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 3/3/2026.
Claim 1-2, 4-13, 15-20 are pending. Claim 1, 2, 4, 10, 12, 13 and 15 are amended. Claim 3 and 14 are cancelled. Claim 1-2, 4-13, 15-20 are rejected.
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 1, 2, 4, 7, 10-13, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Peng, Rui-Qi et al (Chinese Patent Document No. 109299383A), hereafter referred as to “Peng”, in view of Ou, Zhou (PGPUB Document No. 20150012513), hereafter, referred to as “Ou”, in view of Huang, Ru-huan et al(Chinese patent document No. CN 113672791), hereafter, referred to as “Huang”, in view of Brown, Noel (PGPUB Document No. 20160314510), hereafter, referred to as “Brown”, in further view of Kanekawa, Nobuyasu et al(PGPUB Document No. 20200139988), hereafter, referred to as “Kanekawa”.
Claim 1(Currently Amended), Peng teaches A query result display method, comprising: receiving a query content; determining query result quality information corresponding to the query content, the query result quality information representing a matching degree between the query content and query results obtained based on the query content(Peng, page 5 para 6 discloses a process of performing queries and providing results (recommended word sets) based on query “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”; page 4 last para discloses query result quality is being determined by relevance evaluation “Database module further includes relevance evaluation module, for being used by extracting Family calculates the satisfaction feature of search result dependency prediction score of the conjunctive word relative to keyword.”);
wherein as the matching degree represented by the query result quality information goes higher, the display sequence of the recommended word set is more forward, and the display sequence is a sequence between the recommended word set and the query results(Peng, page 2 para 0010 discloses that words are positioned based on their scores higher/forward or lower/backward in sequence “Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”; Peng, page 4 para 12 further discloses recommendation of word set/keywords are being added to the result such as " Liu De China scandal girl friend " and " Liu De China film " in sequence according to the recommended keyword/s scores );
adding the recommended word set to the query results according to the display sequence, to obtain a target query result(Peng, page 4 para 12 discloses recommendation of word set/keywords are being added to the result such as " Liu De China scandal girl friend " and " Liu De China film " in sequence according to the recommended keyword/s scores “For example, recommending original sequence of word is " Liu De China scandal girl friend ", " Liu De China film ", recommend word 1 by calculating " Liu De China scandal girl friend " is scored at -1 relative to the dependency prediction of keyword " Liu Dehua ", ….Thus make the recommendation word sequence more relevant with user's search key more forward, improve user uses body It tests, and significant improves the ratio of user's webpage clicking”);
and transmitting the target query result to the terminal(Peng, page 5 para 2 device/terminal for displaying results “Electronic equipment in the embodiment of the present invention can include but is not limited to such as mobile phone, laptop, number Radio receiver, PDA (personal digital assistant), PAD (tablet computer), PMP (portable media player), car-mounted terminal”), so as to display the recommended word set at a display position corresponding to the display sequence in the query result interface by the terminal(Peng, page 5 para 6 discloses displaying/showing results in sequence/order based on their scores “according to recommendation word relative to the keyword Dependency prediction score is ranked up the recommendation word, and shows the recommendation word according to the sequence after sequence so that with Search key more relevant recommendation word sequence in family is more forward”),
But Peng does not explicitly teach determining a target number of target query results can be displayed in a query result interface of a terminal;
determining a product of the target number and a value resulting from normalizing a difference value between quality standard information of the query result quality information and the query result quality information as a display sequence of a recommended word set corresponding to the query content in the query results according to the query result quality information,
acquiring a target range for the display sequence of the recommended word set, wherein the target range for the display sequence is smaller than a range of the query results to be displayed;
in response to the determined display sequence being smaller than a lower limit value of the target range, correcting the display sequence to be the lower limit value; in response to the determined display sequence being greater than an upper limit value of the target range, correcting the display sequence to be the upper limit value; wherein the display position is a position at which the recommended word set appears in the target query results.
However, in the same field of endeavor of searching and displaying query results Ou teaches determining a target number of target query results can be displayed in a query result interface of a terminal(Qu, para 0020 discloses determining number of search results to be displayed “server 106 is configured to determine a quantity of query results responsive to the current query greater than the desired quantity of query results to display per page to request from search engine 108”);
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 feature of determining number of search results to be displayed of Qu into displaying recommended keywords in sequence of Peng to produce an expected result of displaying only contents which satisfies the various display conditions. The modification would be obvious because one of ordinary skill in the art would be motivated to display query results that are compliant or meets the display conditions(Qu, para 0019).
But Peng and Qu don’t explicitly teach determining a product of the target number and a value resulting from normalizing a difference value between quality standard information of the query result quality information and the query result quality information as a display sequence of a recommended word set corresponding to the query content in the query results according to the query result quality information,
acquiring a target range for the display sequence of the recommended word set, wherein the target range for the display sequence is smaller than a range of the query results to be displayed;
in response to the determined display sequence being smaller than a lower limit value of the target range, correcting the display sequence to be the lower limit value; in response to the determined display sequence being greater than an upper limit value of the target range, correcting the display sequence to be the upper limit value; wherein the display position is a position at which the recommended word set appears in the target query results.
However, in the same field of endeavor of search quality determination Huang teaches determining a product of the target number and a value resulting from normalizing a difference value between quality standard information of the query result quality information and the query result quality information as a display sequence of a recommended word set corresponding to the query content in the query results according to the query result quality information(Huang, claim 1 discloses determination of order sequence of search results by taking difference of two quality values “determining the order of search results, wherein the method comprises: obtaining the quality value of the historical search result in the predefined time period, according to a plurality of the quality value, calculating the average value and the standard difference of the quality value, wherein the quality value is calculated according to the value of one or more quality factors of the historical search result; using the average value and the standard difference” ; where Peng on page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence),
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 feature of determining quality of the search results of Huang into displaying recommended keywords in sequence of Peng and Qu to produce an expected result of displaying search result contents by determined order. The modification would be obvious because one of ordinary skill in the art would be motivated to display ordered list considering newly added data(Huan, Claim 1).
But Peng, Qu and Huang don’t explicitly teach acquiring a target range for the display sequence of the recommended word set, wherein the target range for the display sequence is smaller than a range of the query results to be displayed;
in response to the determined display sequence being smaller than a lower limit value of the target range, correcting the display sequence to be the lower limit value; in response to the determined display sequence being greater than an upper limit value of the target range, correcting the display sequence to be the upper limit value; wherein the display position is a position at which the recommended word set appears in the target query results.
However, in the same field of endeavor of searching and displaying query results Brown teaches acquiring a target range for the display sequence of the recommended word set, wherein the target range for the display sequence is smaller than a range of the query results to be displayed(Brown, abstract and para 0014 discloses determining a display range or threshold and when exceeding the threshold updating sorting or sequencing the result accordingly “A similarity factor is used to determine if the results exceed the display threshold and the best match threshold. The results are sorted and displayed according to the similarity factor”);
wherein the display position is a position at which the recommended word set appears in the target query results(Brown, para 0014 teaches search result display position is being determined by query result relevance “sorting the relevant entities is complete, the present invention displays the sorted relevant entities to the user account through a user interface”).
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 feature of determining the search results display position of Brown into displaying recommended keywords in sequence of Peng, Qu and Huang to produce an expected result of displaying most relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the user search experience effectively by eliminating tedious process of re-entering of queries(Brown, para 0005).
