DETAILED ACTION
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 .
Response to Amendment
This Office action is in response to Applicant's amendment filed on 7/7/2026.
Claim 1-13 are pending. Claim 1-13 are rejected.
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.
Claim 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is directed to statutory category process. The claim recites “obtaining local search results corresponding to the search query…….. after obtaining the local search results: identifying local features for the local search results; and anonymizing the local features to generate anonymized features, wherein the anonymized features are different from the local features….”. The process of obtaining result by searching local sources, identifying local features from local search results and anonymizing features involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “in response to receiving the input corresponding to the search query”, “after generating the anonymized features, sending, to a remote server having a search engine, the search query and the anonymized features; after sending the search query and the anonymized features, receiving, from the remote server having the search engine, remote search results” -above additional elements recite insignificant extra-solution activity of user query specific mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g). The claim also recited additional elements “in response to receiving the remote search results, displaying a ranking of results including the local search results and the remote search results”, above mentioned additional elements considered as insignificant extra solution activity of data output. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 1 is directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements recites insignificant extra-solution activity of data gathering and outputting data such are also well-understood, routine, and conventional. Further, sending user query/data to local and remote server with user specific query is insignificant extra-solution activity of data transmission, such is also well- understood, routine, and conventional (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)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and each claim limitations as a whole does not change this conclusion and claim 1 is not patent ineligible.
Claim 12 differs from claim 1 in that it recites a non-transitory computer readable medium including a sequence of instructions which when executed perform the method of claim 1. For reasons discussed above, the claimed process is directed to mental steps. Use of a non-transitory medium to store instructions which when executed perform the method of claim 1 constitutes use of a component of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 12 is not patent eligible.
Claim 13 differs from claim 1 in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 13 is a system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 13 is not patent eligible.
Dependent claim 2, 6 and 9 are directed to the same abstract idea as the independent claim from which they depend and further recite additional elements “wherein the local search results include one or more locally stored files, one or more remotely stored files, one or more applications, or any combination thereof” and “wherein the local search results include one or more locally stored files, one or more remotely stored files, one or more applications, or any combination thereof” above additional elements recite insignificant extra-solution activity of user query specific mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g).
Claims further recite “wherein displaying the ranking of the results includes displaying a set of categories, wherein a first category of the set of categories includes the local search results, wherein a second category of the set of categories includes the remote search results, and wherein the first category is separate from the second category” above mentioned additional elements considered as insignificant extra solution activity of data output. Claim further recites generic high-level computer components such as remote and local storages for data. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 2, 6 and 9 are directed to an abstract idea..
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements recites insignificant extra-solution activity of data gathering and outputting data such are also well-understood, routine, and conventional. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and each claim limitations as a whole do not change this conclusion and claim 2, 6 and 9 are ineligible..
Dependent claim 3-5, 7-8 and 10-11 are directed to the same abstract idea as the independent claim from which they depend and further recite limitations – “ranking, using at least the local features, the results, wherein the anonymized features are a reduced set of features than the local features”, “ranking, based on at least the remote features, the results”, “anonymizing the local features to generate the anonymized features includes generalizing the local features, obscuring information within the local features, or any combination thereof”, “scoring, based on the search query, the local search results and the remote search results” , “scoring of the local search results and the remote search results is based on scores received from the server having the search engine”, “the first category is ranked relative to the second category” and further “the ranking of the first category relative to the second category is based on category type”. The process of ranking search results based on remote and local features, anonymizing features, ranking based on remote features, scoring search results based on query, local and remote search results, ranking return results categories based on each other and category type involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “receiving the remote search results, receiving, from the server having the search engine, remote features” recites insignificant extra-solution activity of data gathering and further processing the inputted data by machine learning. Query specific mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g). The claims also recited additional elements “displaying the ranking of the results” above mentioned additional elements considered as insignificant extra solution activity of data output. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 3-5, 7-8 and 10-11 are directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements of mere data gathering and outputting data are well-understood, routine or conventional activities. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f) and for applying the machine learning as a tool are carried over and do not provide significantly more than the abstract idea. Looking at the limitations in combination and each claim limitations as a whole do not change this conclusion and claim 3-5, 7-8 and 10-11 are ineligible.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 9 and 12-13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hornkvist, John et al (PGPUB-DOCUMENT 20150347519), hereafter referred as to “Hornkvist”.
