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 .
This Office Action is in response to correspondence filed 19 November 2024 in reference to application 18/952,885. Claims 1-20 are pending and have been examined.
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.
Claims 1, 11, and 20 recite obtaining a trained artificial intelligence (AI) model; obtaining a dataset; obtaining a semantic value characterizing one or more words, wherein the one or more words are indicated by a user input; identifying a portion of the dataset based on the semantic value; generating an input to the trained AI model based on the user input and the portion of the dataset; and generating, using the trained AI model, a response to the user input based on the input to the trained AI model.
The limitation of obtaining a trained artificial intelligence (AI) model, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “computer-implemented” in claim 1, “one more processors” and “at least one computer readable storage medium” in claim 11, and “a non-transitory computer readable storage medium” in claim 20 nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the computer components, “obtaining” in the context of this claim encompasses the user manually opening the interface of an AI model.
The limitation of obtaining a dataset, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “computer components, “obtaining” in the context of this claim encompasses the user reading a dataset record.
The limitation of obtaining a semantic value characterizing one or more words, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “computer components, “obtaining” in the context of this claim encompasses the user listening to a user input and writing out value that represent the input’s meaning.
The limitation of identifying a portion of the dataset based on the semantic value, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “computer components, “identifying” in the context of this claim encompasses the user comparing the input to the dataset to find similarities and selecting data based on the similarities.
The limitation of generating an input to the trained AI model based on the user input and the portion of the dataset, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “generating” in the context of this claim encompasses the user writing a natural language prompt including the input and the selected data describing a task.
The limitation of generating, using the trained AI model, a response to the user input based on the input to the trained AI model, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer components, “generating” in the context of this claim encompasses the user writing a response based on the prompt. Examiner notes this limitation also requires a “trained AI model.” However this model is recited at such high level of granularity, that it can be considered a generic computer component as well, see Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. April 18, 2025) .
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims additionally recite “a trained artificial intelligence model,” along with “computer-implemented” in claim 1, “one more processors” and “at least one computer readable storage medium” in claim 11, and “a non-transitory computer readable storage medium” in claim 20. These components are recited at a high-level of generality such that they amount 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 directed to an abstract idea.
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 of the computer components amount 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 not patent eligible.
Claims 2 and 12 additionally recite the trained AI model includes operational values associated with a particular context. However any trained model will have operational values (i.e. trained parameters) from whatever context of data it was trained on. Thus this does not add limitations that would preclude the model from the considered a generic computer component, and thus the steps recited using the model could still be performed as a mental process. Similar to above, no additional limitations are recited that provide a practical application or amount to significantly more than the abstract idea. These claims are not patent eligible.
Claim 3 and 13 additionally recite wherein identifying the portion of the dataset includes determining that the portion of the dataset is associated with the particular context. However a person can observe a context of training data use to train a model and match the context within other data. Thus this step is still a mental process. Similar to above, no additional limitations are recited that provide a practical application or amount to significantly more than the abstract idea. These claims are not patent eligible.
Claims 4 and 14 additionally recite the trained AI model was trained using a corpus of data that is different from the dataset. However this does not provide specifics about the training data that would make the model specifically trained in a particular fashion. All that is specified is that the training data is different from the dataset. Thus this does not add limitations that would preclude the model from the considered a generic computer component, and thus the steps recited using the model could still be performed as a mental process. Similar to above, no additional limitations are recited that provide a practical application or amount to significantly more than the abstract idea. These claims are not patent eligible.
Claim 5 and 15 additionally recite determining that the portion of the dataset and the semantic value together satisfy a semantic similarity criterion. However a person can compare the dataset to the semantic meaning of the input and determine if they are similar. Thus this step is still a mental process. Similar to above, no additional limitations are recited that provide a practical application or amount to significantly more than the abstract idea. These claims are not patent eligible.
Claim 6 and 16 additionally recite based on respective distances between one or more vectors representing the one or more words and vector embeddings representing textual data in the dataset, determining that a distance between the one or more vectors and a vector embedding from the vector embeddings is below a threshold, the vector embedding being associated with portion of the dataset; and identifying the portion of the dataset based on the determining that the distance between the one or more vectors and the vector embedding associated with the portion of the dataset is below the threshold. However a could assign vectors to the input and the dataset based on meaning of words, calculate the distance between vectors, and compare the distance to a threshold. Thus this step is still a mental process. Similar to above, no additional limitations are recited that provide a practical application or amount to significantly more than the abstract idea. These claims are not patent eligible.
