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
In response to the office action from 11/5/2025, the applicant has submitted an amendment, filed 1/21/2026, amending claims 1, 2, 11, 13, 14, 17, cancelling claims 2 and 12, while arguing to traverse the prior art and 101 rejections. Applicant’s arguments have been fully considered but are moot with respect to new grounds of rejections further in view of Tumuluri (US 2022/0114463) mandated by the latest amendments and for the reasons explained in the response to arguments.
Response to Arguments
In what follows applicant’s arguments and comments will be addressed in the order presented.
Page 7 in the section pertaining to the 101 rejection, in the subsection referring to the “Step 2A” following quoting the amended limitation “the two or more derived queries are derived by syntactic parsing and semantic reasoning based on a self-learned weighted combination operation” (page 7 paragraph before last), in the last paragraph, the last sentence recites: “This type of algorithmic learning with numerical weight optimization cannot be performed in the human mind”. On page 8 following two paragraphs discussing “syntactic parsing” and “semantic parsing” “component[s]”, in the third paragraph last sentence it is recited: “Managing these computational tradeoffs through numerical weight parameters requires computer processing”.
As an initial matter these statements do not represent what is taught in the disclosure; i.e., other than a vague example at the end of the specification ¶ 0066 pertaining to some weight assignments, nowhere in the disclosure one can find any “algorithmic” “learning”, any “optimization” (i.e., involving specific calculations) pertaining to any “weight” calculations to be able to assess if they teach anything significantly more. Furthermore, even if there were any “numerical weight” “algorithms”, presumably behind the “weight” assignments in the specification ¶ 0066, calculating a weight is considered “characterizing data gathering steps as insignificant extra-solution activity” (“Bilski, 561 US 593”). Also a novel computational approach is still basically a mathematical operation and considered abstract.
On page 9 following some broad overview of the “syntactic parsing and semantic reasoning” in paragraph 2, and some more examples in paragraph 3, it is concluded in paragraph 4 that: “These operations involve handling large volumes of electronic documents” “The scale and computational nature of these operations make them incompatible with mental processes”.
Here also it is unclear which step in the claims involves “large volumes of electronic documents”, as almost all steps involve managing basically one “query”, but nevertheless, dealing with a large amount of data does not automatically make a claim patent eligible, it is an application’s solution to dealing with this large amount of the data is what could be patentable.
On page 10 under the section “Step 2A, Prong Two” following some broad remarks in the third paragraph it is recited: “The self-learned weighted combination operation provides a technical improvement to how the system generated derived queries” “This learning mechanism allows the system to adapt to different query domains and use intent patterns. The leaned weights enable the system to make better decisions about how to split queries, which components to prioritize, and how to maintain query independence while ensuring self-completeness”. Page 10 the last paragraph: “The multistage processing architecture creates technical improvements in search efficiency”. Page 11 paragraph 2 lines 1+: “The sequential searching with result appending in the supplementing stage creates a chain-of-searches approach where each derived query’s result informs the processing of subsequent queries”, where “chain-of-Searches technique through multi-stage processing” was discussed on page 11 paragraph before last. Similar arguments are presented in the section “Step 2B” (pages 12+), i.e., regarding “technical improvement” (page 13 paragraph 2, page 15 first and second paragraphs) and “Chain-of-Searches technique” (page 13 paragraph 3).
These assertions, merely point out to some intended or implied use or benefit. What had to be addressed was even if one accepts all these assertions, what impact did they have on the functionality of the “server” (the only additional element in the independent claims); i.e., if they result in “increased flexibility, faster search times, and smaller memory requirements” (Enfish memorandum of 5/19/2016); and/or “improve computer-related technology by allowing computer performance of a function not previously performable by a computer” (MCRO memorandum of 11/2/2016). Unfortunately, one cannot see any reverse impact by the limitations on the functionality of the “server” let alone the specific impacts mentioned above.
On page 14 following some quotations from the specification paragraphs 0039, 0040 and 0047, it is finally concluded in the last 5 lines of that page: “The sorted queries are then searched sequentially, with each query’s results being appended to the next query in the sequence. This specific data flow architecture represents a particular implementation of search technology”. Then on page 15 it is asserted that these arrangements are “analogous” to “BASCOM” and “McRO”.
Respectfully, even if one assumes the said quotations from the said paragraphs in the specification are embedded somehow in the claim limitations (that they are not), how does this last quoted concluding assertion imply “improve computer-related technology by allowing computer performance of a function not previously performable by a computer” (MCRO memorandum of 11/2/2016). And how does a search query or man machine dialog system even compare with a totally technologically different BASCOM case? And even if one assumes this to be a valid comparison, ultimately how does that “particular implementation of search technology” and/or “specific arrangement” (page 15 line 3) alter the performance of the “server” according to the guidelines listed above.
The 112(b) rejections are discussed on page 16 the 2nd paragraph.
Due to the latest amendments the said rejections are overcome.
