Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1-3, 5, 7-9, 11-14 objected to because of the following informalities:
The subsequent recitation of “one or more processors” in the above claims should probably be preceded by: the.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
All claims are directed towards either a method, a system or a non-transitory computer-readable storage media and thus satisfies Step 1 as falling into one of the statutory categories.
Step 2A, Prong One:
Independent Claim 1 recites (the same analysis applies to similar independent Claims 18 and 20):
producing, by one or more processors, generative content based on processing the user query with the machine-learned generative model;
generating, by one or more processors, data indicative of a query association with a target domain based on the user query and a plurality of domain artifacts associated with the target domain;
generating, by one or more processors, data indicative of a response association with the target domain based on the generative content and the plurality of domain artifacts associated with the target domain;
and generating, by one or more processors, a response to the user query based at least in part on the data indicative of the query association with the target domain and the data indicative of the response association with the target domain.
These limitations, under their broadest reasonable interpretation, covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of finding/generating answers (generative content) to questions (queries) and determining if those queries and generative content are valid for a particular target domain using evaluation and judgement.
Step 2A, Prong Two:
Claim 1 recites the additional elements of (the same analysis applies to similar independent Claims 18 and 20):
obtaining, by one or more processors, a user query requesting content generation by a machine-learned generative model;
This limitation is considered as adding insignificant extra-solution activity (obtaining data) to the judicial exception - see MPEP 2106.05(g). The machine-learned generative model is considered as using the model as a tool to perform an abstract idea - see MPEP 2106.05(f). The “one or more processors” is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are considered as appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality (obtaining data), to the judicial exception - see MPEP 2106.05(d), and using a model as a tool to perform an abstract idea - see MPEP 2106.05(f). The further additional element of “one or more processors” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are therefore not patent eligible.
Dependent Claim 2 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining a distance between two embeddings using evaluation.
Dependent Claim 3 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of embedding/placing vectors in a vector space using observation and evaluation.
Dependent Claim 4 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining query distances using evaluation.
Dependent Claim 5 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of classifying queries using observation and evaluation.
Dependent Claim 6 is considered as using a machine learning model as a tool to perform an abstract idea - see MPEP 2106.05(f).
The first limitation of dependent Claim 7 is considered as appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality (obtaining data), to the judicial exception - see MPEP 2106.05(d); the second limitation is considered as using a machine learning model as a tool to perform an abstract idea which also includes its training - see MPEP 2106.05(f).
Dependent Claim 8 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining query distances between two vectors and classifying the query using evaluation.
Dependent Claim 9 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining (response) distances between two vectors using evaluation.
Dependent Claim 10 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining whether embeddings are associated with a particular domain using judgement and evaluation.
Dependent Claim 11 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of classifying responses using evaluation.
Dependent Claim 12 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining a response distance by comparing two vector embeddings and classifying responses using observation and evaluation.
Dependent Claim 13 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining whether or not a response is appropriate to a particular query using judgement and evaluation.
Dependent Claim 14 is considered as appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality (obtaining data, aggregating data, displaying data), to the judicial exception - see MPEP 2106.05(d).
Dependent Claims 15-16 are considered as using a machine learning model as a tool to perform an abstract idea - see MPEP 2106.05(f).
Dependent Claim 17 is considered as appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality (obtaining data), to the judicial exception - see MPEP 2106.05(d).
The first limitation of dependent Claim 19 is considered as appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality (storing data), to the judicial exception - see MPEP 2106.05(d); the second limitation is considered as using a machine learning model as a tool to perform an abstract idea which also includes its training - see MPEP 2106.05(f).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5-7, 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lucas, US 2025/0190801 A1, in view of Steedman, US 2021/0141798 A1.
Regarding Claim 1, Lucas teaches:
A computer-implemented method, comprising: obtaining, by one or more processors, a user query requesting content generation by a machine-learned generative model (paragraph 22: “Computing device 102 may be configured to receive a prompt 101 that may be any human-generated or machine-generated data capable of being used, directly or after a suitable preprocessing, as an input into an LM 122. In some embodiments, LM 122 may be an LLM, e.g., a model with hundreds of millions or one or more billion of learned parameters”);
producing, by one or more processors, generative content based on processing the user query with the machine-learned generative model (paragraph 52: “FIG. 4 illustrates a situation where prompt 101 is provided to LM 424-2. After processing prompt 101 and generating a response 430, the model (e.g., LM 424-2) may deliver response 430 to a user”);
generating, by one or more processors, data indicative of a query association with a target domain based on the user query and a plurality of domain artifacts associated with the target domain (paragraph 19: “where multiple LLMs (e.g., specialized-knowledge models) are available, the prompt analyzer may perform multiple prompt verifications (e.g., in parallel) for the multiple models and then select the model with the highest metric M indicative of the best familiarity of the corresponding model with the subject matter of the user prompt. In some embodiments, rather than selecting from multiple LLMs, the system may select from various prompt tuning models that are trained to adapt the prompt to a specific domain the prompt tuning model is configured for. As such, where the LLM itself may not have a high M score, the LLM in combination with a specific prompt tuning model may have an acceptable M score that may make the combination suitable for addressing the query or particular task at hand”. The subject matter of the user prompt corresponding to the plurality of domain artifacts/data and the specific domain corresponding to the target domain).
