Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is responsive to Applicant’s application filed 02/02/2026. Claims 1, and 11 have been amended. Claims 1-20 are pending in this office action
Response to Arguments
3. Applicant's arguments with respect to amended features in claim, 1 and 11 have been considered but are moot in view of the new ground(s) of rejection.
Applicant argued that, Agrawal and Marchisio, fail to teach or suggest all of the elements of independent claims 1 and 11. Independent claims 1 and 11 recite, inter alia, "each query element comprises one or more words of the natural language query" (newly amendment reject based on new ground(s) of rejection) and "identifying a subset of the query elements based on the query type."
Examiner respectfully disagrees "identifying a subset of the query elements based on the query type." recited in independent claims 1 and 11 fail to teach or suggest all of the elements of independent claims 1 and 11.
In response to Applicant’s argument, Agrawal teaches as the entity list extractor may construct a table schema, e.g., a data entity list including data entities relevant to the query. The entity list extractor may obtain a table summary (e.g., including data types) from the table detector. The entity list extractor may also build a typed table from the grid range and pass it on to the table detector for summarization (paragraphs 0039, 0042-0044). If the column is categorical (e.g., when the unique values in the column is a subset of the entire spectrum of data values), then the column may be used to create an aggregated table (paragraph 0045). The query interpreter may interpret returned query response to an executable formula string using the entity list table view passed on from the get-answer action module. The query interpreter may include various comparable classes for formula builder, e.g., a particular formula builder may correspond to one type of formula. Here a given set and count of fields in the query may correspond to only one formula, e.g., a query with exactly two metrics corresponds to a correlation formula (paragraph 0051).
Marchisio also teaches the SQE parses the data set, identifying entity type tags and the syntax and grammatical roles of terms within the data set as appropriate to the configured parsing level. For the purpose of extending keyword searching to syntactically and semantically annotated data, parsing sufficient to determine at least the subject, object, and verb of each clause is desirable to perform syntactic searches in relationship queries. However, one skilled in the art will recognize that subsets of the capabilities of the SQE could be provided in trade for shorter corpus ingestion times if full syntactic searching is not desired. Deep parsing decomposes a data object into syntactic and grammatical units using sophisticated syntactic and grammatical roles and heuristics. Shallow parsing decomposes a data object to recognize attributes of a portion or all of a data object, such as entity types specified by a default or custom ontology associated with the corpus or the SQE (paragraphs 0081).
For the above reason, examiner believed that rejection of the last office action was proper.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
4. Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-2, 5, 7, and 10 of U.S. Patent No. 12,222,935. Although the conflicting claims are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations.
The following table shows the claims 1-2, 5, 7, and 10 in Instant Application that are rejected by corresponding claim(s) 1-11 in US Patent No. 12,222,935.
Although the conflicting claims are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations.
After analyzing the language of the claims, it is clear that claims 1-20 are merely an obvious variation of claims 1-11 of US Patent No. 12,222,935. It is clear that under the broadest reasonable interpretation of the claims. Therefore, these two sets of claims are not patentably distinct.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
5. Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Agrawal et al. (US Patent Publication No. 2018/0203924 A1, hereinafter “Agrawal”) in view of Spangler et al. (US Patent Publication No. 2019/0251455 A1, hereinafter “Spangler”), and Marchisio et al. (US Patent Publication No. 2005/0267871 A1, hereinafter “Marchisio”). As to
Claim 1, Agrawal teaches the claimed limitations:
“A method for retrieving data, the method being executed by at least one processor, the method comprising:” as systems and methods for processing a natural language query on data tables, a natural language query may be originated by a user via a user interface (paragraphs 0002, 0017).
“Receiving a natural language query” as a server after receiving the natural language query at the server when the data table is stored at the server (paragraph 0004).
“Generating, by providing the query elements to at least one classifier, a query type characterizing the natural language query” as search engines approach data mining typically by offering interactive searches that match the data to one or more keywords using classical pattern matching or string matching techniques. Information extraction engines typically approach the unstructured data mining problem by extracting subsets of the data, based upon formulations of predefined rules, and then converting the extracted data into structured data that can be more easily searched (paragraphs 0047-0050).
“Identifying a subset of the query elements based on the query type” as the entity list extractor may construct a table schema, e.g., a data entity list including data entities relevant to the query. The entity list extractor may obtain a table summary (e.g., including data types) from the table detector. The entity list extractor may also build a typed table from the grid range and pass it on to the table detector for summarization (paragraphs 0039, 0042-0044). If the column is categorical (e.g., when the unique values in the column is a subset of the entire spectrum of data values), then the column may be used to create an aggregated table (paragraph 0045).
“Generating a structured database query including the subset of the query elements” as systems and methods for processing a natural language query allow a user to enter a query for data in natural language. The natural language query may be translated into a structured database query (paragraph 0018). The get-answer action module may also send a parse request including data entity list information and query information to the analytics module, which may generate a parse response. The parse response may include a structured data table/spreadsheet query represented as the query in the protocol buffer” as paragraphs 0045, 0050, 0056). The query interpreter may be structured as a class with multiple smaller formula builders plugged into it. In this way, the query interpreter structure can be expandable with additional formula builders, when a different type of query is received, new formula type may be added to the formula builders without the need to change the existing formula builder (paragraphs 0050-0051, 0055).
Agrawal does not explicitly teach the claimed limitation “decomposing the natural language query into query elements, wherein each query element comprises one or more words of the natural language query”.