Claim 7 additionally recite the threshold is based on at least one of a user preference and an amount of data in the dataset. However a person can select a threshold based on their preference. Thus this step is still a mental process. Similar to above, no additional limitations are recited that provide a practical application or amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim 8 and 17 additionally recite embedding the portion of the dataset in the input to the trained AI model. However a person can assign vectors to data based on meaning and provide them as part of a prompt. Thus this step is still a mental process. Similar to above, no additional limitations are recited that provide a practical application or amount to significantly more than the abstract idea. These claims are not patent eligible.
Claim 9 and 18 additionally recite identifying a pattern in the portion of the dataset; and embedding the pattern in the input to the trained AI model. However a person can observe patterns in data, assign a vector to represent the pattern, and provide it as part of a prompt. Thus this step is still a mental process. Similar to above, no additional limitations are recited that provide a practical application or amount to significantly more than the abstract idea. These claims are not patent eligible.
Claim 10 and 19 additionally recite i applying a bias to a part of the portion of the dataset based on a date when the part of the portion of the dataset was collected; and generating the input to the trained AI model further based on the bias. However a person can decide to discount the importance of older data and select data based on the age of the data. Thus this step is still a mental process. Similar to above, no additional limitations are recited that provide a practical application or amount to significantly more than the abstract idea. These claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8, 11-17, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hooda et al. (US PAP 2026/0135758).
Consider claim 1, Hooda teaches a computer-implemented method (abstract, figures 1 and 2A and B) comprising:
obtaining a trained artificial intelligence (AI) model (0043, 0053, language model deployed);
obtaining a dataset (0035-36, 0054, collecting and retrieving network configuration data);
obtaining a semantic value characterizing one or more words, wherein the one or more words are indicated by a user input (0044, 0056, generating a semantic embedding from the user input prompt);
identifying a portion of the dataset based on the semantic value (0038, 0044, 0057, comparing embedding from user prompt to embeddings from configuration data and retrieving relevant data);
generating an input to the trained AI model based on the user input and the portion of the dataset (0045, 0058, relevant data and prompt provided to the language model); and
generating, using the trained AI model, a response to the user input based on the input to the trained AI model (0045, 0058-59, language model generates a response).
Consider claim 2, Hooda teaches the computer-implemented method of claim 1, wherein the trained AI model includes operational values associated with a particular context (0043, language model may be fine-tuned for networking concepts in general, and thus would include operational values for networking).
Consider claim 3, Hooda teaches the computer-implemented method of claim 2, wherein identifying the portion of the dataset includes determining that the portion of the dataset is associated with the particular context (0038, 0044, 0057, comparing embedding from user prompt to embeddings from configuration data and retrieving relevant data, and thus would be relevant to the particular networking concept that the prompt concerned).
Consider claim 4, Hooda teaches the computer-implemented method of claim 1, wherein the trained AI model was trained using a corpus of data that is different from the dataset (0043, language model pre-trained possibly fine tuned on network concepts in general. 0035, network data can be continually updated, and thus would be available for training).
Consider claim 5, Hooda teaches The computer-implemented method of claim 1, wherein identifying the portion of the dataset includes determining that the portion of the dataset and the semantic value together satisfy a semantic similarity criterion (0044, measuring cosine similarity between embedding of prompt and embedding of data).
Consider claim 6, Hooda teaches The computer-implemented method of claim 1, wherein identifying the portion of the dataset based on the semantic value comprises:
based on respective distances between one or more vectors representing the one or more words and vector embeddings representing textual data in the dataset, determining that a distance between the one or more vectors and a vector embedding from the vector embeddings is below a threshold, the vector embedding being associated with portion of the dataset (0044, measuring cosine similarity between embedding of prompt and embedding of data. Cosine similarity is the inverse of cosine distance, and thus the higher the similarity, the lower the distance. Claim 3 specifically mentions selecting data that is above cosine similarity threshold, and thus is mathematically equivalent to selecting data below a distance threshold as claimed ); and
identifying the portion of the dataset based on the determining that the distance between the one or more vectors and the vector embedding associated with the portion of the dataset is below the threshold (0044, measuring cosine similarity between embedding of prompt and embedding of data. Cosine similarity is the inverse of cosine distance, and thus the higher the similarity, the lower the distance. Claim 3 specifically mentions selecting data that is above cosine similarity threshold, and thus is mathematically equivalent to selecting data below a distance threshold as claimed).
Consider claim 8, Hooda teaches the computer-implemented method of claim 1, wherein generating the input to the trained AI model comprises embedding the portion of the dataset in the input to the trained AI model (0038, 0045, configuration data is embedded and provided to the language model as part of the input).
