Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The instant office action having application number 18/755,980, filed on June 27, 2024, has claims 1-20 pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/11/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
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 to 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A – Prong One: Claim Recites a Judicial ExceptionClaim 1 recites limitations that fall within the abstract idea groupings of mathematical concepts and mental processes.Specifically, the claim recites: “A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform: coordinating at a first system in an online mode: analyzing at least a portion of a search query using one or more query suggestion systems to determine scores for suggested search queries from the one or more query suggestion systems; and coordinating at a second system in the online mode: determining position metrics for the suggested search queries, wherein the position metrics are based on the scores for the suggested search queries; determining efficiency metrics for the one or more query suggestion systems based on the position metrics for the one or more query suggestion systems; analyzing the efficiency metrics for the one or more query suggestion systems to determine a query suggestion system of the one or more query suggestion systems that satisfies a threshold; and transmitting instructions to modify a graphical user interface (GUI) of a user device to display, to a user, one or more suggested search queries from the query suggestion system that is determined to satisfy the threshold.”These limitations constitute mathematical calculations (scores, metrics, threshold comparisons) and evaluations/judgments (selecting a system), which are mental processes.Additionally, the claim is directed to collecting information, analyzing it, and presenting results.Step 2A – Prong Two: No Integration Into a Practical ApplicationThe claim does not integrate the abstract idea into a practical application.Additional elements such as processors, memory, systems, and GUI are generic computer components performing routine functions. The coordination between systems is described at a high level without technical detail. The GUI merely displays results and is insignificant extra-solution activity.Step 2B: No Significantly MoreThe claim does not include an inventive concept.The components are well-understood, routine, and conventional. The steps are generic data processing functions. The ordered combination merely applies the abstract idea using a computer.There is no improvement to computer functionality or technical implementation.
Claim 1 is not patent-eligible under 35 U.S.C. § 101.Claim 11 is rejected based on the same rationale as claim 1.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein analyzing the at least the portion of the search query comprises analyzing the at least the portion of the search query based on historical in-session user activity information.” which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claims 3, 12 and 13.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein: the computing instructions, when executed on the one or more processors, further perform: receiving historical in-session user activity information; and receiving, via the GUI of the user device, the at least the portion of the search query; and analyzing the at least the portion of the search query further comprises: analyzing the at least the portion of the search query based on the historical in-session user activity information.” which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claims 5, 14 and 15.
Claim 6 is dependent on claim 2 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein: the position metrics are determined based on at least one of (1) a number of characters of the at least the portion of the search query, (2) a number of suggested queries that were previously presented to the user, and (3) a number of ranked queries that were previously presented to the user.” which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claim 16.
Claim 7 is dependent on claim 2 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein: determining the position metrics further comprises using a machine learning model to determine the position metrics in a manner to reduce latency of the one or more processors.” which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claim 17.
Claim 8 and 18 similarly recite “wherein at least one of: determining each of the scores for the suggested search queries from the one or more query suggestion systems comprises using an equation comprising: score = 1/(1+e**[-(x1*w1+x2*w2+x3*w3+b1)]) wherein w1 comprises a first weight, w2 comprises a second weight, and w3 comprises a third weight, x1 comprises the first numerical value, x2 comprises the first binary value, x3 comprises the second binary value, and b1 comprises an intercept term; determining the position metrics for the suggested search queries from the one or more query suggestion systems comprises using an equation comprising: posabs=lenprefix-1 numsuggestions+rankingquerywherein prefix comprises a number of characters of the partial search query, numsuggestions comprises a number of suggested queries that were previously presented to the user, and rankingquery comprises a number of ranked queries that were previously presented to the user; or determining each of the efficiency metrics for the one or more query suggestion systems based on the position metrics comprises using an equation comprising: MRR=1N?i=1N1riwherein N comprises a sample of queries, and ri comprises the position metric.” This judicial exception is not integrated into a practical application. The additional elements represent the mental process steps of judging a sum, or mathematical calculation of computing a sum, as in the independent claims. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Mathematical Concepts” groupings of abstract ideas. This additional step is considered an abstract idea (mental process step and/or mathematical concept) and does not integrate the judicial exception into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements represent further mental process steps or mathematical concepts. Therefore, these additional limitations are not sufficient to amount to significantly more than the judicial exception.
