DETAILED ACTION
1. This action is responsive to Application no.19/051,699 filed 2/12/2025. All claims have been examined and are currently pending.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
4. 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 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.
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (12,437,155) in view of Roth er al (2019/0361146).
Regarding claim 1 Zhang teaches A method for generating {equipment} content responses using a generative artificial intelligence (AI) model (col 1 l. 53-60: identifying, by the one or more processors, a relevant table chunk of the one or more table chunks or a relevant text chunk of the one or more text chunks based on a search criterion related to a prompt for a large language model. The method also includes storing a response from the large language model to the prompt and the relevant table chunk or the relevant text chunk), comprising:
identifying a repository including {equipment} documents in an unstructured format {for oil and gas equipment}
(col 2 l. 28-34: Different types of businesses often carefully curate and extract a large volume of documents. For example, a large set of insurance documents or accounting documents (in the form of images and/or PDFs) are typically sent to an insurance broker or a tax preparer, who then has the task of identifying and extracting relevant information from the accounting documents.
Col 4 66 – col 5 l 13 data sources; documents);
generating text-based {equipment} documents based on parsing the equipment documents into a computer-readable text format
(col 5 l. 27-28: The OCR system 106 may be configured to convert the contents of the document to plain text.);
generating a vector index of vector embeddings by using an embedding model to create the vector embeddings from the text-based {equipment} documents
(col 4 l 48: generate a searchable index of documents; col 7 l 24-25: text embedder may be configured to generate a vector embedding for a chunk of text; l 40: after the vectors are created, the text embedder may communicate the vector embeddings of the text chunks to the data extraction manager system for storage in an object (e.g. vector store));
in response to receiving an {equipment} content request, providing the vector index and an {equipment} content prompt generated based on the {equipment} content request to a generative AI model, the {equipment} content prompt including instructions for the generative AI model to generate an {equipment} content response using the vector embeddings from the vector index
(col 7 l 44-63: The LLM 108 may be any type of artificial intelligence (AI) configuration. For example, the LLM 108 may include generative pre-trained transformers (GPT), bidirectional encoder representations from transformers (BERT), text-to-text transfer transformers (T5), recurrent neural networks (RNN), or any other AI architecture suitable for a large language model. The LLM 108 may be configured to output a text response from a textual prompt. For example, the LLM 108 may convert text of a prompt into tokens representing a unit of information (e.g., a character, word, prefix, punctuation, etc.) and use the input sequence tokens to predict each output word (or token) consecutively. The prompt communicated to the LLM 108 may include chunks from the documents gathered from the one or more data sources 104 so that the LLM 108 is able to use that information to generate its response. For example, the LLM 108 may be provided a prompt including a request to determine the range of the market capitalization of a company over the last 6 months and one or more table chunks or text chunks that include information that may be relevant for such a question.); and
based on receiving the {equipment} content response from the generative AI model, providing the {equipment} content response to a client device
(col 1 53-60: The method also includes identifying, by the one or more processors, a relevant table chunk of the one or more table chunks or a relevant text chunk of the one or more text chunks based on a search criterion related to a prompt for a large language model. The method also includes storing a response from the large language model to the prompt and the relevant table chunk or the relevant text chunk.;
Col 7 l. 1-2 index to identify and retrieve potentially related or relevant chunks (e.g. portions of the documents)
Col 7 l 54-63 The prompt communicated to the LLM 108 may include chunks from the documents gathered from the one or more data sources 104 so that the LLM 108 is able to use that information to generate its response. For example, the LLM 108 may be provided a prompt including a request to determine the range of the market capitalization of a company over the last 6 months and one or more table chunks or text chunks that include information that may be relevant for such a question;
Col 8 l. 16-18: UI clients; view results);
but does not specifically teach
identifying a repository including equipment documents in an unstructured format for oil and gas equipment
Zhang Teaches:
Col 2 l. 28-35: Different types of businesses often carefully curate and extract a large volume of documents. For example, a large set of insurance documents or accounting documents (in the form of images and/or PDFs) are typically sent to an insurance broker or a tax preparer, who then has the task of identifying and extracting relevant information from the accounting documents. To provide more efficiency, businesses have tried to automate this workflow;
Col 8 34-48 In one use case where the data extraction and population system 100 is particularly useful is to extract data for the underwriting process of insurance policies. For example, directors and officers liability insurance and/or environmental insurance require extracting large amounts of information for which there is no central repository. The information may be collected about the company, the directors and officers, and/or any business locations. Manually searching for this information is error prone and requires a large time investment for the underwriters. Moreover, much of the data that is to be extracted for insurance underwriting may be found in financial tables of image-based documents (e.g., PDFs) making the systems and methods of separating tabular information and text information described herein particularly useful in such scenarios.