Regarding 103 rejections, pages 16, 17 and 18 (the 1st , 2nd 3rd and 4th paragraphs) merely provide broad overview of the claims 1, and 2, and selected portions of the office action. Then in the last paragraph on page 18, it is asserted: “Tumuluri does not disclose that two or more derived queries are derived by syntactic parsing and semantic reasoning based on a self-learned weighted combination operation”. Similar arguments are presented throughout of page 19.
The claim is silent on the scope of “self-learned weighted combination operation”. According to specification ¶ 0066 lines 19+: “The self-learned weighted combination operation involves assigning weights or scores to each derived query based on certain criteria or factors. Such weights determine the relevance or importance of each derived query in relation to the user search query”.
As an initial matter, this teaching refers to a process after the splitting (i.e., the “split stage”) of a “query” to “derived queries” has taken place and NOT before that. In the instant claim though, the “self-learned weighted combination operation” appears to be used to facilitate the “split stage” operation by “deriv[ing]” from the “query” the “derived queries”, in which case the claim is silent on any post processing associated with each “derived query” let alone one involving an “assign[ment]” of a “score” or “weight”.
Secondly, according to Tumuluri ¶ 0038 page 5 lines 3+: “The soft query” (a query) “may be divided” (splits (in a split stage)) “into multiple micro-queries” (into two or more derived queries) “based on” “entities” (using “entities” which were obtained by semantic and syntactic reasoning) “context, intent, objects and previous responses” (a self-learned weighted combination); ¶ 0048 S1: “The semantic and syntactic” (using semantic and syntactic reasoning) “analyzer 608 may be configured to extract entity information”. In summary: the quoted teaching of ¶ 0048 S1 implies “semantic and syntactic” “analy[sis]” help obtaining “entities”, and ¶ 0038 teaches “entities” help “divid[ing]” a “soft query” (query) into “multiple micro-queries” (into two or more derived queries). Furthermore ¶ 0038 teaches in addition to “entities”, “context, intent, objects and previous responses” (a self-learned weighted combination) are also used in the said “divid[ing]”. Therefore “entities”, “context, intent, objects and previous responses” do map to what amounts to the “self-learned weighted combination” used in the “divid[ing]” (the claim’s split stage). Tumuluri further teaches: ¶ 0038 lines 7+: “the results may be sorted” “for example, based on the relevance scores” (a self-learned weighted parameter used) “of the micro-queries results” (to validate division of a “soft-query” (query) into the plurality of derived queries) “with respect to soft-query). The micro-query score” “is a relevance score”. This “relevance score” maps precisely to what the specification defines the “self-learned weighted combination” to correspond to, namely assignment of a “score” or “weight” to each “derived query”.
On page 20 regarding the dependent claims it is asserted: “Dependent claims 3, 10, 13, and 20 are also allowable at least by virtue of their respective dependencies on independent claims 1 and 11”; on page 21 similar arguments are presented with respect to the dependent claims 7, 17, 8, 9, 18 and 19.
Since applicants have not argued the merits of these dependent claims, but assert patentability solely through their dependence on the allegedly patentable parent claims, they stand or fall with said parent claims and hence no further response to applicant’s arguments is necessary.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 stand rejected:
Claims 1 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a search retrieval method (claim 1) and system (claim11) in which a “search query” is broken into smaller “derived queries”, each with a smaller “length” than the “search query”, but are consistent in “intent” with it. Then for each “derived query” a separate “search” is conducted and plurality of “electronic documents” are determined based on “entities” associated with the said “derived query”, which are used to determine a “sorted” “sequence” of “derived queries” (and/or associated “electronic documents” or “results”)), and in so doing the said “derived queries” are “append[ed]” with each respective “retrieved result” (e.g. like a query result pair) to “obtain” “a final search result”.
These steps are carried out by a “server” (i.e., as the only additional element in both claims) and they involve “electronic documents” (another additional element requiring search to involve “electronic” and not just a regular result). Therefore other than the word “server”, there is nothing in these claims that cannot be done mentally; e.g., suppose I ask you when you are going on vacation, which theme parks will you take your children. This can easily be broken into: 1) when are you going on a vacation?, and 2) which theme parks will you take your children??. Your answer could be June or July (two answers for the first) and (magic mountain, or Disney land or sea world (three answers for the second)), and write them in a sheet of paper starting with the answer to the second question or sorting it according to the second one as it involved more answers and present them with their respective derived queries. Therefore, these limitations, as drafted, under their broadest reasonable interpretation, cover performance of the said limitations in the mind but for the recitation of a generic computer. If a limitation (or limitations), under their broadest reasonable interpretation, cover performance of the said limitations in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, each of the said claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite one additional element – using a server to perform each of the “extracting” “mapping” “sorting” “searching and “appending” steps. The server in all these steps is recited at a high level of generality (i.e., as a generic server or computer) performing generic computer functions of e.g., “sorting” “based on a number of relevant electronic documents related to each query”, such that it amounts no more than mere instructions to appl the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a server to perform the “extracting” “mapping” “sorting” “searching and “appending” steps amount to no more than mere instructions to apply the exception using a generic computer component; i.e., although the words “electronic” and “server” are used, but no technique in any limitations requires a “server” to perform it, and/or nothing in the claim limitations is tailored specifically to “electronic” “documents”. No specific technique is taught to be used to “generate” the “derived queries” from the “search query” to assess it to result in helping to enhance the “server” to run more efficiently and/or do something no machine has done before. Similar thing can be said for the other claim limitations. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Furthermore, in the example above the process of dividing the original query into 1) when are you going on a vacation?, and 2) which theme parks will you take your children??, results in two grammatically correct separate queries (i.e. abides by a syntactic parsing), and both also abide by knowledge of vocabulary (semantic reasoning) and grammatical rules (syntactic reasoning), where the human uses a weighted combination of his knowledge of vocabulary and grammar in a language (a self-learned weighted combination operation).
