input (e.g., image input 102, audio input 104) and refining the search query based on the user's intent.”; ¶130 “The context determination block 62 may identify and/or process metadata, user profile data (e.g., preferences, user search history, user browsing history, user purchase history, and/or user input data), previous interaction data”).
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the search system taught by Kharbana to refine the query based on a user’s intent that is determined using the system taught by RodriguezGarcia as it yields the predictable results of enabling the system to use an expanded repository of known intents related to each domain (RodriguezGarcia, ¶24 “Through multiple iterations with varying test data, the database progressively expands its repository of known intents related to each domain. In some implementations, the test data may be real-world user utterances whose intents needs to be identified”). The search system taught by Kharbana already incorporates user intent in the query refinement process (Kharbana, ¶43). The proposed combination is incorporating the technqiues taught by RodriguezGarcia to use determine an expanded repository of known intents (RodriguezGarcia, ¶24) to use to refine the query (Kharbana, ¶43, ¶120).
With regard to claims 3 and 14 the proposed combination further teaches wherein the historical interaction information comprises at least one of (Please see the 112b rejection above regarding claim interpretation):
a historical query statement corresponding to the user identifier;
a second candidate intent corresponding to a historical query statement as the known utterance-intent pair (RodriguezGarcia, ¶8 “In some implementations, the known intents are a subset of a plurality of known intents stored in a training dataset, where the training dataset includes a plurality of utterance-intent pairs of a particular domain.”);
descriptive information (RodriguezGarcia, ¶52 ) corresponding to the second candidate intent as the known intent (RodriguezGarcia, Figure 2B, 214);
a second target intent corresponding to the historical query statement; or
a response statement corresponding to the historical query statement.
With regard to claim 5 RodriguezGarcia further teaches wherein obtaining the first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”) corresponding to the query statement as for the utterance (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) by matching (RodriguezGarcia, ¶39 “based on a similarity measure (e.g., cosine distance) between the one or more examples and their respective test-batch utterance.”; ¶94 “In some implementations, utterances corresponding to the list of known intents are semantically similar to utterances in the list of test examples. In some implementations, an utterance in the few-shot example is semantically similar to an utterance in at least one of the list of test examples.”) the query statement as the utterance in the test examples (Id) with the preset intent as the list of known intents (Id) and the descriptive information (RodriguezGarcia, ¶52 “the instructions 302 may be a task description that instructs a prompt generator (e.g., the prompt generator 106 described in FIG. 1) to generate a prompt for use by another LLM (e.g., the intent predictor 124 described in FIG. 1).”) of the preset intent as the known intents (RodriguezGarcia, ¶41) comprises:
determining a first as for the utterance (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) encoding vector (RodriguezGarcia, ¶39 “the test batch are embedded into vectors to enable the selection of one or more examples from the few-shot pool 108 for each test-batch utterance, based on a similarity measure (e.g., cosine distance) between the one or more examples and their respective test-batch utterance”) corresponding to the query statement as for the utterance (RodriguezGarcia, ¶39), and a second as the test batch (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) encoding vector (RodriguezGarcia, ¶39) corresponding to the preset intent as the known intents (RodriguezGarcia, ¶41 “the known intent feedback 120 is one or more utterance-intent pairs retrieved by the few-shot sampler 114 from known intents 112, which may be stored in a variety of storage formats, … . In some implementations, the known intents 112 may either be a copy of, or a subset of, the training dataset 104, augmented with one or more intents identified by the intent predictor 124.”) and the descriptive information (RodriguezGarcia, ¶52 “the instructions 302 may be a task description that instructs a prompt generator (e.g., the prompt generator 106 described in FIG. 1) to generate a prompt for use by another LLM (e.g., the intent predictor 124 described in FIG. 1).”) of the preset intent as the known intents (RodriguezGarcia, ¶41);
calculating a similarity (RodriguezGarcia, ¶39 “based on a similarity measure (e.g., cosine distance) between the one or more examples and their respective test-batch utterance.”; ¶94 “In some implementations, utterances corresponding to the list of known intents are semantically similar to utterances in the list of test examples. In some implementations, an utterance in the few-shot example is semantically similar to an utterance in at least one of the list of test examples.”) between the first encoding vector as the test examples (Id) and the second encoding vector as the list of known samples (Id);
and identifying a first number as the selection of one or more examples from the few-shot pool 108 (RodriguezGarcia, ¶39 “the selection of one or more examples from the few-shot pool 108 for each test-batch utterance, based on a similarity measure (e.g., cosine distance) between the one or more examples and their respective test-batch utterance”) of preset intents as from the few-shot pool 108, which is part of the known intent database (RodriguezGarcia, ¶38 “the few-shot sampler 114 may retrieve the one or more few-shot examples 116 from a few-shot pool 108, which includes a subset of the training dataset 104, for example, 10% of the samples for each known intent in the training dataset 104.”) with a [[ as most similar (RodriguezGarcia, ¶39) as the first candidate intent as the discovered intent (RodriguezGarcia, ¶53 “The prompt 400 may include instructions 402 and 406 instructing an LLM (e.g., the intent predictor 124) to discover intents for utterances using samples 404 as a guide”).