But Peng, Qu, Huang and Brown don’t explicitly teach in response to the determined display sequence being smaller than a lower limit value of the target range, correcting the display sequence to be the lower limit value; in response to the determined display sequence being greater than an upper limit value of the target range, correcting the display sequence to be the upper limit value;
However, in the same field of endeavor of output adjustment Kanekawa teaches in response to the determined display sequence being smaller than a lower limit value of the target range, correcting the display sequence to be the lower limit value; in response to the determined display sequence being greater than an upper limit value of the target range, correcting the display sequence to be the upper limit value(Kanekawa, para 0152 discloses correcting output value in the event when the output is above and below the upper and lower range (threshold value) ““OK w/limit” is a case where the automatic control output 21 is out of the range of the control output lower limit and the control output upper limit, but may be corrected to a value between the control output lower limit and the control output upper limit. That is, this value is a case where the automatic control output 21 can be corrected within the range of the control output lower limit and the control output upper limit.”; where Peng, page 2 para 0010 discloses determination of display sequence/position);
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 feature of adjusting output when it falls above or below the threshold of Kanekawa into displaying recommended keywords in sequence of Peng, Qu, Huang and Brown to produce an expected result of displaying out of range relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the output display by including the contents which are around a threshold value(Kanekawa, para 0152).
Claim 2(Currently Amended), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 1 and Qu further teaches wherein a value of the query result quality information is greater than or equal to zero, and the value of the query result quality information is smaller than or equal to the quality standard information, wherein a value of the quality standard information of the query result quality information is a maximum of the query result quality information, and is a constant greater than zero (Qu, para 0020 further teaches the number of query result set to be displayed is determined against a desired number of result set that can be accommodated to a result page “Search engine 108 is configured to return the number of query results responsive to the current query equal to the corrected query results request quantity back to server 106. Server 106 is configured to determine how many of the retrieved query results meet/comply with the one or more display conditions. If server 106 determines that there is at least a number of query results compliant with the display conditions equal to the desired quantity of query results to display per page, then server 106 is configured to send the compliant query results in one query results page to be displayed at client device 102”; where Peng, page 2 para 0010 discloses ranking score and prior art Brown in para 0014 and abstract discloses target threshold).
Claim 3, Cancelled.
Claim 4 (Currently Amended), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 1 and Peng further teaches wherein the query content comprises a plurality of query keywords; determining the display sequence of the recommended word set corresponding to the query content in the query results according to the query result quality information further comprises(Peng, page 5 para 6 discloses performing queries and providing results (recommended word sets) based on query “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”; page 4 last para discloses query result quality is being determined by relevance evaluation “Database module further includes relevance evaluation module, for being used by extracting Family calculates the satisfaction feature of search result dependency prediction score of the conjunctive word relative to keyword.”):
for each of the query keywords, determining a recommended word display sequence corresponding to the query keyword based on the query result quality information corresponding to the query keyword; determining the display sequence corresponding to the query content on the basis of the recommended word display sequences corresponding to each of the query keywords(Peng, page 2 para 0010 discloses based on user inputted keywords, recommendation of word set/keywords are generated; and further, recommended words are being displayed in sequence based on their determined scores “Obtain the keyword of user's input; Incidence relation database is inquired, multiple conjunctive words are generated according to the incidence relation of the keyword, as recommendation word; Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”).
Claim 7(Original), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 1 and Peng further teaches wherein the query result quality information corresponding to the query content is determined based on the query result quality information corresponding to the query keywords comprised in the query content, and the query result quality information corresponding to the query keywords is predetermined by(Peng, page 5 para 6 discloses performing queries and providing results (recommended word sets) based on query “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”; page 4 last para discloses query result quality is being determined by relevance evaluation “Database module further includes relevance evaluation module, for being used by extracting Family calculates the satisfaction feature of search result dependency prediction score of the conjunctive word relative to keyword.”):
acquiring a plurality of history query records corresponding to the query keywords; preprocessing the plurality of history query records to obtain query result confirmation information corresponding to each of the history query records(Peng, claim 2 discloses before confirming the query result (result being shown to users) acquiring/extracting search history/behavior data related to query keywords “wherein the method also includes: first pass through extraction a large number of users in advance Search behavior data establish the incidence relation database, be stored in the incidence relation database keyword column The dependency prediction score of table, the conjunctive word of each keyword and conjunctive word relative to keyword”),
wherein the query result confirmation information characterizes operations executed by a user in a process of completing a query based on the query keywords, the operations comprising at least one of: confirmation operation, page-turning operation, keyword modification operation, and browsing operation(Peng, claim 4 further discloses result confirmation (showing query results) considers browsing operation such as “webpage stay time” or “querying condition rewriting” “it is characterized in that, the satisfaction feature include one in following characteristics or Multiple: number of clicks clicks order, webpage stay time, querying condition rewriting number” ); determining the query result quality information corresponding to the query keywords based on the query result confirmation information corresponding to the plurality of history query records(Peng, claim 4 further discloses result confirmation (showing query results) considers browsing operation such as “webpage stay time” or “querying condition rewriting” as search quality information or satisfaction “it is characterized in that, the satisfaction feature include one in following characteristics or Multiple: number of clicks clicks order, webpage stay time, querying condition rewriting number” ) and a decision-making tree model(Peng, page 4 para 4 discloses using decision tree model for query quality or satisfaction “The mapping relations of above-mentioned satisfaction feature and specific value can be realized by known model training (such as can Realized using decision Tree algorithms), until reach preset accuracy rate”).
Regarding claim 11(Original), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 1 and Peng further teaches A non-transitory computer-readable medium, comprising a computer program stored therein, wherein when the computer program executed by a processing apparatus(Peng, page 3 para 3 discloses computer readable storage media for storing program to be executed processor “the embodiment of the present invention provide a kind of computer readable storage medium, the computer-readable storage medium Computer program is stored in matter, the computer program realizes the described in any item sides of foregoing invention when being executed by processor Method”), causing the processing apparatus to perform the method according to claim 1(see the rejection of method claim 1).