Regarding claim 3 (13), in the example above the process of dividing the original query into 1) when are you going on a vacation?, and 2) which theme parks will you take your children??, results in two queries which have the following common word: “you”.
Regarding claim 4 (14), It would be quite reasonable to record (e.g. write a log (ontology) ontology) of query answer responses tagging them with e.g. date and time and/or based on name or other identifications pertaining to the queries) (e.g. alike a global ontology) in order to use if such queries are encountered again in order to save time in providing responses and/or guessing answers.
Regarding claim 5 (15), any person with basic literacy can recognize names in a query prior to further processing it.
Regarding claim 6 (16), a person who constructs the log (ontology) would know how to access it to recapture information, in particular if it is constructed based on e.g., name (keyword).
Regarding claim 7 (17), for the person who receives the answers to divided or derived queries above could analyze the query that received less answers first simply because there is higher chance that they are incorrect and would be useful to evaluate sooner.
Regarding claim 8 (18) “generating” “database search queries” (i.e., “SQL” ) on derived queries would be simply extra solution activity as providing responses to those derived queries could be made without that extra step. Furthermore, the claimed limitations do not cause “SQL” if implemented to experience enhanced performance, and this application had not invented the “SQL”.
Regarding claim 9 (19) any person with basic knowledge of grammar and vocabulary could handle various query data structures and still divide a query into “derived” or sub-queries according to context. It is very common that a sentence is received which lacks a part of speech component and it is still understandable, and/or there are sentences uttered that do not strictly abide by grammatical rules (i.e., not obey an explicit fixed schema) but their meanings are still inferred and still they could be e.g. divided into smaller portions for more concise analysis.
Regarding claim 10 (20), any person can associate an entity in a query sentence with a certain data type (e.g., medical, business, recreation, etc) in order to analyze the query which includes that entity and to do search for it.
Claim Rejections - 35 USC § 103
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(s) 1, 3, 10-11, 13, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over THOTA ET al. (us 2015/0254357), and further in view of Tumuluri (US 2022/0114463).
Claim(s) 1 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by THOTA et al. (US 2015/0254357).
Regarding claim 1, THOTA et al. do teach a method for multi-stage processing of user queries for enhanced information retrieval (Title, Abstract),
the method comprising:
generating, by a server, two or more derived queries from a user search query in a split stage (¶ 0013 sentence 1: “in response to receiving a query from a user” (from a user query) “where the query specifies a location and an itinerary, a plurality of sub-queries” (two or more derived queries by splitting the user query (split stage)) “is generated” (are generated) “for the query”; these are submitted to “web-based search engines” (a server which is responsible for all the search and retrieval that follow (¶ 0003 sentence 1))),
extracting, by the server, one or more query entities from each derived query of the two or more derived queries (¶ 0013 sentence 3: “Then, for each sub-query” (for each derived query) “a list of query results is generated where each query result in the list of query results indicates a business entity” (an entity is extracted) “located in the vicinity of the specified location that is relevant to the particular activity”; e.g., ¶ 0054 example “New York” “N.Y.” (one or more entities in each derived query));
mapping, by the server, the one or more query entities from each derived query with a plurality of electronic documents from a plurality of diverse data sources in a selection stage to concurrently identify a set of relevant electronic documents for each derived query (¶ 0013 sentence 3: “Then, for each sub-query” (for each derived query) “a list of query results” (a plurality of electronic documents) “is generated” (are identified) “where each query result in the list of query results indicates a business entity” (each mapped to the entity and associated with a “business” (a data source of financial type)) “located in the vicinity of the specified location that is relevant” (and relevant to the search) “to the particular activity”; other data sources the queries could be directed to is search for “park” (a recreation and outdoor activity data source (¶ 0032 last sentence)) and/or search for “daycare” (child care data source (¶ 0024 sentence 2));
sorting, by the server, the derived queries in a sorting stage based on a number of relevant electronic documents related to each derived query to obtain a sorted sequence of derived queries, wherein the sorted sequence of derived queries is indicative of an order in which each derived query is to be resolved (¶ 0013 sentence 4: “The list” (based on a number of) “of sub-query results” (the relevant electronic documents) “for each sub-query” (for each derived query) “may be ranked” (the derived queries are sorted) “based on both the spatial proximity/relevance and the activity relevance of the sub-query results” (to obtain a sorted sequence of derived queries));
searching, by the server, a result associated with each derived query in a search stage sequentially by analyzing the set of relevant electronic documents based on the sorted sequence of derived queries (¶ 0013 last sentence: “Then, all the lists of sub-query results” (following the “ranking” (sorting by analyzing while searching), each “query result” (electronic document) associated with each “sub-query” (derived query)) “may be combined” (to obtain a “top-ranked” “result” (one result (¶ 0054)))) “and provided to the user as query results for the specified itinerary (lists of places matching the activity criteria) at the specified location”);
and appending, by the server, the result retrieved for one derived query with a consequent derived query in the sorted sequence of derived queries in a supplementing stage to obtain a final search result for the user search query (¶ 0054 lines 13+: “For example, a list of itineraries may include a first itinerary that includes” (appending) “the top-ranked query result” (a final search result retrieved for a derived query) “for” "kid-friendly entertainment"(and its associated or consequent derived query and it is the “top” or first in the “ranked” (sorted) list) “and the top-ranked query result for "kid-friendly restaurant").
