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 .
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-, 8-14, 16-20 of U.S. Patent No.12,217,002 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because In re Karlson, 136 USPQ 184 (1963): “Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before”. The present application is directed to a similar invention with minor exceptions which make the application slightly broader in scope than the issued patent.
18/984,848
12,217,002
1. A processor, comprising: one or more processing units to perform one or more operations from a list of operations comprising:
grouping one or more sequences of an input stream into one or more tokens based, at least in part, on simulating a finite state machine; identifying one or more hierarchical relationships between the one or more tokens;
generating a data tree based, at least in part, on the one or more hierarchical relationships;
identifying one or more shared paths of one or more nodes in the data tree;
and storing data from the input stream using at least one shared path of the one or more nodes in the data tree.
2. The processor of claim 1, wherein simulating the finite state machine comprises using at least one logical stack to determine context information of the one or more tokens in the input stream.
3. The processor of claim 2, wherein the one or more operations further comprise generating the data tree using the context information.
4. The processor of claim 1, wherein the one or more operations further comprise inferring a schema based, at least in part, on the data tree.
6. The processor of claim 1, wherein the one or more processing units perform the one or more operations from the list of operations in parallel.
7. The processor of claim 1, wherein the input stream comprises textual data.
8. The processor of claim 7, wherein the input stream comprises at least one of JavaScript object notation (“JSON”) data or extended markup language (“XML”) data.
9. A method, comprising: grouping one or more sequences of an input stream into one or more tokens based, at least in part, on simulating a finite state machine; identifying one or more hierarchical relationships between the one or more tokens; generating a data tree based, at least in part, on the one or more hierarchical relationships; identifying one or more shared paths of one or more nodes in the data tree; and storing data from the input stream using at least one shared path of the one or more nodes in the data tree.
10. The method of claim 9, wherein the generating the data tree is performed using a parallel computing device.
11. The method of claim 9, further comprising: using a second finite state machine to identify the one or more hierarchical relationships between the one or more tokens.
12. The method of claim 9, wherein the simulating the finite state machine comprises using at least one logical stack to determine context of the one or more tokens, based at least in part on one or more parallel operations performed on data obtained from the at least one logical stack.
13. The method of claim 9, wherein the finite state machine comprises a finite state transducer.
14. The method of claim 9, wherein the grouping is performed in parallel.
15. The method of claim 9, wherein the input stream comprises textual data.
16. The method of claim 9, further comprising: identifying schema information by at least performing a parallelized sort operation on information indicative of one or more nodes of the data tree.
17. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by at least one processor of a computing device, cause the computing device to at least: group one or more sequences of an input stream into one or more tokens based, at least in part, on a simulation of a finite state machine; identify one or more hierarchical relationships between the one or more tokens; generate a data tree based, at least in part, on the one or more hierarchical relationships; identify one or more shared paths of one or more nodes in the data tree; and store data from the input stream using at least one shared path of the one or more nodes in the data tree.
18. The non-transitory computer-readable storage medium of claim 17, wherein the simulation assigns one or more categories to each of the one or more tokens.
19. The non-transitory computer-readable storage medium of claim 17, wherein the simulation uses at least one logical stack to determine context of the one or more tokens.
20. The non-transitory computer-readable storage medium of claim 17, having stored thereon further instructions that, when executed by at least one processor of a computing device, cause the computing device to at least: infer context of the one or more tokens recognized in the input stream based, at least in part, on a top-most entry in at least one logical stack.
1. A processor, comprising: a plurality of processing units to perform one or more operations from a list of operations comprising:
grouping one or more sequences of an input stream into one or more tokens based, at least in part, on simulating a finite state transducer; identifying, using at least one logical stack, one or more hierarchical relationships between the one or more tokens;
generating a data tree based, at least in part, on the identified one or more hierarchical relationships;
and analyzing the data tree in parallel to identify one or more shared paths of one or more nodes in the data tree, infer schema information from the one or more shared paths,
and store data from the input stream in accordance with the schema information.
2. The processor of claim 1, wherein a deterministic visibly pushdown automaton (“DVPA”) is used to obtain information to information in the logical stack, and the data tree is generated using context information obtained from the logical stack.
3. The processor of claim 1, the one or more operations further comprising: identifying schema information by at least performing a parallelized sort operation on nodes of the data tree.
5. The processor of claim 1, wherein the one or more operations further comprise: inferring context of a token recognized in the input stream based, at least in part, on a top-most entry in the logical stack, the top-most entry determined using one or more parallel operations on data associated with the logical stack.
8. The processor of claim 1, wherein the input stream comprises at least one of JavaScript object notation (“JSON”) data or extended markup language (“XML”) data.
