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.
Response to Amendment
3. This office action is in response to applicant’s amendment filed on 06/29/2026 in response to the non-final action mailed on 12/30/2025. Claims 1-3, 12-13, 17 and 19-20 have been amended. Claims 4-11,14-16 and 18 have been kept original. Amendment has been entered.
Response to Arguments
4. Applicant's arguments, filed on 06/29/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. §101 an abstract idea (mental process) (Applicant’s arguments, page 8), have been fully considered but are not persuasive. Respectfully, the examiner disagrees, see the clarification below.
The applicant argues that the independent claims integrate into a practical application and/or amount to significantly more than any alleged abstract idea. However, the applicant's argument is vague and fails to provide any limitation or element showing improvement to a technical field or technology that would integrate the claims into a practical application. The Examiner analyzed every limitation in light of the specification and did not recognize any claimed element that would make an improvement to a technical field or technology, see MPEP 2106.04(d). The applicant also failed to provide a limitation or element amounting to significantly more than the clamed abstract idea. The Examiner considered every single element and has not identified any limitation or element amounting to significantly more than any claimed abstract idea, see MPEP 2106.
For the above reasons, the rejection of the claims 1-20 under 35 U.S.C. § 101 as an abstract idea (mental process) is upheld.
Applicant's arguments, filed on 06/29/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. §103 (Applicant’s arguments, pages 9-12), have been fully considered and are but are moot and/or not persuasive.
The arguments regarding the following limitations “generating, using a large language model, a plurality of embeddings corresponding to a plurality of search terms stored for recommendation; determining, in real-time and while the user query is being entered, that the user query exceeds a character count threshold; 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 stored for recommendation; and providing a plurality of recommended search terms” are moot because the limitations have been amended to introduce new limitations not previously presented, and newly found prior art has been applied, see the rejection below. Therefore, the arguments related to those limitations are moot.
The arguments regarding the following limitations “wherein the plurality of recommended search terms are provided according to a ranking generated using (i) the plurality of prefix match results and (ii) the subset of the plurality of search terms” is not persuasive.
The Examiner also noted that the Applicant's representative argued that the prior art of Matson et al. (US 20240419710 A1) does note teach “wherein the plurality of recommended search terms are provided according to a ranking generated using (i) the plurality of prefix match results and (ii) the subset of the plurality of search terms”. The Examiner respectfully disagrees.
The prior art of Matson teaches “the search result manager 238 may order or rank the search results such that the search results are interleaved with one another. In this regard, search results generated via a semantic search are interleaved with search results generated via a lexical search, for example, based on relevance to the search result, date of content item, alphabetical order, and/or the like.”; fig. 2, para. [0103]. Using the broadest reasonable interpretation (BRI), the rank generated in the prior art of Matson can be the same as the rank claimed. A person of ordinary skill in the art can reasonably interpret the plurality of prefix match results as search results generated via a semantic search, and the subset of the plurality of search terms as search results generated via a lexical search.
For those reasons, the Examiner believes that the prior art of Matson teaches this limitation of the claim.
It is noted that the applicant argued that “Ramanath and Li are cited for allegedly teaching certain features of various dependent claims, which Applicant does not agree with or concede.” the Applicant raise the arguments without pointing to any specific element of the claim (Applicant’s arguments, page 12). The Applicant vaguely simply says “Without acquiescing to or agreeing with the alleged teachings of Ramanath and Li, Applicant respectfully submits that Ramanath and Li, alone or in any proper combination, fail to cure the deficiencies of Matson, Chaudhuri, and Teran discussed herein.” The Examiner does not rely on Ramanath and Li to teach any limitation of the independent claims 1, 12 and 20.
Applicant’s remaining arguments with respect to the dependent claims 2-11 and 13-19 have been considered but are moot because the arguments do not apply to the newly introduce references being used in the current 35 U.S.C. §103 rejection of the independent claims 1, 12 and 20.
Examiner's Notes
5. A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for all that it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed were instead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck & Co. v. Biocraft Labs., Inc., 874 F.2d 804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1,215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163 USPQ 545, 549 (CCPA 1969).