RodriguezGarcia does not explicitly teach highest similarity.
Kharbana teaches highest similarity (¶70 “The system can determine that the input audio signature is similar to a first known signature associated with a first known audio file when the similarity value exceeds a threshold value”).
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the similarity taught by RodriguezGarcia to use the threshold evaluation when determining the most similar intents as it yields the predictable results of determining the most similar elements. Please note that one of ordinary skill in the art would recognize that the cosine distance calculation taught by RodriguezGarcia (¶39) may reasonably be expected to be evaluated using a threshold as taught by Kharbana (¶70) to determine the most similar vectors (RodriguezGarcia, ¶39).
With regard to claim 6 the proposed combination further teaches wherein determining the first as for the utterance (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) encoding vector (RodriguezGarcia, ¶39 “the test batch are embedded into vectors to enable the selection of one or more examples from the few-shot pool 108 for each test-batch utterance, based on a similarity measure (e.g., cosine distance) between the one or more examples and their respective test-batch utterance”) corresponding to the query statement as for the utterance (RodriguezGarcia, ¶39), and the second as the test batch (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) encoding vector (RodriguezGarcia, ¶39) corresponding to the preset intent as the known intents (RodriguezGarcia, ¶41 “the known intent feedback 120 is one or more utterance-intent pairs retrieved by the few-shot sampler 114 from known intents 112, which may be stored in a variety of storage formats, … . In some implementations, the known intents 112 may either be a copy of, or a subset of, the training dataset 104, augmented with one or more intents identified by the intent predictor 124.”) and the descriptive information (RodriguezGarcia, ¶52 “the instructions 302 may be a task description that instructs a prompt generator (e.g., the prompt generator 106 described in FIG. 1) to generate a prompt for use by another LLM (e.g., the intent predictor 124 described in FIG. 1).”) of the preset intent as the known intents (RodriguezGarcia, ¶41) comprises:
obtaining the first as for the utterance (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) encoding vector (RodriguezGarcia, ¶39 “the test batch are embedded into vectors to enable the selection of one or more examples from the few-shot pool 108 for each test-batch utterance, based on a similarity measure (e.g., cosine distance) between the one or more examples and their respective test-batch utterance”) by inputting the query statement into an intent retrieval model as the technique for selecting the subset of known intents 112 (RodriguezGarcia, ¶42 “the few-shot sampler 114 may use the KNN semantic sampling technique described earlier to select the subset of the known intents 112 that are semantically similar to the current test batch 118.”); and
obtaining the second as the test batch (RodriguezGarcia, ¶39 “here each utterance in both the few-shot pool 108 and the test batch”) encoding vector (RodriguezGarcia, ¶39) corresponding to the preset intent as the known intents (RodriguezGarcia, ¶41 “the known intent feedback 120 is one or more utterance-intent pairs retrieved by the few-shot sampler 114 from known intents 112, which may be stored in a variety of storage formats, … . In some implementations, the known intents 112 may either be a copy of, or a subset of, the training dataset 104, augmented with one or more intents identified by the intent predictor 124.”) by concatenating (RodriguezGarcia, ¶40 “the few-shot examples 116 then may be concatenated with the samples selected from the training dataset 104 to constitute a sequence of samples to be fed to the intent predictor 124.”) and inputting the preset intent as the training dataset (Id) and the descriptive information (RodriguezGarcia, ¶52 “the instructions 302 may be a task description that instructs a prompt generator (e.g., the prompt generator 106 described in FIG. 1) to generate a prompt for use by another LLM (e.g., the intent predictor 124 described in FIG. 1).”) corresponding to the preset intent into the intent retrieval model (RodriguezGarcia, ¶50 “In some implementations, the one or more software components additionally incorporate into the augmented prompt 216 one or more utterances from test examples 210, which are utterances whose intents are to be discovered by an LLM 218 (e.g., the intent predictor 124 described in FIG. 1).”),
wherein the intent retrieval model is generated by training a pre-trained language model (RodriguezGarcia, ¶84 “the IVR module 806 may send the utterance to an intent retriever 808, which may use a pre-trained neural network model or another mechanism to determine whether an intent matching the utterance exists in a database 810 that stores known intents”) based on a sample statement as the utterance (Id) and a corresponding intent label as the matching intent (Id).
Conclusion
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/AMANDA L WILLIS/ Primary Examiner, Art Unit 2156
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