Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. The action is responsive to the communications filed on 1/23/2026. Claims 21-39 are pending in the case. Claims 21-29, 32, 35 are amended. New Claims 37-39 are cancelled. Claims 21, 29 are independent claims. Claims 21-39 are rejected.
Summary of claims
3. Claims 21-39 are pending,
Claims 21-29, 32, 35 are amended,
Claims 37-39 are newly added,
Claims 21, 29 are independent claims,
Claims 21-39 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 1/23/2026, with respect to the rejection(s) of claim(s) 21-39 under 103 have been fully considered and are not persuasive in view of new rejection ground(s).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 35 U.S.C. 103 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
5. Claims 21-25, 28-33, 36-39 are rejected under 35 U.S.C. 103 as being unpatentable over Trevor Strohman (US Publication 20120047025 A1, hereinafter Strohman), and in view of Timothy Jackson (US Publication 20120100829 A1, hereinafter Jackson), and Rohit Kaul et al (US Publication 20120123863 A1, hereinafter Kaul).
As for claim 21, Strohman discloses: A search engine system (Strohman: Fig. 1 and [0029], an example environment in which a search engine provides search services) comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the search engine system to perform steps comprising: receiving a search request from a user device associated with a user (Strohman: [0008], receiving a query stem from a client device, the query stem being a query input from a client device and being one or more characters ordered in an input sequence that defines an order in which the one or more characters were input as the query input); predicting whether a search intent of the user … or a non-idle intent based on context signals received from the user device, wherein … the non-idle intent is associated with task-specific content (Strohman: [0026], providing query suggestions in response to a query suggestion request and providing search results for at least one of the suggestions if a prediction criterion is met. When the prediction criterion is met, search results are provided to a client device associated with the query suggestion request and presented in a search interface; [0050], For a time based prediction criterion, the search engine 110 can determine that the prediction criterion is met when a timer initialized in response to the query suggestion request expires after a predefined time period and no additional query suggestion requests are received during the predefined time period; please note a timer may determine a user non-idle intent: need query suggestion; [0080], fast typing is interpreted as a signal that the user has a specific intent to enter a particular query; ); optimizing at least one of a selection, placement, or sizing of search content using a machine learning system, wherein the machine learning system is configured to … optimize the task-specific content based on user engagement metrics associated with the non-idle intent (Strohman: [0026], providing query suggestions in response to a query suggestion request and providing search results for at least one of the suggestions if a prediction criterion is met. When the prediction criterion is met, search results are provided to a client device associated with the query suggestion request and presented in a search interface; [0050], For a time based prediction criterion, the search engine 110 can determine that the prediction criterion is met when a timer initialized in response to the query suggestion request expires after a predefined time period and no additional query suggestion requests are received during the predefined time period; please note a timer may determine a user non-idle intent: need query suggestion; [0064], determining if a timer expires and search results should be provided to a client, the timing diagram depicts a process associated with a client and a search service as time elapses downward along a vertical axis; please note the query suggestion is provided and improved based on user engagement monitoring; [0080], fast typing is interpreted as a signal that the user has a specific intent to enter a particular query; [0147], monitoring a long pause; [0201], the function can be a first-degree polynomial function defining a relationship between the stem bid and unit time, as indicated by the line 1104. Alternatively, the function can be an n-degree polynomial function defining a relationship between the stem bid and unit time, with n being greater than 1, as indicated by the curves 1106 and 1108. Other functions can also be used, e.g., a sigmoid function defining a relationship between the stem bid and unit time; [0213], storing historical data in the form of click logs and query logs, also storing historical data indicating when search results resulted in a user interaction; [0218], a variety of quality metrics including a count of the number of times a particular resource was selected from the search results, a value based on the function of selections, importance in a resource graph); automatically generating digital content by receiving data from the search content; and automatically presenting the digital content based on outputs of the machine learning system (Strohman: [0054]-[0057], when search results are to be displayed in response to a prediction criterion being met, the client device generates an indication in the search interface that indicates the query suggestion for which the results are responsive, in some implementations, the indication can be an automatic completion of a query input in the query input field indicating the query suggestion for which the search results are responsive; [0084], the search service provides the search result to the client device automatically with the query suggestions).
Strohman discloses providing query suggests based on user engagement, and Strohman discloses detecting a long pause (idle time), but Strohman does not clearly disclose determining an idle intent based on context signals received from the user device, and optimizing content based on user engagement with the idle intent, in an analogous art of predicting based on user engagement, Jackson discloses: intent of the user if one of an idle intent … based on context signals received from the user device, wherein the idle intent is associated with first content that is likely to engage the user during idle time … optimize the first content based on user engagement metrics associate with the idle intent (Jackson: [0015], This method may actually improve the user experience by entertaining the user during idle times; [0059], During this idle time, the system displays advertising content for Taco Bell.RTM. because the user has initiated a search for food and the user profile database (206) indicates that user likes both fast food and Mexican food; please note an optimized advertising content is presented to user during idle times based on user profile information so user experience is improved);
Strohman and Jackson are in analogous art because they are in the same field of endeavor, generating search results based on user input and contextual information. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Strohman using the teachings of Jackson to clearly include entertaining user with the advertising content fitting user’s preference/need during idle time. It would provide Strohman’s system with the enhanced capability of improving user experience.