Claim 1(Original), Hornkvist teaches A method, comprising: at a client device: receiving an input corresponding to a search query; in response to receiving the input corresponding to the search query, obtaining local search results corresponding to the search query(Hornkvist. para 0061 discloses providing local search results based on the received query “A query can be generated using local search interface 110 and query results can be returned from local database 111, via communication interface 1, and displayed in local search interface 110. Local search subsystem 130 additionally can have a local query service 114, a local search and feedback history 115, and local learning system 116. Local query service 114 can receive a query from local search interface 110”);
after obtaining the local search results: identifying local features for the local search results; and anonymizing the local features to generate anonymized features, wherein the anonymized features are different from the local features(Hornkvist. para 0017-19 discloses computing query features by predictors (learning models) “a client device includes an initial set of one or more predictors that can learn from a user's queries and the user's interaction with both local and remote search query results …….The search engine can also instruct the client device to modify an existing predictor to train on a new feature for the predictor”; where para 0023 further discloses that anonymizing user data “the information is typically sent in an anonymized form such that a particular person is not identified by information sent to the search engine”);
after generating the anonymized features(Hornkvist, para 0020 discloses anonymizing query features “In an embodiment, the information in the session context is anonymized before sending the session context to the search engine” ), sending, to a remote server having a search engine, the search query and the anonymized features(Hornkvist, para 0088 and element 450 for Fig. 4 disclose sending anonymized features to remote search engine “In operation 450, local learning system 116 can optionally send a feature vector to remote search engine 150 based upon a feature identified by local learning system 116”);
after sending the search query and the anonymized features, receiving, from the remote server having the search engine, remote search results(Hornkvist, para 0088 and element 450 for Fig. 4 disclose sending anonymized features to remote search engine and receiving remote search results “Remote results 415 returned in response to the query may include results from The Huffington Post® and Fox News®. Remote search engine 150 may have returned results for Fox News® as the top rated results based upon interaction by many users of remote search engine 150”);
and in response to receiving the remote search results, displaying a ranking of results including the local search results and the remote search results(Hornkvist, para 0080 and Fig. 4 discloses receiving local and remote search results and ranking them together “In operation 420, the local search results and the remote search results can be blended and ranked, then presented to the user on the local search interface 110” ).
Regarding claim 2(Original), Hornkvist teaches all the limitations of claim 1 and further teaches wherein the local search results include one or more locally stored files, one or more remotely stored files, one or more applications, or any combination thereof(Hornkvist, para 0022 discloses obtaining local search results from locally stored sources “the term “local results” (or local query results) refers to results returned from a local database on a client device in response to a query. A “local database” refers to a database of information that is generally considered private to the user of a client device….”).
Regarding claim 3(Original), Hornkvist teaches all the limitations of claim 1 and further teaches further comprising: in conjunction with displaying the ranking of the results, ranking, using at least the local features, the results(Hornkvist, para 0080 discloses considering local features for content ranking for display “ local results 413 matching the query can be ranked higher than remote search engine results 415……….. if a user issues a local query from within his music player application 112, then results returned from the local database 111 that are related to the music player application 112 can be categorized and displayed before other local results 413”), wherein the anonymized features are a reduced set of features than the local features(Hornkvist, para 0018 discloses anonymized local feature such as home address is being reduced by removing it “the fact that the user is at home now (determined through one or more location sensors) and that the user enjoys a particular genre of movies, such as romantic comedies, may be sufficiently anonymized such that the genre and “at home” status can be sent to a remote search engine to improve search results”).
