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 .
Drawings
The applicant’s submitted drawings appear to be acceptable for examination purposes. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the drawings.
Information Disclosure Statement
As required by M.P.E.P. 609(c), the applicant's submission of the Information Disclosure Statement, dated 12 March 2024, is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
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 9-11 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.
Claim 9 recites the limitation “the intent underlying the prompt comprises.” The intended scope is not clear because it is not clear what it “comprises” (the limitation appears to be incomplete). For the purposes of examination, the examiner has assumed that identifying the intent underlying the prompt follows the classification of the prompt being a normal prompt. [Examiner’s Note: the claim also recites the relative terms “normal” and “complex.” The examiner has interpreted these terms, in the context of the claim language, to refer to a “complete” single prompt and a prompt containing multiple sub-prompts, respectively (see also paras. [0023]-[0027] of the specification as filed).]
Claims 10-11 depend upon claim 9 and thus include the aforementioned limitation(s), without providing further clarity as to the intended scope.
Claim 11 further recites the limitation “determining target sub-prompts from the plurality of sub-prompts, each of the target sub-prompts being classified as a normal prompt.” The intended scope of the claim is not clear because it is not clear what relation the classification has to the determination in this limitation (e.g., are the sub-prompts classified, and those classified as normal prompts are determined to be the target sub-prompts, or are the target sub-prompts determined and then classified, etc.). For the purposes of examination, the examiner has assumed that the sub-prompts are classified to determine which sub-prompts are normal prompts.
As per claim 19, see the rejection of claim 9 above.
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(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and/or mathematical concepts. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 1-13 recite a method, claims 14-19 recite a device and claim 20 recites a manufacture. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 1:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process elements:
to generate a classification of the prompt – a data scientist classifies a prompt into one or more categories, which is an observation, evaluation, judgement, or opinion performed in the human mind.
in response to the classification of the prompt being a predetermined category, identifying… an intent underlying the prompt – the data scientist determines an intent underlying the prompt based upon the category of the prompt.
detecting… an implicit constraint for the prompt based on the intent, the implicit constraint representing a domain knowledge associated with the intent and not being included in the prompt – the data scientist determines an implicit constraint of the prompt based on the intent that represents a knowledge domain implicitly associated with the intent.
transforming… the intent in the prompt into a constraint-enhanced intent based on the implicit constraint – the data scientist determines a more specific intent of the prompt constrained by the implicit domain (e.g., generating code for test cases for a specific function, generating certain kinds of images, generating a specific kind of search based upon the context, etc.)
generating… an enhanced prompt based on the constraint-enhanced intent – the data scientist generates an enhanced prompt based upon the constraint-enhanced intent (e.g., clarifies the context in the prompt, adds file extensions, adds contextual parameters, etc.).
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A method comprising:
obtaining… a prompt representing a natural language text for use by a generative artificial intelligence processing model – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
with a processor circuitry – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). [Examiner’s Note: this applies to each of the instances of performing a step “with the processor circuitry” as well.]
obtaining… a prompt classifier trained to evaluate a classification of the prompt – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
inputting… the prompt to the prompt classifier – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
and outputting…the enhanced prompt for the generative artificial intelligence processing model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A method comprising:
obtaining… a prompt representing a natural language text for use by a generative artificial intelligence processing model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
with a processor circuitry – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f). [Examiner’s Note: this applies to each of the instances of performing a step “with the processor circuitry” as well.]