Roth teaches: 0002: systems and methods for predicting the production of a well; and
[0121] Databases from which the device 100 may receive data (whether automatically based on a predetermined schedule and/or preprogrammed query, or in response to specific queries) include databases containing well/well header data; databases containing hydraulic fracture drilling fluid chemical ingredient data; databases containing production test data (e.g. initial production data); databases containing regular production data; databases containing directional survey data; databases containing geologic formations data (e.g., geologic structure maps); databases containing elevation or other geographic data; databases containing real estate data (e.g., property boundary maps); databases containing economic data (including, for example, data about the market price of oil, natural gas, or other natural resources, whether presently or over time; data about the cost of real estate presently or over time; data about the cost of mineral rights presently or over time; data about the cost of drilling equipment presently or over time; and data about the cost of hydraulic fracture drilling fluid presently or over time); government-owned databases; and privately owned databases.
It would have been obvious to one of ordinary skill in the art before the effective filing date to allow for the reference and documents disclosed in the reference to be associated with the oil and gas industry for improved information retrieval of the selected documents, while presenting a reasonable expectation of success in still allowing for improved user accessibility as discussed in the reference.
Zhang already teaches the benefits (of digitizing and indexing industry documents) as applied to the insurance industry, and it would be obvious to merely have the documents from another industry, oil and gas, providing the same benefits, of organizing and indexing a large amount of documents with various components, and allowing for easier search, access, and analysis of the contents of such.
Regarding claim 2 Zhang teaches The method of claim 1, further comprising:
generating the text-based equipment documents based on parsing unstructured text within a first equipment document into text data in a first computer-readable text format associated with parsed text (col 5 l. 27-28: The OCR system 106 may be configured to convert the contents of the document to plain text.); and
creating a first vector embedding from the text data using the embedding model (col 7 l 24-25: text embedder may be configured to generate a vector embedding for a chunk of text).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document)
Regarding claim 3 Zhang teaches The method of claim 2, further comprising:
generating metadata for the text data based on the unstructured text of the first equipment document (col 15 16-18 metadata); and
associating the metadata for the text data with the first vector embedding in the vector index, the metadata including context information of the unstructured text in the first equipment document
(col 15 16-18: the chunk tracer 250 is configured to add metadata to the text chunks and/or the table chunks. The metadata may be added to improve the document retrieval and/or provide traceability of the data that the LLM 108 extracts.).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document)
Regarding claim 4 Zhang teaches The method of claim 3, wherein the context information of the unstructured text identifies a document location of the first equipment document within the repository (col 15 16-18: the chunk tracer 250 is configured to add metadata to the text chunks and/or the table chunks. The metadata may be added to improve the document retrieval and/or provide traceability of the data that the LLM 108 extracts.).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document)
Regarding claim 5 Zhang teaches The method of claim 1, wherein a second equipment document of the equipment documents includes an unstructured table, the method further comprising:
generating the text-based equipment documents based on parsing the unstructured table in the second equipment document into tabular data in a second computer-readable text format associated with parsed tables (col 5 l. 8-9 documents in the database may contain…tables; col 5 l. 27-28: The OCR system 106 may be configured to convert the contents of the document to plain text.; col 5 l 49-53: The documents processed by the OCR system 106 may include non-text-based information (e.g., charts, graphs, trend lines, flow charts, or other graphical elements) and/or special text structures (e.g., tables, rows, columns, etc.). This information may be recognized by the OCR system); and
creating a second vector embedding from the tabular data using the embedding model (col 7 l 24-25: text embedder may be configured to generate a vector embedding for a chunk of text).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document)
Regarding claim 6 Zhang teaches The method of claim 5, further comprising:
generating metadata for the tabular data from the second equipment document (col 15 l. 16-18 metadata); and
associating the metadata for the tabular data with the second vector embedding in the vector index, the metadata including context information for the unstructured table in the second equipment document (col 15 16-18: the chunk tracer 250 is configured to add metadata to the text chunks and/or the table chunks. The metadata may be added to improve the document retrieval and/or provide traceability of the data that the LLM 108 extracts.).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document).