Further, Strohman does not clearly disclose using machine learning to analyze and process search content, in an analogous art of generating search results based on user input and contextual information, Kaul discloses: using a machine learning system, wherein the machine learning system is configured to optimize the first content based on user engagement metrics (Kaul: [0023], by obviating the need for a user (such as an online advertiser or a comparison-shopping engine) to select the keywords, this publishing technique may significantly improve the quality of the keywords that are selected, both from the perspective of their efficacy in attracting paying customers to e-commerce websites and/or comparison-shopping engines, and in terms of their profitability to these entities; [0038], keyword-specific metrics that are computed using machine learning techniques; external factors may include keyword search volume, bid popularity, etc. Some or all of these factors may be combined using machine learning techniques (e.g., regression models) to produce an estimate of the expected revenue per user visit to a comparison-shopping engine, as well as an estimate of a resulting merchant conversion (e.g., whether or not the user will subsequent complete a transaction and purchase a product from a merchant); [0054], a product may be classified based on a category tree, which is determined by combining merchant-provided category and machine learning techniques);
Strohman and Kaul are in analogous art because they are in the same field of endeavor, generating search results based on user input and contextual information. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Strohman using the teachings of Kaul to explicitly include analyzing query and generating search results using machine learning techniques trained basing metrics data. It would provide Strohman’s system with the enhanced capability of generate more relevant search result using machine learning techniques.
As for claim 22, Strohman-Jackson-Kaul discloses: wherein the digital content is automatically generated as at least one of a visual card, a ribbon, or a box (Strohman: [0048], query suggestions can be presented in a query suggestion box 124a according to an order from a highest rank to a lowest rank; [0058], the query suggestion "bank" can be highlighted in the query suggestion box 124a, e.g., with a box or a background color).
As for claim 23, Strohman-Jackson-Kaul discloses: wherein the digital content populates a template with predetermined arrangements based on specific classes or categories of information (Kaul: [0008], a given group of keywords may have a common product classification and a common construction template, which can be used to generate advertising text associated with a given keyword in the given group of keywords based on the construction template and one or more attributes associated with the given keyword; [0049], merchant-feed interface 310 that receives product information (such as titles and descriptions); a keyword-extraction engine 312 that extracts and/or generates keywords (for example, using n-grams, extracted attributes and construction templates)).
As for claim 24, Strohman-Jackson-Kaul discloses: wherein the search intent of the user is predicted based on the context signals and the idle time (Strohman: [0080], fast typing is interpreted as a signal that the user has a specific intent to enter a particular query; please note fast typing means short pause time; [0083], a pause between query suggestion requests (e.g., greater than the threshold time period) may indicate that one of the query suggestions interests the user and the search service 404 should send search results for the highest ranked query suggestion or the query suggestion with the highest probability of being selected by the user).
As for claim 25, Strohman-Jackson-Kaul discloses: wherein the steps further comprise assigning weights to paths for presentation to the user based on user context information (Strohman: [0064], determining if a timer expires and search results should be provided to a client, the timing diagram depicts a process associated with a client and a search service as time elapses downward along a vertical axis; [0147], monitoring a long pause; [0213], storing historical data in the form of click logs and query logs, also storing historical data indicating when search results resulted in a user interaction; [0218], a variety of quality metrics including a count of the number of times a particular resource was selected from the search results, a value based on the function of selections, importance in a resource graph; Kaul: [0061], multiplying the weighted probabilities (Pw) of each token or word belonging to that category; [0064], based on a proximity weighted-ordering frequency, the final keyword order may be determined; [0072], the weight factor may vary between 1 for the product at the top of the ranking to 0.01 for the 50.sup.th product in the ranking).
As for claim 28, Strohman-Jackson-Kaul discloses: wherein the steps further comprise adjusting representation of the search content by varying and selecting different representations based on tracking outcomes (Strohman: [0035], an example search result can include a web page title, a snippet of text extracted from the web page, and the URL of the web page; Fig. 5I and [0142]-[0143], the suggestion search results are displaying as URLs for web pages, as indicated by the web icon adjacent to the URLs, and the content corresponding to the web page is shown in a preview pane, for example, a preview of the webpage at example1.com; please note a preview of a webpage can include a portion of an image and a video, which is extracted from the webpage and is an alternative presentation of the website, for the content of website may include images and/or video; in other words, displaying search results including variants of the visual content elements such as the URL of the web page, a snippet extracted from the web page, and/or a preview content of the web page including images or video content).
As for claim 29, it recites features that are substantially same as those features claimed by claim 21, thus the rationales for rejecting claim 21 are incorporated herein.
As for claim 30, it recites features that are substantially same as those features claimed by claim 22, thus the rationales for rejecting claim 22 are incorporated herein.
As for claim 31, it recites features that are substantially same as those features claimed by claim 23, thus the rationales for rejecting claim 23 are incorporated herein.