obtaining… a prompt classifier trained to evaluate a classification of the prompt – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
inputting… the prompt to the prompt classifier – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
and outputting…the enhanced prompt for the generative artificial intelligence processing model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 2:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
executing the generative artificial intelligence processing model by inputting the enhanced prompt to the generative artificial intelligence processing model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
to generate an artificial intelligence artifact reflecting an intent in the enhanced prompt – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
outputting the artificial intelligence artifact reflecting the intent in the enhanced prompt – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
executing the generative artificial intelligence processing model by inputting the enhanced prompt to the generative artificial intelligence processing model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
to generate an artificial intelligence artifact reflecting an intent in the enhanced prompt – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
outputting the artificial intelligence artifact reflecting the intent in the enhanced prompt – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 3:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
where the outputting the artificial intelligence artifact comprises:
displaying the artificial intelligence artifact via a user interface – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
where the outputting the artificial intelligence artifact comprises:
displaying the artificial intelligence artifact via a user interface – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 4:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
where the artificial intelligence artifact is program codes – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the outputting the artificial intelligence artifact comprises:
executing the program codes to perform a coding function implementing an intent in the enhanced prompt – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
where the artificial intelligence artifact is program codes – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the outputting the artificial intelligence artifact comprises:
executing the program codes to perform a coding function implementing an intent in the enhanced prompt – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 5:
The claim recites the following additional mental process elements:
where the detecting the implicit constraint for the prompt based on the intent comprises:
…to detect a plurality of concepts ontologically associated with the intent – the data scientist determines a plurality of concepts ontologically associated with the intent in order to detect an implicit constraint(s) for the prompt.
and determining at least one of the plurality of concepts as an implicit constraint based on relevancy of the plurality of concepts with the intent – the data scientist determines which of the concepts should act as implicit constraint(s) based on their relevancy to the determined intent.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
utilizing a domain-customized knowledge base – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
utilizing a domain-customized knowledge base – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 6:
The claim recites the following additional mental process elements:
where the detecting the implicit constraint for the prompt based on the intent comprises:
…to obtain a plurality of concepts associated with the intent – the data scientist determines a plurality of concepts associated with the intent in order to detect an implicit constraint(s) for the prompt.
determining at least one of the plurality of concepts as an implicit constraint based on relevancy of the plurality of concepts with the intent – the data scientist determines which of the concepts should act as implicit constraint(s) based on their relevancy to the determined intent.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
obtaining a concept detector trained to detect concepts associated with the intent in a specific knowledge domain – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
inputting the intent to the concept detector – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
obtaining a concept detector trained to detect concepts associated with the intent in a specific knowledge domain – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
inputting the intent to the concept detector – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 7:
The claim recites the following additional mental process and/or mathematical concept elements:
where the determining the at least one of the plurality of concepts as the implicit constraint comprises:
scoring the plurality of concepts based on relevancy of the plurality of concepts with the intent – the data scientist determines relevancy scores for a plurality of concepts with the intent. Alternatively/additionally – scoring a relevancy of concepts is a mathematical calculation.
determining a concept having a score exceeding a predetermined score threshold as the implicit constraint – the data scientist determines which concept is an implicit constraint based on any of the concept scores exceeding a predetermined score. Alternatively/additionally – comparing scores is a mathematical calculation.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 8:
The claim recites the following additional mental process elements:
where the transforming the intent in the prompt into the constraint-enhanced intent based on the implicit constraint comprises:
mapping the implicit constraint to an executable action for concatenating the implicit constraint with the intent – the data scientist maps the implicit constraint to an action to concatenate the constraint with the intent.
generate constraint-enhanced intent for the prompt – the data scientist concatenates the constraint to the intent to generate an constraint-enhanced intent for a prompt.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
and executing the executable action – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
and executing the executable action – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 9:
The claim recites the following additional mental process elements:
where the classification of the prompt comprises a normal prompt or a complex prompt combining a plurality of sub-prompts – the data scientist determines whether the prompt is a normal prompt or a complex prompt containing multiple sub-prompts.
and the identifying the intent underlying the prompt comprises:
in response to the classification of the prompt being a normal prompt, identifying the intent underlying the prompt comprises – the data scientist determines the intent of a normal prompt.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 10:
The claim recites the following additional mental process elements:
where the method further comprises:
in response to the classification of the prompt being a complex prompt, simplifying the prompt into a plurality of sub-prompts – the data scientist simplifies a complex prompt into a plurality of sub-prompts.
