DETAILED ACTION
1. Claims 1-20 are pending in this application.
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 .
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.
Information Disclosure Statement
3. The information disclosure statement filed 07/28/2025 is in compliance with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file and the information referred to therein has been considered as to the merits.
Claim Rejections - 35 USC § 101
4. 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 (Mental Process) without significantly more. The claims describe the steps for recommending search query terms.
The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category?
Claims 1-11 are directed to a computing system.
Claims 12-19 are directed to a method.
Claims 20 is directed to a non-transitory computer-readable medium.
Therefore, claims 1-20 fall into at least one of the four statutory categories.
Step 2A, Prong I: Judicial Exception Recited?
The examiner submits that the foregoing claim limitations constitute a “Mental Process”, as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation.
As per independent claims 1, 12 and 20, the claims similarly recite the limitations of:
“generating, using a large language model, a plurality of embeddings corresponding to a plurality of search terms;” A Humans naturally group concepts based on shared context, experience, and logical relationships. Humans have the capacity to mentally refine complex ideas into core representations that reflect relationships and meaning, which is precisely what an embedding achieves computationally. The large language model is merely a tool used to implement the abstract idea. There is nothing so complex in the limitation that could not be doing in the human mind.
“detecting an initiation of a user query, wherein the user query comprises one or more characters;” A human can observe a user interface and, while observing it, detect that a search term has started to be entered. For example, a human observing a user interface can visually detect the initiation of a search term entry process through immediate on-screen cues like the appearance of a blinking cursor in a text box and the dynamic display of autocomplete suggestions. There is nothing so complex in the limitation that could not be doing in the human mind.
“performing a syntactic search, wherein the syntactic search comprises identifying a plurality of prefix match results based on the one or more characters included in the user query;” A human can observe information on a page and search for terms and other terms associated with the terms the human is looking for. For example, humans actively scan content, identify key information (the initial terms), and mentally branch out to related concepts (associated terms) within the same context. A human can also identify term prefixes based on the terms found in a search. For example, a human can identify common word prefixes by observing patterns across different terms found in search results, such as recognizing the re- in both recycle and repurpose. There is nothing so complex in the limitation that could not be doing in the human mind.
“determining, in real-time with respect to the user query being entered, that the user query exceeds a threshold length;” A human can observe data and make judgments about it, determining if a specific threshold has been reached. For example, a human can observe a data point, such as a computer's 85% memory usage, make a judgment that it is close to the limit, and determine that the critical 90% threshold has not yet been reached. There is nothing so complex in the limitation that could not be doing in the human mind.
“in response to determining that the user query exceeds the threshold length, performing a semantic search, the semantic search comprising:” A human can observe information on a page and search for terms and other terms that match the same context and concepts of the terms the human is looking for. For example, a human searching for "sustainable energy" can browse an article and instinctively identify that related terms like "solar power," "wind turbines," and "renewable resources" all match the same conceptual context. There is nothing so complex in the limitation that could not be doing in the human mind.
“generating at least one embedding for the user query;” A human can mentally create representations of queries based on meaning and context, which is similar to generating an embedding for a user search. For example, when a human hears the query "cold treat," they mentally generate a representation that connects the concept to related items like "ice cream" or "sorbet," effectively mapping the query to a specific semantic space in the same way a vector embedding does for a search engine. There is nothing so complex in the limitation that could not be doing in the human mind.
“matching the at least one embedding for the user query to a subset of the plurality of embeddings, wherein the subset of the plurality of embeddings correspond to a subset of the plurality of search terms;” A human can select a specific term and compare its k-nearest neighbors the most mathematically similar terms between different subsets of data to identify shifts in meaning or context. For example, a researcher can select the search term 'power' and observe that its k-nearest neighbors shift from 'voltage' and 'electricity' in a technical subset to 'influence' and 'authority' in a political subset, effectively identifying a change in semantic context. The subset of the plurality of search terms is a merely element used to implement the abstract idea. The subset of the plurality of search terms is merely an element used to implement the abstract idea. There is nothing so complex in the limitation that could not be doing in the human mind.
“providing a plurality of recommended search terms, wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms.” A human can observe a plurality of search terms, select those of interest, and mentally 'speak' them to reinforce the concept. A human can select terms based on criteria such as the highest-ranked prefix terms identified in a book. For example, a human studying a medical textbook can scan the index to find the most frequent term prefixes, such as 'hyper-' or 'hypo-', and select them as primary search terms to quickly locate all related conditions like hypertension or hypoglycemia. The ranking of the plurality of prefix match results and the subset of the plurality of search terms are merely instructions used to implement the abstract idea. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 2, the claim recites the limitation of:
“wherein in response to determining that the user query is below a threshold length, the semantic search is not performed.” A human can mentally decide to stop looking for a term in a text after observing that the criteria for finding the term are below the established threshold. For example, a researcher might mentally decide to terminate their search for a legal term like 'liability' after scanning several pages and observing that the word only appears in irrelevant contexts, such as general news articles rather than the required case law, thereby falling below their established threshold for information relevance. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 3 and 13, the claims similarly recite the limitations of:
“wherein the threshold length comprises a character count of the one or more characters included in the user query.” The character count of the characters included in the user query is merely an element used to implement the abstract idea.
As per dependent claims 4 and 14, the claims similarly recite the limitations of:
“wherein: the at least one embedding corresponds to at least one user query vector representation;” The at least one user query vector representation is merely an element used to implement the abstract idea.