Lucas may not have explicitly taught the following, however, Steedman shows:
generating, by one or more processors, data indicative of a response association with the target domain based on the generative content and the plurality of domain artifacts associated with the target domain;
and generating, by one or more processors, a response to the user query based at least in part on the data indicative of the query association with the target domain and the data indicative of the response association with the target domain (Abstract: “representing the user inputted query as a sequence of embedding vectors using a first model; encoding the sequence of embedding vectors to produce a context vector using a second model; retrieving responses with associated response vectors; scoring response vectors against the context vector, wherein the scoring is a measure of the similarity between the context vector and a response vector; and outputting the responses with the closest response vectors”. The context vector representative of the response association).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Steedman with that of Lucas for generating data indicative of a response association with the target domain based on the generative content and the plurality of domain artifacts associated with the target domain; and generating a response to the user query based at least in part on the data indicative of the query association with the target domain and the data indicative of the response association with the target domain.
The ordinary artisan would have been motivated to modify Lucas in the manner set forth above for the purposes of having a model that is memory efficient and training efficient, while maintaining performance in a response selection task [Steedman: paragraph 30].
Regarding Claim 5, Lucas further teaches:
The computer-implemented method of claim 1, wherein generating, by one or more processors, data indicative of the query association with the target domain, comprises: generating, by one or more processors, a query classification by processing the user query with a machine-learned classification model that has been trained using the plurality of domain artifacts (paragraph 17: “The evaluation metric M estimates the likelihood that the user prompt is of a type represented in the corpus of training data that was used in LLM training”. The metric representative of the query classification).
Regarding Claim 6, Lucas further teaches:
The computer-implemented method of claim 5, wherein the machine-learned classification model is a large language model (paragraph 17: “The evaluation metric M estimates the likelihood that the user prompt is of a type represented in the corpus of training data that was used in LLM training”. LLM being the large language model).
Regarding Claim 7, Lucas further teaches:
The computer-implemented method of claim 5, further comprising: providing, by one or more processors, at least a portion of the plurality of domain artifacts as training data to the machine-learned classification model (paragraph 26: “LM 122 may be further trained using training data containing a large number of texts, such as human dialogues, newspaper texts, magazine texts, book texts, web-based texts, and/or any other texts”. The training data includes the plurality of domain artifacts/data);
and training, by one or more processors, the machine-learned classification model based at least in part on an output generated by the machine-learned classification model in response to the training data (paragraph 30: “For various training inputs, LM training server 160 may cause LM(s) 122 to generate training output(s). LM training server 160 may then compare training output(s) with the desired target output(s). The resulting error or mismatch, e.g., the difference between the target output(s) and the training output(s), may be backpropagated through various neural layers of LM(s) 122, and the weights and biases of LM(s) 122 may be adjusted to make the training outputs closer to the target (ground truth) outputs”).