to classify the sub-prompts respectively – the data scientist classifies each of the sub-prompts.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
and inputting each of the plurality of sub-prompts to the prompt classifier – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
and inputting each of the plurality of sub-prompts to the prompt classifier – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 11:
The claim recites the following additional mental process elements:
where the method further comprises:
determining target sub-prompts from the plurality of sub-prompts, each of the target sub-prompts being classified as a normal prompt – the data scientist determines whether any of the sub-prompts are normal prompts (which are the target sub-prompts).
and identifying intents underlying the target sub-prompts as intents of the complex prompt – the data scientist determines the intent(s) of the target, normal sub-prompts as intents of the complex prompt.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 12:
The claim recites the following additional mental process elements:
where the identifying the intent underlying the prompt comprises:
performing syntax and semantics analysis on the prompt to derive the intent – the data scientist performs syntax and semantics analysis on the prompt to derive the intent.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 14:
See the rejection of claim 1 above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
where the method further comprises:
storing the enhanced prompt into a storage for validation – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
where the method further comprises:
storing the enhanced prompt into a storage for validation – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 14:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A system comprising:
a memory having stored thereon executable instructions – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and a processor circuitry in communication with the memory – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the processor circuitry when executing the executable instructions configured to: [perform the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A system comprising:
a memory having stored thereon executable instructions – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and a processor circuitry in communication with the memory – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the processor circuitry when executing the executable instructions configured to: [perform the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 15, see the rejection of claim 2 above.
As per claim 16, see the rejection of claim 4 above.
As per claim 17, see the rejection of claim 5 above.
As per claim 18, see the rejection of claim 6 above.
As per claim 19, see the rejection of claim 9 above.
As per claim 20:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A non-transitory machine-readable media, having instructions stored on the machine-readable media – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the instructions configured to, when executed, cause a machine to: [perform the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A non-transitory machine-readable media, having instructions stored on the machine-readable media – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the instructions configured to, when executed, cause a machine to: [perform the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-4, 8-16, 19, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pandita (US 2024/0144922).
As per claim 1, Pandita teaches a method comprising:
obtaining, with a processor circuitry [the method may be implemented as a computer system performing operations, including one or more processors operations from one or more memories (paras. 0147-152; fig. 13; etc.)], a prompt representing a natural language text for use by a generative artificial intelligence processing model [a spoken language utterance(s) is received and converted into (natural language) text which can be used to generate a prompt for an LLM (abstract; para. 0080; etc.) where the LLM is the generative artificial intelligence processing model, which can include an integrated development environment, etc. (para. 0041, etc.)];
obtaining, with the processor circuitry, a prompt classifier trained to evaluate a classification of the prompt [a machine learning engine receives the utterance/prompt and classifies what intent the utterance belongs to, mapping an intent to an incoming utterance before extraction (para. 0027, etc.); where the machine learning engine classifying the utterance is the prompt classifier trained to evaluate the classification of the prompt];
inputting, with the processor circuitry, the prompt to the prompt classifier to generate a classification of the prompt [a machine learning engine receives the utterance/prompt and classifies what intent the utterance belongs to, mapping an intent to an incoming utterance before extraction (para. 0027, etc.); where the machine learning engine classifying the utterance is the prompt classifier];