Consider claim 11, Hooda A system (abstract) comprising:
one or more processors (0087, CPUs); and
at least one computer-readable storage medium having stored therein instructions (0089, 0091-95, memory and computer readable media) which, when executed by the one or more processors, cause the one or more processors to:
obtain a trained artificial intelligence (AI) model (0043, 0053, language model deployed);
obtain a dataset (0035-36, 0054, collecting and retrieving network configuration data);
obtain a semantic value characterizing one or more words, wherein the one or more words are indicated by a user input (0044, 0056, generating a semantic embedding from the user input prompt);
identify a portion of the dataset based on the semantic value (0038, 0044, 0057, comparing embedding from user prompt to embeddings from configuration data and retrieving relevant data);
generate an input to the trained AI model based on the user input and the portion of the dataset (0045, 0058, relevant data and prompt provided to the language model); and
generate, using the trained AI model, a response to the user input based on the input to the trained AI model (0045, 0058-59, language model generates a response).
Claim 12 contains similar limitations as claim 2 and therefore is rejected for the same reasons.
Claim 13 contains similar limitations as claim 3 and therefore is rejected for the same reasons.
Claim 14 contains similar limitations as claim 4 and therefore is rejected for the same reasons.
Claim 15 contains similar limitations as claim 5 and therefore is rejected for the same reasons.
Claim 16 contains similar limitations as claim 6 and therefore is rejected for the same reasons.
Claim 17 contains similar limitations as claim 8 and therefore is rejected for the same reasons.
Consider claim 20, Hooda A non-transitory computer-readable medium having stored thereon instructions (0089, 0091-95, memory and computer readable media) which, when executed by one or more processors, cause the one or more processors to:
obtain a trained artificial intelligence (AI) model (0043, 0053, language model deployed);
obtain a dataset (0035-36, 0054, collecting and retrieving network configuration data);
obtain a semantic value characterizing one or more words, wherein the one or more words are indicated by a user input (0044, 0056, generating a semantic embedding from the user input prompt);
identify a portion of the dataset based on the semantic value (0038, 0044, 0057, comparing embedding from user prompt to embeddings from configuration data and retrieving relevant data);
generate an input to the trained AI model based on the user input and the portion of the dataset (0045, 0058, relevant data and prompt provided to the language model); and
generate, using the trained AI model, a response to the user input based on the input to the trained AI model (0045, 0058-59, language model generates a response).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hooda in view of Zhang et al. (US PAP 2025/0137991).
Consider claim 7, Hooda teaches the computer-implemented method of claim 6, but does not specifically teach wherein the threshold is based on at least one of a user preference and an amount of data in the dataset.
In the same field of data retrieval, Zhang teaches wherein the threshold is based on at least one of a user preference and an amount of data in the dataset (0043, threshold similarity may be selected by the user).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to allow the user to select the similarity threshold as taught by Zhang in the system of Hooda in order to users to better tailor the system to meet their business needs (Zhang 0043).
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hooda in view of Hitesh et al. (US PAP 2025/0245445).
Consider claim 9, Hooda teaches The computer-implemented method of claim 1, but does not specifically teach wherein generating the input to the trained AI model comprises:
identifying a pattern in the portion of the dataset; and embedding the pattern in the input to the trained AI model.
In the same field of generative responses, Hitesh teaches identifying a pattern in the portion of the dataset; and embedding the pattern in the input to the trained AI model (0036, embeddings are created based on semantic patterns).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to embed based on semantic patterns as taught by Hitesh in the system of Hooda in order to better capture semantic relationships within the data.
Claim 18 contains similar limitations as claim 9 and therefore is rejected for the same reasons.
Claim(s) 10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hooda in view of Zhang et al. (US PAP 2026/0073253), herein after Zhang2.
Consider claim 10, Hooda teaches the computer-implemented method of claim 1, but does not specifically teach wherein generating the input to the trained AI model comprises:
applying a bias to a part of the portion of the dataset based on a date when the part of the portion of the dataset was collected; and
generating the input to the trained AI model further based on the bias.
In the same field of retrieval augmented generation, Zhang2 teaches
applying a bias to a part of the portion of the dataset based on a date when the part of the portion of the dataset was collected (0187, prioritizing RAG retrieval based on a recency of data); and
generating the input to the trained AI model further based on the bias (0187, data is used to generate responses, and thus processed by the LLM).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use recency to bias retrieved data as taught by Zhang2 in the system of Hood in order to allow for the most relevant and up to date data to be retrieved (Zhang2 0187).
Claim 19 contains similar limitations as claim 10 and therefore is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lewis et al. (Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks) teaches the basics for this well-known technology. Gao et al (Retrieval-Augmented Generation for Large Language Models: A Survey) gives overviews of several similar systems.
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DOUGLAS GODBOLD
Examiner
Art Unit 2655
/DOUGLAS GODBOLD/ Primary Examiner, Art Unit 2655
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