THOTA et al. do not specifically disclose wherein each of the two or more derived queries has a length less than a first length of the user search query received originally from a client device;
wherein in the split stage, the two or more derived queries are derived by syntactic parsing and semantic reasoning based on a self- learned weighted combination operation.
Tumuluri do teach:
wherein each of the two or more derived queries has a length less than a first length of the user search query received originally from a client device (¶0038, page 5 lines 3+: “The soft-query” (a user search query is used) “may be divided” (to generate by splitting (split stage)) “into multiple micro-queries” (into two or more derived queries of shorter length as they are obtained by the division of the “soft-query” (user search query)) “based on context, intent”);
and wherein in the split stage, the two or more derived queries are derived by syntactic parsing and semantic reasoning based on a self- learned weighted combination operation (¶ 0048 S1: “The semantic and syntactic” (using semantic and syntactic reasoning) “analyzer 608 may be configured to extract entity information”, wherein the “entity” is used to according to ¶ 0038 page 5 lines 3+: “The soft query” (a query) “may be divided” (splits (in a split stage)) “into multiple micro-queries” (into two or more derived queries) “based on” “entities” (using “entities” which were obtained by semantic and syntactic reasoning) “context, intent, objects and previous responses” (a self-learned weighted combination); ¶ 0038 lines 7+: “the results may be sorted” “for example, based on the relevance scores” (a self-learned weighted parameter used) “of the micro-queries results” (to validate division of a “soft-query” (query) into the plurality of derived queries) “with respect to soft-query). The micro-query score” “is a relevance score”).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “micro-queries” generation technique of Tumuluri into the “sub-query” generation of THOTA et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable THOTA et al. to generate from each “query”, “sub-queries” each associated with a unique “context” which could result in more efficiently determining responses to complex multi context queries.
Regarding claim 3, THOTA et al. do teach the method according to claim 1, wherein the two or more derived queries encompass one or more common words or connecting words that connects the two or more derived queries to the user query (¶ 0054: “first sub-query may be "kid-friendly restaurant" in "New York, N.Y."” (first derived query) “and a second sub-query may be "kid-friendly entertainment" in "New York, N.Y."”(and the second derived query have the following common words: “New York”, “kid-friendly” and “N.Y.”).
Regarding claim 10, THOTA et al. do teach the method according to claim 1, wherein in the selection stage, during the mapping of the one or more query entities from each derived query, each of the two or more derived queries is mapped to a relevant data source of the plurality of diverse data sources based on a type of the one or more query entities in each derived query (¶ 0013 sentence 3: “Then, for each sub-query” (for each derived query) “a list of query results” (a plurality of electronic documents) “is generated” (are identified) “where each query result in the list of query results indicates a business entity” (each mapped to the entity and associated with a “business” (a data source of financial type)) “located in the vicinity of the specified location that is relevant” (and relevant to the search) “to the particular activity”; other data sources the queries could be directed to is search for “park” (a recreation and outdoor activity data source (¶ 0032 last sentence)) and/or search for “daycare” (child care data source (¶ 0024 sentence 2)).
