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 .
DETAILED ACTION
This responds to Applicant’s Arguments/Remarks filed 02/26/2026. Claims 1-26 are now pending in this Application.
Response to Arguments
Applicant's arguments filed 2/27/2026 have been fully considered but they are not persuasive.
Applicant argues that “none of LI Arun and Hou whether considered individually or in combination, discloses at least the “query embedding transformation model that transforms the baseline query embeddings from the embedding model into modified query embedding”; “to transform the baseline query embeddings into the modified query embeddings based on a distance between the baseline query embeddings and the training query embeddings” as recited in claims 1, 11 and 21.
In response to Applicant’s argument, the examiner submits that Li discloses “after a search query has been received and converted into a text vector encoding, the text encoding may be transmitted to the matching and selection unit for processing… to compare the text encoding with the visual assets, the matching and selection unit may compute similarity scores between the text encoding and the visual asset encoding of the asset index. This may be done by using the cosine vector similarity metric to estimate the degree of similarity between a text encoding and visual asset encoding on a -1 (highly irrelevant) to a +1 (highly relevant) scale. The most highly ranked matches may then be a selected as the search result” (Par [0045]) (relevant and irrelevant is a distance between the query embeddings and the training query embedding).
Hou discloses “obtain updated query embedding of the query dependency tree graph using the query graph attention diffusion classify the query aspect term based on the updated query embedding of the query dependency tree graph to obtain predicted classification of the query aspect term (Par [0017]). Receiving a query sentence and a query aspect term from the query sentence, converting the query sentence into a query dependency tree graph… obtaining updated query embedding of the query dependency tree graph using the query graph attention diffusion, classifying the query aspect term based on the updated query embedding of the query dependency tree graph to obtain predicted classification of the query aspect term, and labeling the query aspect term with the predicted classification” (Par [0025]).
Therefore, in the combination of Li and Hou disclose this limitation.
The examiner respectfully maintained the rejection.
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.
Claim(s) 1-7, 21-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (U.S. Pub No. 2023/0169110 A1), and Arun Koteeswaran et al (U.S. Pub No. 2025/0005082 A1), and further in view of Hou et al (U.S. Pub No. 2022/0092267 A1).
As per claim 1, Li discloses a server computer configured for embedding based text retrieval, comprising:
one or more processors; one or more databases communicatively coupled with the one or more processors; and a memory communicatively coupled with the one or more processors and storing instructions that, when executed by the one or more processors, causes the one or more processors to be configured as (par [0004]):
an encoder comprising an embedding model that converts text from a corpus into corpus text embeddings that are stored as text embeddings in a text embedding index in the one or more databases, and converts text from queries into baseline query embeddings (Par [0005, 0040, 0043]);
a query embedding transformation model that transforms the baseline query embeddings from the embedding model into modified query embeddings, the query embedding transformation model is trained based on a labeled retrieval dataset comprising training queries and corresponding training texts, wherein the training queries are converted by the embedding model to training query embeddings that are stored in a training query embedding index in the one or more databases, and wherein the training texts are added to the corpus and are converted into training text embeddings by the embedding model and are stored as the text embeddings in the text embedding index in the one or more databases, the query embedding transformation model is trained to transform the baseline query embeddings into the modified query embeddings based on a distance between the baseline query embeddings and the training query embeddings (Par [0045-0047, 0048, 0060]);
searches the text embedding index based on the modified query embeddings to produce one or more texts from the corpus (par [0042]).
Li discloses searching queries, converting queries into a text vector encoding, text embedding. Li does not explicitly disclose a searcher that is configured to perform a nearest neighbor search.
However, Arun Koteeswaran discloses a searcher that is configured to perform a nearest neighbor search (Par [0076]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Arun Koteeswaran into the teachings of Li in order to improve the relevancy based on semantic similarity (Par [0008]).
Li and Arun Koteeswaran do not explicitly disclose modified query embeddings.