Examiner is entitled to give claim limitations their broadest reasonable interpretation in light of the specification. See MPEP 2111 [R-1]. Interpretation of claims during patent examination, the pending claims must be given the broadest reasonable interpretation consistent with the specification. Applicant always has the opportunity to amend the claims during prosecution and broad interpretation by the examiner reduces the possibility that the claim, once issued, will be interpreted more broadly than is justified. In re Prater, 162 USPQ 541,550- 51 (CCPA 1969).
Claim Rejections - 35 USC § 101
6. 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 stored for recommendation;” 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 and while the user query is being entered, that the user query exceeds a character count threshold;” 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 character count threshold, 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 stored for recommendation;” 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 according to a ranking generated using (i) the plurality of prefix match results and (ii) 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 the character count threshold, 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 character count threshold is based on a 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
7. 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.
8. Claims 1, 4, 6, 12, 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 Lichtenberg et al. (US 12566794 B1) in further view of Lewin-Eytan et al. (US 20200012686 A1).
As per claim 1, Matson teaches a computing system comprising (i.e. “computer system”; Abstract):
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]):
performing a syntactic search (i.e. “In some cases, with a prefix search, a wildcard character (e.g., *) is placed at the end of a word in keywords or property: value queries.”; fig. 5, para. [0076]. Further, i.e. “As can be appreciated, in some implementations, the search query may additionally be used to perform a prefix search to identify a second content item, of the content items, as lexically similar to the search query.”; figs. 6-8, para. [0116]-[0117]; Examiner note: the syntactic search is interpreted as the prefix 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 character count threshold (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 in response to determining that the user query exceeds the character count threshold is interpreted as the token threshold (indicating the input size constraint) is met or exceeded),
performing a semantic search, the semantic search comprising (i.e. “performing a semantic search”; para. [0069]):
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);
wherein the plurality of recommended search terms are provided according to a ranking generated using (i) the plurality of prefix match results and (ii) the subset of the plurality of search terms (i.e. “the search result manager 238 may order or rank the search results such that the search results are interleaved with one another. In this regard, search results generated via a semantic search are interleaved with search results generated via a lexical search, for example, based on relevance to the search result, date of content item, alphabetical order, and/or the like.”; fig. 2, para. [0103]; Examiner note: Using BRI the plurality of prefix match results can be interpreted as the search results generated via a semantic search; the subset of the plurality of search terms can be interpreted as the search results generated via a lexical search).
However, it is noted that the prior art of Matson does not explicitly teach “generating, using a large language model, a plurality of embeddings corresponding to a plurality of search terms stored for recommendation; detecting an initiation of a user query, wherein the user query comprises one or more characters; determining, in real-time and while the user query is being entered, that the user query exceeds a character count threshold; 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 stored for recommendation; and providing a plurality of recommended search terms;”
On the other hand, in the same field of endeavor, Lichtenberg teaches generating, using a large language model (i.e. “The suggestion above may be generated by the LLM and include text from actual media items or based on actual media items.”; figs. 6A-D, Column 20, Lines 5-7), a plurality of embeddings corresponding to a plurality of search terms stored for recommendation (i.e. “The service may generate personalized visual search suggestions based at least in part on the user history, as in 708. The personalized visual search suggestions may include virtually any words, such as names of artists, names of songs, genres, moods, personal interests, temporal information, feelings, things to exclude or omit, or other text.”; figs. 2, 6A-D, Column 20, Lines 17-45; Examiner notes: the generating the plurality of embeddings is interpreted as the generate personalized visual search suggestions; the plurality of search terms stored for recommendation is interpreted as the user history);
matching the at least one embedding for the user query (i.e. “The user request 681 may be a natural language request, such as a request for a playlist for a “relaxing dinner party.””; fig. 6D, Column 19, Lines 3-8) to a subset of the plurality of embeddings (i.e. “The system may process the user request as described herein. Since this request is broad, the system may provide different interpretations of the request and generate a playlist for at least some of the interpretations. Even more narrow requests, such as “rock music” may be interpreted in different ways (e.g., guitar rock, classic rock, popular rock, 80s Rock, etc.).”; fig. 6D, Column 19, Lines 8-15; Examiner note: Using BRI the subset of the plurality of embeddings can be interpreted herein as the playlist for at least some of the interpretations; as it also illustrated in fig. 6D),