Claim 9 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein analyzing the efficiency metrics for the one or more query suggestion systems to determine the query suggestion system that satisfies the threshold further comprises selecting the query suggestion system that has a largest efficiency metric value compared to others of the one or more query suggestion systems for the at least the portion of the search query.” which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claim 19.
Claim 10 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of claim 1. The claim recites the additional limitations of “wherein transmitting the instructions to modify the GUI of the user device to display, to the user, the one or more suggested search queries from the query suggestion system that is determined to satisfy the threshold further comprises: displaying one or more first numerical values of the one or more suggested search queries that are output from the query suggestion system; and displaying one or more second numerical values of the one or more suggested search queries that are output by the query suggestion system in response to receiving, via the GUI of the user device, a modification of the at least the portion of the search query.” which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claim 20.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 to 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No.12,038,975. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 under examination are obvious, respectively, by claims 1-20 of the reference Patent. Every limitations in the instant application under examination claims are recited in the conflicting reference patent claims, and the differences or additional limitations between the claims are highlighted below by underlining and bolding all limitations.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the independent claim 1 of the instant application to receiving historical in-session user activity information; and receiving, via a graphical user interface (GUI) of a user device, a partial search query from a user; coordinating at a second system in the online mode in response to receiving a communication from the first system: analyzing the partial search query based on the historical in-session user activity information using one or more query suggestion systems to determine a respective; mode in response to receiving a communication from the first system: analyzing the partial search query based on the historical in-session user activity information using one or more query suggestion systems to determine a respective score for respective suggested search queries from each of the one or more query suggestion systems; and coordinating at a third system in the online mode in response to receiving a communication from the second system: determining a respective absolute position metric for the respective suggested search queries from each of the one or more query suggestion systems, wherein the respective absolute position metric is based on a respective score for the respective suggested search queries, wherein the respective absolute position metric is determined based on a combination of (1) a number of characters of the partial search query, (2) a number of suggested queries that were previously presented to the user, and (3) a number of ranked queries that were previously presented to the user, and wherein the third system comprises a machine learning model that operates in an offline mode to determine the respective absolute position metric to reduce latency of the one or more processors. Note, such deviation would not interfere with the functionality of the claims that are already patented, and would achieve the same end result.
Please, see the comparison table below:
Instant Application 18755980
Patents No. 12038975
1. A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform: coordinating at a first system in an online mode: analyzing at least a portion of a search query using one or more query suggestion systems to determine scores for suggested search queries from the one or more query suggestion systems; and coordinating at a second system in the online mode: determining position metrics for the suggested search queries, wherein the position metrics are based on the scores for the suggested search queries; determining efficiency metrics for the one or more query suggestion systems based on the position metrics for the one or more query suggestion systems; analyzing the efficiency metrics for the one or more query suggestion systems to determine a query suggestion system of the one or more query suggestion systems that satisfies a threshold; and transmitting instructions to modify a graphical user interface (GUI) of a user device to display, to a user, one or more suggested search queries from the query suggestion system that is determined to satisfy the threshold.
11. A method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising: coordinating at a first system in an online mode: analyzing at least a portion of a search query using one or more query suggestion systems to determine scores for suggested search queries from the one or more query suggestion systems; and coordinating at a second system in the online mode: determining position metrics for the suggested search queries, wherein the position metrics are based on the scores for the suggested search queries; determining efficiency metrics for the one or more query suggestion systems based on the position metrics for the one or more query suggestion systems; analyzing the efficiency metrics for the one or more query suggestion systems to determine a query suggestion system of the one or more query suggestion systems that satisfies a threshold; and transmitting instructions to modify a graphical user interface (GUI) of a user device to display, to a user, one or more suggested search queries from the query suggestion system that is determined to satisfy the threshold.