Regarding claim 11, THOTA et al. do teach a system for multi-stage processing of a user query for enhanced information retrieval (Title, Abstract),
the system comprises:
a server configured to:
generate two or more derived queries from a user search query in a split stage (¶ 0013 sentence 1: “in response to receiving a query from a user” (from a user query) “where the query specifies a location and an itinerary, a plurality of sub-queries” (two or more derived queries by splitting the user query (split stage)) “is generated” (are generated) “for the query”; these are submitted to “web-based search engines” (a server which is responsible for all the search and retrieval that follow (¶ 0003 sentence 1))),
extract one or more query entities from each derived query of the two or more derived queries (¶ 0013 sentence 3: “Then, for each sub-query” (for each derived query) “a list of query results is generated where each query result in the list of query results indicates a business entity” (an entity is extracted) “located in the vicinity of the specified location that is relevant to the particular activity”; e.g., ¶ 0054 example “New York” “N.Y.” (one or more entities in each derived query));
map the one or more query entities from each derived query with a plurality of electronic documents to identify a set of relevant electronic documents for each derived query distinctly in a selection stage (¶ 0013 sentence 3: “Then, for each sub-query” (for each derived query) “a list of query results” (a plurality of electronic documents) “is generated” (are identified) “where each query result in the list of query results indicates a business entity” (each mapped to the entity and associated with a “business” (a data source of financial type)) “located in the vicinity of the specified location that is relevant” (and relevant to the search) “to the particular activity”; other data sources the queries could be directed to is search for “park” (a recreation and outdoor activity data source (¶ 0032 last sentence)) and/or search for “daycare” (child care data source (¶ 0024 sentence 2));
sort the two or more derived queries in a sorting stage based on a number of relevant electronic documents related to each derived query to obtain a sorted sequence of derived queries, wherein the sorted sequence of derived queries is indicative of an order in which each derived query is to be resolved (¶ 0013 sentence 4: “The list” (based on a number of) “of sub-query results” (the relevant electronic documents) “for each sub-query” (for each derived query) “may be ranked” (the derived queries are sorted) “based on both the spatial proximity/relevance and the activity relevance of the sub-query results” (to obtain a sorted sequence of derived queries));
search a result associated with each derived query in a search stage sequentially by analyzing the set of relevant electronic documents based on the sorted sequence of derived queries (¶ 0013 last sentence: “Then, all the lists of sub-query results” (following the “ranking” (sorting by analyzing while searching), each “query result” (electronic document) associated with each “sub-query” (derived query)) “may be combined” (to obtain a “top-ranked” “result” (one result (¶ 0054)))) “and provided to the user as query results for the specified itinerary (lists of places matching the activity criteria) at the specified location”),
and append the result retrieved for one derived query with a consequent derived query in the sorted sequence of derived queries in a supplementing stage to obtain a final search result for the user search query (¶ 0054 lines 13+: “For example, a list of itineraries may include a first itinerary that includes” (appending) “the top-ranked query result” (a final search result retrieved for a derived query) “for” "kid-friendly entertainment"(and its associated or consequent derived query and it is the “top” or first in the “ranked” (sorted) list) “and the top-ranked query result for "kid-friendly restaurant").
THOTA et al. do not specifically disclose wherein each of the two or more derived queries have a length less than a first length of the user search query received originally from a client device;
wherein in the split stage, the two or more derived queries are derived by syntactic parsing and semantic reasoning based on a self- learned weighted combination operation.
Tumuluri do teach:
wherein each of the two or more derived queries has a length less than a first length of the user search query received originally from a client device (¶0038, page 5 lines 3+: “The soft-query” (a user search query is used) “may be divided” (to generate) “into multiple micro-queries” (two or more derived queries of shorter length as they are obtained by the division of the “soft-query” (user search query)) “based on context, intent”);
and wherein in the split stage, the two or more derived queries are derived by syntactic parsing and semantic reasoning based on a self- learned weighted combination operation (¶ 0048 S1: “The semantic and syntactic” (using semantic and syntactic reasoning) “analyzer 608 may be configured to extract entity information”, wherein the “entity” is used to according to ¶ 0038 page 5 lines 3+: “The soft query” (a query) “may be divided” (splits (in a split stage)) “into multiple micro-queries” (into two or more derived queries) “based on” “entities” (using “entities” which were obtained by semantic and syntactic reasoning) “context, intent, objects and previous responses” (a self-learned weighted combination); ¶ 0038 lines 7+: “the results may be sorted” “for example, based on the relevance scores” (a self-learned weighted parameter used) “of the micro-queries results” (to validate division of a “soft-query” (query) into the plurality of derived queries) “with respect to soft-query). The micro-query score” “is a relevance score”).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “micro-queries” generation technique of Tumuluri into the “sub-query” generation of THOTA et al. would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable THOTA et al. to generate from each “query”, “sub-queries” each associated with a unique “context” which could result in more efficiently determining responses to complex multi context queries.
Regarding claim 13, THOTA et al. do teach the system according to claim 11, wherein the two or more derived queries encompass one or more common words or connecting words that connects the two or more derived queries to the user query (¶ 0054: “first sub-query may be "kid-friendly restaurant" in "New York, N.Y."” (first derived query) “and a second sub-query may be "kid-friendly entertainment" in "New York, N.Y."”(and the second derived query have the following common words: “New York”, “kid-friendly” and “N.Y.”).
Regarding claim 20, THOTA et al. do teach the system according to claim 11, wherein in the selection stage, during the mapping of the one or more query entities from each derived query, each of the two or more derived queries is mapped to a relevant data source of a plurality of diverse data sources based on a type of the one or more query entities in each derived query (¶ 0013 sentence 3: “Then, for each sub-query” (for each derived query) “a list of query results” (a plurality of electronic documents) “is generated” (are identified) “where each query result in the list of query results indicates a business entity” (each mapped to the entity and associated with a “business” (a data source of financial type)) “located in the vicinity of the specified location that is relevant” (and relevant to the search) “to the particular activity”; other data sources the queries could be directed to is search for “park” (a recreation and outdoor activity data source (¶ 0032 last sentence)) and/or search for “daycare” (child care data source (¶ 0024 sentence 2)).