wherein the subset of the plurality of embeddings correspond to a subset of the plurality of search terms stored for recommendation (i.e. “The UI 680 may provide snippets of playlists, such as snippets 682(1), 682(2), . . . 682(N). A first snippet 682(1) may include an interpretation of “jazz” as fulfilling the request of “relaxing dinner music,” while a second snippet 682(2) may be “pop” and a last snippet 682(N) may be “alternative.” The first snippet 682(1) may include a first title 684(1) and a first partial listing 686(1) that is a partial list of media items or tracks that correspond to the first title 684(1) of the playlist.”; fig. 6D, Column 19, Lines 16-44; Using BRI the subset of the plurality of search terms stored for recommendation is interpreted as the first snippet 682(1) may include an interpretation of “jazz” as fulfilling the request of “relaxing dinner music,”); and
providing a plurality of recommended search terms (i.e. “The UI 680 may provide snippets of playlists, such as snippets 682(1), 682(2), . . . 682(N).”; fig. 6D; Column 19, Lines 16-44; Examiner note: Using BRI the plurality of recommended search terms is interpreted as the snippets 682(1), 682(2), . . . 682(N); the snippets have search terms);
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 Lichtenberg that teaches identifying relevant media content for users in response to natural language requests 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 monitor user interaction with playlists provided to the user, as this can be used as a refinement tool to improve the training data (Lichtenberg, Column 10, Lines 1-23).
However, it is noted that the combination of prior arts of Matson and Lichtenberg do not explicitly teach “detecting an initiation of a user query, wherein the user query comprises one or more characters; determining, in real-time and while the user query is being entered, that the user query exceeds a character count threshold;”
On the other hand, in the same field of endeavor, Lewin-Eytan teaches detecting an initiation of a user query (i.e. “Process 400 begins with Step 402 where input corresponding to a search query to be performed is received or detected.” fig. 4, para. [0097]),
wherein the user query comprises one or more characters (i.e. “An example of such input is illustrated by the entered search term “goals” in search box 502, as illustrated in FIG. 5A.”; fig. 5A, para. [0097]; Examiner note: the entered search term “goals” has one or more characters);
determining, in real-time and while the user query is being entered (i.e. “Therefore, as the user enters characters in a search box of an inbox, a real-time determination is performed that monitors how many characters are being entered, and upon the threshold length value being satisfied, Step 404 is automatically triggered.”; figs. 4, 5A-E, para. [0097]),
that the user query exceeds a character count threshold (i.e. “An example of such input is illustrated by the entered search term “goals” in search box 502, as illustrated in FIG. 5A. As discussed above, such input can be a search term, or a string of characters satisfying a threshold length value.”; figs. 4, 5A-E; Examiner note: the user query exceeds the character count threshold is interpreted as the search term, or a string of characters satisfying a threshold length value);
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 Lewin-Eytan 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 Lichtenberg that teaches identifying relevant media content for users in response to natural language requests. 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 improve the framework for automatically generating and providing a set of interactive autocomplete suggestions used to search a collection of messages because it can provide better interaction between systems and users (Lewin-Eytan, para. [0002]).
As per claim 4, Matson, Lichtenberg and Lewin-Eytan 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, Lichtenberg and Lewin-Eytan 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 12, Matson teaches a method comprising (i.e. “Methods, computer systems, computer-storage media, and graphical user interfaces are provided for providing comprehensive search results.”; Abstract):
performing, by the computing system, a syntactic search (i.e. “In some cases, with a prefix search, a wildcard character (e.g., *) is placed at the end of a word in keywords or property: value queries.”; fig. 5, para. [0076]. Further, i.e. “As can be appreciated, in some implementations, the search query may additionally be used to perform a prefix search to identify a second content item, of the content items, as lexically similar to the search query.”; figs. 6-8, para. [0116]-[0117]; Examiner note: the syntactic search is interpreted as the prefix 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 character count threshold (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 in response to determining that the user query exceeds the character count threshold is interpreted as the token threshold (indicating the input size constraint) is met or exceeded),
performing, by the computing system, a semantic search, the semantic search comprising (i.e. “performing a semantic search”; para. [0069]):
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);
wherein the plurality of recommended search terms are provided according to a ranking generated using (i) the plurality of prefix match results and (ii) the subset of the plurality of search terms (i.e. “the search result manager 238 may order or rank the search results such that the search results are interleaved with one another. In this regard, search results generated via a semantic search are interleaved with search results generated via a lexical search, for example, based on relevance to the search result, date of content item, alphabetical order, and/or the like.”; fig. 2, para. [0103]; Examiner note: Using BRI the plurality of prefix match results can be interpreted as the search results generated via a semantic search; the subset of the plurality of search terms can be interpreted as the search results generated via a lexical search).