Regarding Claim 13, Lucas further teaches:
The computer-implemented method of claim 1, further comprising: comparing, by the one or more processors, the data indicative of the query association and the data indicative of the response association with one or more association criteria (paragraph 25: “prompt analyzer 120 may parse prompt 101 into individual words or tokens and construct one or more verification prompts that include some of the words/tokens of prompt 101 while excluding some other words/tokens of prompt 101 Prompt analyzer 120 may feed the constructed verification prompts to LM 122 and receive, from LM 122 various probabilities (verification scores) indicating likelihoods that one or more words/tokens not included in verification prompts can occur together with words/tokens of verification prompts as part of the same prompt 101. Based on the received probabilities, prompt analyzer 120 may determine if prompt 101 is a valid prompt or invalid prompt. A valid prompt is a prompt of a type that LM 122 is trained to process and whose processing is not against some relevant (public and/or private) policy. An invalid prompt is a prompt that LM 122 has not been adequately trained to process (e.g., a prompt requiring specialized knowledge not learned by LM 122), a prompt whose content violates some relevant policy, a prompt that is likely to generate a response that would violate any relevant policy, an unusual prompt, a prompt that may have been generated by a malicious attacker, and/or the like. Prompt 101 determined to be valid may be forwarded to LM 122 for regular processing. Prompt 101 determined to be invalid may be returned to a (human or machine) user that generated prompt 101 with a suggestion to rephrase prompt 101 or a notification that prompt 101 cannot be processed”. The relevant policy representative of the one or more association criteria);
wherein generating the response to the user query comprises: in response to the data indicative of the query association and the data indicative of the response association satisfying the one or more association criteria, providing the generative content in the response to the user query (paragraph 25: “Prompt 101 determined to be valid may be forwarded to LM 122 for regular processing”);
and in response to the data indicative of the query association and the data indicative of the response association not satisfying the one or more association criteria, filtering the generative content from the response to the user query (paragraph 25: “Prompt 101 determined to be invalid may be returned to a (human or machine) user that generated prompt 101 with a suggestion to rephrase prompt 101 or a notification that prompt 101 cannot be processed”).
Regarding Claim 14, Lucas further teaches:
The computer-implemented method of claim 1, further comprising: obtaining, by the one or more processors, data indicative of a query association with the target domain for a plurality of user queries (paragraph 19: “where multiple LLMs (e.g., specialized-knowledge models) are available, the prompt analyzer may perform multiple prompt verifications (e.g., in parallel) for the multiple models and then select the model with the highest metric M indicative of the best familiarity of the corresponding model with the subject matter of the user prompt. In some embodiments, rather than selecting from multiple LLMs, the system may select from various prompt tuning models that are trained to adapt the prompt to a specific domain the prompt tuning model is configured for. As such, where the LLM itself may not have a high M score, the LLM in combination with a specific prompt tuning model may have an acceptable M score that may make the combination suitable for addressing the query or particular task at hand”. The specific domain corresponding to the target domain);
obtaining, by the one or more processors, data indicative of a response association with the target domain for a plurality of generative content generated by the machine-learned generative model in response to the plurality of user queries (paragraph 34: “Prompt analyzer 120 may be capable of processing prompt 101 and generating a response”);
generating, by the one or more processors, aggregated results based on the data indicative of the query association with the target domain for the plurality of user queries and the data indicative of the response association with the target domain for the plurality of generative content; and generating, by the one or more processors, a visualization of the aggregated results (paragraph 115: “an application may summarize results before completing, which may include, without limitation, a single confidence score, pixel level-segmentation, voxel-level segmentation, generating a visualization, or generating text to summarize findings”).
Regarding Claim 15, Lucas further teaches:
The computer-implemented method of claim 1, wherein: the machine-learned generative model includes a large language model; and the user query includes a prompt for the large language model (paragraph 18: “the prompt analyzer may determine that the LLM is likely to generate an acceptable, reliable, and accurate response and may pass the user prompt to LLM for regular processing”. LLM being the large language model).
Regarding Claim 16, Lucas further teaches:
The computer-implemented method of claim 1, wherein: the machine-learned generative model includes an image generation model; and the user query includes a prompt for the image generation model (paragraph 38: “systems for performing collaborative content creation for 3D assets, systems implementing one or more language models, such as large language models (LLMs) (which may process text, voice, image, and/or other data types to generate outputs in one or more formats)”).
Regarding Claim 17, Lucas further teaches:
The computer-implemented method of claim 16, wherein: the prompt includes image data (paragraph 22: “Prompt 101 may include a text (e.g., a sequence of one or more typed words), a speech (e.g., a sequence of one or more spoken words), an image (e.g., a drawing or a picture), a video or series of images”).
Regarding Claim 19, Steedman further teaches:
The computing system of claim 18, wherein the one or more non-transitory computer-readable media collectively store: a database comprising a plurality of vector embeddings corresponding to the plurality of domain artifacts (paragraph 61: “the response vectors and their associated responses, and various parameters of the response selection model 109 such as the embeddings and weights and bias vectors in the encoder may be stored in the storage”. The response vectors and their associated responses representative of the plurality of domain artifacts/data);
And Lucas further teaches:
and a machine-learned classification model that has been trained using the plurality of domain artifacts (paragraph 26: “LM 122 may be further trained using training data containing a large number of texts, such as human dialogues, newspaper texts, magazine texts, book texts, web-based texts, and/or any other texts”. The training data representative of the plurality of domain artifacts/data. See also Steedman, for example paragraph 26, “the second model has been trained using corresponding queries and responses”, the corresponding queries and responses representative of the plurality of domain artifacts/data).