Regarding claim 7 Zhang teaches The method of claim 6, wherein the context information provides a copy of the tabular data in the second computer-readable text format (col 15 16-18: the chunk tracer 250 is configured to add metadata to the text chunks and/or the table chunks. The metadata may be added to improve the document retrieval and/or provide traceability of the data that the LLM 108 extracts.).
Regarding claim 8 Zhang teaches The method of claim 7, wherein:
parsing the unstructured table includes generating a textual description of the unstructured table (col 5 l. 8-9 documents in the database may contain…tables; col 5 l. 27-28: The OCR system 106 may be configured to convert the contents of the document to plain text.; col 5 l 49-53: The documents processed by the OCR system 106 may include non-text-based information (e.g., charts, graphs, trend lines, flow charts, or other graphical elements) and/or special text structures (e.g., tables, rows, columns, etc.). This information may be recognized by the OCR system); and
the metadata includes a link to access the unstructured table (col 15 16-18: the chunk tracer 250 is configured to add metadata to the text chunks and/or the table chunks. The metadata may be added to improve the document retrieval and/or provide traceability of the data that the LLM 108 extracts.).
Regarding claim 9 Zhang teaches The method of claim 1, wherein a third equipment document includes an image, the method further comprising:
generating the text-based equipment documents based on generating an image description of the image in the third equipment document in a third computer-readable text format associated with image descriptions (col 5 l 8-10: the documents in the database may contain…graphics, images; col 5 l. 34-36: convert an image based document…plain text); and
creating a third vector embedding from the image description using the embedding model (col 7 l 24-25: text embedder may be configured to generate a vector embedding for a chunk of text).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document).
Regarding claim 10 Zhang teaches The method of claim 9, further comprising:
generating metadata for the image description from the third equipment document (col 15 l 16-18: metadata); and
associating the metadata for the image description with the third vector embedding in the vector index, the metadata including context information of the image in the third equipment document (col 15 16-18: the chunk tracer 250 is configured to add metadata to the text chunks and/or the table chunks. The metadata may be added to improve the document retrieval and/or provide traceability of the data that the LLM 108 extracts.).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document).
Regarding claim 11 Zhang teaches The method of claim 10, wherein the context information identifies text in the third equipment document surrounding the image (col 5 l 8-10: the documents in the database may contain…graphics, images; col 5 l. 34-36: convert an image based document…plain text; col 15 16-18: the chunk tracer 250 is configured to add metadata to the text chunks and/or the table chunks. The metadata may be added to improve the document retrieval and/or provide traceability of the data that the LLM 108 extracts.).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document).
Regarding claim 12 Zhang teaches The method of claim 1, further comprising segmenting text of the text-based equipment documents into blocks, wherein the vector embeddings are created based on the blocks (col 7 l. 24-27: The text embedder 110 may be configured to generate a vector embedding for a chunk of text. The vector embedding may refer to a vector representation of the semantic content of the chunk of text.).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document).
Regarding claim 13 Zhang teaches The method of claim 12, wherein:
segmenting the text into the blocks is based on applying a token allocation to the text-based equipment documents (col 14 l 51-56: The text chunker 248 may be configured to generate chunks of text from the plain text remaining after tables have been removed from the document. A number of text chunks may be generated from a single document. The text chunks may be of a fixed length (e.g., 500 words, 500 characters, 1000 tokens, etc.). ); and
the blocks include a token overlap between adjacent text blocks (col 14 l. 56: the text chunks may be overlapping).
