DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The Office has withdrawn the previous rejections of the claims under 35 USC §103, in light of Applicant's amendments and arguments, filed 3/12/2026.
The previous 35 USC §101 rejections of the claims have been withdrawn, and new rejections have been set forth under 35 USC §101 to address the newly amended claim language.
Applicant's arguments, filed 3/12/2026, concerning the previous rejection of the claims under 35 USC §101 have been fully considered but they are not persuasive. .
Regarding the rejections of the claims under 35 USC §101, Applicants argue on pages 9-11 that the newly amended claims are not abstract because they explicitly require computing elements.
The Office respectfully disagrees. First, it is noted that generic computers performing generic computer functions to apply an abstract idea do not amount to significantly more than the abstract idea of generating further data (i.e., suggestions) from initial/partial data. Additionally, Applicants indicate that “specifics” are recited, but refer to the reception of data, analysis of that data and the generation of suggestions. There are no recited “specifics” (i.e., implementation details), only insignificant extra-solution activity (e.g., data reception) and the abstract concepts of analysis and generation. Applicants’ further asserts that the functioning of a computer is improved, however, the claims merely reflect the use of computing elements. And, viewing the limitations as a combination does not add anything further than looking at the limitations individually. Therefore, the previous rejection of the claims under 35 USC §101 is maintained.
Allowable Subject Matter
Claims 1-20 are allowable over the prior art. However, the claims remain rejected under 35 USC §101.
Reasons For Allowance
The cited references do not disclose obtaining search results from a prior search query executed against a dataset of event data, wherein the search results comprise a subset of the event data that matches search criteria of the prior search query, and generating, using a first computer node of an observability analysis system, a plurality of autocomplete suggestions based on the partial search query and metadata obtained from the search results of the prior search query, wherein the plurality of autocomplete suggestions includes at least one of: a suggested field selected from a plurality of fields; and a suggested value selected from values.
Claim Rejections – 35 U.S.C. § 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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
These claims are rejected under 35 USC §101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites at a very high level, formulating a search string for event data in a piecemeal manner, generating optional complete strings to complete the piecemeal string using previous data, and passing on / displaying the optional strings. Thus, the claims encompass the performance of the limitations in the mind, and are not tied to a practical application. (There are no claimed implementation details, nor a claimed tie to / integration with a practical application.)
Regarding the independent claims:
Step 1: Yes, claim 1 recites a method (therefore a process). Claim 9 is directed to a storage medium (therefore a product), and claim 17 is directed to a computer node comprising processors, display and memory (therefore a product/machine). Thus, each of these claims is directed to a statutory category.
Step 2A, Prong 1 (Judicial Exception Recited?): Yes. Claims 1, 9 and 17 recite limitations directed to an abstract idea: “generating, … , a plurality of autocomplete suggestions based on the partial search query and metadata obtained from the search results of the prior search query, wherein the plurality of autocomplete suggestions includes at least one of: a suggested field selected from a plurality of fields; and a suggested value selected from values;”, and “wherein the metadata obtained from the search results is generated by analyzing the subset of the event data in the search results of the prior search query to identify fields and values present in the subset of the event data”. As drafted, these limitations recite mentally performable processes as one can generate new words/sentences from partial words/sentences and parse a dataset to generate a subset of data by analyzing/identifying data in the dataset via a mental process or using paper and pencil.
Step 2A, Prong 2 (Integrated into a Practical Application?): No. Claim 1 recites the following additional elements: “user interface” (software), “computer node” (hardware), and “client device for display” (hardware). Claim 9 additionally recites: “non-transitory computer-readable medium” (hardware), “data procession apparatus” (hardware), “user interface” (software), and “client device for display” (hardware). And, claim 17 additionally recites : “a computer node” (hardware), “processors” (hardware), “display device” (hardware), “memory” (hardware), “user interface” (software), “a first computer node” (hardware), and “client device for display” (hardware). Each of these are merely high-level recitations of generic computer components and represent mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Additionally, claims 1, 9 and 17 each recites “receiving …”, “obtaining [receiving] …”, and “transmitting …” various data elements. These elements represent insignificant extra-solution activity as the receiving and transmitting of data (i.e., mere data gathering) are steps for 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Viewing the additional limitations together and the claims as a whole, nothing provides integration into a practical application. Therefore, each claim is directed to an abstract idea.
Step 2B (Inventive Concept Provided?): No. As discussed with respect to Step 2A, the elements (i.e., steps of receiving, obtaining, transmitting) in each independent claim amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using generic computer components (e.g., generic hardware or software) cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
With respect to the receiving/obtaining/transmitting limitations discussed above, and when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, each of these claims, taken as a whole, does not change this conclusion and the claims are ineligible.