“the plurality of embeddings correspond to a plurality of search entity vector representations, and matching the at least one embedding for the user query to the subset of the plurality of embeddings comprises determining a plurality of cosine similarities between the at least one user query vector representation and the plurality of search entity vector representations.” The plurality of search entity vector representations is merely an element used to implement the abstract idea. Humans can mentally identify data similarities by comparing one piece of information to another. A human can mentally visualize vectors and compare them to identify similar values. For example, a physicist can mentally visualize two force vectors as arrows on a 2D plane and compare their lengths and angles to identify if the forces have similar magnitudes and directions. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 5 and 15, the claims similarly recite the limitations of:
“wherein the subset of the plurality of search terms are ranked according to the plurality of cosine similarities, the plurality of cosine similarities being based on distances between the at least one user query vector representation and the plurality of search entity vector representations, wherein a smaller distance between the at least one user query vector representation and one of the plurality of search entity vector representations correlates to a greater cosine similarity.” A human can observe terms and mentally rank them. The plurality of cosine similarities being based on distances between the at least one user query vector representation and the plurality of search entity vector representations are merely instructions used to implement the abstract ideas. The smaller distance between the at least one user query vector representation and one of the pluralities of search entity vector representations correlates to a greater cosine similarity are merely instructions used to implement the abstract ideas. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 6, the claim recites the limitation of:
“wherein the syntactic search further comprises applying weights to the plurality of prefix match results.” A human can define and apply a criterion mentally to find prefix matches of terms. For example, a human can mentally use the prefix "tri-" to quickly identify words like "triangle" or "triple" from a list. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 7, the claim recites the limitations of:
“wherein the weights are based on a popularity of the plurality of prefix match results, wherein the popularity is based on historical user interaction with the plurality of prefix match results.” The popularity of a plurality of prefix-match results is a simple element used to implement the abstract idea. The historical user interaction with the plurality of prefix match results is a simple element used to implement the abstract idea.
As per dependent claims 8 and 17, the claims similarly recite the limitations of:
“wherein the plurality of recommended search terms include a number of prefix match results and a number of semantic search results, wherein the number of semantic search results increases and the number of prefix match results decreases as a number of characters in the user query increases.” The number of prefix match results and the number of semantic search results are simple elements used to implement the abstract idea. The wherein the number of semantic search results increases and the number of prefix match results decreases as a number of characters in the user query increases is a simple instruction used to implement the abstract idea.
As per dependent claim 9, the claim recites the limitations of:
“wherein the plurality of search terms comprises a plurality of service categories and a plurality of service entities, wherein each of the plurality of service entities corresponds to at least one of the plurality of service categories.” The plurality of service categories and the plurality of service entities are simple elements used to implement the abstract idea. The wherein each of the plurality of service entities corresponds to at least one of the plurality of service categories is a simple instruction used to implement the abstract idea.
As per dependent claims 10 and 19, the claims similarly recite the limitations of:
“wherein the instructions cause the processing circuit to perform operations comprising: determining a prefix match result matches a search term from the subset of the plurality of search terms;” A human can observe terms and mentally identify those that have a prefix the human is looking for. For example, a reader scanning a grocery list can mentally apply the prefix "blue-" to instantly pick out "blueberries" and "blue cheese" from the other items. There is nothing so complex in the limitation that could not be doing in the human mind.
“responsive to determining the prefix match result matches the search term, removing one of the prefix match result or the search term from the plurality of recommended search terms.” A human can quickly scan a list of related terms—such as "interstellar," "interactive," and "intervene"—to identify that "inter-" is the common prefix shared among them. For example, a person looking at a list containing "disagree," "disappear," and "dislike" can instantly observe that they all share the common prefix "dis-." A human can also, after defining several term prefixes, decide to forget or eliminate one of them. For example, a researcher might identify "bio-," "eco-," and "geo-" as relevant prefixes for a project but then decide to eliminate "geo-" once they narrow their focus to biology alone. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 11 and 18, the claims similarly recite the limitations of:
“wherein providing the plurality of recommended search terms further comprises: scoring each of the plurality of prefix match results and the subset of the plurality of search terms;” A human can mentally observe terms and order them based on a specific criterion, such as alphabetizing a short list of names in their head. For example, a person looking at a list of random grocery items can mentally reorder them by aisle, such as grouping "apples," "bananas," and "carrots" together under the criterion of "produce" to make shopping more efficient. There is nothing so complex in the limitation that could not be doing in the human mind.
“ordering each of the plurality of prefix match results and the subset of the plurality of search terms based on the scoring.” The each of the plurality of prefix match results and the subset of the plurality of search terms based on the scoring is a simple instruction used to implement the abstract idea.
As per dependent claim 16, the claim recites the limitation of:
“wherein the syntactic search further comprises applying weights to the plurality of prefix match results.” A human can define and apply a criterion mentally to find prefix matches of terms. For example, a human can mentally use the prefix "tri-" to quickly identify words like "triangle" or "triple" from a list. There is nothing so complex in the limitation that could not be doing in the human mind.
“wherein the weights are based on a popularity of the plurality of prefix match results, wherein the popularity is based on historical user interaction with the plurality of prefix match results.” The popularity of a plurality of prefix-match results is a simple element used to implement the abstract idea. The historical user interaction with the plurality of prefix match results is a simple element used to implement the abstract idea.
Accordingly, claims 1-20 recite at least one abstract idea.
Step 2A, Prong II: Integrated into a Practical Application?
The claims recite the following additional limitations/elements:
As per independent claim 1, the claim recites the limitation of:
“a processing circuit; one or more processors; and one or more memory devices.” This element is example of mere instruction to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
As per independent claim 12, the claim recites the limitation of:
“a computing system.” This element is example of mere instruction to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
As per independent claim 20, the claim recites the limitation of:
“a non-transitory computer-readable medium; and one or more processors of a processing circuit.” This element is example of mere instruction to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
Therefore, claims 1-20 do not integrate the recited abstract ideas into a practical application.
Step 2B: Claim provides an Inventive Concept?
With respect to the elements identified as insignificant extra-solution activity above the conclusions are carried over.
It is noted that in Alice Corp. v. CLS Bank (2014), the U.S. Supreme Court established that merely requiring generic computer implementation cannot transform an abstract idea into a patent-eligible invention, see MPEP 2106 - “The programmed computer or "special purpose computer" test of In re Alappat, 33 F.3d 1526, 31 USPQ2d 1545 (Fed. Cir. 1994) (i.e., the rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim for the "special purpose" of executing the algorithm or software) was also superseded by the Supreme Court’s Bilski and Alice Corp. decisions. Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) ("[W]e note that Alappat has been superseded by Bilski, 561 U.S. at 605–06, and Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 110 USPQ2d 1976 (2014)"); Intellectual Ventures I LLC v. Capital One Bank (USA), N.A., 792 F.3d 1363, 1366, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) ("An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). Lastly, eligibility should not be evaluated based on whether the claimed invention has utility, because "[u]tility is not the test for patent-eligible subject matter." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1380, 118 USPQ2d 1541, 1548 (Fed. Cir. 2016).”
Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible.
Therefore, the claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
5. 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. § 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
6. Claims 1-4, 6-7, 9, 11-14, 16, 18 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Matson et al. (US 20240419710 A1) in view of Chaudhuri et al. (US 20250005083 A1) in further view of Teran et al. (US 20100131902 A1).
As per claim 1, Matson teaches a computing system (i.e. “computer system”; Abstract) comprising:
a processing circuit (i.e. “computing device 900”; fig. 9, para. [0145]; Examiner note: the processing circuit is interpreted as the computing device)
having one or more processors (i.e. “one or more processors 914”; fig. 9, para. [0145])
coupled to one or more memory devices storing instructions thereon that (i.e. “computing device 900 includes a bus 910 that directly or indirectly couples the following devices: memory 912, one or more processors 914,”; fig. 9, para. [0145]. Further, i.e. “Memory 912 includes computer storage media”; fig. 9, para. [0149]; Examiner note: the memory devices storing instructions is interpreted as the Memory 912 includes computer storage media),
when executed by the one or more processors (i.e. “when executed by one or more processors,”; fig. 9, para. [0137]),
cause the processing circuit to perform operations comprising (i.e. “cause the one or more processors to perform a method”; fig. 9, para. [0137]):
generating, using a large language model (i.e. “Large Language Models (LLM)”; fig. 2, para. [0029], [0060]), a plurality of embeddings corresponding to a plurality of search terms (i.e. “the text summary is generated, via a large language model (LLM), by providing, to the LLM, a model prompt including at least a portion of the text of the content item. At block 406, a text embedding representing the text summary is generated,”; fig. 4, para. [0114]. Further, i.e. “text embeddings associated with text summaries of content items”; para. [0090]; Examiner note: the embeddings are interpreted as the text embeddings. The plurality of search terms are interpreted as the text summaries of content items);
wherein the user query comprises one or more characters (i.e. “for a query of “park,””; para. [0076]; Examiner note: the one or more characters is interpreted as the “park”);
performing a syntactic search, wherein the syntactic search comprises identifying a plurality of prefix match results based on the one or more characters included in the user query (i.e. “In prefix searches, the search returns results with terms that contain the word followed by zero or more characters. For example, for a query of “park,” search results that contain the word ‘park,’ ‘parked,’ and ‘parking’ (and other words that start with ‘park’) are returned.”; para. [0076]; Examiner note: the prefix is ‘park’. The syntactic search is the prefix searches. The prefix match results are search results that contain the word ‘park’ ‘parked,’ and ‘parking’ (and other words that start with ‘park’);
in response to determining that the user query exceeds the threshold length (i.e. “Responsively, the text summarization manager 226 can then tokenize text of the content item to generate tokens, and then responsively and progressively add tokens until the token threshold (indicating the input size constraint) is met or exceeded, at which point the model prompt is generated”; fig. 2, para. [0062]-[0063]; Examiner note: the user query being entered is interpreted as the add tokens. The user query exceeds the threshold length is interpreted as the token threshold (indicating the input size constraint) is met or exceeded. The in response to determining is interpreted as the responsively),
performing a semantic search (i.e. “performing a semantic search”; para. [0069]), the semantic search comprising:
generating at least one embedding for the user query (i.e. “to generate a query text embedding”; para. [0088]; Examiner note: the user query is interpreted as the query text); and
matching the at least one embedding for the user query to a subset of the plurality of embeddings (i.e. “As such, a semantic search includes comparing text embeddings or vectors and determining a similarity distance between the embeddings or vectors.”; para. [0070]; Examiner note: the matching is the determining a similarity. The at least one embedding for the user query to a subset of the plurality of embeddings is interpreted as the between the embeddings or vectors),
wherein the subset of the plurality of embeddings correspond to a subset of the plurality of search terms (i.e. “semantic search data includes text embeddings representing the content items.”; para. [0112]; Examiner note: the plurality of search terms in interpreted as the semantic search data);
However, it is noted that the prior art of Matson does not explicitly teach “detecting an initiation of a user query; determining, in real-time with respect to the user query being entered, that the user query exceeds a threshold length; providing a plurality of recommended search terms, wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms.”
On the other hand, in the same field of endeavor, Chaudhuri teaches detecting an initiation of a user query (i.e. “when a system detects a recommendation-generation trigger, such as a user navigating to a search browser page,”; para. [0022]. Further, i.e. “a user may begin entering text into a text search field. The system may detect the trigger prior to the user completing a text entry action “; para. [0053]; Examiner note: the initiation of a user query is interpreted as the user navigating to a search browser page and user may begin entering text into a text search field);
determining, in real-time with respect to the user query being entered (i.e. “As the system detects that the number of search history records meets the respective thresholds, the system automatically, without user intervention, modifies the values in the respective fields of the data object resource to enable/disable different types of search filter recommendations.”; fig. 2A, para. [0067]; Examiner note: the in real-time is interpreted as the automatically),
that the user query exceeds a threshold length (i.e. “the system determines if a set of machine learning-generated search filter recommendation criteria are met (Operation 210).”; fig. 2A, para. [0067]-[0069]; Examiner note: the determining that the user query exceeds a threshold length is interpreted as the system determines if the set of machine learning-generated search filter recommendation criteria are met. Further, “The policy may assign a highest weight to a user-specific machine learning model recommendation if the recommendation has a confidence score exceeding a threshold.”; para. [0080], [0093]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Chaudhuri that teaches generating and presenting search filter recommendations into the prior art of Matson that teaches providing comprehensive search results. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to have a search engine that attempts to suggest search terms based on what the user frequently retrieves because it can reduce the burden on the user by providing suggestions for words to enter into a search field (Chaudhuri, para. [0003]).