However, it is noted that the prior art of Matson does not explicitly teach “generating, by a computing system using a large language model, a plurality of embeddings corresponding to a plurality of search terms stored for recommendation; detecting, by the computing system, an initiation of a user query, wherein the user query comprises one or more characters; determining, by the computing system and in real-time and while the user query is being entered, that the user query exceeds a character count threshold; and 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 stored for recommendation; and providing, by the computing system, a plurality of recommended search terms;”
On the other hand, in the same field of endeavor, Lichtenberg teaches generating, by a computing system using a large language model (i.e. “The suggestion above may be generated by the LLM and include text from actual media items or based on actual media items.”; figs. 6A-D, Column 20, Lines 5-7), a plurality of embeddings corresponding to a plurality of search terms stored for recommendation (i.e. “The service may generate personalized visual search suggestions based at least in part on the user history, as in 708. The personalized visual search suggestions may include virtually any words, such as names of artists, names of songs, genres, moods, personal interests, temporal information, feelings, things to exclude or omit, or other text.”; figs. 2, 6A-D, Column 20, Lines 17-45; Examiner notes: the generating the plurality of embeddings is interpreted as the generate personalized visual search suggestions; the plurality of search terms stored for recommendation is interpreted as the user history);
matching the at least one embedding for the user query (i.e. “The user request 681 may be a natural language request, such as a request for a playlist for a “relaxing dinner party.””; fig. 6D, Column 19, Lines 3-8) to a subset of the plurality of embeddings (i.e. “The system may process the user request as described herein. Since this request is broad, the system may provide different interpretations of the request and generate a playlist for at least some of the interpretations. Even more narrow requests, such as “rock music” may be interpreted in different ways (e.g., guitar rock, classic rock, popular rock, 80s Rock, etc.).”; fig. 6D, Column 19, Lines 8-15; Examiner note: Using BRI the subset of the plurality of embeddings can be interpreted herein as the playlist for at least some of the interpretations; as it also illustrated in fig. 6D),
wherein the subset of the plurality of embeddings correspond to a subset of the plurality of search terms stored for recommendation (i.e. “The UI 680 may provide snippets of playlists, such as snippets 682(1), 682(2), . . . 682(N). A first snippet 682(1) may include an interpretation of “jazz” as fulfilling the request of “relaxing dinner music,” while a second snippet 682(2) may be “pop” and a last snippet 682(N) may be “alternative.” The first snippet 682(1) may include a first title 684(1) and a first partial listing 686(1) that is a partial list of media items or tracks that correspond to the first title 684(1) of the playlist.”; fig. 6D, Column 19, Lines 16-44; Using BRI the subset of the plurality of search terms stored for recommendation is interpreted as the first snippet 682(1) may include an interpretation of “jazz” as fulfilling the request of “relaxing dinner music,”); and
providing, by the computing system, a plurality of recommended search terms (i.e. “The UI 680 may provide snippets of playlists, such as snippets 682(1), 682(2), . . . 682(N).”; fig. 6D; Column 19, Lines 16-44; Examiner note: Using BRI the plurality of recommended search terms is interpreted as the snippets 682(1), 682(2), . . . 682(N); the snippets have search terms);
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 Lichtenberg that teaches identifying relevant media content for users in response to natural language requests 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 monitor user interaction with playlists provided to the user, as this can be used as a refinement tool to improve the training data (Lichtenberg, Column 10, Lines 1-23).