Regarding claim 4(Original), Hornkvist teaches all the limitations of claim 1 and further teaches further comprising: in conjunction with receiving the remote search results, receiving, from the server having the search engine, remote features;
and in conjunction with displaying the ranking of the results, ranking, based on at least the remote features, the results(Hornkvist, para 0084 discloses obtaining remote search results and ranking them according to remote search feature “The remote results 415 returned in response to the query may include results from The Huffington Post® and Fox News®. In operation 425, local learning system 116 can learn from the locally stored feedback on any/all results that this user rarely, or never, interacts with Fox News® results. The local learning system 116 can determine a new feature to train upon, “News Source.” The learning system 116 can learn to exclude, or lower the ranking of, Fox News® results from future remote results 415 when blending, ranking, and presenting results on the local device in operation 420).
Regarding claim 5(Original), Hornkvist teaches all the limitations of claim 1 and further teaches wherein anonymizing the local features to generate the anonymized features includes generalizing the local features, obscuring information within the local features, or any combination thereof (Hornkvist, para 0018 discloses anonymized local feature such as home address is being reduced by removing it “the fact that the user is at home now (determined through one or more location sensors) and that the user enjoys a particular genre of movies, such as romantic comedies, may be sufficiently anonymized such that the genre and “at home” status can be sent to a remote search engine to improve search results”).
Regarding claim 6(Original), Hornkvist teaches all the limitations of claim 1 and further teaches wherein the local features are based on the local search results(Hornkvist, para 0022 discloses local features are from local search results “A feature can be learned either on local results or on remote search results. When a predictor is generated to learn on a new feature, the feature can be tagged with “local” or “remote” based upon whether the feature was learned on local results or remote search results”).
Regarding claim 9(Original), Hornkvist teaches all the limitations of claim 1 and Hornkvist further teaches wherein displaying the ranking of the results includes displaying a set of categories, wherein a first category of the set of categories includes the local search results(Hornkvist. para 0080 further discloses displaying local search results ranked by category “local results 413 can be presented in categories, such as emails, contacts, iTunes®, movies, Tweets, text messages, documents, images, spreadsheets, et al. and ordered within each category”),
Hornkvist further teaches wherein a second category of the set of categories includes the remote search results, and wherein the first category is separate from the second category(Hornkvist, para 0026 discloses category for remote search results (second category) where local results is different category (first category) “There can be a predictor for each category of results, e.g. local email results, local text results, local contact results, remote results from Yelp®, remote results from Wikipedia®, remote results for media, remote results for maps, etc.”).
Claim 12(Original), Hornkvist teaches A non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a client device, the one or more programs including instructions for(Hornkvist. Fig. 13 discloses a search processing system that executes instructions stored in machine-readable medium): receiving an input corresponding to a search query; in response to receiving the input corresponding to the search query, obtaining local search results corresponding to the search query(Hornkvist. para 0061 discloses providing local search results based on the received query “A query can be generated using local search interface 110 and query results can be returned from local database 111, via communication interface 1, and displayed in local search interface 110. Local search subsystem 130 additionally can have a local query service 114, a local search and feedback history 115, and local learning system 116. Local query service 114 can receive a query from local search interface 110”); after obtaining the local search results: identifying local features for the local search results; and anonymizing the local features to generate anonymized features, wherein the anonymized features are different from the local features(Hornkvist. para 0017-19 discloses computing query features by predictors (learning models) “a client device includes an initial set of one or more predictors that can learn from a user's queries and the user's interaction with both local and remote search query results …….The search engine can also instruct the client device to modify an existing predictor to train on a new feature for the predictor”; where para 0023 further discloses that anonymizing user data “the information is typically sent in an anonymized form such that a particular person is not identified by information sent to the search engine”); after generating the anonymized features(Hornkvist, para 0020 discloses anonymizing query features “In an embodiment, the information in the session context is anonymized before sending the session context to the search engine” ), sending, to a remote server having a search engine, the search query and the anonymized features(Hornkvist, para 0088 and element 450 for Fig. 4 disclose sending anonymized features to remote search engine “In operation 450, local learning system 116 can optionally send a feature vector to remote search engine 150 based upon a feature identified by local learning system 116”); after sending the search query and the anonymized features, receiving, from the remote server having the search engine, remote search results(Hornkvist, para 0088 and element 450 for Fig. 4 disclose sending anonymized features to remote search engine and receiving remote search results “Remote results 415 returned in response to the query may include results from The Huffington Post® and Fox News®. Remote search engine 150 may have returned results for Fox News® as the top rated results based upon interaction by many users of remote search engine 150”); and in response to receiving the remote search results, displaying a ranking of results including the local search results and the remote search results(Hornkvist, para 0080 and Fig. 4 discloses receiving local and remote search results and ranking them together “In operation 420, the local search results and the remote search results can be blended and ranked, then presented to the user on the local search interface 110” ).