Claim 10(Currently Amended), Peng teaches A query result display method, comprising: in response to receiving a target query result transmitted by the server, determining a display position corresponding to a recommended word set according to a display sequence corresponding to the recommended word set indicated by the target query result(Peng, page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence “Obtain the keyword of user's input; Incidence relation database is inquired, multiple conjunctive words are generated according to the incidence relation of the keyword, as recommendation word; Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”; where transmitting results by server is disclosed by prior art Zhu discussed later), wherein the target query result comprises the recommended word set and the display sequence, and a query result obtained based on the query content(Peng, page 4 para 12 discloses recommendation of word set/keywords are being added to the result in response to query such as " Liu De China scandal girl friend " and " Liu De China film " in sequence according to the recommended keyword/s scores “For example, recommending original sequence of word is " Liu De China scandal girl friend ", " Liu De China film ", recommend word 1 by calculating " Liu De China scandal girl friend " is scored at -1 relative to the dependency prediction of keyword " Liu Dehua ", ….Thus make the recommendation word sequence more relevant with user's search key more forward, improve user uses body It tests, and significant improves the ratio of user's webpage clicking”), and displaying, at the display position in the query result interface, the recommended word set corresponding to the query content(Peng, page 5 para 6 discloses displaying/showing results in sequence/order based on their scores “according to recommendation word relative to the keyword Dependency prediction score is ranked up the recommendation word, and shows the recommendation word according to the sequence after sequence so that with Search key more relevant recommendation word sequence in family is more forward”), determining the query result quality information representing a matching degree between the query content and the query result (Peng, page 5 para 6 discloses a process of performing queries and providing results (recommended word sets) based on query “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”; page 4 last para discloses query result quality is being determined by relevance evaluation “Database module further includes relevance evaluation module, for being used by extracting Family calculates the satisfaction feature of search result dependency prediction score of the conjunctive word relative to keyword.”),
But Peng does not explicitly teach the display sequence is determined as a product of a target number and a value resulting from normalizing a difference value between quality standard information of query result quality information and the query result quality information, the target number being a number of target query results can be displayed in a query result interface of a terminal;
wherein in response to the determined display sequence being smaller than a lower limit value of the target range, the display sequence is corrected to be the lower limit value, and in response to the determined display sequence being greater than an upper limit value of the target range, the display sequence is corrected to be the upper limit value (Kanekawa, para 0152 discloses correcting output value in the event when the output is above and below the upper and lower range (threshold value) ““OK w/limit” is a case where the automatic control output 21 is out of the range of the control output lower limit and the control output upper limit, but may be corrected to a value between the control output lower limit and the control output upper limit. That is, this value is a case where the automatic control output 21 can be corrected within the range of the control output lower limit and the control output upper limit.”; where Peng, page 2 para 0010 discloses determination of display sequence/position),
and the target range for the display sequence is smaller than a number of the query results to be displayed (Brown, abstract and para 0014 discloses determining a display range or threshold and when exceeding the threshold updating sorting or sequencing the result accordingly “A similarity factor is used to determine if the results exceed the display threshold and the best match threshold. The results are sorted and displayed according to the similarity factor”); wherein the display position is a position at which the recommended word set appears in the target query results (Brown, para 0014 teaches search result display position is being determined by query result relevance “sorting the relevant entities is complete, the present invention displays the sorted relevant entities to the user account through a user interface”).
However, in the same field of endeavor of searching and displaying query results Ou teaches the target number being a number of target query results can be displayed in a query result interface of a terminal(Qu, para 0020 discloses determining number of search results to be displayed “server 106 is configured to determine a quantity of query results responsive to the current query greater than the desired quantity of query results to display per page to request from search engine 108”);
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 feature of determining number of search results to be displayed of Qu into displaying recommended keywords in sequence of Peng to produce an expected result of displaying only contents which satisfies the various display conditions. The modification would be obvious because one of ordinary skill in the art would be motivated to display query results that are compliant or meets the display conditions(Qu, para 0019).
But Peng and Qu don’t explicitly teach the display sequence is determined as a product of a target number and a value resulting from normalizing a difference value between quality standard information of query result quality information and the query result quality information,
determining the query result quality information representing a matching degree between the query content and the query result, wherein in response to the determined display sequence being smaller than a lower limit value of the target range, the display sequence is corrected to be the lower limit value, and in response to the determined display sequence being greater than an upper limit value of the target range, the display sequence is corrected to be the upper limit value, and the target range for the display sequence is smaller than a number of the query results to be displayed; wherein the display position is a position at which the recommended word set appears in the target query results.
However, in the same field of endeavor of search quality determination Huang teaches the display sequence is determined as a product of a target number and a value resulting from normalizing a difference value between quality standard information of query result quality information and the query result quality information (Huang, claim 1 discloses determination of order sequence of search results by taking difference of two quality values “determining the order of search results, wherein the method comprises: obtaining the quality value of the historical search result in the predefined time period, according to a plurality of the quality value, calculating the average value and the standard difference of the quality value, wherein the quality value is calculated according to the value of one or more quality factors of the historical search result; using the average value and the standard difference” ; where Peng on page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence and prior art Qu and determination of target number for display),
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 feature of determining quality of the search results of Huang into displaying recommended keywords in sequence of Peng and Qu to produce an expected result of displaying search result contents by determined order. The modification would be obvious because one of ordinary skill in the art would be motivated to display ordered list considering newly added data(Huang, Claim 1).
But Peng, Qu and Huang don’t explicitly teach wherein in response to the determined display sequence being smaller than a lower limit value of the target range, the display sequence is corrected to be the lower limit value, and in response to the determined display sequence being greater than an upper limit value of the target range, the display sequence is corrected to be the upper limit value, and the target range for the display sequence is smaller than a number of the query results to be displayed; wherein the display position is a position at which the recommended word set appears in the target query results.
However, in the same field of endeavor of output adjustment Kanekawa teaches wherein in response to the determined display sequence being smaller than a lower limit value of the target range, the display sequence is corrected to be the lower limit value, and in response to the determined display sequence being greater than an upper limit value of the target range, the display sequence is corrected to be the upper limit value (Kanekawa, para 0152 discloses correcting output value in the event when the output is above and below the upper and lower range (threshold value) ““OK w/limit” is a case where the automatic control output 21 is out of the range of the control output lower limit and the control output upper limit, but may be corrected to a value between the control output lower limit and the control output upper limit. That is, this value is a case where the automatic control output 21 can be corrected within the range of the control output lower limit and the control output upper limit.”; where Peng, page 2 para 0010 discloses determination of display sequence/position);
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 feature of adjusting output when it falls above or below the threshold of Kanekawa into displaying recommended keywords in sequence of Peng, Qu and Huang to produce an expected result of displaying out of range relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the output display by including the contents which are around a threshold value(Kanekawa, para 0152).
But Peng, Qu, Huang and Kanekawa don’t explicitly teach and the target range for the display sequence is smaller than a number of the query results to be displayed; wherein the display position is a position at which the recommended word set appears in the target query results.
However, in the same field of endeavor of searching and displaying query results Brown teaches and the target range for the display sequence is smaller than a number of the query results to be displayed (Brown, abstract and para 0014 discloses determining a display range or threshold and when exceeding the threshold updating sorting or sequencing the result accordingly “A similarity factor is used to determine if the results exceed the display threshold and the best match threshold. The results are sorted and displayed according to the similarity factor”); wherein the display position is a position at which the recommended word set appears in the target query results (Brown, para 0014 teaches search result display position is being determined by query result relevance “sorting the relevant entities is complete, the present invention displays the sorted relevant entities to the user account through a user interface”).
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 feature of determining the search results display position of Brown into displaying recommended keywords in sequence of Peng, Qu, Huang and Kanekawa to produce an expected result of displaying most relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the user search experience effectively by eliminating tedious process of re-entering of queries(Brown, para 0005).
Regarding claim 20(Original), Peng, Qu, Huang, Kanekawa and Brown teach all the limitations of claim 10 and Peng further teaches An electronic device(Peng, page 2 para 19 discloses a device for generating and recommending words for queries “the embodiment of the present invention provide a kind of device for generating and recommending word”), comprising: a memory storing a computer program thereon (Peng, page 3 para 3 discloses computer readable storage media for storing program to be executed processor “the embodiment of the present invention provide a kind of computer readable storage medium, the computer-readable storage medium Computer program is stored in matter, the computer program realizes the described in any item sides of foregoing invention when being executed by processor Method”), and a processor for execution of the computer program in the memory to perform the method of claim 10 (see the rejection of claim 10).