Claim(s) 4-6, 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over THOTA et al. in view of Tumuluri, and further in view of LASTRA DIAZ et al. (US 2016/0179945).
Regarding claim 4 THOTA et al. in view of Tumuluri do teach:
tagging the one or more query entities in each derived query (Tumuluri: ¶ 0028 sentence 2: “Annotating 320 the inputs includes tagging” (tagging) “the inputs” (e.g. queries including their entities)).
Regarding claim 4 THOTA et al. in view of Tumuluri do not specifically disclose the method according to claim 1, wherein the mapping of the one or more query entities from each derived query is performed by:
creating a global ontology based on information present in the plurality of electronic documents and corresponding metadata;
tagging the one or more query entities in each derived query with corresponding relevant sections of information from the global ontology; and
identifying the set of relevant electronic documents from the plurality of electronic documents for each entity based on the relevant sections of information.
LASTRA DIAZ et al. do teach:
creating a global ontology based on information present in the plurality of electronic documents and corresponding metadata (¶ 0081 lines 15+: “select documents that match query terms” “use a set of semantic distance functions on the ontology” (a global ontology created based on information (e.g. metadata) present on “documents” (a plurality of documents) which are obtained in response to search “queries”; ¶ 0075 sentence 3: “each document is represented by a set of semantic annotations within an ontology” (i.e., the “ontology” has stored thereon the “documents” with “annotation[s]” (tags)));
tagging the one or more query entities in each derived query with corresponding relevant sections of information from the global ontology (¶ 0081 lines 15+: “To select the documents that match the query terms” “use a set of semantic distance functions on the ontology” (using relevant sections of the global ontology associated with “documents”) “to compute closeness among the concepts in the query and concepts annotated” (and “annotated” (tagged) one or more “concepts” (e.g., entities) in the “query”) “by the document”); and
identifying the set of relevant electronic documents from the plurality of electronic documents for each entity based on the relevant sections of information ((¶ 0081 lines 15+: “To select” (identifying) “the documents” (relevant documents) “that match the query terms” (for “query terms” (e.g. query entities)) “use a set of semantic distance functions on the ontology” (using relevant sections of the ontology)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “ontology” use of LASTRA DIAZ et al. in obtaining results for “query” search into the “sub-query result” “list” generation of THOTA et al. in THOTA et al. in view of Tumuluri would enable the combined systems and their associated methods to perform in combination as they do separately and to result in “efficient search and ranking methods” as disclosed in LASTRA DIAZ et al. ¶ 0099 last sentence.
Regarding claim 5, THOTA et al. in view of Tumuluri do teach the method according to claim 4, wherein the tagging of the one or more query entities in each derived query with corresponding relevant sections of information from the global ontology is performed based on a named entity recognition (NER) model (Tumuluri: ¶ 0030 sentence before last: “The Name Entity Recognition” (using named entity recognition) “may be used for identifying the entities” (for entities) “in the text portion of the query”(in a query input, e.g., ¶ 0028 sentence 2: “Annotating 320 the inputs includes tagging” (tagged) “the inputs” (input))).
Regarding claim 6, THOTA et al. in view of Tumuluri do not specifically disclose the method according to claim 5, wherein the relevant sections of information from the global ontology are determined based on a similarity between keywords or key phrases in the one or more entities in each derived query and the global ontology.
LASTRA DIAZ et al. do teach:
the method according to claim 5, wherein the relevant sections of information from the global ontology are determined based on a similarity between keywords or key phrases in the one or more entities in each derived query and the global ontology (¶ 0081 lines 15+: “select the documents that match query terms” “use a set of semantic distance functions” (using a similarity between) “on the ontology”(the global ontology) “to compute the closeness among the concepts in the query” (and “concepts” (e.g., keywords) or entities in each query) “and the concepts annotated by the document”; ¶ 0105 lines 7-8: “Any similarity function can be converted in a distance function, and vice versa”).
For obviousness to combine THOTA et al. in view of Tumuluri and LASTRA DIAZ et al. see claim 4.
Regarding claim 14 THOTA et al. in view of Tumuluri do teach:
tag the one or more query entities in each derived query (Tumuluri: ¶ 0028 sentence 2: “Annotating 320 the inputs includes tagging” (tagging) “the inputs” (e.g. queries including their entities)).
Regarding claim 14 THOTA et al. in view of Tumuluri do not specifically disclose the system according to claim 11, wherein in order to perform the mapping of the one or more query entities from each derived query, the server is further configured to:
create a global ontology based on information present in the plurality of electronic documents and corresponding metadata;
tag the one or more query entities in each derived query with corresponding relevant sections of information from the global ontology; and
identify the set of relevant electronic documents from the plurality of electronic documents from the plurality of electronic document from the plurality of diverse data sources for each query entity based on the relevant sections of information.