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “providing a plurality of recommended search terms, wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms.”
On the other hand, in the same field of endeavor, Teran teaches providing a plurality of recommended search terms (i.e. “determine a plurality of suggested search queries.”; fig.10, para. [0082]; Examiner note: the recommended search terms is interpreted as the suggested search queries),
wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms (i.e. “In the example of FIG. 4, window 404 displays the suggested search queries of “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” and “cnnsi” for the entered characters of “cnn.” Typically, the suggested search queries are ordered according to a ranking of their respective popularity to the general population of users of search engine 106”; fig. 4, para. [0044]; Examiner note: the plurality of prefix match results is interpreted as the “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” and “cnnsi” for the entered characters of “cnn.”. The subset of the plurality of search terms is “cnn”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
As per claim 2, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “wherein in response to determining that the user query is below a threshold length, the semantic search is not performed.”
On the other hand, in the same field of endeavor, Teran teaches wherein in response to determining that the user query is below a threshold length, the semantic search is not performed (i.e. “If the predetermined percentage is 75%, navigational query determiner 506 may determine that “cnn” is not a navigational query because the percentage of clicks (57%) for “www.cnn.com” is less than 75%.”; para. [0070]; Examiner note: the below a threshold length is interpreted as the percentage of clicks (57%) for “www.cnn.com” is less than 75%. The semantic search is not performed is interpreted as the navigational query determiner 506 may determine that “cnn” is not a navigational query).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
As per claim 3, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “wherein the threshold length comprises a character count of the one or more characters included in the user query.”
On the other hand, in the same field of endeavor, Teran teaches wherein the threshold length comprises a character count of the one or more characters included in the user query (i.e. “after a suitable number of characters is/are entered into entry box 402, including one character, two characters, three characters, or further numbers of characters.”; para. [0044]; Examiner note: the threshold length is interpreted as the suitable number of characters).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
As per claim 4, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 1 above.
Additionally, Matson teaches wherein: the at least one embedding corresponds to at least one user query vector representation (i.e. “A text embedding is generally in the form of a vector.”; para. [0087]);
the plurality of embeddings correspond to a plurality of search entity vector representations (i.e. “generate text embeddings, for example, in the form of vectors,”; para. [0071]), and
matching the at least one embedding for the user query to the subset of the plurality of embeddings comprises determining a plurality of cosine similarities between the at least one user query vector representation and the plurality of search entity vector representations (i.e. “cosine similarity may be used to determine similarity between a query embedding and a content embedding.”; para. [0019], [0070], [0087], [0090]-[0092]; Examiner note: the determining the plurality of cosine similarities between the at least one user query vector representation and the plurality of search entity vector representations is interpreted as the determine similarity between the query embedding and the content embedding).
As per claim 6, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 1 above.
Additionally, Matson teaches wherein the syntactic search further comprises applying weights to the plurality of prefix match results (i.e. “semantic similarity or distance may be used to identify or determine a weight or rank associated with a search result.”; para. [0099]).
As per claim 7, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 6 above.
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “wherein the weights are based on a popularity of the plurality of prefix match results, wherein the popularity is based on historical user interaction with the plurality of prefix match results.”
On the other hand, in the same field of endeavor, Teran teaches wherein the weights are based on a popularity of the plurality of prefix match results, wherein the popularity is based on historical user interaction with the plurality of prefix match results (i.e. “the suggested search queries of “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” and “cnnsi” for the entered characters of “cnn.” Typically, the suggested search queries are ordered according to a ranking of their respective popularity to the general population of users”; para. [0044]; Examiner note: the weights are based on the plurality of prefix match results is interpreted as the ranking of their respective popularity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
As per claim 9, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “wherein the plurality of search terms comprises a plurality of service categories and a plurality of service entities, wherein each of the plurality of service entities corresponds to at least one of the plurality of service categories.”
On the other hand, in the same field of endeavor, Teran teaches wherein the plurality of search terms comprises a plurality of service categories and a plurality of service entities, wherein each of the plurality of service entities corresponds to at least one of the plurality of service categories (i.e. “FIG. 4, window 404 displays the suggested search queries of “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” and “cnnsi” for the entered characters of “cnn.””; fig.4, para. [0044]; Examiner note: the services entities is cnn. The service categories are “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
As per claim 11, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “wherein providing the plurality of recommended search terms further comprises: scoring each of the plurality of prefix match results and the subset of the plurality of search terms; and ordering each of the plurality of prefix match results and the subset of the plurality of search terms based on the scoring.”