However, Hou discloses modified query embeddings (par [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hou into the teachings of Li as modified by Arun Koteeswaran in order to improve the search system.
As per claim 2, Li discloses the server computer of claim 1, wherein the text embeddings, the baseline query embeddings, and the modified query embeddings are numeric vectors of a same fixed dimension (Par [0043-0044]).
Li and Arun Koteeswaran do not explicitly disclose modified query embeddings.
However, Hou discloses modified query embeddings (par [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hou into the teachings of Li as modified by Arun Koteeswaran in order to improve the search system.
As per claim 3, Li discloses the server computer of claim 1, wherein the searcher searches the text embedding index based on a distance between the modified query embeddings and the text embeddings in the text embedding index (par [0045, 0060]).
As per claim 4, Li discloses the server computer of claim 1, wherein the distance is determined based on at least one of a cosine distance, a Euclidean distance, a squared Euclidean distance, a vector dot product, a Manhattan distance, and a Hamming distance (par [0045] cosine vector similarity metric).
As per claim 5, Li discloses the server computer of claim 1, wherein the query embedding transformation model is trained to transform the baseline query embeddings into the modified query embeddings based on the distance between the baseline query embeddings and the training query embeddings according to a set of parameters such that as the distance between the baseline query embeddings and the training query embeddings decreases, the modified query embeddings are less distant to the training text embeddings (par [0045, 0060] Scoring the similarity).
As per claim 6, Li discloses the server computer of claim 1, wherein the query embedding transformation model is trained to transform the baseline query embeddings with a set of parameters such that: an exact match between a baseline query embedding and a training query embedding results in a modified query embedding that is the training text embedding; and as the distance between a baseline query embedding and a training query embedding approaches infinity, the modified query embedding is less distant to the baseline query embedding (Par [0039, 0045-0046, 0060]).
As per claim 7, Li discloses the server computer of claim 1, wherein the query embedding transformation model is trained to transform the baseline query embeddings according to parameters such that:
an exact match between a baseline query embedding and a training query embedding results in a modified query embedding that is the training text embedding; a distance between a baseline query embedding and a training query embedding that is greater than a threshold results in a modified query embedding that is the baseline query embedding; and a distance between a baseline query embedding and a training query embedding that is less than a threshold results in a modified query embedding that is the training text embedding (Par [0039, 0045-0046, 0060]).
As per claim 21, Li discloses a method for embedding based retrieval of text with a text retrieval system, comprising:
receiving a user query via an electronic interface (Par [0036]);
encoding the user query into a baseline query embedding with an embedding model, wherein the embedding model encodes text from a corpus into corpus text embeddings and the corpus text embeddings are stored as text embeddings in a text embedding index (Par [0005, 0040, 0043]);
transforming the baseline query embedding to a modified query embedding with a query embedding transformation model, the query embedding transformation model is trained based on a labeled retrieval dataset comprising training queries and corresponding training texts, wherein the training queries are converted by the embedding model to training query embeddings that are stored in a training query embedding index, and wherein the training texts are added to the corpus and are converted into training text embeddings by the embedding model and are stored as the text embeddings in the text embedding index, the query embedding transformation model transforms the baseline query embeddings into the modified query embeddings based on a distance between the baseline query embeddings and the training query embeddings Par [0045-0047, 0048, 0060]);
retrieving one or more texts from the corpus based on a nearest neighbor search of the text embeddings in the text embedding index using the modified query embedding searches the text embedding index based on the modified query embeddings to produce one or more texts from the corpus (par [0042]).
Li discloses searching queries, converting queries into a text vector encoding, text embedding. Li does not explicitly disclose a searcher that is configured to perform a nearest neighbor search.
However, Arun Koteeswaran discloses a searcher that is configured to perform a nearest neighbor search (Par [0076]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Arun Koteeswaran into the teachings of Li in order to improve the relevancy based on semantic similarity (Par [0008]).
Li and Arun Koteeswaran do not explicitly disclose modified query embeddings.