However, it is noted that the combination of prior arts of Matson and Lichtenberg do not explicitly teach “detecting, by the computing system, an initiation of a user query; determining, by the computing system and in real-time and while the user query is being entered, that the user query exceeds a character count threshold;”
On the other hand, in the same field of endeavor, Lewin-Eytan teaches detecting, by the computing system, an initiation of a user query (i.e. “Process 400 begins with Step 402 where input corresponding to a search query to be performed is received or detected.” fig. 4, para. [0097]),
wherein the user query comprises one or more characters (i.e. “An example of such input is illustrated by the entered search term “goals” in search box 502, as illustrated in FIG. 5A.”; fig. 5A, para. [0097]; Examiner note: the entered search term “goals” has one or more characters);
determining, by the computing system and in real-time and while the user query is being entered (i.e. “Therefore, as the user enters characters in a search box of an inbox, a real-time determination is performed that monitors how many characters are being entered, and upon the threshold length value being satisfied, Step 404 is automatically triggered.”; figs. 4, 5A-E, para. [0097]),
that the user query exceeds a character count threshold (i.e. “An example of such input is illustrated by the entered search term “goals” in search box 502, as illustrated in FIG. 5A. As discussed above, such input can be a search term, or a string of characters satisfying a threshold length value.”; figs. 4, 5A-E; Examiner note: the user query exceeds the character count threshold is interpreted as the search term, or a string of characters satisfying a threshold length value);
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 Lewin-Eytan 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 Lichtenberg that teaches identifying relevant media content for users in response to natural language requests. 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 improve the framework for automatically generating and providing a set of interactive autocomplete suggestions used to search a collection of messages because it can provide better interaction between systems and users (Lewin-Eytan, para. [0002]).
As per claim 14, Matson, Lichtenberg and Lewin-Eytan 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, Lichtenberg and Lewin-Eytan 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, Lichtenberg and Lewin-Eytan 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 that (i.e. “The computer-readable media may include computer-readable instructions”; para. [0034]),
when executed by one or more processors of a processing circuit, cause the processing circuit to (i.e. “executable by one or more processors”; para. [0034]):
perform a syntactic search (i.e. “In some cases, with a prefix search, a wildcard character (e.g., *) is placed at the end of a word in keywords or property: value queries.”; fig. 5, para. [0076]. Further, i.e. “As can be appreciated, in some implementations, the search query may additionally be used to perform a prefix search to identify a second content item, of the content items, as lexically similar to the search query.”; figs. 6-8, para. [0116]-[0117]; Examiner note: the syntactic search is interpreted as the prefix 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 character count threshold (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 in response to determining that the user query exceeds the character count threshold is interpreted as the token threshold (indicating the input size constraint) is met or exceeded),
perform a semantic search, the semantic search comprising (i.e. “performing a semantic search”; para. [0069]):
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
wherein the plurality of recommended search terms are provided according to a ranking generated using (i) the plurality of prefix match results and (ii) the subset of the plurality of search terms (i.e. “the search result manager 238 may order or rank the search results such that the search results are interleaved with one another. In this regard, search results generated via a semantic search are interleaved with search results generated via a lexical search, for example, based on relevance to the search result, date of content item, alphabetical order, and/or the like.”; fig. 2, para. [0103]; Examiner note: Using BRI the plurality of prefix match results can be interpreted as the search results generated via a semantic search; the subset of the plurality of search terms can be interpreted as the search results generated via a lexical search).