Further Rejected for similar rationale and reasoning as claim 1 (equipment document).
Regarding claim 14 Zhang does not specifically teach where Roth teaches The method of claim 1, wherein the generative AI model generates the equipment content response by synthesizing content from the vector embeddings within the vector index to generate new equipment content supported by content in the equipment documents, wherein the new equipment content is not included in the equipment documents
(Roth [0008] The present disclosure describes a system and method for predicting well production at various stages in the life cycle of the well. Each prediction is accompanied by a determined certainty level to show the probability of the predicted result actually occurring. Additionally, users of the system and method disclosed herein can apply economic parameters to the prediction to model the economic return of the well over time. In addition to providing insight regarding existing wells, the present disclosure may be used to design new wells and to predict the production thereof before breaking ground on the well, thus allowing users of embodiments of the present disclosure to prioritize the expenditure of resources on wells that are most likely to be the most productive.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate Roth and its machine learning and prediction for an improved system allowing for prediction and more computer intelligence based on the provided information/documents. Zhang already teaches using an LLM to provide analysis and responses to prompts based on industry related documents in a repository, and one could look to Roth to allow further processing using the documents and machine learning of Roth to provide additional predictions and computer intelligence based on the information of the repository.
Regarding claim 15 Zhang teaches The method of claim 14, wherein the equipment content response includes metadata identifying a set of equipment documents from the repository that supports the new equipment content within the equipment content response (col 15 16-18: the chunk tracer 250 is configured to add metadata to the text chunks and/or the table chunks. The metadata may be added to improve the document retrieval and/or provide traceability of the data that the LLM 108 extracts.
Col 7 l 54-63 The prompt communicated to the LLM 108 may include chunks from the documents gathered from the one or more data sources 104 so that the LLM 108 is able to use that information to generate its response. For example, the LLM 108 may be provided a prompt including a request to determine the range of the market capitalization of a company over the last 6 months and one or more table chunks or text chunks that include information that may be relevant for such a question).
Further Rejected for similar rationale and reasoning as claim 1 and 14.
Regarding claim 16 Zhang teaches The method of claim 1, further comprising:
providing the vector index and the equipment content prompt to a large language model (LLM) (col 7 l 44-63: The LLM 108 may be any type of artificial intelligence (AI) configuration. For example, the LLM 108 may include generative pre-trained transformers (GPT), bidirectional encoder representations from transformers (BERT), text-to-text transfer transformers (T5), recurrent neural networks (RNN), or any other AI architecture suitable for a large language model. The LLM 108 may be configured to output a text response from a textual prompt. For example, the LLM 108 may convert text of a prompt into tokens representing a unit of information (e.g., a character, word, prefix, punctuation, etc.) and use the input sequence tokens to predict each output word (or token) consecutively. The prompt communicated to the LLM 108 may include chunks from the documents gathered from the one or more data sources 104 so that the LLM 108 is able to use that information to generate its response. For example, the LLM 108 may be provided a prompt including a request to determine the range of the market capitalization of a company over the last 6 months and one or more table chunks or text chunks that include information that may be relevant for such a question.); and
receiving, from the LLM, the equipment content response {including a natural language response of a quote for providing an oil and gas service or product responsive to the equipment content request} (col 7 l 44-63 The prompt communicated to the LLM 108 may include chunks from the documents gathered from the one or more data sources 104 so that the LLM 108 is able to use that information to generate its response. For example, the LLM 108 may be provided a prompt including a request to determine the range of the market capitalization of a company over the last 6 months and one or more table chunks or text chunks that include information that may be relevant for such a question)
but does not specifically teach where Roth teaches
receiving, from the LLM, the equipment content response including a natural language response of a quote for providing an oil and gas service or product responsive to the equipment content request
(Roth [0008] The present disclosure describes a system and method for predicting well production at various stages in the life cycle of the well. Each prediction is accompanied by a determined certainty level to show the probability of the predicted result actually occurring. Additionally, users of the system and method disclosed herein can apply economic parameters to the prediction to model the economic return of the well over time. In addition to providing insight regarding existing wells, the present disclosure may be used to design new wells and to predict the production thereof before breaking ground on the well, thus allowing users of embodiments of the present disclosure to prioritize the expenditure of resources on wells that are most likely to be the most productive.; 121).
