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 1-20 of U.S. Patent No. 11,487,943 and claims 1-18 of U.S. Patent No. 12,182,514. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious variations of each other.
Regarding Claim 1 (drawn to a method):
Current Application
Claim 1:
A method of processing natural language commands, performed at a computer system that includes one or more processors and memory, the method comprising:
receiving a user input to specify a natural language command;
wherein the trained word model is trained to identify similar words based on a plurality of processed representations corresponding to a set of words of a natural language;
in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word model, based on semantic annotations for a published data source,
querying the published data source based on the semantic interpretation, thereby retrieving a dataset; and
generating and displaying a data visualization based on the retrieved dataset.
Claim 1:
A method of processing natural language commands, performed at a computer system that includes one or more processors and memory, the method comprising:
receiving a user input to specify a natural language command;
in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word model, based on semantic annotations for a published data source, wherein the trained word model is trained to identify similar words based on a plurality of processed representations corresponding to a set of words of a natural language;
querying the published data source based on the semantic interpretation, thereby retrieving a dataset; and
generating and displaying a data visualization based on the retrieved dataset.
‘943
Claim 1:
A method of processing natural language commands, comprising:
obtaining a plurality of word embeddings for a set of words of a natural language;
training a word similarity model to identify similar words based on the plurality of word embeddings and a synonym database;
generating semantic annotations for a published data source using the trained word similarity model, based on the synonym database, and the plurality of word embeddings, and indexing the plurality of word embeddings;
generating a semantic interpretation for a natural language command based on the semantic annotations for the published data source; and
querying the published data source based on the sematic interpretation, thereby retrieving a dataset.
Claim 16:
… further comprising displaying a data visualization based on the retrieved dataset.
‘514
Claim 1:
A method of processing natural language commands, comprising:
receiving a user input to specify a natural language command;
in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word similarity model, based on semantic annotations for a published data source, wherein the trained word similarity model is trained to identify similar words based on (i) a plurality of word embeddings corresponding to a set of words of a natural language and (ii) a synonym database;
querying the published data source based on the semantic interpretation, thereby retrieving a dataset; and
generating and displaying a data visualization based on the retrieved dataset.
Regarding Claim 10 (drawn to a system):
Current Application
Claim 10:
A computer system for processing natural language commands, comprising:
one or more processors; and memory coupled to the one or more processors,
the memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for:
receiving a user input to specify a natural language command; in response to receiving the user input,
wherein the trained word model is trained to identify similar words based on a plurality of processed representations corresponding to a set of words of a natural language;
generating a semantic interpretation for the natural language command using a trained word model, based on semantic annotations for a published data source,
querying the published data source based on the semantic interpretation, thereby retrieving a dataset; and
a display; generating and displaying a data visualization based on the retrieved dataset.
Claim 10:
A computer system for processing natural language commands, comprising:
a display;
one or more processors; and
memory coupled to the one or more processors, the memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for:
receiving a user input to specify a natural language command;
in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word model, based on semantic annotations for a published data source,
querying the published data source based on the semantic interpretation, thereby retrieving a dataset; and
generating and displaying a data visualization based on the retrieved dataset.
wherein the trained word model is trained to identify similar words based on a plurality of processed representations corresponding to a set of words of a natural language;
‘943
Claim 19:
A computer system for processing natural language commands, comprising:
one or more processors; and
memory; wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprise instructions for:
obtaining a plurality of word embeddings for a set of words of a natural language;
training a word similarity model to identify similar words based on the plurality of word embeddings and a synonym database; and
generating semantic annotations for a published data source using the trained word similarity model, based on the synonym database, and the plurality of word embeddings, and indexing the plurality of word embeddings;
generating a semantic interpretation of a natural language command based on the semantic annotations for the published data source; and
querying the published data source based on the sematic interpretation, thereby retrieving a dataset.
Claim 16:
… further comprising displaying a data visualization based on the retrieved dataset.