On the other hand, in the same field of endeavor, Teran teaches wherein providing the plurality of recommended search terms further comprises: scoring each of the plurality of prefix match results and the subset of the plurality of search terms; and ordering each of the plurality of prefix match results and the subset of the plurality of search terms based on the scoring (i.e. “search results, of the set of search results, corresponding with the first set of content items semantically similar to the search query and search results, of the set of search results, corresponding with the second set of content items lexically similar to the search query are interleaved with one another based on a relevance ranking indicating relevance of the corresponding content item to the search query.”; para. [0120]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
As per claim 12, Matson teaches a method (i.e. “Methods, computer systems, computer-storage media, and graphical user interfaces are provided for providing comprehensive search results.”; Abstract) comprising:
generating, by a computing system using a large language model (i.e. “Large Language Models (LLM)”; fig. 2, para. [0029], [0060]), a plurality of embeddings corresponding to a plurality of search terms (i.e. “the text summary is generated, via a large language model (LLM), by providing, to the LLM, a model prompt including at least a portion of the text of the content item. At block 406, a text embedding representing the text summary is generated,”; fig. 4, para. [0114]. Further, i.e. “text embeddings associated with text summaries of content items”; para. [0090]. Further, i.e. “computer system”; Abstract; Examiner note: the emdeddings are interpreted as the text embeddings. The plurality of search terms are interpreted as the text summaries of the content items);
wherein the user query comprises one or more characters (i.e. “for a query of “park,””; para. [0076]; Examiner note: the one or more characters is interpreted as the “park”);
performing, by the computing system, a syntactic search, wherein the syntactic search comprises identifying a plurality of prefix match results based on the one or more characters included in the user query (i.e. “In prefix searches, the search returns results with terms that contain the word followed by zero or more characters. For example, for a query of “park,” search results that contain the word ‘park,’ ‘parked,’ and ‘parking’ (and other words that start with ‘park’) are returned.”; para. [0076]; Examiner note: the prefix is ‘park’. The syntactic search in the prefix searches. The prefix match results are search results that contain the word ‘park’ ‘parked,’ and ‘parking’ (and other words that start with ‘park’);
in response to determining that the user query exceeds the threshold length (i.e. “Responsively, the text summarization manager 226 can then tokenize text of the content item to generate tokens, and then responsively and progressively add tokens until the token threshold (indicating the input size constraint) is met or exceeded, at which point the model prompt is generated”; para. [0063]; Examiner note: the user query being entered is interpreted as the add tokens. The user query exceeds the threshold length is interpreted as the token threshold (indicating the input size constraint) is met or exceeded. The in response to determining is interpreted as the responsively),
performing, by the computing system, a semantic search (i.e. “performing a semantic search”; para. [0069]), the semantic search comprising:
generating at least one embedding for the user query (i.e. “to generate a query text embedding”; para. [0088]; Examiner note: the user query is interpreted as the query text); and
matching the at least one embedding for the user query to a subset of the plurality of embeddings (i.e. “As such, a semantic search includes comparing text embeddings or vectors and determining a similarity distance between the embeddings or vectors.”; para. [0070]. Examiner note: the matching is the determining a similarity. The at least one embedding for the user query to a subset of the plurality of embeddings is interpreted as the between the embeddings or vectors),
wherein the subset of the plurality of embeddings correspond to a subset of the plurality of search terms (i.e. “semantic search data includes text embeddings representing the content items.”; para. [0112]; Examiner note: the plurality of search terms in interpreted as the semantic search data);
However, it is noted that the prior art of Matson does not explicitly teach “detecting, by the computing system, an initiation of a user query; determining, by the computing system and in real-time with respect to the user query being entered, that the user query exceeds a threshold length; providing, by the computing system, a plurality of recommended search terms, wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms.”
On the other hand, in the same field of endeavor, Chaudhuri teaches detecting, by the computing system, an initiation of a user query (i.e. “when a system detects a recommendation-generation trigger, such as a user navigating to a search browser page,”; para. [0022]. Further, i.e. “a user may begin entering text into a text search field. The system may detect the trigger prior to the user completing a text entry action “; para. [0053]; Examiner note: the initiation of a user query is interpreted as the user navigating to a search browser page and user may begin entering text into a text search field);
determining, by the computing system and in real-time with respect to the user query being entered (i.e. “As the system detects that the number of search history records meets the respective thresholds, the system automatically, without user intervention, modifies the values in the respective fields of the data object resource to enable/disable different types of search filter recommendations.”; fig. 2A, para. [0067]; Examiner note: the in real-time is interpreted as the automatically),
that the user query exceeds a threshold length (i.e. “the system determines if a set of machine learning-generated search filter recommendation criteria are met (Operation 210).”; fig. 2A, para. [0067]-[0069]; Examiner note: the determining that the user query exceeds a threshold length is interpreted as the system determines if the set of machine learning-generated search filter recommendation criteria are met. Further, “The policy may assign a highest weight to a user-specific machine learning model recommendation if the recommendation has a confidence score exceeding a threshold.”; para. [0080], [0093]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Chaudhuri that teaches generating and presenting search filter recommendations into the prior art of Matson that teaches providing comprehensive search results. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to have a search engine that attempts to suggest search terms based on what the user frequently retrieves because it can reduce the burden on the user by providing suggestions for words to enter into a search field (Chaudhuri, para. [0003]).
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “providing, by the computing system, a plurality of recommended search terms, wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms.”
On the other hand, in the same field of endeavor, Teran teaches providing, by the computing system, a plurality of recommended search terms (i.e. “determine a plurality of suggested search queries.”; fig.10, para. [0082]; Examiner note: the recommended search terms are interpreted as the suggested search queries),
wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms (i.e. “In the example of FIG. 4, window 404 displays the suggested search queries of “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” and “cnnsi” for the entered characters of “cnn.” Typically, the suggested search queries are ordered according to a ranking of their respective popularity to the general population of users of search engine 106”; fig. 4, para. [0044]; Examiner note: the plurality of prefix match results is interpreted as the “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” and “cnnsi” for the entered characters of “cnn.”. The subset of the plurality of search terms is “cnn”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
As per claim 13, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 12 above.
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “wherein the threshold length comprises a character count of the one or more characters included in the user query.”
On the other hand, in the same field of endeavor, Teran teaches wherein the threshold length comprises a character count of the one or more characters included in the user query (i.e. “after a suitable number of characters is/are entered into entry box 402, including one character, two characters, three characters, or further numbers of characters.”; para. [0044]; Examiner note: the threshold length is interpreted as the a suitable number of characters).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
As per claim 14, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 12 above.
Additionally, Matson teaches wherein: the at least one embedding corresponds to at least one user query vector representation (i.e. “A text embedding is generally in the form of a vector.”; para. [0087]);
the plurality of embeddings correspond to a plurality of search entity vector representations (i.e. “generate text embeddings, for example, in the form of vectors,”; [0071]), and
matching the at least one embedding for the user query to the subset of the plurality of embeddings comprises determining a plurality of cosine similarities between the at least one user query vector representation and the plurality of search entity vector representations (i.e. “cosine similarity may be used to determine similarity between a query embedding and a content embedding.”; para. [0019], [0070], [0087], [0090]-[0092]; Examiner note: the determining the plurality of cosine similarities between the at least one user query vector representation and the plurality of search entity vector representations is interpreted as the determine similarity between the query embedding and the content embedding).