in response to the classification of the prompt being a predetermined category, identifying, with the processor circuitry, an intent underlying the prompt [a machine learning engine receives the utterance/prompt and classifies what intent the utterance belongs to, then extracts a corresponding slot(s) from the utterance based upon the intent classification (paras. 0027, 0036, etc.) where the slots are arguments associated with the intent (paras. 0023-27, etc.); where the intent classification is the predetermined category of the prompt and the intent and associated slot argument(s) is the intent underlying the prompt];
detecting, with the processor circuitry, an implicit constraint for the prompt based on the intent, the implicit constraint representing a domain knowledge associated with the intent and not being included in the prompt [additional context may be provided inside the prompt or via contextual understanding of the LLM (not included in the prompt) (paras. 0041-42, 0077, etc.) which is used as a bound/constraint, based upon the determined domain, so that the intent is predicted based upon the determined context of the utterance (paras. 0042, 0074, etc.); where the context information (from outside the prompt) is the implicit constraint which can represent domain knowledge associated with the intent (e.g., the IDE)];
transforming, with the processor circuitry, the intent in the prompt into a constraint-enhanced intent based on the implicit constraint [additional context may be provided inside the prompt or via contextual understanding of the LLM (not included in the prompt) (paras. 0041-42, 0077, etc.) which acts as a bound/constraint based upon the domain so that the intent is predicted based upon the determined context of the utterance (paras. 0042, 0074, etc.); where the intent prediction based upon the determined context (constraint) is the transformation of the prompt into a constraint-enhanced intent];
generating, with the processor circuitry, an enhanced prompt based on the constraint-enhanced intent [the identified contextualized intent and slots extracted from the utterance are used to provide an engineered prompt with supplemental, enhanced, or augmented contextual awareness to the LLM (abstract; paras. 0076, 0079-80; etc.)]; and
outputting, with the processor circuitry, the enhanced prompt for the generative artificial intelligence processing model [the identified contextualized intent and slots extracted from the utterance are added to the prompt and used to provide an engineered prompt with supplemental, enhanced, or augmented contextual awareness to the LLM (abstract; paras. 0076, 0079-80; etc.)].
As per claim 2, Pandita teaches the method further comprises:
executing the generative artificial intelligence processing model by inputting the enhanced prompt to the generative artificial intelligence processing model to generate an artificial intelligence artifact reflecting an intent in the enhanced prompt; and
outputting the artificial intelligence artifact reflecting the intent in the enhanced prompt [the identified contextualized intent and slots extracted from the utterance are added to the prompt and used to provide an engineered prompt with supplemental, enhanced, or augmented contextual awareness to the LLM (abstract; paras. 0076, 0079-80; etc.) and the LLM can then also associate another intent with the ones provided by the prompt (paras. 0040, 0048, etc.); where this cycle can be performed any number of times desired].
As per claim 3, Pandita teaches where the outputting the artificial intelligence artifact comprises:
displaying the artificial intelligence artifact via a user interface [a service/application can display the generated prompts/variations (para. 0119, etc.)].
As per claim 4, Pandita teaches where the artificial intelligence artifact is program codes [the generated prompts (from the LLM) are used to generate code (paras. 0081-83, 0123-129, etc.)], and the outputting the artificial intelligence artifact comprises:
executing the program codes to perform a coding function implementing an intent in the enhanced prompt [the generated prompts (from the LLM) are used to generate code, which code can also be used to fine tune a model (paras. 0081-83, 0123-129, etc.), can include providing a specific executable meaning of the prompt intent within the context (para. 0033, etc.), and the actions can be performed (para. 0063, etc.)].
As per claim 8, Pandita teaches where the transforming the intent in the prompt into the constraint-enhanced intent based on the implicit constraint comprises:
mapping the implicit constraint to an executable action for concatenating the implicit constraint with the intent; and
executing the executable action to generate constraint-enhanced intent for the prompt [the identified contextualized intent and slots extracted from the utterance are added (concatenated) to the prompt and used to provide an engineered prompt with supplemental, enhanced, or augmented contextual awareness to the LLM (abstract; paras. 0076, 0079-80; etc.), which can be performed by executable actions from the prompt (para. 0063, etc.); where the generation of the executable code/actions by the model is the mapping of the constraint to the executable action to produce the prompt with added context information].