‘514
Claim 14:
A computer device for processing natural language commands, comprising:
a display;
one or more processors; and
memory coupled to the one or more processors, the memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for:
receiving a user input to specify a natural language command;
in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word similarity model, based on semantic annotations for a published data source;
querying the published data source based on the semantic interpretation, thereby retrieving a dataset;
generating and displaying a data visualization based on the retrieved dataset;
detecting that a new data source has been published; and
in response to detecting that the new data source has been published, generating semantic annotations for the new data source using the trained word similarity model, based on (i) a plurality of word embeddings corresponding to a set of words of a natural language and (ii) a synonym database.
Regarding Claim 16 (drawn to a non-transitory CRM):
Current Application
Claim 16:
A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computer system, cause the computer system to perform operations comprising:
receiving a user input configured to specify a natural language command;
wherein the trained word model is trained to identify similar words based on a plurality of processed representations corresponding to a set of words of a natural language;
in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word model, based on semantic annotations for a published data source,
querying the published data source based on the semantic interpretation, thereby retrieving a dataset; and
generating and displaying a data visualization based on the retrieved dataset.
Claim 16:
A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computer system, cause the computer system to perform operations comprising:
wherein the trained word model is trained to identify similar words based on a plurality of processed representations corresponding to a set of words of a natural language;
receiving a user input to specify a natural language command;
in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word model, based on semantic annotations for a published data source,
querying the published data source based on the semantic interpretation, thereby retrieving a dataset;
and generating and displaying a data visualization based on the retrieved dataset.
‘943
Claim 20:
A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having a display, one or more processors, and memory, the one or more programs comprising instructions for:
obtaining a plurality of word embeddings for a set of words of a natural language;
training a word similarity model to identify similar words based on the plurality of word embeddings and a synonym database;
generating semantic annotations for a published data source using the trained word similarity model, based on the synonym database, and the plurality of word embeddings, and indexing the plurality of word embeddings;
generating a semantic interpretation of a natural language command based on the semantic annotations for the published data source; and
querying the published data source based on the sematic interpretation, thereby retrieving a dataset.
Claim 16:
… further comprising displaying a data visualization based on the retrieved dataset.
‘514
Claim 18:
A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computer device having a display, cause the computer device to perform operations comprising:
generating semantic annotations for a published data source using a trained word similarity model, based on (i) a plurality of word embeddings corresponding to a set of words of a natural language and (ii) a synonym database;
receiving a user input to specify a natural language command;
in response to receiving the user input, generating a semantic interpretation for the natural language command using the trained word similarity model, based on the semantic annotations for the published data source;
querying the published data source based on the semantic interpretation, thereby retrieving a dataset;
and generating and displaying a data visualization based on the retrieved dataset.
As shown in the tables above, it is clear that all the elements of the application claims 1, 10, and 16 are to be found in patent claims 1, 16, 19, and 20 and patent claims 1, 14, and 18, as the application claims 1, 10, and 16 fully encompasses patent claims1, 16, 19, and 20 and patent claims 1, 14, and 18. The difference between the application claims 1, 10, and 16 and the patent claims 1, 16, 19, and 20 and patent claims 1, 14, and 18 lies in the fact that the patent claims includes more elements and is thus more specific. Thus the invention of claims 1, 16, 19, and 20 and claims 1, 14, and 18 of the patents is in effect a “species” of the “generic” invention of the application claims 1, 10, and 16. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claims 2, 12, and 17 of the current application corresponds to claim 2 of U.S. Patent No. 11,487,943 and claim 10 of U.S. Patent No. 12,182,514.
Claim 3 of the current application corresponds to claim 3 of U.S. Patent No. 11,487,943 and claim 10 of U.S. Patent No. 12,182,514.
Claims 4 and 13 of the current application corresponds to claim 4 of U.S. Patent No. 11,487,943 and claim 12 of U.S. Patent No. 12,182,514.