9. A method, comprising: grouping one or more sequences of an input stream into one or more tokens based, at least in part, on simulation of one or more finite state machines; identifying one or more hierarchical relationships between the one or more tokens, using at least one logical stack; generating a data tree based, at least in part, on the one or more identified hierarchical relationships; identifying one or more shared paths of one or more nodes in the data tree; inferring schema information from the shared paths; and storing data from the input stream in accordance with the schema information.
11. The method of claim 9, further comprising: using a second finite state machine to identify the one or more hierarchical relationships between the tokens.
12. The method of claim 9, wherein the simulation uses the at least one logical stack to determine context of a token in the input stream, based at least in part on one or more parallel operations performed on data obtained from the logical stack.
13. The method of claim 9, wherein one or more portions of the input stream are parsed by respective finite state machines using context information obtained using a finite state transducer.
10. The method of claim 9, wherein at least one of the one or more finite state machines is a finite state transducer.
16. The method of claim 9, further comprising: identifying schema information by at least performing a parallelized sort operation on information indicative of one or more nodes of the data tree.
17. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by at least one processor of a computing device, cause the computing device to at least: group sequences of an input stream into tokens based, at least in part, on simulation of a finite state machine; identify parent-child relationships between the tokens, using at least one logical stack; generate a tree based, at least in part, on the identified parent-child relationships; identify shared paths of nodes in the tree; infer schema information from the shared paths; and store data from the input stream in accordance with the schema information.
18. The non-transitory computer-readable storage medium of claim 17, wherein the simulation assigns one or more categories to each token.
19. The non-transitory computer-readable storage medium of claim 17, wherein the simulation uses the at least one logical stack to determine context of a token in the input stream.
20. The non-transitory computer-readable storage medium of claim 17, having stored thereon further instructions that, when executed by at least one processor of a computing device, cause the computing device to at least: infer context of a token recognized in the input stream based, at least in part, on a top-most entry in the at least one logical stack.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to methods for grouping one or more sequences into tokens, generating a data tree, identifying paths of nodes of the trees and storing the data from the input stream without significantly more. The claim(s) recite(s) algorithmic steps for performing data processing and storage. This judicial exception is not integrated into a practical application because the claims are considered an abstract idea, for example a person could receive information in the form of text divide it into tokens or categories, draw a tree and record the paths of the tree. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are mere algorithmic steps for performing data processing, a person receiving information, and storing it without significantly more, a person can divide the information into tokens (or categories) draw a tree, follow tree paths and store the information.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7, 9-12, 14-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yadav U.S. PAP 2021/0019309 A1.
Regarding claim 1 Yadav teaches a processor (processor , see par. [0004]), comprising:
one or more processing units to perform one or more operations from a list of operations (memory stores instructions executable by the processor, see par. [0004] comprising:
grouping one or more sequences of an input stream into one or more tokens based, at least in part, on simulating a finite state machine (generate, based on a string, a set of tokens of a database syntax, wherein the tokens are each matched to a respective fragment of the string, generate a graph using a finite state machine, see par. [0004]);
identifying one or more hierarchical relationships between the one or more tokens (wherein nodes of the finite state machine represent token types, directed edges of the finite state machine represent valid transitions between token types in the query grammar, vertices of the query graph correspond to respective tokens of the set of tokens, and directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens, see par. [0004]);
generating a data tree based, at least in part, on the one or more hierarchical relationships (generate a query graph for the set of tokens using a finite state machine representing a query grammar, see par. [0004]);
identifying one or more shared paths of one or more nodes in the data tree ( directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens, see par. [0004]);
and storing data from the input stream using at least one shared path of the one or more nodes in the data tree (determining, based on the query graph, a sequence of the tokens in the set of tokens to form a database query, see par. [0004]).
Regarding claim 2 Yadav teaches the processor of claim 1, wherein simulating the finite state machine comprises using at least one logical stack to determine context information of the one or more tokens in the input stream ( A token may represent an entity or another concept meaningful in the context of a database, such as, for example, a measure (e.g., a column storing values of a metric); an attribute (e.g., a column storing categorical information); a value (e.g., a value from any column, such as, ‘California’ from a state column); a keyword (a language specific keyword, such as, “sum”); an operator (e.g., language specific operators, such as, “<” or “before”); a positive integer, a date bucket (e.g., monthly or Weekly); or a constant (e.g., numeric/string/Boolean constants, such as, −100.2 or ‘john’ or true). A token may have an associated data structure that includes data describing the token, storing data, such as descriptive text in a natural language, a token type, and/or an identifier or pointer for an entity in a database, see par. [0036]).
Regarding claim 3 Yadav teaches the processor of claim 2, wherein the one or more operations further comprise generating the data tree using the context information (determine a weight for a directed edge from a source vertex of the query graph corresponding to a first token of the set of tokens to a destination vertex of the query graph corresponding to a second token of the set of tokens, see par. [0005]).