As per claim 16, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 12 above.
Additionally, Matson teaches wherein the syntactic search further comprises applying weights to the plurality of prefix match results (i.e. “semantic similarity or distance may be used to identify or determine a weight or rank associated with a search result.”; para. [0099]), and
wherein the weights are based on a popularity of the plurality of prefix match results, wherein the popularity is based on historical user interaction with the plurality of prefix match results (i.e. “the suggested search queries of “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” and “cnnsi” for the entered characters of “cnn.” Typically, the suggested search queries are ordered according to a ranking of their respective popularity to the general population of users”; para. [0044]; Examiner note: the weights are based on the plurality of prefix match results is interpreted as the ranking of their respective popularity).
As per claim 18, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 12 above.
Additionally, Matson teaches wherein providing the plurality of recommended search terms further comprises: scoring, by the computing system, each of the plurality of prefix match results and the subset of the plurality of search terms; and ordering, by the computing system, each of the plurality of prefix match results and the subset of the plurality of search terms based on the scoring (i.e. “search results, of the set of search results, corresponding with the first set of content items semantically similar to the search query and search results, of the set of search results, corresponding with the second set of content items lexically similar to the search query are interleaved with one another based on a relevance ranking indicating relevance of the corresponding content item to the search query.”; para. [0120]).
As per claim 20, Matson teaches a non-transitory computer-readable medium storing instructions (i.e. “The computer-readable media may include computer-readable instructions”; para. [0034]) that,
when executed by one or more processors of a processing circuit (i.e. “executable by one or more processors”; para. [0034]),
cause the processing circuit to: generate, using a large language model, a plurality of embeddings corresponding to a plurality of search terms (i.e. “the text summary is generated, via a large language model (LLM), by providing, to the LLM, a model prompt including at least a portion of the text of the content item. At block 406, a text embedding representing the text summary is generated,”; fig. 4, para. [0114]. Further, i.e. “text embeddings associated with text summaries of content items”; para. [0090]; Examiner note: the embeddings are interpreted as the text embeddings. The search terms are interpreted as the text summaries of the content items);
wherein the user query comprises one or more characters (i.e. “for a query of “park,””; para. [0076]; Examiner note: the one or more characters is interpreted as the “park”);
perform a syntactic search, wherein the syntactic search comprises identifying a plurality of prefix match results based on the one or more characters included in the user query (i.e. “In prefix searches, the search returns results with terms that contain the word followed by zero or more characters. For example, for a query of “park,” search results that contain the word ‘park,’ ‘parked,’ and ‘parking’ (and other words that start with ‘park’) are returned.”; para. [0076]; Examiner note: the prefix is ‘park’. The syntactic search in the prefix searches. The prefix match results are search results that contain the word ‘park’ ‘parked,’ and ‘parking’ (and other words that start with ‘park’);
in response to determining that the user query exceeds the threshold length (i.e. “Responsively, the text summarization manager 226 can then tokenize text of the content item to generate tokens, and then responsively and progressively add tokens until the token threshold (indicating the input size constraint) is met or exceeded, at which point the model prompt is generated”; para. [0063]; Examiner note: the user query being entered is interpreted as the add tokens. The user query exceeds the threshold length is interpreted as the token threshold (indicating the input size constraint) is met or exceeded. The in response to determining is interpreted as the responsively),
perform a semantic search (i.e. “performing a semantic search”; para. [0069]), the semantic search comprising:
generating at least one embedding for the user query (i.e. “to generate a query text embedding”; para. [0088]; Examiner note: the user query is interpreted as the query text); and
matching the at least one embedding for the user query to a subset of the plurality of embeddings (i.e. “As such, a semantic search includes comparing text embeddings or vectors and determining a similarity distance between the embeddings or vectors.”; para. [0070]; Examiner note: the matching is the determining a similarity. The at least one embedding for the user query to a subset of the plurality of embeddings is interpreted as the between the embeddings or vectors.),
wherein the subset of the plurality of embeddings correspond to a subset of the plurality of search terms (i.e. “semantic search data includes text embeddings representing the content items.”; para. [0112]; Examiner note: the plurality of search terms in interpreted as the semantic search data);
However, it is noted that the prior art of Matson does not explicitly teach “detect an initiation of a user query; determine, in real-time with respect to the user query being entered, that the user query exceeds a threshold length; provide a plurality of recommended search terms, wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms.”
On the other hand, in the same field of endeavor, Chaudhuri teaches detect an initiation of a user query (i.e. “when a system detects a recommendation-generation trigger, such as a user navigating to a search browser page,”; para. [0022]. Further, i.e. “a user may begin entering text into a text search field. The system may detect the trigger prior to the user completing a text entry action “; para. [0053]; Examiner note: the initiation of a user query is interpreted as the user navigating to a search browser page and user may begin entering text into a text search field);
determine, in real-time with respect to the user query being entered (i.e. “As the system detects that the number of search history records meets the respective thresholds, the system automatically, without user intervention, modifies the values in the respective fields of the data object resource to enable/disable different types of search filter recommendations.”; fig. 2A, para. [0067]; Examiner note: the in real-time is interpreted as the automatically),
that the user query exceeds a threshold length (i.e. “the system determines if a set of machine learning-generated search filter recommendation criteria are met (Operation 210).”; fig. 2A, para. [0067]-[0069]; Examiner note: the determining that the user query exceeds a threshold length is interpreted as the system determines if the set of machine learning-generated search filter recommendation criteria are met. Further, “The policy may assign a highest weight to a user-specific machine learning model recommendation if the recommendation has a confidence score exceeding a threshold.”; para. [0080], [0093]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Chaudhuri that teaches generating and presenting search filter recommendations into the prior art of Matson that teaches providing comprehensive search results. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to have a search engine that attempts to suggest search terms based on what the user frequently retrieves because it can reduce the burden on the user by providing suggestions for words to enter into a search field (Chaudhuri, para. [0003]).
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “provide a plurality of recommended search terms, wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms.”