Claim 13(Original), Hornkvist teaches A client device, comprising: one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for(Hornkvist. Fig. 13 discloses a search processing system that executes instructions stored in machine-readable medium):
receiving an input corresponding to a search query; in response to receiving the input corresponding to the search query, obtaining local search results corresponding to the search query(Hornkvist. para 0061 discloses providing local search results based on the received query “A query can be generated using local search interface 110 and query results can be returned from local database 111, via communication interface 1, and displayed in local search interface 110. Local search subsystem 130 additionally can have a local query service 114, a local search and feedback history 115, and local learning system 116. Local query service 114 can receive a query from local search interface 110”); after obtaining the local search results: identifying local features for the local search results; and anonymizing the local features to generate anonymized features, wherein the anonymized features are different from the local features(Hornkvist. para 0017-19 discloses computing query features by predictors (learning models) “a client device includes an initial set of one or more predictors that can learn from a user's queries and the user's interaction with both local and remote search query results …….The search engine can also instruct the client device to modify an existing predictor to train on a new feature for the predictor”; where para 0023 further discloses that anonymizing user data “the information is typically sent in an anonymized form such that a particular person is not identified by information sent to the search engine”);
after generating the anonymized features(Hornkvist, para 0020 discloses anonymizing query features “In an embodiment, the information in the session context is anonymized before sending the session context to the search engine” ), sending, to a remote server having a search engine, the search query and the anonymized features(Hornkvist, para 0088 and element 450 for Fig. 4 disclose sending anonymized features to remote search engine “In operation 450, local learning system 116 can optionally send a feature vector to remote search engine 150 based upon a feature identified by local learning system 116”); after sending the search query and the anonymized features, receiving, from the remote server having the search engine, remote search results(Hornkvist, para 0088 and element 450 for Fig. 4 disclose sending anonymized features to remote search engine and receiving remote search results “Remote results 415 returned in response to the query may include results from The Huffington Post® and Fox News®. Remote search engine 150 may have returned results for Fox News® as the top rated results based upon interaction by many users of remote search engine 150”); and in response to receiving the remote search results, displaying a ranking of results including the local search results and the remote search results(Hornkvist, para 0080 and Fig. 4 discloses receiving local and remote search results and ranking them together “In operation 420, the local search results and the remote search results can be blended and ranked, then presented to the user on the local search interface 110” ).
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Hornkvist, John et al (PGPUB Document No. US20150347519), hereafter referred as to “Hornkvist”, in further view of Zheng, Zhaohui et al (PGPUB Document No. US 20090248667), hereafter, referred to as “Zheng”.
Regarding claim 7(Original), Hornkvist teaches all the limitations of claim 1 but does explicitly teach further comprising: before displaying the ranking of the results, scoring, based on the search query, the local search results and the remote search results.
However, in the same field of endeavor of query feature retrieval Zheng teaches further comprising: before displaying the ranking of the results, scoring, based on the search query, the local search results and the remote search results(Zheng, claim 32 discloses ranking contents according to the generated score ”search engine is operative to order the given item in the result set according to the ranking score for the given content item” and para 0015 further discloses that search features are from both local or remote “the ranking engine 180 may comprise one or more processing elements…….the search provider 140 may utilize more or fewer components and data stores, which may be local or remote with regard to a given component or data store”);
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 scoring of query features and ranking search results based on score of Zheng into the computation of query features of Hornkvist to produce an expected result of ranking search results with features from local and remote resources. The modification would be obvious because one of the ordinary skill in the art would be motivated to implement a search function that is operative to retrieve and efficiently rank relevant contents based on changing subjective factors such that important contents appears at the top of the search results(Zheng, para 0005).
