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 .
Claims 1-20 are active in this application.
Response to Arguments
Applicant arguments with respect to the 35 U.S.C 102 rejections and the amendments have been fully considered but are addressed and rejected below in view of Tizhoosh.
Examiner's Note
The Examiner respectfully requests of the Applicants in preparing responses, to fully consider the entirety of the references as potentially teaching all or part of the claimed invention.
It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. "The use of patents as references is not Limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the Literature of the art, relevant for all they contain." In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including non-preferred embodiments (see MPEP 2123).
The Examiner has cited particular locations in the reference(s) as applied to the claims below for the convenience of the Applicants. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claims, typically other passages and figures will apply as well.
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 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 of this title, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree (US 2025/0156898) and in view of Tizhoosh (US 2024/0194328).
Regarding claims 1, 10, and 18, Crabtree discloses a system, comprising: one or more processors (Figure 56) and a method to:
obtain a configuration dataset and an evaluation dataset, the configuration dataset identifying a pipeline and the evaluation dataset comprising a set of content and at least one query ([0133]-[0134], the pipeline orchestration subsystem periodically generates and persists auditable “temporal snapshots” of the entire DAG, capturing both the execution state and intermediate results. This is achieved through time-indexed graph storage that supports querying historical pipeline configurations, their respective output qualities, and resource utilization patterns. Such a temporal record also informs optimization strategies for future pipeline runs, allowing data scientists and DevOps engineers to analyze historical trends in model performance, [0179]-[0181], data pipeline);
generate, using a first artificial intelligence (AI) model of the pipeline, a plurality of embeddings of the set of content ([0179]-[0182], “the context data 601 is broken into chunks, passed through and embedding model 615, then stored in a specialized database called a vector database 620. Embedding models are a class of models used in many tasks such as natural language processing (NLP) to convert words, phrases, or documents into numerical representations (embeddings) that capture similarity which often correlates semantic meaning. Exemplary embedding models can include, but are not limited to, text-embedding-ada-002 model (i.e., OpenAI API), bidirectional encoder representations form transformers, Word2Vec, FastText, transformer-based models, and/or the like. The vector database 615 is responsible for efficiently storing, comparing, and retrieving a large plurality of embeddings (i.e., vectors). Vector database 615 may be any suitable vector database system known to those with skill in the art including, but not limited to, both closed and open source systems like Pinecone, Weaviate, Vespa, and Qdrant. According to the embodiment, embedding model 615 may also receive a user query from experience curation 640 and vectorize it where it may be stored in vector database 620. This provides another useful datapoint to provide deeper context when comparing received queries against stored query embeddings”);
retrieve, from a vector database, at least one candidate content from the plurality of embeddings based at least on a similarity metric between the at least one candidate content and the at least one query ([0179]-[0182], “the context data 601 is broken into chunks, passed through and embedding model 615, then stored in a specialized database called a vector database 620. Embedding models are a class of models used in many tasks such as natural language processing (NLP) to convert words, phrases, or documents into numerical representations (embeddings) that capture similarity which often correlates semantic meaning. Exemplary embedding models can include, but are not limited to, text-embedding-ada-002 model (i.e., OpenAI API), bidirectional encoder representations form transformers, Word2Vec, FastText, transformer-based models, and/or the like. The vector database 615 is responsible for efficiently storing, comparing, and retrieving a large plurality of embeddings (i.e., vectors). Vector database 615 may be any suitable vector database system known to those with skill in the art including, but not limited to, both closed and open source systems like Pinecone, Weaviate, Vespa, and Qdrant. According to the embodiment, embedding model 615 may also receive a user query from experience curation 640 and vectorize it where it may be stored in vector database 620. This provides another useful datapoint to provide deeper context when comparing received queries against stored query embeddings” and [0251]-[0253], similarity search such as cosine, dot product, Euclidean);
determine a performance metric based at least on the retrieved at least one candidate content([0117], [0138], performance metrics); and
provide the performance metric ([0117], [0138], performance metrics).
Crabtree is silent as to determine a performance metric based at least on a retrieval effectiveness corresponding to the retrieved at least one candidate content. On the other hand, Tizhoosh discloses determine a performance metric based at least on a retrieval effectiveness corresponding to the retrieved at least one candidate content ([0105]-[0108], [0105] “One of the key metrics, recall@K (R@k),.... The successful retrieval was defined by the premise that the correct item was found within the top K retrieved items”, [0106], “the recall@K evaluation was applied to ascertain if the accurate primary diagnosis was among the top “K” retrieved items. This layered evaluation technique, termed Recall@K@V, provided a multidimensional and more nuanced analysis of retrieval success and presented a more comprehensive picture of retrieval performance”, Tizhoosh). It would have been obvious to one of ordinary skill in the art at the time the invention was made to determine a performance metric based at least on a retrieval effectiveness corresponding to the retrieved at least one candidate content as suggested by Tizhoosh. The motivation would have been to ensure that the retrieved data is unique and accurate.