However, Hou discloses modified query embeddings (par [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hou into the teachings of Li as modified by Arun Koteeswaran in order to improve the search system.
Li, Arun Koteeswaran and Hou do not explicitly disclose provide the one or more texts and the user query to a prompt constructor for a Large Language Model (LLM) in a Retrieval Augmented Generation application that produces a prompt to the LLM that integrates the one or more texts and the user query.
However, Shepherd discloses provide the one or more texts and the user query to a prompt constructor for a Large Language Model (LLM) in a Retrieval Augmented Generation application that produces a prompt to the LLM that integrates the one or more texts and the user query (par [0057]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Arun Koteeswaran into the teachings of Li in order provide significant advantages relative to conventional techniques (Par [0003]).
Li, Arun Koteeswaran and Hou do not explicitly disclose provide the one or more texts and the user query to a prompt constructor for a Large Language Model (LLM) in a Retrieval Augmented Generation application that produces a prompt to the LLM that integrates the one or more texts and the user query.
However, Shepherd discloses provide the one or more texts and the user query to a prompt constructor for a Large Language Model (LLM) in a Retrieval Augmented Generation application that produces a prompt to the LLM that integrates the one or more texts and the user query (par [0057]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Arun Koteeswaran into the teachings of Li in order provide significant advantages relative to conventional techniques (Par [0003]).
As per claim 22, Li discloses the method of claim 21, wherein the query embedding transformation model is trained to transform the baseline query embedding into the modified query embedding based on the distance between the baseline query embedding and the training query embedding according to a set of parameters that as the distance between baseline query embeddings and a training query embedding decreases, the modified query embeddings are less distant to the training text embedding (par [0045, 0060] Scoring the similarity).
As per claim 23. The method of claim 21, wherein the query embedding transformation model is trained to transform the baseline query embedding with a set of parameters such that: an exact match between the baseline query embedding and a training query embedding results in the modified query embedding that is the training text embedding; and as the distance between the baseline query embedding and a training query embedding approaches infinity, the modified query embedding is less distant to the baseline query embedding (Par [0039, 0045-0046, 0060]).
As per claim 24, Li discloses the method of claim 21, wherein the query embedding transformation model is trained to transform the baseline query embedding according to parameters such that:
an exact match between the baseline query embedding and a training query embedding results in the modified query embedding that is the training text embedding; a distance between the baseline query embedding and a training query embedding that is greater than a threshold results in the modified query embedding that is the baseline query embedding; and a distance between the baseline query embedding and a training query embedding that is less than a threshold results in the modified query embedding that is the training text embedding (Par [0039, 0045-0046, 0060]).
Claim(s) 8, 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al, Arun Koteeswaran et al and Hou et al, and further in view of Hiranandani et al (U.S. Pub No. 2021/0366016).
As per claim 8, Li discloses the server computer of claim 1, wherein the query embedding transformation model with parameters trained on the labeled retrieval dataset (Par [0039, 0045-0046, 0060]).
Li, Arun Koteeswaran and Hou do not explicitly disclose transformation model is a parameterized interpolation model.
However, Hiranandani discloses transformation model is a parameterized interpolation model (Par [0082]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hiranandani into the teachings of Li as modified by Arun Koteeswaran and Hou in order to improve in existing technologies that generate search result (Par [0003]).
As per claim 25, Li discloses the method of claim 21, wherein the query embedding transformation model with parameters trained on the labeled retrieval dataset (Par [0039, 0045-0046, 0060]).
Li, Arun Koteeswaran and Hou do not explicitly disclose transformation model is a parameterized interpolation model.
However, Hiranandani discloses transformation model is a parameterized interpolation model (Par [0082]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hiranandani into the teachings of Li as modified by Arun Koteeswaran and Hou in order to improve in existing technologies that generate search result (Par [0003]).
Claim(s) 9 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al, Arun Koteeswaran et al and Hou et al, and further in view of Xiong et al (U.S. Pub No. 2020/0234199 A1).