Regarding claim 8(Original), Hornkvist and Zheng teach all the limitations of claim 7 and Zheng further teaches wherein the scoring of the local search results and the remote search results is based on scores received from the server having the search engine (Zheng, claim 31 discloses generating ranking score based on features of result set and ranking the contents accordingly “ranking engine is operative to determine a feature vector for a given content item in the result set, apply the ranking function to the feature vector for the given content item, and generate a ranking score for the given content item…” and para 0015 further discloses that search features are from both local or remote “the ranking engine 180 may comprise one or more processing elements…….the search provider 140 may utilize more or fewer components and data stores, which may be local or remote with regard to a given component or data store”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Hornkvist, John et al (PGPUB Document No. US20150347519), hereafter referred as to “Hornkvist”, in view of Kumar, Mukul Raj et al(US Patent No. 9875740 ), hereafter, referred to as “Kumar”.
Regarding claim 10(Original), Hornkvist teaches all the limitations of claim 1 but does not explicitly teach wherein the first category is ranked relative to the second category,
However, in the same field of endeavor of query feature retrieval Zheng teaches wherein the first category is ranked relative to the second category (Kumar, claim 1 discloses ranking category according to their respective scores and thus, categories are ordered relative to one with other according to their scores ”the set of categories including two or more categories, each of the two or more categories being ranked according to respective relevance scores to the first search query; identify a first set of results associated with a first category of the set of categories, the first category having a largest relevance score to the first search 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 category ranking features of Kumar into the computation of query features of Hornkvist to produce an expected result of having ranked search results into ordered categories on retrieved contents. The modification would be obvious because one of the ordinary skill in the art would be motivated to categorize contents by their relevance so that users can easily locate contents by the level of theirs relevance(Kumar, claim 1).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Hornkvist, John et al (PGPUB Document No. US20150347519), hereafter referred as to “Hornkvist”, in view of Kumar, Mukul Raj et al(US Patent No. 9875740 ), hereafter, referred to as “Kumar”, in further view of Lavie, Avishay et al(PGPUB Document No. 20180046703), hereafter, referred to as “Lavie”.
Regarding claim 11(Original), Hornkvist and Kumar teach all the limitations of claim 10 but don’t explicitly teach wherein the ranking of the first category relative to the second category is based on category type.
However, in the same field of endeavor of query and categorization of retrieved contents Lavie teaches wherein the ranking of the first category relative to the second category is based on category type(Lavie, para 0049 discloses ranking category according to their types “categories may be ordered according to the type of the category, such as, for example, product category, brand, technical feature, material, color, and or any other suitable type of category”);
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 ordering searched contents by types of Lavie into the computation of query features of Hornkvist and Kumar to produce an expected result of ordering categories of retrieved contents. The modification would be obvious because one of the ordinary skill in the art would be motivated to implement a time-efficient search system to improve the search performance by reducing the burden on search servers and the computational power consumption (Lavie, para 0037).
Response to Arguments
I. 35 U.S.C §101- Abstract idea rejection
The examiner admits the typo mentioned in the paragraph 4 of page 8 of REMARKS and applicant is requested to disregard the typos. However, all recited claim limitations were analyzed either as mental steps or as additional elements under “Claim Rejections- 35 U.S.C §103” section according to 2019 PEG in the office action mailed on 4/6/2026.
The crux of the applicant’s arguments presented on page 9 paragraph 2-4 argued that “The Office Action's characterization of the claims as involving "observation, judgment and evaluation" improperly abstracts away technological benefits of the claims. The claims require the client device to use local search results as the basis for generating local features, anonymize those local features into different anonymized features, and send those anonymized features to a remote search engine for use in a remote-search workflow. The ordered relationship among these operations is not merely "observation, judgment and evaluation"”.