Claim 12(Currently Amended), Peng teaches An electronic device(Peng, page 2 para 19 discloses a device for generating and recommending words for queries “the embodiment of the present invention provide a kind of device for generating and recommending word”), comprising: a memory storing a computer program thereon; and a processor for execution of the computer program in the memory to cause the electronic device to(Peng, page 3 para 3 discloses computer readable storage media for storing program to be executed processor “the embodiment of the present invention provide a kind of computer readable storage medium, the computer-readable storage medium Computer program is stored in matter, the computer program realizes the described in any item sides of foregoing invention when being executed by processor Method”): receive a query content; determine query result quality information corresponding to the query content, the query result quality information representing a matching degree between the query content and query results obtained based on the query content(Peng, page 5 para 6 discloses performing received inputted queries by users and providing results (recommended word sets) based on query “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”; page 4 last para discloses query result quality is being determined by relevance evaluation “Database module further includes relevance evaluation module, for being used by extracting Family calculates the satisfaction feature of search result dependency prediction score of the conjunctive word relative to keyword.”);
wherein as the matching degree represented by the query result quality information goes higher, the display sequence of the recommended word set is more forward, and the display sequence is a sequence between the recommended word set and the query results(Peng, page 2 para 0010 discloses that words are positioned based on their scores higher/forward or lower/backward in sequence “Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”; Peng, page 4 para 12 further discloses recommendation of word set/keywords are being added to the result such as " Liu De China scandal girl friend " and " Liu De China film " in sequence according to the recommended keyword/s scores );
add the recommended word set to the query results according to the display sequence, to obtain a target query result(Peng, page 4 para 12 discloses recommendation of word set/keywords are being added to the result such as " Liu De China scandal girl friend " and " Liu De China film " in sequence according to the recommended keyword/s scores “For example, recommending original sequence of word is " Liu De China scandal girl friend ", " Liu De China film ", recommend word 1 by calculating " Liu De China scandal girl friend " is scored at -1 relative to the dependency prediction of keyword " Liu Dehua ", ….Thus make the recommendation word sequence more relevant with user's search key more forward, improve user uses body It tests, and significant improves the ratio of user's webpage clicking”); and transmit the target query result to the terminal(Peng, page 5 para 2 device/terminal for displaying results “Electronic equipment in the embodiment of the present invention can include but is not limited to such as mobile phone, laptop, number Radio receiver, PDA (personal digital assistant), PAD (tablet computer), PMP (portable media player), car-mounted terminal”), so as to display the recommended word set at a display position corresponding to the display sequence in the query result interface by the terminal(Peng, page 5 para 6 discloses displaying/showing results in sequence/order based on their scores “according to recommendation word relative to the keyword Dependency prediction score is ranked up the recommendation word, and shows the recommendation word according to the sequence after sequence so that with Search key more relevant recommendation word sequence in family is more forward”),
But Peng does not explicitly teach determine a target number of target query results can be displayed in a query result interface of a terminal; determine a product of the target number and a value resulting from normalizing a difference value between quality standard information of the query result quality information and the query result quality information as a display sequence of a recommended word set corresponding to the query content in the query results, acquire a target range for the display sequence of the recommended word set; in response to the determined display sequence being smaller than a lower limit value of the target range, correct the display sequence to be the lower limit value; in response to the determined display sequence being greater than an upper limit value of the target range, correct the display sequence to be the upper limit value; wherein the display position is a position at which the recommended word set appears in the target query results.
However, in the same field of endeavor of searching and displaying query results Ou teaches determine a target number of target query results can be displayed in a query result interface of a terminal (Qu, para 0020 discloses determining number of search results to be displayed “server 106 is configured to determine a quantity of query results responsive to the current query greater than the desired quantity of query results to display per page to request from search engine 108”);
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 feature of determining number of search results to be displayed of Qu into displaying recommended keywords in sequence of Peng to produce an expected result of displaying only contents which satisfies the various display conditions. The modification would be obvious because one of ordinary skill in the art would be motivated to display query results that are compliant or meets the display conditions(Qu, para 0019).
But Peng and Qu don’t explicitly teach determine a product of the target number and a value resulting from normalizing a difference value between quality standard information of the query result quality information and the query result quality information as a display sequence of a recommended word set corresponding to the query content in the query results,
However, in the same field of endeavor of search quality determination Huang teaches determine a product of the target number and a value resulting from normalizing a difference value between quality standard information of the query result quality information and the query result quality information as a display sequence of a recommended word set corresponding to the query content in the query results (Huang, claim 1 discloses determination of order sequence of search results by taking difference of two quality values “determining the order of search results, wherein the method comprises: obtaining the quality value of the historical search result in the predefined time period, according to a plurality of the quality value, calculating the average value and the standard difference of the quality value, wherein the quality value is calculated according to the value of one or more quality factors of the historical search result; using the average value and the standard difference” ; where Peng on page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence),
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 feature of determining quality of the search results of Huang into displaying recommended keywords in sequence of Peng and Qu to produce an expected result of displaying search result contents by determined order. The modification would be obvious because one of ordinary skill in the art would be motivated to display ordered list considering newly added data(Huang, Claim 1).
But Peng, Qu and Huang don’t explicitly teach acquire a target range for the display sequence of the recommended word set; in response to the determined display sequence being smaller than a lower limit value of the target range, correct the display sequence to be the lower limit value; in response to the determined display sequence being greater than an upper limit value of the target range, correct the display sequence to be the upper limit value; wherein the display position is a position at which the recommended word set appears in the target query results.
However, in the same field of endeavor of searching and displaying query results Brown teaches acquire a target range for the display sequence of the recommended word set; in response to the determined display sequence being smaller than a lower limit value of the target range, correct the display sequence to be the lower limit value (Brown, abstract and para 0014 discloses determining a display range or threshold and when exceeding the threshold updating sorting or sequencing the result accordingly “A similarity factor is used to determine if the results exceed the display threshold and the best match threshold. The results are sorted and displayed according to the similarity factor”); wherein the display position is a position at which the recommended word set appears in the target query results (Brown, para 0014 teaches search result display position is being determined by query result relevance “sorting the relevant entities is complete, the present invention displays the sorted relevant entities to the user account through a user interface”).
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 feature of determining the search results display position of Brown into displaying recommended keywords in sequence of Peng, Qu and Huang to produce an expected result of displaying most relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the user search experience effectively by eliminating tedious process of re-entering of queries(Brown, para 0005).
But Peng, Qu, Huang and Brown don’t explicitly teach in response to the determined display sequence being smaller than a lower limit value of the target range, correcting the display sequence to be the lower limit value; in response to the determined display sequence being greater than an upper limit value of the target range, correcting the display sequence to be the upper limit value;
However, in the same field of endeavor of output adjustment Kanekawa teaches in response to the determined display sequence being smaller than a lower limit value of the target range, correcting the display sequence to be the lower limit value; in response to the determined display sequence being greater than an upper limit value of the target range, correcting the display sequence to be the upper limit value (Kanekawa, para 0152 discloses correcting output value in the event when the output is above and below the upper and lower range (threshold value) ““OK w/limit” is a case where the automatic control output 21 is out of the range of the control output lower limit and the control output upper limit, but may be corrected to a value between the control output lower limit and the control output upper limit. That is, this value is a case where the automatic control output 21 can be corrected within the range of the control output lower limit and the control output upper limit.”; where Peng, page 2 para 0010 discloses determination of display sequence/position);
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 feature of adjusting output when it falls above or below the threshold of Kanekawa into displaying recommended keywords in sequence of Peng, Qu, Huang and Brown to produce an expected result of displaying out of range relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the output display by including the contents which are around a threshold value(Kanekawa, para 0152).