As per claim 9. The server computer of claim 1, wherein the query embedding transformation model is a process model with parameters trained on the labeled retrieval dataset (Par [0039, 0045-0046, 0060]).
Li, Arun Koteeswaran and Hou do not explicitly disclose transformation model is a parameterized multivariate Gaussian process model.
However, Xiong discloses transformation model is a parameterized multivariate Gaussian process model (Par [0055, 0109]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hiranandani into the teachings of Li as modified by Arun Koteeswaran and Hou in order to improve the diversity performance (Par [0006]).
As per claim 26. The method of claim 21, wherein the query embedding transformation model is a process model with parameters trained on the labeled retrieval dataset (Par [0039, 0045-0046, 0060]).
Li, Arun Koteeswaran and Hou do not explicitly disclose transformation model is a parameterized multivariate Gaussian process model.
However, Xiong discloses transformation model is a parameterized multivariate Gaussian process model (Par [0055, 0109]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hiranandani into the teachings of Li as modified by Arun Koteeswaran and Hou in order to improve the diversity performance (Par [0006]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al, Arun Koteeswaran et al and Hou et al, and further in view of Shepherd et al (U.S. Pub No. 2025/0343825 A1).
As per claim 10, Li discloses the server computer of claim 1, wherein the one or more processors are further configured to:
receive a user query via an electronic interface; encode the user query into a baseline query embedding with the embedding model; transform the baseline query embedding to a modified query embedding based on the distance between the baseline query embedding and the training query embeddings; retrieve one or more texts from the corpus based on a nearest neighbor search of the text embedding index using the modified query embedding (Par [0045-0047, 0048, 0060]).
Li discloses searching queries, converting queries into a text vector encoding, text embedding. Li does not explicitly disclose a searcher that is configured to perform a nearest neighbor search.
However, Arun Koteeswaran discloses a searcher that is configured to perform a nearest neighbor search (Par [0076]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Arun Koteeswaran into the teachings of Li in order to improve the relevancy based on semantic similarity (Par [0008]).
Li, Arun Koteeswaran and Hou do not explicitly disclose provide the one or more texts and the user query to a prompt constructor for a Large Language Model (LLM) in a Retrieval Augmented Generation application that produces a prompt to the LLM that integrates the one or more texts and the user query.
However, Shepherd discloses provide the one or more texts and the user query to a prompt constructor for a Large Language Model (LLM) in a Retrieval Augmented Generation application that produces a prompt to the LLM that integrates the one or more texts and the user query (par [0057]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Arun Koteeswaran into the teachings of Li in order provide significant advantages relative to conventional techniques (Par [0003]).
Claim(s) 11-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (U.S. Pub No. 2023/0169110 A1), and in view of Hou et al (U.S> Pub No. 2022/0092267 A1).
As per claim 11, Li discloses a method for training a text retrieval system for embedding based retrieval of text, comprising:
encoding text from a corpus into corpus text embeddings with an embedding model that is pretrained, wherein the embedding model converts text from user queries into baseline query embeddings (Par [0005, 0040, 0043]);
receiving a labeled retrieval dataset comprising training queries and corresponding training texts, wherein the training texts are added in the corpus; encoding the training queries into training query embeddings and the training texts into training text embeddings (par [0022, 0028-0031, 0048-0050]);
storing the training query embeddings in a training query embedding index; storing the corpus text embeddings and the training text embeddings as text embedding in a text embedding index; and
training a query embedding transformation model using the labeled retrieval dataset to transform the baseline query embeddings produced by the embedding model into modified query embeddings based on a distance between the baseline query embeddings and the training query embeddings (par [0022, 0028-0031, 0048-0050]).
Li does not explicitly disclose modified query embeddings.
However, Hou discloses modified query embeddings (par [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hou into the teachings of Li in order to improve the search system.
As per claim 12, Li discloses the method of claim 11, wherein the text embeddings, the baseline query embeddings, and the modified query embeddings are numeric vectors of a same fixed dimension (Par [0043-0044]).