On the other hand, in the same field of endeavor, Teran teaches provide a plurality of recommended search terms (i.e. “determine a plurality of suggested search queries.”; fig.10, para. [0082]; Examiner note: the recommended search terms is interpreted as the suggested search queries),
wherein the plurality of recommended search terms are provided based on a ranking of the plurality of prefix match results and the subset of the plurality of search terms (i.e. “In the example of FIG. 4, window 404 displays the suggested search queries of “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” and “cnnsi” for the entered characters of “cnn.” Typically, the suggested search queries are ordered according to a ranking of their respective popularity to the general population of users of search engine 106”; fig. 4, para. [0044]; Examiner note: the plurality of prefix match results is interpreted as the “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” and “cnnsi” for the entered characters of “cnn.”. The subset of the plurality of search terms is “cnn”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
7. Claims 5 and 15 are rejected under 35 U.S.C. § 103 as being unpatentable over Matson et al. (US 20240419710 A1) in view of Chaudhuri et al. (US 20250005083 A1) in further view of Teran et al. (US 20100131902 A1) still in further view of Ramanath et al. (US 20200004886 A1).
As per claim 5, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 4 above.
Additionally, Matson teaches wherein the subset of the plurality of search terms are ranked according to the plurality of cosine similarities (i.e. “Based on determined similarities, a set of content items, or search results, can be identified as relevant to a search query. In embodiments, the search results may correspond with a weight or a rank indicating an extent of relevance or relatedness to the search query.”; para. [0093]),
the plurality of cosine similarities being based on distances between the at least one user query vector representation and the plurality of search entity vector representations (i.e. “semantic search includes comparing vectors and determining a similarity distance between the vectors.”; para. [0092]);
However, it is noted that the combination of prior arts of Matson, Chaudhuri and Teran do not explicitly teach “wherein a smaller distance between the at least one user query vector representation and one of the plurality of search entity vector representations correlates to a greater cosine similarity.”
On the other hand, in the same field of endeavor, Ramanath teaches wherein a smaller distance between the at least one user query vector representation and one of the plurality of search entity vector representations correlates to a greater cosine similarity (i.e. “the third neural network determines the level of similarity between the profile vector representation of the one of the plurality of user profiles and the query vector representation based on a cosine similarity calculation”; para. [0046]. Further, i.e. “a member that is similar to the query will have a very high cosine similarity”; para. [0099]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramanath that teaches implementing an architecture for neural networks used for search into the combination of the prior arts of Matson that teaches providing comprehensive search results, Chaudhuri that teaches generating and presenting search filter recommendations, and Teran that teaches a search assistance is provided to users that submit search queries to search engines. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to generating supervised embedding representations for search because it can maximize the relevance of the search results, while avoiding latency issues that hinder other search systems (Ramanath, para. [0018]-[0019]).
As per claim 15, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 14 above.
Additionally, Matson teaches wherein the subset of the plurality of search terms are ranked according to the plurality of cosine similarities (i.e. “Based on determined similarities, a set of content items, or search results, can be identified as relevant to a search query. In embodiments, the search results may correspond with a weight or a rank indicating an extent of relevance or relatedness to the search query.”; para. [0093]),
the plurality of cosine similarities being based on distances between the at least one user query vector representation and the plurality of search entity vector representations (i.e. “semantic search includes comparing vectors and determining a similarity distance between the vectors.”; para. [0092]);
However, it is noted that the combination of prior arts of Matson, Chaudhuri and Teran do not explicitly teach “wherein a smaller distance between the at least one user query vector representation and one of the plurality of search entity vector representations correlates to a greater cosine similarity.”
On the other hand, in the same field of endeavor, Ramanath teaches wherein a smaller distance between the at least one user query vector representation and one of the plurality of search entity vector representations correlates to a greater cosine similarity (i.e. “the third neural network determines the level of similarity between the profile vector representation of the one of the plurality of user profiles and the query vector representation based on a cosine similarity calculation”; para. [0046]. Further, i.e. “a member that is similar to the query will have a very high cosine similarity”; para. [0099]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramanath that teaches implementing an architecture for neural networks used for search into the combination of the prior arts of Matson that teaches providing comprehensive search results, Chaudhuri that teaches generating and presenting search filter recommendations, and Teran that teaches a search assistance is provided to users that submit search queries to search engines. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to generating supervised embedding representations for search because it can maximize the relevance of the search results, while avoiding latency issues that hinder other search systems (Ramanath, para. [0018]-[0019]).
8. Claims 8, 10, 17 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Matson et al. (US 20240419710 A1) in view of Chaudhuri et al. (US 20250005083 A1) in further view of Teran et al. (US 20100131902 A1) still in further view of Li et al. (US 20210319068 A1).
As per claim 8, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson and Chaudhuri do not explicitly teach “wherein the plurality of recommended search terms include a number of prefix match results and a number of semantic search results, wherein the number of semantic search results increases and the number of prefix match results decreases as a number of characters in the user query.”
On the other hand, in the same field of endeavor, Teran teaches wherein the number of semantic search results increases and the number of prefix match results decreases as a number of characters in the user query increases (i.e. “suggested search manager 504 may determine the following suggested search queries for “cnn,” listed in order of decreasing popularity (e.g., based on number of clicks) among users having search history tracked in world search history 716: “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” “cnnsi,” and “cnn sports.””; para. [0083]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Teran that teaches a search assistance is provided to users that submit search queries to search engines into the combination of the prior arts of Matson that teaches providing comprehensive search results, and Chaudhuri that teaches generating and presenting search filter recommendations. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
However, it is noted that the combination of prior arts of Matson, Chaudhuri and Teran do not explicitly teach “wherein the plurality of recommended search terms include a number of prefix match results and a number of semantic search results;”
On the other hand, in the same field of endeavor, Li teaches wherein the plurality of recommended search terms include a number of prefix match results and a number of semantic search results (i.e. “aggregating search results from both the term search and the semantic search;”; para. [0251]; Examiner note: number of prefix match results and a number of semantic search results is interpreted as the term search and the semantic search);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Li that teaches application searching into the combination of the prior arts of Matson that teaches providing comprehensive search results, Chaudhuri that teaches generating and presenting search filter recommendations, and Teran that teaches a search assistance is provided to users that submit search queries to search engines. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to enable users to open a document and submit a query to the in-app search tool to locate relevant sections within the file because it can dramatically reduce information retrieval time, allowing users to bypass manual scrolling and find specific data points in seconds (Li, para. [0002]).