Claims 2-4, 8-12 are rejected under 35 U.S.C. 103 as being unpatentable over Lucas, US 2025/0190801 A1, in view of Steedman, US 2021/0141798 A1, and further in view of Hemington, US 2024/0320251 A1.
Regarding Claim 2, with Lucas and Steedman teaching those limitations of the claim as previously pointed out, neither Lucas nor Steedman may have taught all of the following, however, Hemington shows:
The computer-implemented method of claim 1, wherein generating, by the one or more processors, data indicative of the query association with the target domain, comprises: determining, by one or more processors, a query distance based on comparing a vector embedding of the user query with a plurality of vector embeddings corresponding to the plurality of domain artifacts (paragraph 52: “Each token 56 in the token sequence is converted into an embedding vector 60 (also referred to simply as an embedding). An embedding 60 is a learned numerical representation (such as, for example, a vector) of a token that captures some semantic meaning of the text segment represented by the token 56. The embedding 60 represents the text segment corresponding to the token 56 in a way such that embeddings corresponding to semantically-related text are closer to each other in a vector space than embeddings corresponding to semantically-unrelated text. For example, assuming that the words “look”, “see”, and “cake” each correspond to, respectively, a “look” token, a “see” token, and a “cake” token when tokenized, the embedding 60 corresponding to the “look” token will be closer to another embedding corresponding to the “see” token in the vector space, as compared to the distance between the embedding”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Hemington with that of Lucas and Steedman for determining a query distance based on comparing a vector embedding of the user query with a plurality of vector embeddings corresponding to the plurality of domain artifacts.
The ordinary artisan would have been motivated to modify Lucas and Steedman in the manner set forth above for the purposes of determining semantically related and/or dissimilar queries [Hemington: Abstract; paragraph 52].
Regarding Claim 3, with Lucas and Steedman teaching those limitations of the claim as previously pointed out, Hemington further teaches:
The computer-implemented method of claim 2, further comprising: generating, by one or more processors, the vector embedding of the user query by embedding the user query into a vector embedding space using one or more machine-learned embedding models (paragraph 52: “The embedding 60 represents the text segment corresponding to the token 56 in a way such that embeddings corresponding to semantically-related text are closer to each other in a vector space”);
and generating, by one or more processors, the plurality of vector embeddings by embedding the plurality of domain artifacts into the vector embedding space using the one or more machine-learned embedding models (paragraph 52: “The vector space may be defined by the dimensions and values of the embedding vectors. Various techniques may be used to convert a token 56 to an embedding 60. For example, another trained ML model may be used to convert the token 56 into an embedding”).
Regarding Claim 4, with Lucas and Steedman teaching those limitations of the claim as previously pointed out, Hemington further teaches:
The computer-implemented method of claim 3, wherein the query distance is a semantic distance between the vector embedding of the user query and the plurality of vector embeddings associated with the target domain (paragraph 52: “The embedding 60 represents the text segment corresponding to the token 56 in a way such that embeddings corresponding to semantically-related text are closer to each other in a vector space than embeddings corresponding to semantically-unrelated text. For example, assuming that the words “look”, “see”, and “cake” each correspond to, respectively, a “look” token, a “see” token, and a “cake” token when tokenized, the embedding 60 corresponding to the “look” token will be closer to another embedding corresponding to the “see” token in the vector space, as compared to the distance between the embedding”).
Regarding Claim 8, with Lucas and Steedman teaching those limitations of the claim as previously pointed out, Hemington further teaches:
The computer-implemented method of claim 1, wherein generating, by one or more processors, data indicative of the query association with the target domain, comprises: determining, by one or more processors, a query distance based on comparing a vector embedding of the user query with a plurality of vector embeddings corresponding to the plurality of domain artifacts (paragraph 52: “Each token 56 in the token sequence is converted into an embedding vector 60 (also referred to simply as an embedding). An embedding 60 is a learned numerical representation (such as, for example, a vector) of a token that captures some semantic meaning of the text segment represented by the token 56. The embedding 60 represents the text segment corresponding to the token 56 in a way such that embeddings corresponding to semantically-related text are closer to each other in a vector space than embeddings corresponding to semantically-unrelated text. For example, assuming that the words “look”, “see”, and “cake” each correspond to, respectively, a “look” token, a “see” token, and a “cake” token when tokenized, the embedding 60 corresponding to the “look” token will be closer to another embedding corresponding to the “see” token in the vector space, as compared to the distance between the embedding”);
And Lucas further teaches:
and generating, by one or more processors, a query classification by processing the user query with a machine-learned classification model that has been trained using at least a portion of the plurality of domain artifacts (paragraph 17: “The evaluation metric M estimates the likelihood that the user prompt is of a type represented in the corpus of training data that was used in LLM training”. The metric representative of the query classification).