Rejected for similar rationale and reasoning as claim 14
Regarding claim 17 Zhang teaches The method of claim 1, wherein generating the equipment content prompt includes:
identifying one or more relevant vector embeddings in the vector index that are relevant to a context of the equipment content request (col 7 l 44-63); and
providing the equipment content prompt and the one or more relevant vector embeddings to the generative AI model to generate the equipment content response based on the equipment content prompt and the one or more relevant vector embeddings.
(col 7 l 44-63: The LLM 108 may be any type of artificial intelligence (AI) configuration. For example, the LLM 108 may include generative pre-trained transformers (GPT), bidirectional encoder representations from transformers (BERT), text-to-text transfer transformers (T5), recurrent neural networks (RNN), or any other AI architecture suitable for a large language model. The LLM 108 may be configured to output a text response from a textual prompt. For example, the LLM 108 may convert text of a prompt into tokens representing a unit of information (e.g., a character, word, prefix, punctuation, etc.) and use the input sequence tokens to predict each output word (or token) consecutively. The prompt communicated to the LLM 108 may include chunks from the documents gathered from the one or more data sources 104 so that the LLM 108 is able to use that information to generate its response. For example, the LLM 108 may be provided a prompt including a request to determine the range of the market capitalization of a company over the last 6 months and one or more table chunks or text chunks that include information that may be relevant for such a question.)
Further Rejected for similar rationale and reasoning as claim 1
Regarding claim 18 Zhang does not specifically teach where Roth teaches The method of claim 1, wherein:
one or more of the equipment documents include one or more PDF equipment documents that include vendor quotes for various oil and gas equipment defined by equipment type and equipment specifications (121); and
the generative AI model generates the equipment content response based on the vendor quotes as represented by one or more vector embeddings for the one or more PDF equipment documents in the vector index.
(Roth [0008] The present disclosure describes a system and method for predicting well production at various stages in the life cycle of the well. Each prediction is accompanied by a determined certainty level to show the probability of the predicted result actually occurring. Additionally, users of the system and method disclosed herein can apply economic parameters to the prediction to model the economic return of the well over time. In addition to providing insight regarding existing wells, the present disclosure may be used to design new wells and to predict the production thereof before breaking ground on the well, thus allowing users of embodiments of the present disclosure to prioritize the expenditure of resources on wells that are most likely to be the most productive.).
Rejected for similar rationale and reasoning as claim 1 and 14 (where Zhang teaches PDF documents (col 5 l 11) and vector embeddings)
Regarding claim 19 Zhang teaches A system (fig 2), comprising:
at least one processor (fig 2);
memory in electronic communication with the at least one processor (fig 2); and
instructions stored in the memory, the instructions being executable by the at least one processor (fig 2) to:
identify a repository including equipment documents in an unstructured format for oil and gas equipment, the equipment documents including unstructured text information and unstructured table information (col 2 l. 28-34: Different types of businesses often carefully curate and extract a large volume of documents. For example, a large set of insurance documents or accounting documents (in the form of images and/or PDFs) are typically sent to an insurance broker or a tax preparer, who then has the task of identifying and extracting relevant information from the accounting documents.