Claim 13(Currently Amended), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 12 and Qu further teaches wherein a value of the query result quality information is greater than or equal to zero, and the value of the query result quality information is smaller than or equal to the quality standard information, wherein a value of the quality standard information of the query result quality information is a maximum of the query result quality information, and is a constant greater than zero (Qu, para 0020 further teaches the number of query result set to be displayed is determined against a desired number of result set that can be accommodated to a result page “Search engine 108 is configured to return the number of query results responsive to the current query equal to the corrected query results request quantity back to server 106. Server 106 is configured to determine how many of the retrieved query results meet/comply with the one or more display conditions. If server 106 determines that there is at least a number of query results compliant with the display conditions equal to the desired quantity of query results to display per page, then server 106 is configured to send the compliant query results in one query results page to be displayed at client device 102”; where Peng, page 2 para 0010 discloses ranking score and prior art Brown in para 0014 and abstract discloses target threshold).
Claim 14, cancelled.
Regarding claim 15(Currently Amended), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 12 and Peng further teaches wherein the query content comprises a plurality of query keywords; the electronic device being caused to determine the display sequence of the recommended word set corresponding to the query content in the query results according to the query result quality information further comprises being caused to(Peng, page 5 para 6 discloses performing queries and providing results (recommended word sets) based on query “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”; page 4 last para discloses query result quality is being determined by relevance evaluation “Database module further includes relevance evaluation module, for being used by extracting Family calculates the satisfaction feature of search result dependency prediction score of the conjunctive word relative to keyword”; where page 4 para 13 discloses computing device “As shown in Fig. 2, for the structural schematic diagram of the device provided in an embodiment of the present invention for generating recommendation word”):
for each of the query keywords, determine a recommended word display sequence corresponding to the query keyword based on the query result quality information corresponding to the query keyword; determine the display sequence corresponding to the query content on the basis of the recommended word display sequences corresponding to each of the query keywords(Peng, page 2 para 0010 discloses based on user inputted keywords, recommendation of word set/keywords are generated; and further, recommended words are being displayed in sequence based on their determined scores “Obtain the keyword of user's input; Incidence relation database is inquired, multiple conjunctive words are generated according to the incidence relation of the keyword, as recommendation word; Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”).
Claim 18(Original), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 12 and Peng further teaches wherein the query result quality information corresponding to the query content is determined based on the query result quality information corresponding to the query keywords comprised in the query content, and the query result quality information corresponding to the query keywords is predetermined by (Peng, page 5 para 6 discloses performing queries and providing results (recommended word sets) based on query “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”; page 4 last para discloses query result quality is being determined by relevance evaluation “Database module further includes relevance evaluation module, for being used by extracting Family calculates the satisfaction feature of search result dependency prediction score of the conjunctive word relative to keyword.”):
acquiring a plurality of history query records corresponding to the query keywords; preprocessing the plurality of history query records to obtain query result confirmation information corresponding to each of the history query records(Peng, claim 2 discloses before confirming the query result (result being shown to users) acquiring/extracting search history/behavior data related to query keywords “wherein the method also includes: first pass through extraction a large number of users in advance Search behavior data establish the incidence relation database, be stored in the incidence relation database keyword column The dependency prediction score of table, the conjunctive word of each keyword and conjunctive word relative to keyword”),
wherein the query result confirmation information characterizes operations executed by a user in a process of completing a query based on the query keywords, the operations comprising at least one of: confirmation operation, page-turning operation, keyword modification operation, and browsing operation (Peng, claim 4 further discloses result confirmation (showing query results) considers browsing operation such as “webpage stay time” or “querying condition rewriting” “it is characterized in that, the satisfaction feature include one in following characteristics or Multiple: number of clicks clicks order, webpage stay time, querying condition rewriting number” ); determining the query result quality information corresponding to the query keywords based on the query result confirmation information corresponding to the plurality of history query records (Peng, claim 4 further discloses result confirmation (showing query results) considers browsing operation such as “webpage stay time” or “querying condition rewriting” as search quality information or satisfaction “it is characterized in that, the satisfaction feature include one in following characteristics or Multiple: number of clicks clicks order, webpage stay time, querying condition rewriting number” ) and a decision-making tree model(Peng, page 3 para 16 discloses using decision tree model for query quality or satisfaction “The mapping relations of above-mentioned satisfaction feature and specific value can be realized by known model training (such as can Realized using decision Tree algorithms), until reach preset accuracy rate”).
Claim 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Peng, Rui-Qi et al (Chinese Patent Document No. 109299383A), hereafter referred as to “Peng”, in view of Ou, Zhou (PGPUB Document No. 20150012513), hereafter, referred to as “Ou”, in view of Huang, Ru-huan et al(Chinese patent document No. CN 113672791), hereafter, referred to as “Huang”, in view of Brown, Noel (PGPUB Document No. 20160314510), hereafter, referred to as “Brown”, in further view of Kanekawa, Nobuyasu et al(PGPUB Document No. 20200139988), hereafter, referred to as “Kanekawa”, in view of Du, Liang et al (PGPUB Document No. 20200159860), hereafter, referred to as “Du”, in further view of Zhu, Li et al (PGPUB Document No. 20120173562), hereafter, referred to as “Zhu”.
Claim 5(Original), Peng, Ou, Huang, Brown and Kanekawa teach all the limitations of claim 4 and Peng further teaches determining the display sequence corresponding to the query content on the basis of the recommended word display sequences corresponding to each of the query keywords comprises(Peng, page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence “Obtain the keyword of user's input; Incidence relation database is inquired, multiple conjunctive words are generated according to the incidence relation of the keyword, as recommendation word; Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”): in response to the query keywords corresponding to different recommended word display sequences(Peng, page 5 para 6 discloses performing queries and providing results (recommended word sets) based on query “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”), determining each of the recommended word display sequences corresponding to the query keywords as a display sequence corresponding to the query content, and determining recommended words in the recommended word set corresponding to the recommended word display sequence on the basis of the query keywords corresponding to the recommended word display sequence(Peng, page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence “Obtain the keyword of user's input; Incidence relation database is inquired, multiple conjunctive words are generated according to the incidence relation of the keyword, as recommendation word; Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”,
and adding the recommended words to the target query result; in response to a plurality of the query keywords corresponding to a same recommended word display sequence, determining the recommended word display sequence as a display sequence for the plurality of the keywords(Peng, page 4 para 12 discloses recommendation of word set/keywords are being added to the result such as " Liu De China scandal girl friend " and " Liu De China film " in sequence according to the recommended keyword/s scores “For example, recommending original sequence of word is " Liu De China scandal girl friend ", " Liu De China film ", recommend word 1 by calculating " Liu De China scandal girl friend " is scored at -1 relative to the dependency prediction of keyword " Liu Dehua ", ….Thus make the recommendation word sequence more relevant with user's search key more forward, improve user uses body It tests, and significant improves the ratio of user's webpage clicking”),
so as to acquire recommended words in an amount respectively corresponding to the plurality of query keywords(Peng, page 5 para 6 discloses acquiring or generating recommended words “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”), and add the recommended words to the target query result(Peng, page 4 para 12 discloses recommendation of word set/keywords are being added to the result such as " Liu De China scandal girl friend " and " Liu De China film " in sequence according to the recommended keyword/s scores “For example, recommending original sequence of word is " Liu De China scandal girl friend ", " Liu De China film ", recommend word 1 by calculating " Liu De China scandal girl friend " is scored at -1 relative to the dependency prediction of keyword " Liu Dehua ", ….Thus make the recommendation word sequence more relevant with user's search key more forward, improve user uses body It tests, and significant improves the ratio of user's webpage clicking”).