1. A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform: coordinating at a first system in an online mode: receiving historical in-session user activity information; and receiving, via a graphical user interface (GUI) of a user device, a partial search query from a user; coordinating at a second system in the online mode in response to receiving a communication from the first system: analyzing the partial search query based on the historical in-session user activity information using one or more query suggestion systems to determine a respective score for respective suggested search queries from each of the one or more query suggestion systems; and coordinating at a third system in the online mode in response to receiving a communication from the second system: determining a respective absolute position metric for the respective suggested search queries from each of the one or more query suggestion systems, wherein the respective absolute position metric is based on a respective score for the respective suggested search queries, wherein the respective absolute position metric is determined based on a combination of (1) a number of characters of the partial search query, (2) a number of suggested queries that were previously presented to the user, and (3) a number of ranked queries that were previously presented to the user, and wherein the third system comprises a machine learning model that operates in an offline mode to determine the respective absolute position metric to reduce latency of the one or more processors; determining a respective efficiency metric for each of the one or more query suggestion systems based on the respective absolute position metrics for the each of the one or more query suggestion systems; analyzing the respective efficiency metric for the each of the one or more query suggestion systems to determine a query suggestion system of the one or more query suggestion systems that satisfies a threshold; and transmitting instructions to the first system to modify the GUI to display the respective suggested search queries from the query suggestion system that satisfied the threshold.
11. A method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising: coordinating at a first system in an online mode: receiving historical in-session user activity information; and receiving, via a graphical user interface (GUI) of a user device, a partial search query from a user; coordinating at a second system in the online mode in response to receiving a communication from the first system: analyzing the partial search query based on the historical in-session user activity information using one or more query suggestion systems to determine a respective score for respective suggested search queries from each of the one or more query suggestion systems; and coordinating at a third system in the online mode in response to receiving a communication from the second system: determining a respective absolute position metric for the respective suggested search queries from each of the one or more query suggestion systems, wherein the respective absolute position metric is based on a respective score for the respective suggested search queries, wherein the respective absolute position metric is determined based on a combination of (1) a number of characters of the partial search query, (2) a number of suggested queries that were previously presented to the user, and (3) a number of ranked queries that were previously presented to the user, and wherein the third system comprises a machine learning model that operates in an offline mode to determine the respective absolute position metric to reduce latency of the one or more processors; determining a respective efficiency metric for each of the one or more query suggestion systems based on the respective absolute position metrics for the each of the one or more query suggestion systems; analyzing the respective efficiency metric for the each of the one or more query suggestion systems to determine a query suggestion system of the one or more query suggestion systems that satisfies a threshold; and transmitting instructions to the first system to modify the GUI to display the respective suggested search queries from the query suggestion system that satisfied the threshold.
"A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). " ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
The application claim 1 does not contain specific limitations as shown in the patent claim 1; however, according to In re Goodman, the application claim 1 is generic to the species of information covered by claim 1 of the patent. Thus, the generic invention is anticipated by the species of the patented invention.
The application claim 11 does not contain specific limitations as shown in the patent claim 11; however, according to In re Goodman, the application claim 11 is generic to the species of information covered by claim 11 of the patent. Thus, the generic invention is anticipated by the species of the patented invention.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 4-5, 7, 11-12, 14-15 and 17 are rejected under 35 USC 102(a)(1) as being anticipated by Long et al. (US 2016/0246805 A1) (hereinafter Long).