Regarding Claim 9, with Lucas and Steedman teaching those limitations of the claim as previously pointed out, Hemington further teaches:
The computer-implemented method of claim 1, wherein generating, by one or more processors, data indicative of the response association with the target domain, comprises: determining, by one or more processors, a response distance based on comparing a vector embedding of the generative content with a plurality of vector embeddings associated with the target domain (paragraph 52: “Each token 56 in the token sequence is converted into an embedding vector 60 (also referred to simply as an embedding). An embedding 60 is a learned numerical representation (such as, for example, a vector) of a token that captures some semantic meaning of the text segment represented by the token 56. The embedding 60 represents the text segment corresponding to the token 56 in a way such that embeddings corresponding to semantically-related text are closer to each other in a vector space than embeddings corresponding to semantically-unrelated text. For example, assuming that the words “look”, “see”, and “cake” each correspond to, respectively, a “look” token, a “see” token, and a “cake” token when tokenized, the embedding 60 corresponding to the “look” token will be closer to another embedding corresponding to the “see” token in the vector space, as compared to the distance between the embedding”).
Regarding Claim 10, with Lucas and Steedman teaching those limitations of the claim as previously pointed out, Hemington further teaches:
The computer-implemented method of claim 9, wherein the response distance is a semantic distance between the vector embedding of the generative content and the plurality of vector embeddings associated with the target domain (paragraph 52: “The embedding 60 represents the text segment corresponding to the token 56 in a way such that embeddings corresponding to semantically-related text are closer to each other in a vector space than embeddings corresponding to semantically-unrelated text. For example, assuming that the words “look”, “see”, and “cake” each correspond to, respectively, a “look” token, a “see” token, and a “cake” token when tokenized, the embedding 60 corresponding to the “look” token will be closer to another embedding corresponding to the “see” token in the vector space, as compared to the distance between the embedding”).
Regarding Claim 11, with Steedman and Hemington teaching those limitations of the claim as previously pointed out, Lucas further teaches:
The computer-implemented method of claim 9, wherein generating, by one or more processors, data indicative of the response association with the target domain, comprises: generating, by one or more processors, a response classification by processing the user query with a machine-learned classification model that has been trained using at least a portion of the plurality of domain artifacts (paragraph 18: “the prompt analyzer may determine that the LLM is likely to generate an acceptable, reliable, and accurate response and may pass the user prompt to LLM for regular processing”).
Regarding Claim 12, with Lucas and Steedman teaching those limitations of the claim as previously pointed out, Hemington further teaches:
The computer-implemented method of claim 1, wherein generating, by one or more processors, data indicative of the response association with the target domain, comprises: determining, by one or more processors, a response distance based on comparing a vector embedding of the generative content with a plurality of vector embeddings associated with the target domain (paragraph 52: “The embedding 60 represents the text segment corresponding to the token 56 in a way such that embeddings corresponding to semantically-related text are closer to each other in a vector space than embeddings corresponding to semantically-unrelated text. For example, assuming that the words “look”, “see”, and “cake” each correspond to, respectively, a “look” token, a “see” token, and a “cake” token when tokenized, the embedding 60 corresponding to the “look” token will be closer to another embedding corresponding to the “see” token in the vector space, as compared to the distance between the embedding”);
And Lucas further teaches:
and generating, by one or more processors, a response classification by processing the user query with a machine-learned classification model that has been trained using at least a portion of the plurality of domain artifacts ((paragraph 18: “the prompt analyzer may determine that the LLM is likely to generate an acceptable, reliable, and accurate response and may pass the user prompt to LLM for regular processing”).
Claims 18 and 20 are similar to Claim 1 and are rejected under the same rationale as stated above for that claim.
Examiner's Note:
The Examiner cites particular pages, sections, columns, line numbers, and/or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner and the additional related prior arts made of record that are considered pertinent to applicant's disclosure to further show the general state of the art. The Examiner's interpretations in parenthesis are provided with the cited references to assist the applicants to better understand how the examiner interprets the prior art to read on the claims. Such comments are entirely consistent with the intent and spirit of compact prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 for the relevant prior art where for example Padgett, US 2024/0160902 A1, teaches generating output content using a generative artificial intelligence (AI) model based on an input.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVE MISIR whose telephone number is (571)272-5243. The examiner can normally be reached M-R 8-5 pm, F some hours.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at 5712703169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVE MISIR/
Primary Examiner, Art Unit 2127
Prosecution Insights