Regarding claim 4 Yadav teaches the processor of claim 1, wherein the one or more operations further comprise inferring a schema based, at least in part, on the data tree ( the system can learn to make context specific inferences to fill in missing information for a query that is not explicitly stated in a string. In some implementations, the context for such an inference includes words from a string and tokens from an associated query, see par. [0040]).
Regarding claim 5 Yadav teaches the processor of claim 1, wherein the one or more tokens represent one or more input categories recognized in the input stream (nodes of the finite state machine represent token types, see par. [0004]).
Regarding claim 6 Yadav teaches the processor of claim 1, wherein the one or more processing units perform the one or more operations from the list of operations in parallel (the an initial version of the database query that is generated and/or selected based on the string may be used to invoke a search of one or more databases in parallel with presenting 230 the data query for review and/or editing by a user, see par. [0052]).
Regarding claim 7 Yadav teaches the processor of claim 1, wherein the input stream comprises textual data (natural text representations of the tokens of the query may be presented to the user to help the user more easily read and understand the structure of the query, see par. [0039]).
Regarding claim 9 Yadav teaches a method (Systems and methods for mapping natural language to queries using a query grammar, see abstract), comprising:
grouping one or more sequences of an input stream into one or more tokens based, at least in part, on simulating a finite state machine (generate, based on a string, a set of tokens of a database syntax, wherein the tokens are each matched to a respective fragment of the string, generate a graph using a finite state machine, see par. [0004]);
identifying one or more hierarchical relationships between the one or more tokens (wherein nodes of the finite state machine represent token types, directed edges of the finite state machine represent valid transitions between token types in the query grammar, vertices of the query graph correspond to respective tokens of the set of tokens, and directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens, see par. [0004]);
generating a data tree based, at least in part, on the one or more hierarchical relationships (generate a query graph for the set of tokens using a finite state machine representing a query grammar, see par. [0004]);
identifying one or more shared paths of one or more nodes in the data tree ( directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens, see par. [0004]);
and storing data from the input stream using at least one shared path of the one or more nodes in the data tree (determining, based on the query graph, a sequence of the tokens in the set of tokens to form a database query, see par. [0004]).
Regarding claim 10 Yadav teaches the method of claim 9, wherein the generating the data tree is performed using a parallel computing device (the an initial version of the database query that is generated and/or selected based on the string may be used to invoke a search of one or more databases in parallel with presenting 230 the data query for review and/or editing by a user, see par. [0052])..
Regarding claim 11 Yadav teaches the method of claim 9, further comprising: using a second finite state machine to identify the one or more hierarchical relationships between the one or more tokens (The relational search engine unit 1850 may implement one or more finite state machines, see par. [0453]).
Regarding claim 12 Yadav teaches the method of claim 9, wherein the simulating the finite state machine comprises using at least one logical stack to determine context of the one or more tokens, based at least in part on one or more parallel operations performed on data obtained from the at least one logical stack (determine a weight for a directed edge from a source vertex of the query graph corresponding to a first token of the set of tokens to a destination vertex of the query graph corresponding to a second token of the set of tokens, see par. [0005])..
Regarding claim 14 Yadav teaches the method of claim 9, wherein the grouping is performed in parallel (the an initial version of the database query that is generated and/or selected based on the string may be used to invoke a search of one or more databases in parallel with presenting 230 the data query for review and/or editing by a user, see par. [0052]).
Regarding claim 15 Yadav teaches the method of claim 9, wherein the input stream comprises textual data (natural text representations of the tokens of the query may be presented to the user to help the user more easily read and understand the structure of the query, see par. [0039])..
Regarding claim 16 Yadav teaches the method of claim 9, further comprising: identifying schema information by at least performing a parallelized sort operation on information indicative of one or more nodes of the data tree ((1.) Matching: In this part we try and match fragments of input string to known tokens. Each string fragment may match multiple tokens, including no token at all. (2.) Parts of Speech, Syntax Tree Generation: the input string may be input into a natural language processing system (e.g., SyntaxNet) to get both part-of-speech tags and syntax tree data for the input string. This may happen in parallel with #1 above, see par. [0103]).