As for claim 32, it recites features that are substantially same as those features claimed by claim 24, thus the rationales for rejecting claim 24 are incorporated herein.
As for claim 33, it recites features that are substantially same as those features claimed by claim 25, thus the rationales for rejecting claim 25 are incorporated herein.
As for claim 36, it recites features that are substantially same as those features claimed by claim 28, thus the rationales for rejecting claim 28 are incorporated herein.
As for claim 37, Strohman-Jackson-Kaul discloses: an advertising module configured to associated advertisements with keyword fragments, wherein the advertising module is configured to : determine that the search request comprises a fragment of a keyword; determine, using machine learning, a minimum bid amount for the fragment based on a relevance score and the search intent of the user, and select an advertisement associated with the keyword for display based on the minimum bid amount (Strohman: Abstract, the targeting to the corresponding word stem is independent of keyword targeting; [0039], Example targeting rules include keyword targeting, in which advertiser provide bids for keywords that are present in either search queries or webpage content. Advertisements that are associated with keywords having bids that result in an advertisement slot being awarded in response to an auction are selected for displaying in the advertisement slots; [0041], advertisements can also be provided based on a current query input that does not constitute a completed query input, e.g., advertisements can be provided based on a single character input, or on a current input that forms a stem for many different words. In a manner similar to keyword targeting, advertiser submit stem bids for word stems)
As for claim 38, Strohman-Jackson-Kaul discloses: wherein predicting whether the search intent is a non-idle intent comprises predicting whether the search intent is one of a plurality of task-specific intents (Strohman: [0026], providing query suggestions in response to a query suggestion request and providing search results for at least one of the suggestions if a prediction criterion is met. When the prediction criterion is met, search results are provided to a client device associated with the query suggestion request and presented in a search interface; [0050], For a time based prediction criterion, the search engine 110 can determine that the prediction criterion is met when a timer initialized in response to the query suggestion request expires after a predefined time period and no additional query suggestion requests are received during the predefined time period; please note a timer may determine a user non-idle intent: need query suggestion)
As for claim 39, Strohman-Jackson-Kaul discloses: wherein the predicting uses a classifier, wherein the plurality of task-specific intents are organized in a user intent ontology (Kaul: [0008], a given group of keywords may have a common product classification and a common construction template, which can be used to generate advertising text associated with a given keyword in the given group of keywords based on the construction template and one or more attributes associated with the given keyword; [0054], a product may be classified based on a category tree, which is determined by combining merchant-provided category and machine learning techniques)
6. Claims 26, 27, 34, 35 are rejected under 35 U.S.C. 103 as being unpatentable over Strohman, Jackson and Kaul as applied on claims 21 and 29, and further in view of Hassan Sawaf (US Publication 20160188575 A1, hereinafter).
As for claim 26, Strohman-Jackson-Kaul disclose presenting the result based on users information, but does not clearly disclose using a group of users’ context information, Sawaf discloses: wherein assigning the weights to the paths is based on a group to which the user belongs (Sawaf: [0006], Importantly, the server can store information on search requests to help improve future translations. Such information is shown as click-through logs 180. For example, suppose that many users submit an English language query which we will denote as “qEn”, and after obtaining the search results the users frequently select from the search results a given URL (Uniform Resource Locator), e.g. www.fedex.com, which is an English-language home page of a U.S. company. Suppose also that many other users, possibly Chinese-speakers, submit a Chinese language query qCn, obtain search results, and select the URL www.fedex.com/cn, which is the Chinese-language home page of the same company; please note data is collected and processed based on different group of users).
Strohman and Sawaf are in analogous art because they are in the same field of endeavor, generating and presenting search results based on user input and contextual information. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Strohman using the teachings of Sawaf to explicitly include training machine learning model based on the different group of users. It would provide Strohman’s system with the enhanced capability of optimizing relevant search result contents to maximize the success indicators (Sawaf: [0043]).
As for claim 27, Strohman-Jackson-Kaul-Sawaf discloses: wherein assigning the weights to the paths is based on elapsed time to completion for a group to which the user belongs (Sawaf: [0014]-[0015], in some embodiments, the server applies computer-based data-mining and machine-learning techniques to the click-through data to find what portion of a flow before the query is statistically related to success of a translation, success can be measured by indicators derived from the user’s action after receiving the search result, a possible success indicator is the amount of time that the user spend on reviewing the documents in the search results produced for the query translated in a particular way: more time corresponds to a greater success; please note the machine learning is trained based on the amount of time between user receiving the search result and completing review).
Strohman and Sawaf are in analogous art because they are in the same field of endeavor, generating and presenting search results based on user input and contextual information. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Strohman using the teachings of Sawaf to explicitly include training machine learning model based on the amount of time that the user spend on reviewing the search results. It would provide Strohman’s system with the enhanced capability of optimizing relevant search result contents to maximize the success indicators (Sawaf: [0043]).
As for claim 34, it recites features that are substantially same as those features claimed by claim 26, thus the rationales for rejecting claim 26 are incorporated herein.
As for claim 35, it recites features that are substantially same as those features claimed by claim 27, thus the rationales for rejecting claim 27 are incorporated herein.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118