However, it is noted that the prior art of Matson does not explicitly teach “generate, using a large language model, a plurality of embeddings corresponding to a plurality of search terms stored for recommendation; detect an initiation of a user query, wherein the user query comprises one or more characters; determine, in real-time and while the user query is being entered, that the user query exceeds a character count threshold; 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 stored for recommendation; and provide a plurality of recommended search terms;”
On the other hand, in the same field of endeavor, Lichtenberg teaches generate, using a large language model (i.e. “The suggestion above may be generated by the LLM and include text from actual media items or based on actual media items.”; figs. 6A-D, Column 20, Lines 5-7), a plurality of embeddings corresponding to a plurality of search terms stored for recommendation (i.e. “The service may generate personalized visual search suggestions based at least in part on the user history, as in 708. The personalized visual search suggestions may include virtually any words, such as names of artists, names of songs, genres, moods, personal interests, temporal information, feelings, things to exclude or omit, or other text.”; figs. 2, 6A-D, Column 20, Lines 17-45; Examiner notes: the generating the plurality of embeddings is interpreted as the generate personalized visual search suggestions; the plurality of search terms stored for recommendation is interpreted as the user history);
matching the at least one embedding for the user query (i.e. “The user request 681 may be a natural language request, such as a request for a playlist for a “relaxing dinner party.””; fig. 6D, Column 19, Lines 3-8) to a subset of the plurality of embeddings (i.e. “The system may process the user request as described herein. Since this request is broad, the system may provide different interpretations of the request and generate a playlist for at least some of the interpretations. Even more narrow requests, such as “rock music” may be interpreted in different ways (e.g., guitar rock, classic rock, popular rock, 80s Rock, etc.).”; fig. 6D, Column 19, Lines 8-15; Examiner note: Using BRI the subset of the plurality of embeddings can be interpreted herein as the playlist for at least some of the interpretations; as it also illustrated in fig. 6D),
wherein the subset of the plurality of embeddings correspond to a subset of the plurality of search terms stored for recommendation (i.e. “The UI 680 may provide snippets of playlists, such as snippets 682(1), 682(2), . . . 682(N). A first snippet 682(1) may include an interpretation of “jazz” as fulfilling the request of “relaxing dinner music,” while a second snippet 682(2) may be “pop” and a last snippet 682(N) may be “alternative.” The first snippet 682(1) may include a first title 684(1) and a first partial listing 686(1) that is a partial list of media items or tracks that correspond to the first title 684(1) of the playlist.”; fig. 6D, Column 19, Lines 16-44; Using BRI the subset of the plurality of search terms stored for recommendation is interpreted as the first snippet 682(1) may include an interpretation of “jazz” as fulfilling the request of “relaxing dinner music,”); and
provide a plurality of recommended search terms (i.e. “The UI 680 may provide snippets of playlists, such as snippets 682(1), 682(2), . . . 682(N).”; fig. 6D; Column 19, Lines 16-44; Examiner note: Using BRI the plurality of recommended search terms is interpreted as the snippets 682(1), 682(2), . . . 682(N); the snippets have search terms).
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 Lichtenberg that teaches identifying relevant media content for users in response to natural language requests 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 monitor user interaction with playlists provided to the user, as this can be used as a refinement tool to improve the training data (Lichtenberg, Column 10, Lines 1-23).
However, it is noted that the combination of prior arts of Matson and Lichtenberg do not explicitly teach “detect an initiation of a user query, wherein the user query comprises one or more characters; determine, in real-time and while the user query is being entered, that the user query exceeds a character count threshold;”
On the other hand, in the same field of endeavor, Lewin-Eytan teaches detect an initiation of a user query (i.e. “Process 400 begins with Step 402 where input corresponding to a search query to be performed is received or detected.” fig. 4, para. [0097]),
wherein the user query comprises one or more characters (i.e. “An example of such input is illustrated by the entered search term “goals” in search box 502, as illustrated in FIG. 5A.”; fig. 5A, para. [0097]; Examiner note: the entered search term “goals” has one or more characters);
determine, in real-time and while the user query is being entered (i.e. “Therefore, as the user enters characters in a search box of an inbox, a real-time determination is performed that monitors how many characters are being entered, and upon the threshold length value being satisfied, Step 404 is automatically triggered.”; figs. 4, 5A-E, para. [0097]),
that the user query exceeds a character count threshold (i.e. “An example of such input is illustrated by the entered search term “goals” in search box 502, as illustrated in FIG. 5A. As discussed above, such input can be a search term, or a string of characters satisfying a threshold length value.”; figs. 4, 5A-E; Examiner note: the user query exceeds the character count threshold is interpreted as the search term, or a string of characters satisfying a threshold length value);
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 Lewin-Eytan 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 Lichtenberg that teaches identifying relevant media content for users in response to natural language requests. 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 improve the framework for automatically generating and providing a set of interactive autocomplete suggestions used to search a collection of messages because it can provide better interaction between systems and users (Lewin-Eytan, para. [0002]).