Claims 5 and 18 of the current application corresponds to claim 5 of U.S. Patent No. 11,487,943 and claim 13 of U.S. Patent No. 12,182,514.
Claim 6 of the current application corresponds to the corresponding portion of claim 9 of U.S. Patent No. 11,487,943 the corresponding portion of claim of U.S. Patent No. 12,182,514.
Claim 7 of the current application corresponds to the corresponding portion of claim 9 of U.S. Patent No. 11,487,943 and the corresponding portion of claim 3 of U.S. Patent No. 12,182,514.
Claims 8 and 19 of the current application corresponds to claim 9 of U.S. Patent No. 11,487,943 and claim 3 of U.S. Patent No. 12,182,514.
Claims 9 and 20 of the current application corresponds to claim 11 of U.S. Patent No. 11,487,943 and claim 4 of U.S. Patent No. 12,182,514.
Claim 11 of the current application corresponds to the corresponding portion of claim 19 of U.S. Patent No. 11,487,943 and the corresponding portion of claim 9 of U.S. Patent No. 12,182,514.
Claim 14 of the current application corresponds to the corresponding portion of claim 2 of U.S. Patent No. 11,487,943 and the corresponding portion of claim 9 of U.S. Patent No. 12,182,514.
Claim 15 of the current application corresponds to claim 18 of U.S. Patent No. 11,487,943 and claim 8 of U.S. Patent No. 12,182,514.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 10, and 16 relate to the statutory category of method/process and machine/apparatus. The independent claims 1, 10, and 16 recite “receiving a user input to specify a natural language command; in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word model, based on semantic annotations for a published data source, wherein the trained word model is trained to identify similar words based on a plurality of processed representations corresponding to a set of words of a natural language; querying the published data source based on the semantic interpretation, thereby retrieving a dataset; and generating and displaying a data visualization based on the retrieved dataset”.
The limitations of “receiving…”, “…receiving…”, “querying…”, and “generating…” as drafted covers mental activity. More specifically, a human after receiving a command or instruction can generate a grammatic representation of the command based on annotations for data that has been published and using the annotations to identify similar words. The published data is searched based on the grammatic representation and the applicable data is retrieved and displayed.
This judicial exception is not integrated into a practical application. In particular, claims 1, 10, and 16 recite the additional elements of “computer system”, “processor”, “memory”, and “display” which are recited generally in the specification. For example, in paragraph [0045] of the as published specification, there is a description of using a general purpose operating system. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional element of “trained word model” in claims 1, 10 and 16 can be interpreted as humans being able to identify similar words based on previous experience and knowledge. The claims are 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 the integration of the abstract idea int a practical application, the additional element of using a computer as a general computer is noted. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. The claims are not patent eligible.
With respect to claims 2, 12, and 17, the claims relate to when given data field name, determining similar word to the data field name and associating these words with the data field name. The claims relate to amental activity of when given a name associated with a data set, determining similar words that describe the data set and associating these words with the data set. No additional limitations are present.
With respect to claim 3, the claim relates to the similar words being characteristics that are defined by the user or previously used and inherited. The claim relates to a mental activity of using traits or characteristics to describe the similar word. No additional limitations are present.
With respect to claims 4 and 13, the claims relate to determining a set of similar words to describe the data field name and computing a similarity score for the similar words and selecting the similar words that exceed a predetermined threshold. The claims relate to a mental activity of selecting the similar words describing the data set which are the closest to the actual description. No additional limitations are present.
With respect to claims 5 and 18, the claims relate to generating a list of synonyms for the data field name and from the list, generating a list of similar words based on the how similar the words are to the synonyms which exceed a predetermined threshold. The claims relate to a mental activity of selecting the similar words describing the list of synonyms. No additional limitations are present.
With respect to claim 6, the claim relates to embedding the similar words which correspond to the natural language. The claim relates to a mental activity of embedding the similar word into a natural language description of the words. No additional limitations are present.