As per claim 9, Pandita teaches where the classification of the prompt comprises a normal prompt or a complex prompt combining a plurality of sub-prompts [a complexity of the intent of the prompt may be determined which includes determining the size/number of prompt phrases (paras. 0039, 0052, etc.)], and the identifying the intent underlying the prompt comprises:
in response to the classification of the prompt being a normal prompt, identifying the intent underlying the prompt comprises [a complexity of the intent of the prompt may be determined which includes determining the size/number of prompt phrases (paras. 0039, 0052, etc.) and a machine learning engine receives the utterance/prompt and classifies what intent the utterance belongs to, then extracts a corresponding slot(s) from the utterance based upon the intent classification (paras. 0027, 0036, etc.) where the slots are arguments associated with the intent (paras. 0023-27, etc.)].
As per claim 10, Pandita teaches where the method further comprises:
in response to the classification of the prompt being a complex prompt, simplifying the prompt into a plurality of sub-prompts [a complexity of the intent of the prompt may be determined which includes determining the size/number of prompt phrases and limiting the number of prompt phrases based upon the determined complexity (paras. 0039, 0052, 0090, etc.); where each of the prompt phrases are sub-prompts]; and
inputting each of the plurality of sub-prompts to the prompt classifier to classify the sub-prompts respectively [a machine learning engine receives the utterance/prompt and classifies what intent the utterance belongs to, then extracts a corresponding slot(s) from the utterance based upon the intent classification (paras. 0027, 0036, etc.) where the slots are arguments associated with the intent (paras. 0023-27, etc.), which can include providing the specified number of prompt phrases (based upon determined complexity) to the model (para. 0090, etc.)].
As per claim 11, Pandita teaches where the method further comprises:
determining target sub-prompts from the plurality of sub-prompts, each of the target sub-prompts being classified as a normal prompt [a complexity of the intent of the prompt may be determined which includes determining the size/number of prompt phrases and limiting the number of prompt phrases based upon the determined complexity (paras. 0039, 0052, 0090, etc.); where the selected limited number of prompt phrases are the determined target sub-prompts]; and
identifying intents underlying the target sub-prompts as intents of the complex prompt [a machine learning engine receives the utterance/prompt and classifies what intent the utterance belongs to, then extracts a corresponding slot(s) from the utterance based upon the intent classification (paras. 0027, 0036, etc.) where the slots are arguments associated with the intent (paras. 0023-27, etc.), which can include providing the specified number of prompt phrases (based upon determined complexity) to the model (para. 0090, etc.)].
As per claim 12, Pandita teaches where the identifying the intent underlying the prompt comprises:
performing syntax and semantics analysis on the prompt to derive the intent [determining the context can include syntax analysis (paras. 0041, 0108, etc.) and semantic analysis (paras. 0006, 0023, 0048, etc.)].
As per claim 13, Pandita teaches where the method further comprises:
storing the enhanced prompt into a storage for validation [the generated intent(s) and slot(s) can be stored in a repository for subsequent reference or use (para. 0049, etc.)].
As per claim 14, see the rejection of claim 1 above, wherein Pandita also teaches a system comprising:
a memory having stored thereon executable instructions; and
a processor circuitry in communication with the memory, the processor circuitry when executing the executable instructions configured to: [perform the method] [the system can be implemented as executable instructions stored in one or more memories and executed by one or more processors of a computer system (paras. 0151-153, etc.)].
As per claim 15, see the rejection of claim 2 above.
As per claim 16, see the rejection of claim 4 above.
As per claim 19, see the rejection of claim 9 above.
As per claim 20, see the rejection of claim 1 above, wherein Pandita also teaches a non-transitory machine-readable media, having instructions stored on the machine-readable media, the instructions configured to, when executed, cause a machine to: [perform the method] [the system can be implemented as executable instructions stored in one or more memories and executed by one or more processors of a computer system (paras. 0151-153, etc.)].
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 5, 6, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandita (US 2024/0144922) in view of Gruber (US 2013/0275164).
As per claim 5, Pandita teaches the method of claim 1, as described above.