Regarding claims 2, 11 and 19, Crabtree/izhoosh discloses wherein the configuration dataset identifies at least one configuration template to implement the pipeline, wherein the first AI model is identified from the at least one configuration template (Crabtree: [0086], [0130], pipeline configurations and [0198], configuration of neuro-symbolic Gen AI models).
Regarding claims 3, 12 and 20, Crabtree/izhoosh discloses wherein the one or more processors are to: store the plurality of embeddings in the vector database, wherein generating the plurality of embeddings comprises generating a vector index for the set of content (Crabtree: [0251]-[0253], index embeddings for efficient retrieval and similarity search).
Regarding claims 4 and 13, Crabtree/izhoosh discloses wherein the one or more processors are to: generate, using the first AI model, a plurality of query embeddings representing the at least one query, wherein the at least one candidate content is retrieved based at least on the similarity metric determined between the plurality of query embeddings and the plurality of embeddings stored in the vector database (Crabtree: [0180]-[0182], [0251]-[0253] and [0276], similarity scoring).
Regarding claims 5 and 14, Crabtree/izhoosh discloses wherein the at least one candidate content comprises a plurality of candidate content, and wherein the one or more processors are to: update, using a second AI model, an order of the plurality of candidate content based at least on ranking the plurality of candidate content using at least one of (i) a vector similarity metric, (ii) query relevance metric, (iii) retrieval confidence metric, or (iv) aggregation metric. Please see Crabtree: ([0129], [0137]-[0138], relevance scoring, [0131], top-k aggregated results, [0135], confidence score, [0180]-[0182], [0251]-[0253] and [0276], similarity scoring).
Regarding claims 6 and 15, Crabtree/izhoosh discloses wherein the one or more processors are to: apply the at least one candidate content and the at least one query as input to a third AI model of the pipeline to cause the third AI model to generate an output, wherein generating the performance metric is further based on the output and corresponding ground truth data (Crabtree: [0117], [0130], [0137]-[0138]).
Regarding claims 7 and 16, Crabtree/izhoosh discloses wherein the one or more processors are to: generate, based on the configuration dataset, a plurality of pipeline configurations, at least one pipeline configuration of the plurality of pipeline configurations comprising at least one of the first AI model, the second AI model, or the third AI model (Crabtree: [0086], [0129]-[0130], [0133], [0137]-[0138]); perform at least one pipeline modeling job corresponding to at least one of the plurality of pipeline configurations (Crabtree: [0133], [0137]-[0138]); determine, for the at least one pipeline modeling job, the performance metric corresponding to the retrieved at least one candidate content or the output generated by the third AI model (Crabtree: [0137]-[0138]); and update at least one of the plurality of pipeline configurations based at least on the performance metric across the plurality of pipeline configurations Crabtree: ([0137]-[0138]).
Regarding claims 8 and 17, Crabtree/izhoosh discloses wherein the one or more processors are to: receive, from a user device, a status request corresponding to a pipeline modeling job (Crabtree: [0207]-[0208]); determine a status of the pipeline modeling job (Crabtree: [0207]-[0208]); and provide, to the user device, the status (Crabtree: [0207]-[0208]).
Regarding claim 9, Crabtree/izhoosh discloses wherein the one or more processors are to execute operations comprising: a system for performing one or more retrieval-augmented generation (RAG) pipelines (Crabtree: [0134], [0153], [0270], RAG); a system for tuning one or more processing architectures; a system for updating one or more processing configurations; a system for implementing distributed or concurrent processing operations (Crabtree: [0118]); a system implementing one or more multi-model language models (MMLMs); a system implementing one or more large language models (LLMs) (Crabtree: [0280], LLM); a system implementing one or more small language models (SLMs); a system implementing one or more vision language models (VLMs); a system for generating synthetic data (Crabtree: [0268], [0278]); a system for generating synthetic data using AI (Crabtree: [0268], [0278]); a co0ntrol system for an autonomous or semi-autonomous machine (Crabtree: [0092], automated bot); a perception system for an autonomous or semi-autonomous machine; a system for performing digital twin operations; a system for performing light transport simulation (Crabtree: [0414]); a system for performing remote operations; a system implemented using an edge device (Crabtree: [0183], [0185], edge devices); a system implemented using a robot (Crabtree: [0092], bot); a system for performing conversational AI operations (Crabtree: [0153], [0287], conversational AI or chatbots); a system incorporating one or more virtual machines (VMs) (Crabtree: [0252] and [0271]); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (Crabtree: [0165]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sampaio (US 20230073843) discloses data compatibility for text-enhanced visual retrieval.
Shalmashi (US 20240241902) discloses NLP-based recommender system for efficient analysis of trouble tickets.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MERILYN P NGUYEN whose telephone number is 571-272-4026. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571) 272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MERILYN P NGUYEN/ Primary Examiner, Art Unit 2153
October 02, 2026
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