Li does not explicitly disclose modified query embeddings.
However, Hou discloses modified query embeddings (par [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hou into the teachings of Li as modified by Hou in order to improve the search system.
As per claim 13, Li discloses the method of claim 11, wherein a searcher in the text retrieval system searches the text embedding index based on the distance between the modified query embeddings and the text embeddings in the text embedding index (Par [0045, 0060])
As per claim 14, Li discloses the method of claim 11, wherein the query embedding transformation model is trained to transform the baseline query embeddings into modified query embeddings based on a distance between the baseline query embeddings and the training query embeddings according to a set of parameters that as the distance between the baseline query embeddings and the training query embeddings decreases, the modified query embeddings are less distant to the training text embeddings (par [0045, 0060] Scoring the similarity).
As per claim 15, Li discloses the method of claim 11, wherein the query embedding transformation model is trained to transform the baseline query embeddings according to a set of parameters such that: an exact match between a baseline query embedding and a training query embedding results in a modified query embedding that is the training text embedding; and as the distance between a baseline query embedding and a training query embedding approaches infinity, the modified query embedding is less distant to the baseline query embedding (Par [0039, 0045-0046, 0060]).
As per claim 16, Li discloses the method of claim 11, wherein the query embedding transformation model is trained to transform the baseline query embeddings according to parameters such that:
an exact match between a baseline query embedding and a training query embedding results in a modified query embedding that is the transformed query embedding; a distance between a baseline query embedding and a training query embedding that is greater than a threshold results in a modified query embedding that is the baseline query embedding; and a distance between a baseline query embedding and a training query embedding that is less than a threshold results in a modified query embedding that is the training text embedding (Par [0039, 0045-0046, 0060]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al, Hou et al, and further in view of Arun Koteeswaran et al (U.S. Pub NO. 2025/0005082 A1).
As per claim 19, Li discloses the method of claim 11, wherein training the query embedding transformation model comprises the labeled retrieval dataset (Par [0039, 0045-0046, 0060]).
Li, Arun Koteeswaran and Hou do not explicitly disclose transformation model is a parameterized interpolation model.
However, Hiranandani discloses transformation model is a parameterized interpolation model (Par [0082]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hiranandani into the teachings of Li as modified by Hou in order to improve in existing technologies that generate search result (Par [0003]).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al, Hou et al, and further in view of Kosuru et al (U.S. Pub No. 2018/0089271).
As per claim 17, Li and Hou do not explicitly disclose the method of claim 11, wherein the query embedding transformation model is trained using F-fold cross validation.
However, Kosuru discloses wherein the query embedding transformation model is trained using F-fold cross validation (par [0032]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Kosuru into the teachings of Li as modified by Hou in order to improve the quality of service (Par [0011]).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al, Hou et al, and further in view of Xie et al (U.S. Pub No. 2011/0255858 A1).
As per claim 18, Li discloses the method of claim 11, wherein training the query embedding transformation model (Par [0039, 0045-0046, 0060]).
Li and Hou do not explicitly disclose minimizing a negative log marginal likelihood function.
However, Xie discloses minimizing a negative log marginal likelihood function (par [0032]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Kosuru into the teachings of Li as modified by Hou in order to improve the quality of service (Par [0011]).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al, Hou et al,, and further in view of Xiong et al (U.S. Pub No. 2020/0234199 A1).
As per claim 20, Li discloses the method of claim 11, wherein training the query embedding transformation model comprises the labeled retrieval dataset (Par [0039, 0045-0046, 0060]).
Li, and Hou do not explicitly disclose transformation model is a parameterized multivariate Gaussian process model.
However, Xiong discloses transformation model is a parameterized multivariate Gaussian process model (Par [0055, 0109]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Hiranandani into the teachings of Li as modified by Hou in order to improve the diversity performance (Par [0006]).
Conclusion
THIS ACTION IS MADE FINAL. 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.
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May 19, 2026
/THU N NGUYEN/Examiner, Art Unit 2154
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