While Pandita teaches detecting the implicit constraint for the prompt based on the intent (see above), it has not been relied upon for teaching where the detecting the implicit constraint for the prompt based on the intent comprises: utilizing a domain-customized knowledge base to detect a plurality of concepts ontologically associated with the intent; and determining at least one of the plurality of concepts as an implicit constraint based on relevancy of the plurality of concepts with the intent.
Gruber teaches where the detecting the implicit constraint for the prompt based on the intent comprises:
utilizing a domain-customized knowledge base to detect a plurality of concepts ontologically associated with the intent [the speech to text and natural language understanding system can include constraining the understanding/interpretation of the natural language using a set of domain models to determine appropriate sources of information within domains based upon active ontologies, which models can then be used for mappings from intent to services parameters (paras. 0132-142, etc.); for determining the appropriate context/domain information in Pandita, above]; and
determining at least one of the plurality of concepts as an implicit constraint based on relevancy of the plurality of concepts with the intent [the speech to text and natural language understanding system can include constraining the understanding/interpretation of the natural language using a set of domain models to determine appropriate/relevant sources of information within domains based upon active ontologies, which models can then be used for mappings from intent to services parameters (paras. 0132-142, 0319, etc.); for determining the appropriate context/domain information in Pandita, above].
Pandita and Gruber are analogous art, as they are within the same field of endeavor, namely utilizing language models for interpreting natural language prompts/speech.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize active ontologies to determine relevant domain models/context information for constraining/understanding the natural language inputs, as taught by Gruber, in the determination of context information for constraining the interpretation of the natural language inputs in the system/method taught by Pandita.
Gruber provides motivation as [Unlike assistant technology that attempts to implement a general-purpose artificial intelligence system, the embodiments described herein may apply the multiple sources of constraints to reduce the number of solutions to a more tractable size. This results in fewer ambiguous interpretations of language, fewer relevant domains or tasks, and fewer ways to operationalize the intent in services. The focus on specific domains, tasks, and dialogs also makes it feasible to achieve coverage over domains and tasks with human-managed vocabulary and mappings from intent to services parameters. (para. 0133, etc.) and active ontologies can unify elements of various components included and/or referenced by other components of the intelligent automated assistant (para. 0226, etc.)].
As per claim 6, Pandita teaches the method of claim 1, as described above.
While Pandita teaches detecting the implicit constraint for the prompt based on the intent (see above), it has not been relied upon for teaching where the detecting the implicit constraint for the prompt based on the intent comprises: obtaining a concept detector trained to detect concepts associated with the intent in a specific knowledge domain; inputting the intent to the concept detector to obtain a plurality of concepts associated with the intent; and determining at least one of the plurality of concepts as an implicit constraint based on relevancy of the plurality of concepts with the intent.
Gruber teaches where the detecting the implicit constraint for the prompt based on the intent comprises:
obtaining a concept detector trained to detect concepts associated with the intent in a specific knowledge domain [the speech to text and natural language understanding system can include utilizing an active ontology to choose appropriate domain models (concept detector) associated with different concepts (paras. 0224-231, etc.); for determining the appropriate context/domain information in Pandita, above];
inputting the intent to the concept detector to obtain a plurality of concepts associated with the intent [the speech to text and natural language understanding system can include utilizing an active ontology to choose appropriate domain models (concept detector) associated with different concepts (paras. 0224-231, etc.) and use the domain models to determine appropriate/relevant concepts and for determining mappings from intent to services parameters (paras. 0132-142, 0319, etc.); for determining the appropriate slot and/or context information based upon intent in Pandita, above]; and
determining at least one of the plurality of concepts as an implicit constraint based on relevancy of the plurality of concepts with the intent [the speech to text and natural language understanding system can include utilizing an active ontology to choose appropriate domain models (concept detector) associated with different concepts (paras. 0224-231, etc.) and use the domain models to determine appropriate/relevant concepts and for determining mappings from intent to services parameters (paras. 0132-142, 0319, etc.); for determining the appropriate context/domain information in Pandita, above].