Regarding claim 17 Yadav teaches a non-transitory computer-readable storage medium having stored thereon instructions that, when executed by at least one processor of a computing device (a non-transitory computer-readable storage medium is provided for providing a search interface for databases. The non-transitory computer-readable storage medium may include executable instructions that, when executed by a processor, facilitate performance of operations, see par. [0008]), cause the computing device to at least:
group one or more sequences of an input stream into one or more tokens based, at least in part, on simulating a finite state machine (generate, based on a string, a set of tokens of a database syntax, wherein the tokens are each matched to a respective fragment of the string, generate a graph using a finite state machine, see par. [0004]);
identify one or more hierarchical relationships between the one or more tokens (wherein nodes of the finite state machine represent token types, directed edges of the finite state machine represent valid transitions between token types in the query grammar, vertices of the query graph correspond to respective tokens of the set of tokens, and directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens, see par. [0004]);
generate a data tree based, at least in part, on the one or more hierarchical relationships (generate a query graph for the set of tokens using a finite state machine representing a query grammar, see par. [0004]);
identify one or more shared paths of one or more nodes in the data tree ( directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens, see par. [0004]);
and store data from the input stream using at least one shared path of the one or more nodes in the data tree (determining, based on the query graph, a sequence of the tokens in the set of tokens to form a database query, see par. [0004]).
Regarding claim 18 Yadav teaches the non-transitory computer-readable storage medium of claim 17, wherein the simulation assigns one or more categories to each of the one or more tokens (nodes of the finite state machine represent token types, see par. [0004]).
Regarding claim 19 Yadav teaches the non-transitory computer-readable storage medium of claim 17, wherein the simulation uses at least one logical stack to determine context of the one or more tokens ( A token may represent an entity or another concept meaningful in the context of a database, such as, for example, a measure (e.g., a column storing values of a metric); an attribute (e.g., a column storing categorical information); a value (e.g., a value from any column, such as, ‘California’ from a state column); a keyword (a language specific keyword, such as, “sum”); an operator (e.g., language specific operators, such as, “<” or “before”); a positive integer, a date bucket (e.g., monthly or Weekly); or a constant (e.g., numeric/string/Boolean constants, such as, −100.2 or ‘john’ or true). A token may have an associated data structure that includes data describing the token, storing data, such as descriptive text in a natural language, a token type, and/or an identifier or pointer for an entity in a database, see par. [0036]).
Regarding claim 20 Yadav teaches the non-transitory computer-readable storage medium of claim 17, having stored thereon further instructions that, when executed by at least one processor of a computing device, cause the computing device to at least: infer context of the one or more tokens recognized in the input stream based, at least in part, on a top-most entry in at least one logical stack ( the system can learn to make context specific inferences to fill in missing information for a query that is not explicitly stated in a string. In some implementations, the context for such an inference includes words from a string and tokens from an associated query, see par. [0040]).
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) 8 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yadav U.S. PAP 2021/0019309 A1 in view of Lucco WO 2008/034075 A2.
Regarding claim 8 Yadav does not teach the processor of claim 7, wherein the input stream comprises at least one of JavaScript object notation (“JSON”) data or extended markup language (“XML”) data.
In the same field of endeavor Lucco teaches performing efficient tree transformation in a generalized manner that achieves preservation of action semantics for FSTs that support action information in their representations across a diverse set of types of representations for FSTs, see abstract. Presently, some graph transducers and tree transducers exist that accomplish some aspects of transforming a set of systems modeled by FSTs, e.g., for some types of directed graph data structure(s), to form a new FST or set of FSTs, e.g., new directed graph data structure(s), however, such systems are limited for various reasons. The Extensible Stylesheet Language Transformation (XSLT), for instance, is an XML-based language used for the transformation of XML documents. When used to transform other XML documents, the document is not changed; rather, a new document is created based on the content of the existing XML document, see par. [0003].
It would have been obvious to one of ordinary skill in the art to combine the Yadav invention with the teachings of Lucco for the benefit of performing efficient tree transformation in a generalized manner, see abstract.
Regarding claim 13 Yadav does not teach the method of claim 9, wherein the finite state machine comprises a finite state transducer.
In the same field of endeavor Lucco teaches performing efficient tree transformation in a generalized manner that achieves preservation of action semantics for FSTs that support action information in their representations across a diverse set of types of representations for FSTs, see abstract.
It would have been obvious to one of ordinary skill in the art to combine the Yadav invention with the teachings of Lucco for the benefit of performing efficient tree transformation in a generalized manner, see abstract.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Atasu 060’ teaches a cascaded finite-state-transducer array comprises a plurality of finite-state-transducers, the finite-state-transducers being distributed in space, wherein the array is configured with dedicated data transfer channels between the finite-state-transducers to transfer specific data types. Each data stream on a dedicated data transfer channel may transmit a particular data type, which may be sorted in increasing order of start offsets or token IDs, see par. [0005].
Roulland ‘155 teaches ranking component ranks the identified entity clusters. An entity suggestion component proposes a set of the ranked clusters as a refinement tree linked by paths to their parent and child clusters, see par. [0017].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Ortiz-Sanchez whose telephone number is (571)270-3711. The examiner can normally be reached Monday- Friday 9AM-6PM.
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/MICHAEL ORTIZ-SANCHEZ/ Primary Examiner, Art Unit 2656
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