But Peng, Ou, Huang, Brown and Kanekawa don’t explicitly teach wherein the plurality of query keywords have an OR relationship; and determining the number of recommended words respectively corresponding to the plurality of query keywords in the recommended word set in accordance with a total number of query keywords corresponding to the recommended word display sequence and a display number of recommended words can be displayed in the recommended word set,
However, in the same field of endeavor of searching and displaying query results Du teaches wherein the plurality of query keywords have an OR relationship(Du, para 0033 discloses inputting search terms having “OR” relationship “A user at the client machine 106 can enter into a search box 108 various search terms. The search terms are provided to the search engine 102. The search engine 102 uses an index 110 to match search terms, operators (such as AND, XOR, OR, etc.), and/or filters (such as time filters, location filters, etc.) entered into the search box 108 to entries in the index 110”);
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 feature of inputting query terms having “OR” relation of Du into inputting query term/keyword of Peng, Ou, Huang, Brown and Kanekawa to produce an expected result of displaying query results that matches any of the keywords. The modification would be obvious because one of ordinary skill in the art would be motivated to make the query to search additional data to identify additional relevant search results(Du, abstract).
But Peng, Ou, Huang, Brown, Kanekawa and Du don’t explicitly teach and determining the number of recommended words respectively corresponding to the plurality of query keywords in the recommended word set in accordance with a total number of query keywords corresponding to the recommended word display sequence and a display number of recommended words can be displayed in the recommended word set,
However, in the same field of endeavor of search key recommendation Zhu teaches and determining the number of recommended words respectively corresponding to the plurality of query keywords in the recommended word set in accordance with a total number of query keywords corresponding to the recommended word display sequence and a display number of recommended words can be displayed in the recommended word set(Zhu, para 0098 discloses determination of total number of recommended keywords “based on the determined probabilities and the total number of recommended search keywords, the number of search keywords that need to be recommended for each corresponding category of industry is also determined”; where Peng discloses display of recommended words in sequence page 5 para 6 discloses displaying/showing results in sequence/order based on their scores “according to recommendation word relative to the keyword Dependency prediction score is ranked up the recommendation word, and shows the recommendation word according to the sequence after sequence so that with Search key more relevant recommendation word sequence in family is more forward”),
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 feature of determination of total number of recommended keywords of Zhu into generation of recommended search keywords of Peng, Ou, Huang, Brown, Kanekawa and Du to produce an expected result of considering more dataset to query. The modification would be obvious because one of ordinary skill in the art would be motivated to determine number of recommended keywords for category wise display of keywords/results within a fixed number of results to be displayed(Zhu, para 0107).
Claim 16(Original), Peng, Ou, Huang, Brown and Kanekawa teach all the limitations of claim 15 and Peng further teaches the electronic device being caused to determine the display sequence corresponding to the query content on the basis of the recommended word display sequences corresponding to each of the query keywords comprises being caused to (Peng, page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence “Obtain the keyword of user's input; Incidence relation database is inquired, multiple conjunctive words are generated according to the incidence relation of the keyword, as recommendation word; Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”):
in response to the query keywords corresponding to different recommended word display sequences(Peng, page 5 para 6 discloses performing queries and providing results (recommended word sets) based on query “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”), determine each of the recommended word display sequences corresponding to the query keywords as a display sequence corresponding to the query content, and determine recommended words in the recommended word set corresponding to the recommended word display sequence on the basis of the query keywords corresponding to the recommended word display sequence (Peng, page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence “Obtain the keyword of user's input; Incidence relation database is inquired, multiple conjunctive words are generated according to the incidence relation of the keyword, as recommendation word; Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”,
and add the recommended words to the target query result; in response to a plurality of the query keywords corresponding to a same recommended word display sequence, determine the recommended word display sequence as a display sequence for the plurality of the keywords (Peng, page 4 para 12 discloses recommendation of word set/keywords are being added to the result such as " Liu De China scandal girl friend " and " Liu De China film " in sequence according to the recommended keyword/s scores “For example, recommending original sequence of word is " Liu De China scandal girl friend ", " Liu De China film ", recommend word 1 by calculating " Liu De China scandal girl friend " is scored at -1 relative to the dependency prediction of keyword " Liu Dehua ", ….Thus make the recommendation word sequence more relevant with user's search key more forward, improve user uses body It tests, and significant improves the ratio of user's webpage clicking”), so as to acquire recommended words in an amount respectively corresponding to the plurality of query keywords(Peng, page 5 para 6 discloses acquiring or generating recommended words “The embodiment of the present invention obtains the keyword of user's input first, then by inquiring incidence relation database, according to The incidence relation of the keyword generates multiple conjunctive words, as recommendation word”), and add the recommended words to the target query result(Peng, page 4 para 12 discloses recommendation of word set/keywords are being added to the result such as " Liu De China scandal girl friend " and " Liu De China film " in sequence according to the recommended keyword/s scores “For example, recommending original sequence of word is " Liu De China scandal girl friend ", " Liu De China film ", recommend word 1 by calculating " Liu De China scandal girl friend " is scored at -1 relative to the dependency prediction of keyword " Liu Dehua ", ….Thus make the recommendation word sequence more relevant with user's search key more forward, improve user uses body It tests, and significant improves the ratio of user's webpage clicking”).
But Peng, Ou, Huang, Brown and Kanekawa don’t explicitly teach wherein the plurality of query keywords have an OR relationship; and determine the number of recommended words respectively corresponding to the plurality of query keywords in the recommended word set in accordance with a total number of query keywords corresponding to the recommended word display sequence and a display number of recommended words can be displayed in the recommended word set,
However, in the same field of endeavor of searching and displaying query results Du teaches wherein the plurality of query keywords have an OR relationship (Du, para 0033 discloses inputting search terms having “OR” relationship “A user at the client machine 106 can enter into a search box 108 various search terms. The search terms are provided to the search engine 102. The search engine 102 uses an index 110 to match search terms, operators (such as AND, XOR, OR, etc.), and/or filters (such as time filters, location filters, etc.) entered into the search box 108 to entries in the index 110”);
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 feature of inputting query terms having “OR” relation of Du into inputting query term/keyword of Peng, Ou, Huang, Brown and Kanekawa to produce an expected result of displaying query results that matches any of the keywords. The modification would be obvious because one of ordinary skill in the art would be motivated to make the query to search additional data to identify additional relevant search results(Du, abstract).
But Peng, Ou, Huang, Brown and Kanekawa and Du don’t explicitly teach and determine the number of recommended words respectively corresponding to the plurality of query keywords in the recommended word set in accordance with a total number of query keywords corresponding to the recommended word display sequence and a display number of recommended words can be displayed in the recommended word set,
However, in the same field of endeavor of search key recommendation Zhu teaches and determine the number of recommended words respectively corresponding to the plurality of query keywords in the recommended word set in accordance with a total number of query keywords corresponding to the recommended word display sequence and a display number of recommended words can be displayed in the recommended word set (Zhu, para 0098 discloses determination of total number of recommended keywords “based on the determined probabilities and the total number of recommended search keywords, the number of search keywords that need to be recommended for each corresponding category of industry is also determined”; where Peng discloses display of recommended words in sequence page 5 para 6 discloses displaying/showing results in sequence/order based on their scores “according to recommendation word relative to the keyword Dependency prediction score is ranked up the recommendation word, and shows the recommendation word according to the sequence after sequence so that with Search key more relevant recommendation word sequence in family is more forward”),
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 feature of determination of total number of recommended keywords of Zhu into generation of recommended search keywords of Peng, Ou, Huang, Brown, Kanekawa and Du to produce an expected result of considering more dataset to query. The modification would be obvious because one of ordinary skill in the art would be motivated to determine number of recommended keywords for category wise display of keywords/results within a fixed number of results to be displayed(Zhu, para 0107).