As per claims 1 and 11, Long discloses one or more processors [Hardware processor 302 can include any suitable hardware processor, such as a microprocessor, a micro-controller, digital signal processor(s), paragraph 41]; and one or more non-transitory computer-readable media storing computing instructions [computer-readable medium containing computer-executable instructions, paragraph 7] that, when executed on the one or more processors, perform: coordinating at a first system in an online mode [user interface 100 is associated with a site, paragraph 32]: analyzing at least a portion of a search query using one or more query suggestion systems to determine scores for suggested search queries from the one or more query suggestion systems [the request can include an indication of a content rating to be used by safety score server 204 to determine if a search suggestion is to be allowed, paragraph 54, (it is understood that a safety score server evaluates suggested queries using a content rating to decide whether they are allowed, which reasonably corresponds to determining scores for suggested search queries generated by a query suggested system)]; and coordinating at a second system in the online mode: determining position metrics for the suggested search queries, wherein the position metrics are based on the scores for the suggested search queries [Suggestion completion server 202 can transmit a subset of the allowed candidate suggestions to user device 208 at 416. The subset can include any suitable number (e.g., zero, one, five, ten, and/or any other suitable number) of search suggestions. Suggestion completion server 202 can select the subset based on any suitable information and using any suitable technique(s). For example, in some embodiments, suggestion completion server 202 can rank the allowed candidate suggestions based on any suitable information, such as relevance to a particular topic, relevance to a particular demographic group (e.g., children within a particular age range, and/or any other suitable demographic group), popularity of the search query and/or search results associated with the query (e.g., based on a number of times a particular query has been entered, a number of times a particular search result has been viewed, and/or any other suitable metric) and/or any other suitable information. As another example, in some embodiments, suggestion completion server 202 can rank the allowed candidate suggestions based on relevance of associated content items to a particular topic, demographic group, and/or any other suitable information. Suggestion completion server 202 can then select any suitable number (e.g., one, two, five, ten, and/or any other suitable number) of the ranked candidate suggestions, paragraph 59, (It is understood that the suggestion completion server ranks the allowed candidate suggestions based on metrics such as relevance, demographic fit, and popularity, then selects a subset of the ranked suggestion. That ranking determines the order or placement of the suggested search queries, i.e., their position metrics, and the ranking is based on the previously determined suggestion scores or suitability information)]; determining efficiency metrics for the one or more query suggestion systems based on the position metrics for the one or more query suggestion systems; analyzing the efficiency metrics for the one or more query suggestion systems to determine a query suggestion system of the one or more query suggestion systems that satisfies a threshold [Process 500 can generate a list of search queries that are not to be presented based on the stored number of results at 510 using any suitable technique or combination of techniques. For example, in some embodiments, the list can include search queries associated with a number of search results associated with a particular content rating that is below a predetermined threshold. As a specific example, if the content rating indicates that the content is suitable for all ages, the list can include search queries where the number of search results associated with content that is indicated as suitable for all ages is below a predetermined threshold (e.g., less than 50 search results, less than 50% of the search results, and/or any other suitable number and/or proportion). As another example, in some embodiments, the list can include search queries associated with a number of search results associated with a particular content rating that is greater than a predetermined threshold, paragraph 71 (it is understood that the reference evaluates search queries using quantitative thresholds (e.g., number or portion of acceptable results), which corresponds to determining efficiency metrics, and then analyzes those metrics to select or exclude queries based on whether they satisfy a predetermined threshold, as required by claim)]; and transmitting instructions to modify a graphical user interface (GUI) of a user device to display, to a user, one or more suggested search queries from the query suggestion system that is determined to satisfy the threshold [At 414, suggestion completion server 202 can remove candidate search suggestions indicated to not be allowed to be presented from the group of candidate search suggestions. In instances where safety score server 204 transmits a list of allowed or not allowed search suggestions, suggestion completion server 202 can update the group of candidate search suggestions based on the received list. In instances where safety score server 204 transmits a safety score associated with each of the candidate search suggestions, suggestion completion server 202 can filter the group of candidate search suggestions based on the received safety score. For example, in some embodiments, suggestion completion server 202 can remove any search suggestions with safety scores below a predetermined threshold, paragraph 57 (it is understood that the suggestion completion server filters candidate search suggestions using safety scores and a predetermined threshold, and then updates the set of suggestions to be presented to the user device, which corresponds to transmitting instructions that modify the GUI to display only those suggested queries that satisfy the threshold)].