As per claim 10, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 1 above.
Additionally, Matson teaches wherein the instructions cause the processing circuit to perform operations comprising: determining a prefix match result matches a search term from the subset of the plurality of search terms (i.e. “In prefix searches, the search returns results with terms that contain the word followed by zero or more characters. For example, for a query of “park,” search results that contain the word ‘park,’ ‘parked,’ and ‘parking’ (and other words that start with ‘park’) are returned.”; para. [0076]);
However, it is noted that the combination of prior arts of Matson, Chaudhuri and Teran do not explicitly teach “responsive to determining the prefix match result matches the search term, removing one of the prefix match result or the search term from the plurality of recommended search terms.”
On the other hand, in the same field of endeavor, Li teaches responsive to determining the prefix match result matches the search term, removing one of the prefix match result or the search term from the plurality of recommended search terms (i.e. “Operation 810 combines the search results from the semantic search and term search and removes duplicate results. In some embodiments, preference is given to the term search result so that semantic result is removed. In other embodiments, preference is given to the semantic search result so that the term search result is removed.”; para. [0100]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Li that teaches application searching into the combination of the prior arts of Matson that teaches providing comprehensive search results, Chaudhuri that teaches generating and presenting search filter recommendations, and Teran that teaches a search assistance is provided to users that submit search queries to search engines. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to enable users to open a document and submit a query to the in-app search tool to locate relevant sections within the file because it can dramatically reduce information retrieval time, allowing users to bypass manual scrolling and find specific data points in seconds (Li, para. [0002]).
As per claim 17, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 12 above.
However, it is noted that the combination of prior arts of Matson, Chaudhuri and Teran do not explicitly teach “wherein the plurality of recommended search terms include a number of prefix match results and a number of semantic search results, wherein the number of semantic search results increases and the number of prefix match results decreases as a number of characters in the query increases.”
On the other hand, in the same field of endeavor, Li teaches wherein the plurality of recommended search terms include a number of prefix match results and a number of semantic search results (i.e. “aggregating search results from both the term search and the semantic search;”; para. [0251]; Examinr note: number of prefix match results and a number of semantic search results is interpreted as the term search and the semantic search),
wherein the number of semantic search results increases and the number of prefix match results decreases as a number of characters in the query increases (i.e. “suggested search manager 504 may determine the following suggested search queries for “cnn,” listed in order of decreasing popularity (e.g., based on number of clicks) among users having search history tracked in world search history 716: “cnn news,” “cnn money,” “cnn headline news,” “cnn politics,” “cnnsi,” and “cnn sports.””; para. [0083]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Li that teaches application searching into the combination of the prior arts of Matson that teaches providing comprehensive search results, Chaudhuri that teaches generating and presenting search filter recommendations, and Teran that teaches a search assistance is provided to users that submit search queries to search engines. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to enable users to open a document and submit a query to the in-app search tool to locate relevant sections within the file because it can dramatically reduce information retrieval time, allowing users to bypass manual scrolling and find specific data points in seconds (Li, para. [0002]).
As per claim 19, Matson, Chaudhuri and Teran teach all the limitations as discussed in claim 12 above.
Additionally, Matson teaches further comprises: wherein determining, by the computing system, a prefix match result matches a search term from the subset of the plurality of search terms (i.e. “In prefix searches, the search returns results with terms that contain the word followed by zero or more characters. For example, for a query of “park,” search results that contain the word ‘park,’ ‘parked,’ and ‘parking’ (and other words that start with ‘park’) are returned.”; para. [0076]);
However, it is noted that the combination of prior arts of Matson, Chaudhuri and Teran do not explicitly teach “responsive to identifying the prefix match result matches the search term, removing, by the computing system, removing one of the prefix match result or the search term from the plurality of recommended search terms.”
On the other hand, in the same field of endeavor, Li teaches responsive to identifying the prefix match result matches the search term, removing, by the computing system, removing one of the prefix match result or the search term from the plurality of recommended search terms (i.e. “Operation 810 combines the search results from the semantic search and term search and removes duplicate results. In some embodiments, preference is given to the term search result so that semantic result is removed. In other embodiments, preference is given to the semantic search result so that the term search result is removed.”; para. [0100]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Li that teaches application searching into the combination of the prior arts of Matson that teaches providing comprehensive search results, Chaudhuri that teaches generating and presenting search filter recommendations, and Teran that teaches a search assistance is provided to users that submit search queries to search engines. Additionally, this can perform a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.
The motivation for doing so would be to enable users to open a document and submit a query to the in-app search tool to locate relevant sections within the file because it can dramatically reduce information retrieval time, allowing users to bypass manual scrolling and find specific data points in seconds (Li, para. [0002]).
Prior Art of Record
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
He et al. (US 20240362286 A1), teaches an artificial intelligence (AI) platform to search a document collection.
Venkateshwaran et al. (US 20230117206 A1), teaches a computerized method for extracting domain specific insights from a corpus of files containing large documents.
Sen et al. (US 20210286850 A1), teaches generating a search query using flexible autocomplete suggestions.
Holt et al. (US 20180189297 A1), teaches generating a search query.
Yoo et al. (US 20160306898 A1), teaches recommending a query word using a domain property.
Roskind (US 8924409 B1), teaches search terms may be presented to the user of a search engine by automatically populating the search term data field with relevant search terms or combinations of search terms (e.g., "auto-completion").
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTONIO CAIA DO whose telephone number is (469)295-9251. The examiner can normally be reached on Monday - Friday / 06:30 to 16:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ng, Amy can be reached on (571) 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANTONIO J CAIA DO/
Examiner, Art Unit 2164