LASTRA DIAZ et al. do teach:
create a global ontology based on information present in the plurality of electronic documents and corresponding metadata (¶ 0081 lines 15+: “select documents that match query terms” “use a set of semantic distance functions on the ontology” (a global ontology created based on information (e.g. metadata) present on “documents” (a plurality of documents) which are obtained in response to search “queries”; ¶ 0075 sentence 3: “each document is represented by a set of semantic annotations within an ontology” (i.e., the “ontology” has stored thereon the “documents” with “annotation[s]” (tags)));
tag the one or more query entities in each derived query with corresponding relevant sections of information from the global ontology (¶ 0081 lines 15+: “To select the documents that match the query terms” “use a set of semantic distance functions on the ontology” (using relevant sections of the global ontology associated with “documents”) “to compute closeness among the concepts in the query and concepts annotated” (and “annotated” (tagged) one or more “concepts” (e.g., entities) in the “query”) “by the document”); and
identify the set of relevant electronic documents from the plurality of electronic documents from a plurality of diverse data sources for each query entity based on the relevant sections of information ((¶ 0081 lines 15+: “To select” (identifying) “the documents” (relevant documents) “that match the query terms” (for “query terms” (e.g. query entities)) “use a set of semantic distance functions on the ontology” (using relevant sections of the ontology)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “ontology” use of LASTRA DIAZ et al. in obtaining results for “query” search into the “sub-query result” “list” generation of THOTA et al. in THOTA et al. in view of Tumuluri would enable the combined systems and their associated methods to perform in combination as they do separately and to result in “efficient search and ranking methods” as disclosed in LASTRA DIAZ et al. ¶ 0099 last sentence.
Regarding claim 15, THOTA et al. in view of Tumuluri do teach the system according to claim 14, wherein the server is further configured to train a named entity recognition (NER) model to tag the one or more query entities in each derived query with corresponding relevant sections of information from the global ontology is performed based on a named entity recognition (NER) model (Tumuluri: ¶ 0030 sentence before last: “The Name Entity Recognition” (using named entity recognition) “may be used for identifying the entities” (for entities) “in the text portion of the query”(in a query input, e.g., ¶ 0028 sentence 2: “Annotating 320 the inputs includes tagging” (tagged) “the inputs” (input))).
Regarding claim 16, THOTA et al. in view of Tumuluri do not specifically disclose the system according to claim 15, wherein the server is further configured to determine relevant sections of information from the global ontology based on a similarity between keywords or key phrases in the one or more entities in each derived query and the global ontology.
LASTRA DIAZ et al. do teach:
the system according to claim 15, wherein the server is further configured to determine relevant sections of information from the global ontology based on a similarity between keywords or key phrases in the one or more entities in each derived query and the global ontology (¶ 0081 lines 15+: “select the documents that match query terms” “use a set of semantic distance functions” (using a similarity between) “on the ontology”(the global ontology) “to compute the closeness among the concepts in the query” (and “concepts” (e.g., keywords) or entities in each query) “and the concepts annotated by the document”; ¶ 0105 lines 7-8: “Any similarity function can be converted in a distance function, and vice versa”).
For obviousness to combine THOTA et al. in view of Tumuluri and LASTRA DIAZ et al. see claim 14.
Claim(s) 7, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over THOTA et al. in view of Tumuluri, and further in view of Tiyyagura et al. (US 2019/0236185).
Regarding claim 7, THOTA et al. in view of Tumuluri do not specifically disclose teach the method according to claim 1, wherein the order in which each derived query is to be resolved is determined based on a lowest to highest number of relevant electronic documents retrieved for each derived query, wherein the derived query associated with the lowest number of relevant electronic documents is resolved initially followed by other derived queries.
Tiyyagura et al. do teach the method according to claim 1, wherein the order in which each derived query is to be resolved is determined based on a lowest to highest number of relevant electronic documents retrieved for each derived query, wherein the derived query associated with the lowest number of relevant electronic documents is resolved initially followed by other derived queries (Abstract lines 5+: “sub-queries” (derived queries) “are generated from the user query”(from a search query) “a number of items” (with a plurality of resulting in response documents) “obtained from executing the first sub-query” (obtained for each derived query); ¶ 0007 last 2 sentences: “sorting” (resolving) “the items obtained from executing the first sub-query and the one or more subsequent sub-queries” (the derived queries) “into chronological or reverse chronological order” (starting from the lowest) “based on” “number of the sorted items” (relevant number electronic documents)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “query” “sub-query” methods of Tiyyagura et al. into the respective ones of THOTA et al. in THOTA et al. in view of Tumuluri would enable the combined systems and their associated methods to perform in combination as they do separately and to give greater flexibility to them in how to tailor outputting the results.
Regarding claim 17, THOTA et al. in view of Tumuluri do not specifically disclose teach the system according to claim 11, wherein the order in which each derived query is to be resolved is determined based on a count of lowest to highest number of relevant electronic documents related to each derived query, wherein the derived query associated with the lowest number of relevant electronic documents is resolved initially followed by other derived queries.