As per claim 2, Long discloses wherein analyzing the at least the portion of the search query comprises analyzing the at least the portion of the search query based on historical in-session user activity information [collect personal information about users, or make use of personal information, the users may be provided with an opportunity to control whether programs or features collect user information, paragraph 76].
As per claim 4, Long discloses wherein: the computing instructions, when executed on the one or more processors, further perform: receiving historical in-session user activity information; and receiving, via the GUI of the user device, the at least the portion of the search query; and analyzing the at least the portion of the search query further comprises: analyzing the at least the portion of the search query based on the historical in-session user activity information [a user interface for presenting search suggestions in accordance with some embodiments of the disclosed subject matter is shown. As illustrated, user interface 100 can include a logo 102, a search text input 104, a portion of a search query 106, a group of search suggestions 108, and page content 112, paragraph 27].
As per claim 5, Long discloses receiving the historical in-session user activity information and receiving the at least portion of the search query are performed by another system in the online mode [the mechanisms can receive user input that includes one or more characters of a partial search query entered on a user device, and can determine candidate search suggestions corresponding to the received characters., paragraph 23].
As per claim 7, Long discloses determining the position metrics further comprises using a machine learning model to determine the position metrics in a manner to reduce latency of the one or more processors [popularity of the search query and/or search results associated with the query (e.g., based on a number of times a particular query has been entered, a number of times a particular search result has been viewed, and/or any other suitable metric) and/or any other suitable information., paragraph 59].
Allowable Subject Matter
Claim 3, 6, 8-10, 13 and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if overcome the 35 USC 101 rejection and rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The primary reason for objecting to claims 3, 6, 9-10, 13 and 19-20 is because the prior arts of record do not teach or suggest wherein at least one of: the historical in-session user activity information comprises at least one or more of: (i) add-to-cart (ATC) history information for the user and prior users, (ii) previous queries for the user, or (iii) affinity information for the user; analyzing the at least the portion of the search query further comprises: converting the ATC history information for the user and the prior users to a first numerical value; converting the previous queries for the user to a first binary value; and converting the affinity information for the user to a second binary value; converting the ATC history information for the user and the prior users to the first numerical value further comprises determining a ratio between a minimum baseline score of the ATC history information and a maximum baseline score of the ATC history information; or converting the affinity information for the user to the second binary value further comprises: determining one or more categories corresponding to each of the previous purchases of the user; and determining an affinity probability for each of the one or more categories; the position metrics are determined based on at least one of (1) a number of characters of the at least the portion of the search query, (2) a number of suggested queries that were previously presented to the user, and (3) a number of ranked queries that were previously presented to the user;
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; wherein analyzing the efficiency metrics for the one or more query suggestion systems to determine the query suggestion system that satisfies the threshold further comprises selecting the query suggestion system that has a largest efficiency metric value compared to others of the one or more query suggestion systems for the at least the portion of the search query; wherein transmitting the instructions to modify the GUI of the user device to display, to the user, the one or more suggested search queries from the query suggestion system that is determined to satisfy the threshold further comprises: displaying one or more first numerical values of the one or more suggested search queries that are output from the query suggestion system; and displaying one or more second numerical values of the one or more suggested search queries that are output by the query suggestion system in response to receiving, via the GUI of the user device, a modification of the at least the portion of the search query.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOOSHA ARJOMANDI whose telephone number is (571)272-9784. The examiner can normally be reached on (571)272-9784.
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April 29, 2026
/NOOSHA ARJOMANDI/Primary Examiner, Art Unit 2166
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