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 .
1. Claims 1 – 13 are currently pending in this application.
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) 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over McCann et al. (Pre-Grant Publication No. 2024/0184834 A1), hereinafter McCann, in view of Anushiravani et al. (Patent No. US 12,235,912 B1), hereinafter Anush, and further in view of Yudin et al. (Pre-Grant Publication No. US 2024/0202202 A1), hereinafter Yudin.
2. With respect to claims 1 and 10, McCann taught a system for providing privacy-preserving search suggestions (0077-0078), comprising: at least one computing device comprising at least one storage device for storing one or more program modules, wherein the computing device comprises Large Language Models (0106), wherein the program modules executed by the computing device causes the computing device to: receive an input data comprising a plurality of input having text content (0110 & 0043, the words presented to the search query); generate at least one first word embedding for each input (0119, the tokens); generate at least one second word embedding for each first search phrase (0110, the phrases being compared to the user input, which would also rely on tokenized data).
However, McCann did not explicitly state to compare each first word embedding to the corresponding second word embedding to rank the first search phrases based on similarity to the input and create a plurality of ranked search phrases for each document and to generate a list of first search phrases for each document using Large Language Models. On the other hand, Anush did teach to compare each first word embedding to the corresponding second word embedding to rank the first search phrases based on similarity to the input and create a plurality of ranked search phrases for each document (14:62 to 15:5, where the generated search terms can be seen in 13:21-28) and to generate a list of first search phrases for each document using Large Language Models (13:21-28). Both of the systems of McCann and Anush are directed towards improving user search queries and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings McCann, to utilize specifically suggesting search terms, in order to improve the efficiency of the user’s searching.
However, McCann did not explicitly state that the input was a document and to deduplicate one or more ranked search phrases having a rank lower than a first predefined rank, and execute remaining ranked search phrases after deduplication in a search engine to evaluate search results and determine a set of final search phrases from the remaining ranked search phrases based on the search results. On the other hand, Yudin did teach that the input was a document (0011) and to deduplicate one or more ranked search phrases having a rank lower than a first predefined rank, and execute remaining ranked search phrases after deduplication in a search engine to evaluate search results and determine a set of final search phrases from the remaining ranked search phrases based on the search results (0088). Both of the systems of McCann and Yudin are directed towards improving user search queries and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings McCann, to utilize specifically suggesting search terms, in order to improve the efficiency of the user’s searching.
3. As for claims 2, 7, and 11, they are rejected on the same basis as claims 1, 5, and 10 (respectively). In addition, Yudin taught wherein the computing device is further configured to refine the set of final search phrases by providing a set of final search phrases having a rank higher than a second predefined rank (0083, where the cutoff is implicitly taught such that the list does not appear to be infinite under broadest reasonable interpretation).
4. As for claims 3, 8, and 12, they are rejected on the same basis as claims 1, 5, and 10 (respectively). In addition, Anush taught wherein the plurality of ranked search phrases is an arrangement of first search phrases in an order based on similarity to the documents (14:62 to 15:5).
5. As for claims 4, 9, and 13, they are rejected on the same basis as claims 1, 5, and 10 (respectively). In addition, Yudin taught wherein the deduplication involves conducting pair-wise comparisons of the embeddings associated with each search phrase to determine conceptual duplicates (0088, where this is part of the processing of removing duplicates under broadest reasonable interpretation of a pair-wise comparison).
6. With respect to claim 5, McCann taught a method for providing privacy-preserving search suggestions executed in a system (0077-0078) comprising at least one computing device comprising at least one storage device for storing one or more program modules (0047), wherein the program modules are executed by the computing device to perform one or more operations, wherein the method comprising the steps of: receiving an input data comprising a plurality of input having text content (0110, the words presented to the search query); feeding each input into one or more Large Language Models executed at the computing device (0106).
However, McCann did not explicitly state to generate a list of first search phrases for each document using Large Language Models, and filtering the list of first search phrases based on similarity to the documents and providing a set of final search phrases. On the other hand, Anush did teach generate a list of first search phrases for each input using Large Language Models (13:21-28), and filtering the list of first search phrases based on similarity to the documents and providing a set of final search phrases (14:62 to 15:5, where the generated search terms can be seen in 13:21-28). Both of the systems of McCann and Anush are directed towards improving user search queries and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings McCann, to utilize specifically suggesting search terms, in order to improve the efficiency of the user’s searching.
However, McCann did not explicitly state that the input was a document. On the other hand, Yudin did teach that the input was a document (0011). Both of the systems of McCann and Yudin are directed towards improving user search queries and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings McCann, to utilize specifically suggesting search terms, in order to improve the efficiency of the user’s searching.
7. As for claim 6, it is rejected on the same basis as claims 5. In addition, McCann taught wherein the step of filtering further comprising the steps of: generating at least one first word embedding for each document (0119, the token); generating at least one second word embedding for each first search phrase (0110, the phrases being compared to the user input, which would also rely on tokenized data); comparing each first word embedding to the corresponding second word embedding to rank the first search phrases based on similarity to the documents and creating a plurality of ranked search phrases for each document (14:62 to 15:5, where the generated search terms can be seen in 13:21-28); deduplicating one or more ranked search phrases having a rank lower than a first predefined rank, and executing remaining ranked search phrases after deduplication in a search engine to evaluate search results and determining a set of final search phrases from the remaining ranked search phrases based on the search results (Yudin: 0011).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(a) Davis et al. (Pre-Grant Publication No. US 2025/0342216 A1), 0034.
(b) Rofouei et al. (Pre-Grant Publication No. US 2024/0289407 A1), 0007-0008, 0166.
(c) Chrysanthou (Pre-Grant Publication No. US 2024/0289396 A1), 0160-0161.
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/JOSEPH L GREENE/Primary Examiner, Art Unit 2443