Claims 2-8 depend upon claim 1, and do not correct the issues set forth above. These claims essentially further recite previously discussed abstract concepts, insignificant extra-solution activity, and manipulation of particular data types. Therefore, these claims are likewise rejected.
Claims 10-16 depend upon claim 9, and do not correct the issues set forth above. These claims essentially further recite previously discussed abstract concepts, insignificant extra-solution activity, and manipulation of particular data types. Therefore, these claims are likewise rejected.
Claims 18-20 depend upon claim 17, and do not correct the issues set forth above. These claims essentially further recite previously discussed abstract concepts, insignificant extra-solution activity, and manipulation of particular data types. Therefore, these claims are likewise rejected.
Therefore, the claims are not patent eligible, and are reasonably rejected under 35 USC §101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Relevance is provided in at least the Abstract of each cited document.
Non-Patent Literature
Whiting, Stewart, et al., “Recent and Robust Query Auto-Completion”, WWW ‘14, Seoul. Korea, April 7-11, 2014, pp. 971-981.
Query auto-completion (QAC) is a common interactive feature that assists users in formulating queries by providing completion suggestions as they type. In order for QAC to minimise the user's cognitive and physical effort, it must: (i) suggest the user's intended query after minimal input keystrokes, and (ii) rank the user's intended query highly in completion suggestions. Typically, QAC approaches rank completion suggestions by their past popularity. Accordingly, QAC is usually very effective for previously seen and consistently popular queries. Users are increasingly turning to search engines to find out about unpredictable emerging and ongoing events and phenomena, often using previously unseen or unpopular queries. Consequently, QAC must be both robust and time-sensitive -- that is, able to sufficiently rank both consistently and recently popular queries in completion suggestions. To address this trade-off, we propose several practical completion suggestion ranking approaches, including: (i) a sliding window of query popularity evidence from the past 2-28 days, (ii) the query popularity distribution in the last N queries observed with a given prefix, and (iii) short-range query popularity prediction based on recently observed trends. Using real-time simulation experiments, we extensively investigated the parameters necessary to maximise QAC effectiveness for three openly available query log datasets with prefixes of 2-5 characters: MSN and AOL (both English), and Sogou 2008 (Chinese). Optimal parameters vary for each query log, capturing the differing temporal dynamics and querying distributions. (page 971, Abstract).
US Patent Application Publications
Burke 2008/0065617
A search entry system and method of search entry where the query terms can be autocompleted based on entries in a search history query log. A search system can include one or more entry modules, that can include a query window, configured to accept a search query. A query log can store one or more queries each query containing one or more query terms. Concurrent with entry of query terms in a query window, a query searcher can be configured to search one or more query logs for stored queries having the query terms contained anywhere within the query. An autocompletion module can be configured to process the results obtained from the query searcher and display or otherwise indicate to a user a selection of autocompletion options containing the current query terms. (Abstract). A supplier of content might determine the interests of its users and provide relevant content, such as current news, sports, weather, search services, calendaring, messaging, information retrieval and the like. Content might be in the form of pages that are static (i.e., existing prior to a query for the page), dynamic (i.e., generated in response to a query) or partially static, partially dynamic. Thus, a news report about an event in a particular city might exist as a static page, but that same content might also be generated dynamically in response to a query, taking into account the context of the content and/or demographics of the user making the query. As an example of a dynamically generated page, if the news report was being viewed by a user known to live in city in which the event is to occur, the resulting page might include information about how to drive to the location of the event or to purchase tickets, however if the user is known to live far from that city, the resulting page might include information about the weather in that remote city and how to purchase an airline ticket to that city. (paras 0006-0007).
Mishra 2019/0228038
For example, publication system 120 and payment system 122 may each be associated with separate autocomplete systems. Each of these separate autocomplete systems may use separate databases 126 which store data about periodic events which are associated with shifts in user search selections. In other embodiments, a shared database or system that tracks and updates search systems based on historical data regarding periodic or event-based shifts in user search preferences may be shared by multiple different autocomplete systems associated with different search applications. (para 0030). In certain embodiments, both explicit and implicit information from any of the above elements may be combined with a partial user input and an autocomplete event time period to determine autocomplete search results that are provided to a client device. For example, publication engine 202 and an associated auction engine 204 may provide records of listings or published information that matches search result variations based on temporal shifts in system user interest as described herein. Similarly, the listing creation engine may track information about author listings of items, including changes in author listings of items that are associated with periodic or predictably reoccurring events. The information about predictably reoccurring events and shifts in user searches around these events may then be provided to a search engine 222 or comparison shopping engine 224. Results from search engine 222 or comparison shopping engine 224 are then updated and adjusting based on the identified changes that occur during the reoccurring event. In some embodiments, any module described above for publication system 120 may include forms with autocomplete services, or search services to supplement the basic operation of the module. Feedback based on reoccurring events to provide autocomplete results more may be used in any form element or search element of any of these modules of the publication system 120. (paras 0044-0045).