Tiyyagura et al. do teach the system according to claim 11, wherein the order in which each derived query is to be resolved is determined based on a count of lowest to highest number of relevant electronic documents related to each derived query, wherein the derived query associated with the lowest number of relevant electronic documents is resolved initially followed by other derived queries (Abstract lines 5+: “sub-queries” (derived queries) “are generated from the user query”(from a search query) “a number of items” (with a plurality of resulting in response documents) “obtained from executing the first sub-query” (obtained for each derived query); ¶ 0007 last 2 sentences: “sorting” (resolving) “the items obtained from executing the first sub-query and the one or more subsequent sub-queries” (the derived queries) “into chronological or reverse chronological order” (starting from the lowest) “based on” “number of the sorted items” (relevant number electronic documents)).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “query” “sub-query” methods of Tiyyagura et al. into the respective ones of THOTA et al. in THOTA et al. in view of Tumuluri would enable the combined systems and their associated methods to perform in combination as they do separately and to give greater flexibility to them in how to tailor outputting the results.
Claim(s) 8-9, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over THOTA et al. in view of Tumuluri, and further in view of Xu et al. (WO 2018/064442 A1).
Regarding claim 8, THOTA et al. in view of Tumuluri do not specifically disclose the method according to claim 1, wherein the searching of the result associated with each derived query in the search stage is performed by generating a combination of database search queries for each derived query and further performing a semantic search for structured and unstructured electronic documents for each derived query.
Xu et al. do teach the method according to claim 1, wherein the searching of the result associated with each derived query in the search stage is performed by generating a combination of query database search queries for each derived query (¶ 0016 last sentence: “to formulate semantic queries in syntax similar to SQL” (generating a combination of query database search queries)
and further performing a semantic search for structured and unstructured electronic documents for each derived query (¶ 0013 sentence 1: “Semantic Search” (performing semantic search) “seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results” (on e.g., electronic documents which can comprise of “unstructured” (¶ 0015 sentence 2) as well as “structured” “data” (¶ 0016 sentence 2)))).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the search techniques of Xu et al. into those of THOTA et al. in view of Tumuluri would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable THOTA et al. in view of Tumuluri to “produce highly relevant search results as disclosed in Xu et al. ¶ 0013 lines 7-8.
Regarding claim 9, THOTA et al. in view of Tumuluri do not specifically disclose the method according to claim 1, wherein in the search stage, the searching is independent of a requirement of data to follow an explicit fixed schema.
Xu et al. do teach the method according to claim 1, wherein in the search stage, the searching is independent of a requirement of data to follow an explicit fixed schema ( ¶ 0009: “Semantic Search” (search stage searching) “and Semantic Query” use both “unstructured” (¶ 0015 sentence 2) as well as “structured” “data” (¶ 0016 sentence 2), which implies it does not follow an explicit fixed schema).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the search techniques of Xu et al. into those of THOTA et al. in view of Tumuluri would enable the combined systems and their associated methods to perform in combination as they do separately and to enable it to deal with both “structured data” (e.g., “named graphs, linked data, or triplets” (¶ 0015) as well data not in the formats quoted such as regular text resulting in greater flexibility.
Regarding claim 18, THOTA et al. in view of Tumuluri do not specifically disclose the system according to claim 11, wherein the searching of the result associated with each derived query in the search stage is performed by generating a combination of database search queries for each derived query and further performing a semantic search for structured and unstructured electronic documents for each derived query.
Xu et al. do teach the system according to claim 11, wherein the searching of the result associated with each derived query in the search stage is performed by generating a combination of database search queries for each derived query (¶ 0016 last sentence: “to formulate semantic queries in syntax similar to SQL” (generating a combination of query database search queries)
and further performing a semantic search for structured and unstructured electronic documents for each derived query (¶ 0013 sentence 1: “Semantic Search” (performing semantic search) “seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results” (on e.g., electronic documents which can comprise of “unstructured” (¶ 0015 sentence 2) as well as “structured” “data” (¶ 0016 sentence 2)))).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the search techniques of Xu et al. into those of THOTA et al. in view of Tumuluri would enable the combined systems and their associated methods to perform in combination as they do separately and to further enable THOTA et al. in view of Tumuluri to “produce highly relevant search results as disclosed in Xu et al. ¶ 0013 lines 7-8.
Regarding claim 19, THOTA et al. in view of Tumuluri do not specifically disclose the system according to claim 11, wherein in the search stage, the searching is independent of a requirement of data to follow an explicit fixed schema.
Xu et al. do teach the system according to claim 11, wherein in the search stage, the searching is independent of a requirement of data to follow an explicit fixed schema ( ¶ 0009: “Semantic Search” (search stage searching) “and Semantic Query” use both “unstructured” (¶ 0015 sentence 2) as well as “structured” “data” (¶ 0016 sentence 2), which implies it does not follow an explicit fixed schema).
It would have therefore been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the search techniques of Xu et al. into those of THOTA et al. in view of Tumuluri would enable the combined systems and their associated methods to perform in combination as they do separately and to enable it to deal with both “structured data” (e.g., “named graphs, linked data, or triplets” (¶ 0015) as well data not in the formats quoted such as regular text resulting in greater flexibility.
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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/Farzad Kazeminezhad/
Art Unit 2653
April 25th 2026.
Prosecution Insights