Pandita and Gruber are analogous art, as they are within the same field of endeavor, namely utilizing language models for interpreting natural language prompts/speech.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize active ontologies to determine relevant domain models for providing concepts/associations for the natural language inputs, as taught by Gruber, in the determination of context information for constraining the interpretation of the natural language inputs in the system/method taught by Pandita, including providing the intent to the selected domain model(s) for determining the appropriate context/concepts.
Gruber provides motivation as [Unlike assistant technology that attempts to implement a general-purpose artificial intelligence system, the embodiments described herein may apply the multiple sources of constraints to reduce the number of solutions to a more tractable size. This results in fewer ambiguous interpretations of language, fewer relevant domains or tasks, and fewer ways to operationalize the intent in services. The focus on specific domains, tasks, and dialogs also makes it feasible to achieve coverage over domains and tasks with human-managed vocabulary and mappings from intent to services parameters. (para. 0133, etc.) and active ontologies can unify elements of various components included and/or referenced by other components of the intelligent automated assistant (para. 0226, etc.)].
As per claim 17, see the rejection of claim 5 above.
As per claim 18, see the rejection of claim 6 above.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandita and Gruber as applied to claim 6 above, and further in view of Martelaro (US 2021/0264296).
As per claim 7, Pandita/Gruber teaches the method of claim 6, above.
While Pandita/Gruber teaches determining at least one of a plurality of concepts as an implicit constraint based upon relevance (see above), it has not been relied upon for teaching where the determining the at least one of the plurality of concepts as the implicit constraint comprises: scoring the plurality of concepts based on relevancy of the plurality of concepts with the intent; and determining a concept having a score exceeding a predetermined score threshold as the implicit constraint.
Martelaro teaches where the determining the at least one of the plurality of concepts as the implicit constraint comprises:
scoring the plurality of concepts based on relevancy of the plurality of concepts with the intent; and
determining a concept having a score exceeding a predetermined score threshold as the implicit constraint [the system for providing recommendations based upon natural language queries (para. 0003, etc.) can include a selection module for selecting a subset of recommendations based upon a defined threshold of relevance scoring with respect to the determined user’s intent (paras. 0061-62); for selecting the concepts/context of the implicit constraint for the user intent in Pandita/Gruber, above].
Pandita/Gruber and Martelaro are analogous art, as they are within the same field of endeavor, namely interpreting natural language inputs from users, including determining user intent and context information.
It would have been obvious to one of ordinary skill in the art, before the invention was made, to include determining relevant recommendations using a relevance score threshold from a determined user intent, as taught by Martelaro, in determining the relevant concepts/context for providing implicit constraints based upon the user intent in the system/method taught by Pandita/Gruber.
Martelaro provides motivation as [different scoring/ranking allows prioritizing different goals (para. 0023, etc.) while various optimization criteria can be utilized and modified to provide better recommendations/fit, including allowing the user to customize selection parameters to meet their specific requirements (paras. 0047-49, etc.)].
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-20 are rejected
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Packer (US 2016/0239568) – discloses a system/method wherein ranking and relevance functions are used in determining which of the available domain models are best suited for determination of the correct intents of a user query for generating a response.
Srivastava (US 2021/0056973) – discloses natural language/voice processing including determining user intent and sub-intent(s) for querying a knowledge graph.
Hewavitharana (US 2018/0068031) – discloses a personal assistant determining explicit and implicit user intent(s) and context/knowledge of the user.
Baldwin (US 2008/0091406) – discloses a user interaction system including context domain agents determining user intent from a shared knowledge model.
Lahiri et al. (Interactive Code Generation via Test-Driven User-Intent Formalization, Aug 2022, pgs. 1-18) – discloses a system using LLMs for producing code from natural language intent via user-intent formalization.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE GIROUX whose telephone number is (571)272-9769. The examiner can normally be reached M-F 10am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128
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