Claim 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Peng, Rui-Qi et al (Chinese Patent Document No. 109299383A), hereafter referred as to “Peng”, in view of Ou, Zhou (PGPUB Document No. 20150012513), hereafter, referred to as “Ou”, in view of Huang, Ru-huan et al(Chinese patent document No. CN 113672791), hereafter, referred to as “Huang”, in view of Brown, Noel (PGPUB Document No. 20160314510), hereafter, referred to as “Brown”, in further view of Kanekawa, Nobuyasu et al(PGPUB Document No. 20200139988), hereafter, referred to as “Kanekawa”, in further view of Kwok, Thomas et al(PGPUB Document No. 20020165873), hereafter, referred to as “Kwok”.
Claim 6(Original), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 4 and Peng further teaches wherein determining the display sequence corresponding to the query content on the basis of the recommended word display sequences corresponding to each of the query keywords comprises(Peng, page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence “Obtain the keyword of user's input; Incidence relation database is inquired, multiple conjunctive words are generated according to the incidence relation of the keyword, as recommendation word; Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”):
But Peng, Qu, Huang, Brown and Kanekawa don’t explicitly teach determining a sequence resulted from averaging and rounding off the recommended word display sequences corresponding to each of the query keywords as the display sequence corresponding to the query content.
However, in the same field of endeavor of searching and displaying query results Kwok teaches determining a sequence resulted from averaging and rounding off the recommended word display sequences corresponding to each of the query keywords as the display sequence corresponding to the query content(Kwok, para 0052 discloses averaging and rounding off scores for words/parameters for ranking “for optimization is performed by substituting the word score at each rank with a linear function of the word score … The sets of parameters that generated the best results are determined. These sets are then averaged and rounded off”; where Peng discloses display of recommended words in sequence page 5 para 6 discloses displaying/showing results in sequence/order based on their scores “according to recommendation word relative to the keyword Dependency prediction score is ranked up the recommendation word, and shows the recommendation word according to the sequence after sequence so that with Search key more relevant recommendation word sequence in family is more forward” and Kwok’s teaching can similarly be applied for recommended query keywords).
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 feature of averaging and rounding scores of Kwok into in scoring of recommended words of Peng, Qu, Huang, Brown and Kanekawa to produce an expected result of displaying recommended words in sequence by their scores. The modification would be obvious because one of ordinary skill in the art would be motivated improve the retrieval process by utilizing multiple recognizers for retrieving words from documents (Kwok, para 0013).
Claim 17(Original), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 15 and further teaches wherein the electronic device being caused to determine the display sequence corresponding to the query content on the basis of the recommended word display sequences corresponding to each of the query keywords comprises being caused to (Peng, page 2 para 0010 discloses recommendation of word set/keywords are generated and based on their scores recommended words are getting displayed in sequence “Obtain the keyword of user's input; Incidence relation database is inquired, multiple conjunctive words are generated according to the incidence relation of the keyword, as recommendation word; Dependency prediction score according to the recommendation word relative to the keyword, is ranked up the recommendation word, And the recommendation word is shown according to the sequence after sequence”):
But Peng, Qu, Huang, Brown and Kanekawa don’t explicitly teach determine a sequence resulted from averaging and rounding off the recommended word display sequences corresponding to each of the query keywords as the display sequence corresponding to the query content.
However, in the same field of endeavor of searching and displaying query results Kwok teaches determine a sequence resulted from averaging and rounding off the recommended word display sequences corresponding to each of the query keywords as the display sequence corresponding to the query content (Kwok, para 0052 discloses averaging and rounding off scores for words/parameters for ranking “for optimization is performed by substituting the word score at each rank with a linear function of the word score, with a separate linear function for each rank. For this case, a Monte Carlo optimization of a few thousand trials was run, concentrating the variation in the parameters for the higher ranks. The sets of parameters that generated the best results are determined. These sets are then averaged and rounded off”; Kwok’s teaching can similarly be applied for recommended query keywords).
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 feature of averaging and rounding scores of Kwok into in scoring of recommended words of Peng, Qu, Huang, Brown and Kanekawa to produce an expected result of displaying recommended words in sequence by their scores. The modification would be obvious because one of ordinary skill in the art would be motivated improve the retrieval process by utilizing multiple recognizers for retrieving words from documents (Kwok, para 0013).
Claim 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Peng, Rui-Qi et al (Chinese Patent Document No. 109299383A), hereafter referred as to “Peng”, in view of Ou, Zhou (PGPUB Document No. 20150012513), hereafter, referred to as “Ou”, in view of Huang, Ru-huan et al(Chinese patent document No. CN 113672791), hereafter, referred to as “Huang”, in view of Brown, Noel (PGPUB Document No. 20160314510), hereafter, referred to as “Brown”, in further view of Kanekawa, Nobuyasu et al(PGPUB Document No. 20200139988), hereafter, referred to as “Kanekawa”, in further view of Zilka, Lukas (US Patent No. 11741191), hereafter, referred to as “Zilka”.
Claim 8(Original), Peng, Ou, Huang, Brown and Kanekawa teach all the limitations of claim 7 and further teaches wherein the decision-making tree model is trained and obtained by(Peng, page 3 para 16 discloses using decision tree model training for query quality or satisfaction “The mapping relations of above-mentioned satisfaction feature and specific value can be realized by known model training (such as can Realized using decision Tree algorithms), until reach preset accuracy rate”):
preprocessing the training query record to obtain query result confirmation information corresponding to each of the training query record(Peng, claim 2 discloses preprocessing or before confirming the query result (result being shown to users) acquiring/extracting search history/behavior data related to query keywords “wherein the method also includes: first pass through extraction a large number of users in advance Search behavior data establish the incidence relation database, be stored in the incidence relation database keyword column The dependency prediction score of table, the conjunctive word of each keyword and conjunctive word relative to keyword”; where Peng further on page 3 para 16 discloses using decision tree model training for query quality or satisfaction);
But Peng, Ou, Huang, Brown and Kanekawa don’t explicitly teach acquiring a training sample set, wherein each training sample in the training sample set comprises a training query record corresponding to the training sample and marked query result quality information corresponding to the training query record; performing model training with query result confirmation information corresponding to the training query record as input and marked query result quality information corresponding to the training query record as target output, so as to obtain the decision-making tree model.
However, in the same field of endeavor of model training with query data for obtaining result quality Zilka teaches acquiring a training sample set, wherein each training sample in the training sample set comprises a training query record corresponding to the training sample and marked query result quality information corresponding to the training query record(Zilka, col 9:23-27 discloses acquiring or generating training data of queries “The local training system 202 can continuously generate the local training data 206 by monitoring the interaction of the user with webpages identified by search results responsive to search queries submitted by the user to a search system”; col 12:42-46 discloses further teaches marking or scoring query result quality “the ranking engine 414 processes the data element 506 and the search query 402 using the machine learning model 108, in accordance with the trained parameter values of the machine learning model 108, to generate a respective interaction score 508”);
performing model training with query result confirmation information corresponding to the training query record as input and marked query result quality information corresponding to the training query record as target output, so as to obtain the decision-making tree model(Zilka, col 12:42-46 discloses further teaches marking or scoring query result quality model training “the ranking engine 414 processes the data element 506 and the search query 402 using the machine learning model 108, in accordance with the trained parameter values of the machine learning model 108, to generate a respective interaction score 508”; where Peng further on page 3 para 16 discloses using decision tree model training for query quality or satisfaction “The mapping relations of above-mentioned satisfaction feature and specific value can be realized by known model training (such as can Realized using decision Tree algorithms), until reach preset accuracy rate”).