Col 4 66 – col 5 l 13 data sources; documents; col 5 l. 8-9 documents in the database may contain…tables;);
generate text-based equipment documents based on parsing the unstructured text information into text data in a first computer-readable text format (col 5 l. 27-28: The OCR system 106 may be configured to convert the contents of the document to plain text.);
parse the unstructured table information into tabular data in a second computer-readable text format (col 5 l. 8-9 documents in the database may contain…tables; col 5 l. 27-28: The OCR system 106 may be configured to convert the contents of the document to plain text.; col 5 l 49-53: The documents processed by the OCR system 106 may include non-text-based information (e.g., charts, graphs, trend lines, flow charts, or other graphical elements) and/or special text structures (e.g., tables, rows, columns, etc.). This information may be recognized by the OCR system);
segment text of the text data and of the tabular data into blocks based on applying a token allocation to the text of the blocks that implements a token overlap between adjacent blocks (col 14 l 51-56: The text chunker 248 may be configured to generate chunks of text from the plain text remaining after tables have been removed from the document. A number of text chunks may be generated from a single document. The text chunks may be of a fixed length (e.g., 500 words, 500 characters, 1000 tokens, etc.). the text chunks may be overlapping);
generate a vector index of vector embeddings by using an embedding model to create the vector embeddings from the blocks (col 4 l 48: generate a searchable index of documents; col 7 l 24-25: text embedder may be configured to generate a vector embedding for a chunk of text; l 40: after the vectors are created, the text embedder may communicate the vector embeddings of the text chunks to the data extraction manager system for storage in an object (e.g. vector store));
in response to receiving an equipment content request, provide the vector index and an equipment content prompt generated based on the equipment content request to a generative AI model, the equipment content prompt including instructions for the generative AI model to generate an equipment content response using the vector embeddings from the vector index (col 7 l 44-63: The LLM 108 may be any type of artificial intelligence (AI) configuration. For example, the LLM 108 may include generative pre-trained transformers (GPT), bidirectional encoder representations from transformers (BERT), text-to-text transfer transformers (T5), recurrent neural networks (RNN), or any other AI architecture suitable for a large language model. The LLM 108 may be configured to output a text response from a textual prompt. For example, the LLM 108 may convert text of a prompt into tokens representing a unit of information (e.g., a character, word, prefix, punctuation, etc.) and use the input sequence tokens to predict each output word (or token) consecutively. The prompt communicated to the LLM 108 may include chunks from the documents gathered from the one or more data sources 104 so that the LLM 108 is able to use that information to generate its response. For example, the LLM 108 may be provided a prompt including a request to determine the range of the market capitalization of a company over the last 6 months and one or more table chunks or text chunks that include information that may be relevant for such a question.); and
based on receiving the equipment content response from the generative AI model, provide the equipment content response to a client device (col 1 53-60: The method also includes identifying, by the one or more processors, a relevant table chunk of the one or more table chunks or a relevant text chunk of the one or more text chunks based on a search criterion related to a prompt for a large language model. The method also includes storing a response from the large language model to the prompt and the relevant table chunk or the relevant text chunk.;
Col 7 l. 1-2 index to identify and retrieve potentially related or relevant chunks (e.g. portions of the documents)
Col 7 l 54-63 The prompt communicated to the LLM 108 may include chunks from the documents gathered from the one or more data sources 104 so that the LLM 108 is able to use that information to generate its response. For example, the LLM 108 may be provided a prompt including a request to determine the range of the market capitalization of a company over the last 6 months and one or more table chunks or text chunks that include information that may be relevant for such a question;
Col 8 l. 16-18: UI clients; view results).
Rejected for similar rationale and reasoning as claim 1 where Roth teaches documents for oil and gas equipment
Regarding claim 20 Zhang and Roth teach A non-transitory computer-readable storage medium including instruction that, when executed by a processor, cause the processor to:
identify a repository including equipment documents in an unstructured format for oil and gas equipment;
generate text-based equipment documents based on parsing the equipment documents into a computer-readable text format;
generate a vector index of vector embeddings by using an embedding model to create vector embeddings from the text-based equipment documents;
in response to receiving an equipment content request, provide the vector index and an equipment content prompt generated based on the equipment content request to a generative AI model, the equipment content prompt including instructions for the generative AI model to generate an equipment content response using the vector embeddings from the vector index; and
based on receiving the equipment content response from the generative AI model, provide the equipment content response to a client device.
Claim recites limitations similar to claim 1 and is rejected for similar rationale and reasoning
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: See PTO-892.
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/SHAUN ROBERTS/Primary Examiner, Art Unit 2655
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