Applicant’s above mentioned arguments have been fully considered but the examiner respectfully disagrees as recited claim limitations such as “obtaining local search results corresponding to the search query….. after obtaining the local search results: identifying local features for the local search results; and anonymizing the local features to generate anonymized features, wherein the anonymized features are different from the local features….” as drafted, are processes that under broadest reasonable interpretation, cover performance of the limitations in the human mind. Further, throughout the specification “remote search engine” is recited as generic component and not sufficient to amount significantly more than the judicial exception. The claim also recited “remote server” for performing the method but, that is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications. This is no more than mere instructions to apply the exception using generic computer component.
The applicant further on page 10 paragraph 1 stated “Prong Two. The additional claim elements are not insignificant extra-solution activity. …..In particular, the claimed anonymizing operation is not a generic post-processing step. It occurs after local search results are obtained and before transmission to the remote server, and it changes the data sent to the remote search engine. That claimed transformation of local features into different anonymized features is part of the claimed search workflow”.
In response to the above statement the examiner likes to mentioned that the limitation “anonymizing the local features to generate anonymized features….”, was analyzed under Step 2A Prong I as a mental step but not considered under Step 2A Prong II.
Applicant’s arguments regarding the sending, receiving and displaying data presented in paragraph 2-3 of page 10 are not found persuasive as argued recited additional elements are insignificant extra-solution activity of user query specific mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g), and further displaying data is insignificant post solution activity of outputting data. Their collective functions merely provide conventional computer implementation but not an improvement to computer or other technology.
Applicant’s arguments regarding Step 2B presented through paragraph 4 of page 10 through paragraph 3 of page 11 are also not found persuasive as both insignificant extra-solution activity of data gathering and outputting data are also well-understood, routine, and conventional. Further, sending user query/data to local and remote server with user specific query is insignificant extra-solution activity of data transmission, such is also well- understood, routine, and conventional (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)). Also, presenting data is WURC based on OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed. Cir. 2015) (presenting offers and gathering statistics). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible.
II. 35 U.S.C §102
The applicant in paragraph 4-6 of page 12 of REMARKS filed on 7/7/2026 argued that “The Office Action has not shown that the cited portions disclose the claimed ordered sequence of claim 1. In particular, claim 1 requires, after obtaining the local search results, "identifying local features for the local search results" and "anonymizing the local features to generate anonymized features," where the anonymized features are different from the local features. The claim then requires, after generating the anonymized features, sending the search query and the anonymized features to a remote server having a search engine.
The Office Action's cited mapping does not establish disclosure of that ordered
relationship. The rejection cites different portions for alleged query features, user data, session context, predictor modification, and sending a feature vector. But the rejection does not identify a disclosure in which local search results are first obtained, local features for those local search results are then identified, those same local features are anonymized to generate anonymized features different from the local features, and the search query and those anonymized features are then sent to the remote search engine
……The claim requires the specific relationship among the local search results, the local features identified for those local search results, the anonymized features generated from those local features, and the subsequent transmission of the search query with those anonymized features”.
Applicant’s above mentioned arguments have been fully considered but the examiner respectfully disagrees for following reasons;
Firstly, Hornkvist in para 0018 discloses getting features from user local search results such as movies that user frequently selects from the search result and place where user watches it. Subsequently, the movie selection gets anonymized by movie genre and place (address) is anonymized generic location such as “at home” to hide personal information of a user.
Secondly, Hornkvist further in para 0018 discloses anonymized features from local search result is getting sent to remote search engine as following “The anonymized private information preserves the privacy of the user of the client device while improving the relevance to the user of search results returned to the user in response to a query to the remote search engine”.
Thirdly, Hornkvist in Fig. 4, paragraph 0088 and 0018 disclose sending anonymized features to remote search engine after performing the local search and feature anonymization processes. Therefore, Hornkvist’s cited teachings retains the argued order of operations as claimed.
No additional arguments are presented other than discussed above therefore; the examiner maintains the rejection to claim 1-13 mailed on 4/6/2026.
Conclusion
THIS ACTION IS MADE FINAL. 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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/ABDULLAH A DAUD/Examiner, Art Unit 2164
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
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