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 feature of generating query training data for result quality of Zilka into displaying query result of Peng, Ou, Huang, Brown and Kanekawa to produce an expected result of displaying only contents which satisfies the various display conditions. The modification would be obvious because one of ordinary skill in the art would be motivated to display quality query results that are more likely to be interacted by users(Zilka, col 12:46-48).
Claim 19(Original), Peng, Ou, Huang, Brown and Kanekawa teach all the limitations of claim 18 and Peng further teaches wherein the decision-making tree model is trained and obtained by (Peng, page 3 para 16 discloses using decision tree model training for query quality or satisfaction “The mapping relations of above-mentioned satisfaction feature and specific value can be realized by known model training (such as can Realized using decision Tree algorithms), until reach preset accuracy rate”):
preprocessing the training query record to obtain query result confirmation information corresponding to each of the training query record (Peng, claim 2 discloses preprocessing or before confirming the query result (result being shown to users) acquiring/extracting search history/behavior data related to query keywords “wherein the method also includes: first pass through extraction a large number of users in advance Search behavior data establish the incidence relation database, be stored in the incidence relation database keyword column The dependency prediction score of table, the conjunctive word of each keyword and conjunctive word relative to keyword”; where Peng further on page 3 para 16 discloses using decision tree model training for query quality or satisfaction);
But Peng, Ou, Huang, Brown and Kanekawa don’t explicitly teach acquiring a training sample set, wherein each training sample in the training sample set comprises a training query record corresponding to the training sample and marked query result quality information corresponding to the training query record; performing model training with query result confirmation information corresponding to the training query record as input and marked query result quality information corresponding to the training query record as target output, so as to obtain the decision-making tree model.
However, in the same field of endeavor of model training with query data for obtaining result quality Zilka teaches acquiring a training sample set, wherein each training sample in the training sample set comprises a training query record corresponding to the training sample and marked query result quality information corresponding to the training query record (Zilka, col 9:23-27 discloses acquiring or generating training data of queries “The local training system 202 can continuously generate the local training data 206 by monitoring the interaction of the user with webpages identified by search results responsive to search queries submitted by the user to a search system”; col 12:42-46 discloses further teaches marking or scoring query result quality “the ranking engine 414 processes the data element 506 and the search query 402 using the machine learning model 108, in accordance with the trained parameter values of the machine learning model 108, to generate a respective interaction score 508”);
performing model training with query result confirmation information corresponding to the training query record as input and marked query result quality information corresponding to the training query record as target output, so as to obtain the decision-making tree model (Zilka, col 12:42-46 discloses further teaches marking or scoring query result quality model training “the ranking engine 414 processes the data element 506 and the search query 402 using the machine learning model 108, in accordance with the trained parameter values of the machine learning model 108, to generate a respective interaction score 508”; where Peng further on page 3 para 16 discloses using decision tree model training for query quality or satisfaction “The mapping relations of above-mentioned satisfaction feature and specific value can be realized by known model training (such as can Realized using decision Tree algorithms), until reach preset accuracy rate”).
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 feature of generating query training data for result quality of Zilka into displaying query result of Peng, Ou, Huang, Brown and Kanekawa to produce an expected result of displaying only contents which satisfies the various display conditions. The modification would be obvious because one of ordinary skill in the art would be motivated to display quality query results that are more likely to be interacted by users(Zilka, col 12:46-48).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Peng, Rui-Qi et al (Chinese Patent Document No. 109299383A), hereafter referred as to “Peng”, in view of Ou, Zhou (PGPUB Document No. 20150012513), hereafter, referred to as “Ou”, in view of Huang, Ru-huan et al(Chinese patent document No. CN 113672791), hereafter, referred to as “Huang”, in view of Brown, Noel (PGPUB Document No. 20160314510), hereafter, referred to as “Brown”, in further view of Kanekawa, Nobuyasu et al(PGPUB Document No. 20200139988), hereafter, referred to as “Kanekawa”, in further view of Liu, Lin et al (PGPUB Document No. 20220358089), hereafter, referred to as “Lin”.
Claim 9(Original), Peng, Qu, Huang, Brown and Kanekawa teach all the limitations of claim 7 and further teaches wherein each of the query result quality information corresponding to the query keywords is stored by a quality information set, and the method further comprises(Peng, claim 2 discloses recommended words which are to be included in the results get stored in the database with their quality information or scores “Search behavior data establish the incidence relation database, be stored in the incidence relation database keyword column The dependency prediction score of table, the conjunctive word of each keyword and conjunctive word relative to keyword” ): acquiring a plurality of target query records for updating; preprocessing the plurality of target query records, and obtaining a target keyword in each of the target query records and query result confirmation information corresponding to the target keyword(Peng, claim 2 discloses before confirming the query result (result being shown to users) acquiring/extracting search history/behavior data related to query keywords “wherein the method also includes: first pass through extraction a large number of users in advance Search behavior data establish the incidence relation database, be stored in the incidence relation database keyword column The dependency prediction score of table, the conjunctive word of each keyword and conjunctive word relative to keyword”),
determining query result quality information corresponding to the target keyword based on the decision-making tree model (Peng, page 3 para 16 discloses determining query quality or satisfaction using decision tree model “The mapping relations of above-mentioned satisfaction feature and specific value can be realized by known model training (such as can Realized using decision Tree algorithms), until reach preset accuracy rate”)and query result confirmation information corresponding to the same target keyword(Peng, claim 2 discloses before confirming the query result (result being shown to users) acquiring/extracting search history/behavior data related to query keywords “wherein the method also includes: first pass through extraction a large number of users in advance Search behavior data establish the incidence relation database, be stored in the incidence relation database keyword column The dependency prediction score of table, the conjunctive word of each keyword and conjunctive word relative to keyword”); updating the quality information set with the query result quality information corresponding to the target keyword(Peng, page 3 para 16 discloses updating quality information until an accuracy level is reached “The mapping relations of above-mentioned satisfaction feature and specific value can be realized by known model training (such as can Realized using decision Tree algorithms), until reach preset accuracy rate”).
But Peng, Brown and Kanekawa don’t explicitly teach wherein the target keyword is a first keyword in the query content corresponding to the target query records;
However, in the same field of endeavor of searching and displaying query results Lin teaches wherein the target keyword is a first keyword in the query content corresponding to the target query records(Lin, para 0232 discloses target query records/results related to first keyword (“notepad”) “FIG. 7(A) and FIG. 7(B) are a schematic diagram of still another application interface according to an embodiment of this application. As shown in FIG. 7(A), in the foregoing example, the first keyword includes a keyword “notepad”, and a search result 205 of the keyword “notepad” is displayed in response to the keyword “notepad” detected in the application search box 201. The search result 205 includes an icon 2041 b of the first application (that is, the Memo application)”).
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 feature of extracting result set based on first keyword of Liu into displaying query result of Peng, Qu, Huang, Brown and Kanekawa to produce an expected result of displaying only contents which are related to a particular keyword. The modification would be obvious because one of ordinary skill in the art would be motivated to display query results that are both exact match or related to a particular keyword for the improvement of search convenience(Lin, para 0013).
Response to Arguments
I. 35 U.S.C §103
Applicant’s arguments presented on 3/3/2026 have been fully considered but are moot because the independent claim 1, 10 and 12 have been amended with newly added features which applicant’s arguments are directed towards. Since claims have been amended with new features, a new ground of rejection is presented.
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 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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