Bauer 2022/0083686
A method includes sequentially generating fragment records for a user device according to fragment generation rules specifying that each subsequent fragment record be generated for user device events that occur within a defined period of time. Each fragment record includes event data for a series of user device events and includes a fragment ID generated using a non-deterministic ID generation algorithm. The method includes generating an ID-fragment record associating a chain ID with the fragment IDs. The chain ID is associated with device IDs that identify the user device. The method includes removing associations between the chain ID and fragment IDs according to removal parameters indicating that associations be removed based on an age of the fragment records. The method includes generating at least one of search results and advertisements for the user device based on the event data in the fragment records that remain associated with the chain ID. (Abstract). Search event data may include any data associated with the local search application 128 and/or remote search system 104. The search event data may include data associated with search requests, such as the search query, search context data, and autosuggest/autocomplete data. The search event data may also include data associated with the search results, such as the application/web uniform resource indicators/locators (URIs/URLs) associated with the results, relevance scores associated with the results, and ranking of the results. The search event data may also include user interaction data associated with the search results, such as data that indicates which results were viewed and/or selected. (para 0041).
US Patents
Balasubramanian 10,380,190
If search program 112 determines that the input is detected within the certain time range (decision 208, “YES” branch), search program 112 provides an autocomplete suggestion for input into the search field (step 210). In the example embodiment, search program 112 provides autocomplete suggestions based on search terms utilized by the social media contacts associated with the user and the event(s). For example, search program 112 identifies search terms, social media comments, comments on social forums, and the like utilized by the determined social media contacts associated with the user and the event(s). In the example embodiment, search program 112 then utilizes natural language processing techniques and/or string matching techniques to determine one or more terms from the terms utilized by the determined social media contacts that are related to the event(s), such as the location of the event, speakers of the event, social interests/purpose for visit in relation to the event, or metadata related to the event (event invite, etc). For example, search program 112 may utilize the techniques described above to identify terms or phrases which contain the name of the event, the name of the city, names of the speakers attending the event, names of participants attending the event, or any other terms related to the event (such as terms found in metadata associated with the event). Search program 112 then provides autocomplete suggestions utilizing the identified terms or phrases which are associated with the event. In addition, search program 112 utilizes character matching techniques in order to provide autocomplete suggestions that predict what the user of computing device 110 desires to input. For example, if event 1 is a football game and the identified terms and phrases utilized by the social media contacts of the user include the team name the “Frogs”, the stadium name “Blenley Stadium”, and “frogs stadium parking”, if the user of computing device 110 inputs the character “f”, search program 112 may provide autocomplete suggestion “Frogs”, “The Frogs”, or “frogs stadium parking”. Additionally, if user of computing device 110 inputs “frogs s”, search program 112 may provide the autocomplete suggestions “frogs stadium parking”. In the example embodiment, search program 112 provides autocomplete suggestions related to the event associated with the certain time range. Furthermore, if input corresponds to more than one time range detailed in event data 116, search program 112 may provide autocomplete suggestions based on multiple events. In addition, in another embodiment, search program 112 may identify social media contacts associated with the user whose location matches the location of an event(s) associated with the certain time range. Search program 112 may then provide autocomplete suggestions based on one or more terms utilized by the identified social media contacts. For example, if event 1 or the destination of the user of computing device 110 is location A, search program 112 determines one or more social media contacts of the user of computing device 110 that are associated or are located in location A (by referencing location information provided on social media sites, professional networking sites, and the like). Furthermore, if search program 112, identifies the one or more terms utilized by the identified social media contacts to be “downtown festival”, “all-day truck marathon”, and “all you can eat rice”, and the user of computing device 110 inputs the character “d”, search program 112 may provide autocomplete suggestion “downtown festival”. Additionally, if user of computing device 110 inputs “all”, search program 112 may provide the autocomplete suggestions “all-day truck marathon” and “all you can eat rice”. In this embodiment, search program 112 may utilize character matching techniques as described above. Furthermore, in the example embodiment, search program 112 may utilize natural language processing techniques when analyzing terms utilized by social media contacts in order to identify certain terms such as the subject or object of a sentence. (col. 4 line 19 – col. 5 line 25).
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner ROBERT STEVENS whose telephone number is (571) 272-4102. The examiner can normally be reached Mon - Fri 6:00 - 2:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on (571) 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT STEVENS/Primary Examiner, Art Unit 2164
May 29, 2026
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