Spangler teaches semantic analysis engine may generate semantic feature vectors describing the characteristics of the entities from analysis of the corpus of unstructured data, e.g., text-based data. A feature vector for an entity may include information which links the entity to the development and progression of particular diseases, to the treatment of particular diseases, to text-based names of particular chemical structures, to other molecules or macromolecules that the entity binds to, or any other property that may be described in a text-based representation. For an entity that is mentioned in a document, the context of the entity within the document can be semantically analyzed, e.g., decomposed into terms used in the local or global vicinity of the entity to establish the context of the entity. For example, NLP techniques include embedding words, aggregating terms locally, aggregating terms across the entire content of the document, determining the frequency of a term within a document, extraction of individual words or N-grams (phrases of length N), etc., which may be used to generate feature elements to include in the feature vector describing the entity (paragraph 0027).
Agrawal does not explicitly teach the claimed limitation “retrieving data based on the structured database query”.
Marchisio teaches information extraction engines typically approach the unstructured data mining problem by extracting subsets of the data, based upon formulations of predefined rules, and then converting the extracted data into structured data that can be more easily searched. Typically, the extracted structured data is stored in a relational database management system and accessed by database languages and tools. Other techniques, products, offer greater accuracy and truer information discovery tools, because they employ generalized syntactic indexing with the ability to interactively search for relationships and events in the data, including particular syntactic patterns. Some embodiments support a natural language query interface, which parses natural language queries in much the same manner as the underlying data, in addition to a streamlined relationship and event searching interface that focuses on retrieving information associated with particular grammatical roles (paragraph 0047).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, having the teachings of Agrawal, Spangler and Marchisio before him/her, to modify Agrawal decomposing the natural language query into query elements, wherein each query element comprises one or more words because that would provide for combining chemical structure-based and unstructured text-based analytics for similarity-based comparison of entities for predictive analytics in a cognitive system as taught by Spangler (paragraph 0015). Or retrieving data based on the structured database query because that would provide enhanced methods and systems for syntactically indexing and searching data sets to achieve more accurate search results with greater flexibility and efficiency as taught by Marchisio (paragraph 0048).
As to Claim 2, Agrawal teaches the claimed limitations:
“Wherein said identifying the subset of the query elements is performed further based on one or more parameters associated with the query elements” as (paragraphs 0038, 0045, 0047, 0052).
Marchisio teaches (paragraphs 0047, 0080, 0168. 0184).
As to Claim 3, Agrawal teaches the claimed limitations:
“Wherein the structured database query includes fewer elements than the natural language query” as (paragraphs 0019-0020, 0025, 0052-0054, 0104), claim 16.
As to Claim 4, Agrawal teaches the claimed limitations:
“Processing the retrieved data for presentation based on the natural language query, thereby obtaining processed data” as (paragraphs 0002-0003, 0021-0022, 0025, 0071).
Marchisio teaches (paragraphs 0047, 0051, 0082-0083, 0088, 0143-0148).
As to Claim 5, Agrawal teaches the claimed limitations:
“Wherein said processing the retrieved data comprises formatting the retrieved data” as (paragraphs 0006, 0017, 0060, 0071). And Claims 9
Marchisio teaches (paragraphs 000087-0088, 0114-0116, 0145, 0167-0168).
As to Claim 6, Agrawal teaches the claimed limitations:
“Generating a graphical representation of the processed data” as (paragraphs 0040).
Marchisio teaches (paragraphs 0047, 0051, 0082-0086, 0088, 0142-0148, 0166, 0171).
As to Claim 7, Agrawal teaches the claimed limitations:
“Wherein said generating the structured database query comprises selecting a suitable function from a set of functions based on the query type” as (paragraphs 0018, 0069), and claim 15.
Marchisio teaches (paragraphs 0080, 0154, 0166, 0168, 0172, 0212). And claims 214, 218-219, 220-222).
As to Claim 8, Agrawal teaches the claimed limitations:
“Wherein said selecting the suitable function is performed further based on at least one query parameters and relationships between the subset of the query elements” as (paragraphs 0018, 0038, 0041, 0047, 0052, 0069, 0081), and claims 15, 214, 218-219, 220-222).
As to Claim 9, Agrawal teaches the claimed limitations:
“Wherein said retrieving the data comprises retrieving the data from at least one knowledge graph database operatively connected to the at least one processor using the structured database query” as (paragraphs 0001, 0040).
Marchisio teaches (paragraph 0151).
As to Claim 10, Agrawal teaches the claimed limitations:
“Wherein the query type comprises one of: an identification query, an amount query, a strategy query, a counting query, a listing query, a grouping query, a time query, and a calculation query” as (paragraphs 0001, 0019, 0025, 0035, 0037-0039, 0052, 0056).
Marchisio teaches (paragraphs 0006, 0062, 0142-0145) and claims 42, 77, 103.
As to claims 11-20 are rejected under 35 U.S.C 103(a), the limitations therein have substantially the same scope as claims 1-10. In addition, Agrawal teaches systems and methods for processing a natural language query on data tables, a natural language query may be originated by a user via a user interface (paragraphs 0002, 0017). Therefore, these claims are rejected for at least the same reasons as claims 1-10.
Conclusion
6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Examiner’s Note
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James Hwa whose telephone number is 571-270-1285 or email address james.hwa@uspto.gov. The examiner can normally be reached on 9:00 am – 5:30 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached on 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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04/04/2026
/SHYUE JIUNN HWA/
Primary Examiner, Art Unit 2156
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