9. Claims 2-3, 7, 9, 11 and 13 are rejected under 35 U.S.C. § 103 as being unpatentable over Matson et al. (US 20240419710 A1) in view of Lichtenberg et al. (US 12566794 B1) in further view of Lewin-Eytan et al. (US 20200012686 A1) still in further view of Teran et al. (US 20100131902 A1).
As per claim 2, Matson, Lichtenberg and Lewin-Eytan teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson, Lichtenberg and Lewin-Eytan do not explicitly teach “wherein in response to determining that the user query is below the character count threshold, 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 the character count threshold, 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 the character count threshold 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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 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, Lichtenberg and Lewin-Eytan teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson, Lichtenberg and Lewin-Eytan do not explicitly teach “wherein the character count threshold is based on a 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 character count threshold is based on a 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 count of the one or more characters included in the user query 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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 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 7, Matson, Lichtenberg and Lewin-Eytan teach all the limitations as discussed in claim 6 above.
However, it is noted that the combination of prior arts of Matson, Lichtenberg and Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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 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, Lichtenberg and Lewin-Eytan teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson, Lichtenberg and Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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 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, Lichtenberg and Lewin-Eytan teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson, Lichtenberg and Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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 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, Lichtenberg and Lewin-Eytan teach all the limitations as discussed in claim 12 above.
However, it is noted that the combination of prior arts of Matson, Lichtenberg and Lewin-Eytan do not explicitly teach “wherein the character count threshold is based on a 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 character count threshold is based on a count of the one or more characters included in the user query (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 the character count threshold 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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 provide search assistance, thereby minimizing the interaction cost by reducing the amount of manual input required from the user (Teran, para. [0004]-[0007]).
10. 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 Lichtenberg et al. (US 12566794 B1) in further view of Lewin-Eytan et al. (US 20200012686 A1) still in further view of Ramanath et al. (US 20200004886 A1).
As per claim 5, Matson, Lichtenberg and Lewin-Eytan 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, Lichtenberg and Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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, Lichtenberg and Lewin-Eytan 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, Lichtenberg and Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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]).
11. Claims 8 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Matson et al. (US 20240419710 A1) in view of Lichtenberg et al. (US 12566794 B1) in further view of Lewin-Eytan et al. (US 20200012686 A1) still 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, Lichtenberg and Lewin-Eytan teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Matson, Lichtenberg and Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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 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, Lichtenberg, Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, Lewin-Eytan that teaches a search assistance is provided to users that submit search queries to search engines, 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, Lichtenberg and Lewin-Eytan teach all the limitations as discussed in claim 12 above.
However, it is noted that the combination of prior arts of Matson, Lichtenberg and Lewin-Eytan 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 increases.”
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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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 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, Lichtenberg, Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, Lewin-Eytan that teaches a search assistance is provided to users that submit search queries to search engines, 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]).
12. Claims 10 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Matson et al. (US 20240419710 A1) in view of Lichtenberg et al. (US 12566794 B1) in further view of Lewin-Eytan et al. (US 20200012686 A1) still in further view of Li et al. (US 20210319068 A1).
As per claim 10, Matson, Lichtenberg and Lewin-Eytan 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, Lichtenberg and Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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, Lichtenberg and Lewin-Eytan teach all the limitations as discussed in claim 12 above.
Additionally, Matson teaches further comprising: 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, Lichtenberg and Lewin-Eytan 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, Lichtenberg that teaches identifying relevant media content for users in response to natural language requests, and Lewin-Eytan 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
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jensen et al. (US 20260140971 A1), teaches a search query to be executed against a database.
Lu et al. (US 20250209266 A1), teaches evaluating typeahead suggestions using a partial search query.
Tca et al. (US 20250077582 A1), teaches providing contextual suggestions and automated responses to users managing incidents within production or security environments.
Yushkina et al. (US 20240281481 A1), teaches providing context-based assistance during web browsing.
Conclusion
14. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANTONIO J CAIA DO/
Examiner, Art Unit 2164
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