With respect to claim 7, the claim relates to identifying similar word based on the synonyms list. The claim relates to a mental activity of associating the similar words to the words on the synonym list. No additional limitations are present.
With respect to claims 8 and 19, the claims relate to detecting if new data has been published and generating grammatic annotations for the new data. The claims relate to a mental activity of determining that new data is available and annotating the new data. No additional limitations are present.
With respect to claims 9 and 20, the claims relate to generating similarity data using a neural network trained on a large amount of data. The claims relate to a mental activity of learning similarity data based on experience and knowledge by being accessible to large amounts of data. The additional element of “trained neural network model” can be interpreted as humans being able to identify similar words based on previous experience and knowledge.
With respect to claim 11, the claim relates to indexing the published data and identifying the data fields and filling the data fields with metadata. The claim relates to a mental activity of indexing the data set and associating the data set with characteristics of the data. No additional limitations are present.
With respect to claim 14, the claim relates to generating annotations for the published data based on how the data is represented. The claim relates to a mental activity of annotating the data based on the description of the data. No additional limitations are present.
With respect to claim 15, the claim relates to generating the annotations concurrently for a plurality of entity names for the published data. The claim relates to a mental activity of simultaneously annotating the plurality of annotations needed based on multiple entity names in the data. No additional limitations are present.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3 and 6-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Setlur et al. (US 10,817,527).
Regarding Claim 1, Setlur et al discloses a method of processing natural language commands, performed at a computer system that includes one or more processors and memory, the method comprising:
receiving a user input to specify a natural language command (The natural language processing region 124 includes an input bar (also referred to herein as a command bar) for receiving natural language commands) (col. 5, lines 5-9);
in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word model (For example, a pattern such as “boy is to girl as prince is to princess” can be generated through algebraic calculations on the vector representations of the words ‘boy,’ ‘girl,’ ‘prince’ and ‘princess.’ The algebraic expression ‘prince’−‘boy’+‘girl’ produces a result that is closest to the vector representation of ‘princess,’ in this example) (col. 8, lines 24-34), based on semantic annotations for a published data source (Word embeddings capture similarity between words and enable efficient computation of semantic similarity using vector arithmetic) (col. 8, lines 21-24), wherein the trained word model is trained to identify similar words based on a plurality of processed representations corresponding to a set of words of a natural language (In implementations that use neural network models 312, semantic and syntactic relatedness are computed by initially training a neural network on a large corpus of text (generally computed in advance)) (col. 8, lines 13-17);
querying the published data source based on the semantic interpretation, thereby retrieving a dataset (Some implementations use synonym lookup 314, such as by querying a lexical database, to find semantic equivalents or synonyms of query words) (col. 9, line 63-col. 10, line 6); and
generating and displaying a data visualization based on the retrieved dataset (In response to receiving the user associations, the application 230 queries the database using a set of queries corresponding to the received user associations, and then displays one or more data visualizations 408) (col. 12, lines 1-17).
Regarding Claim 2, Setlur discloses the method, further comprising:
for a data field name corresponding to a data field of a plurality of data fields of the published data source ( Queries often contain direct, clear references to particular columns of data, such as when the user types “nation” to refer to a “nation” data column. But a user may use an alternative word, such as “country” in reference to the “nation” column) (col. 7, line 59-col. 8, line 12):
determining a set of similar words for the data field name (In this case, natural language processing helps to compute semantically related words) (col. 7, line 59-col. 8, line 12); and
associating the set of similar words with the data field name (In some implementations, the language processing module 238 uses neural network models 312 to generate semantically similar words (sometimes referred to as synonyms)) (col. 7, line 59-col. 8, line 12).
Regarding Claim 3, Setlur discloses the method, wherein the set of similar words includes at least one of: user-defined metadata provided by a user via a user interface or inherited metadata Queries often contain direct, clear references to particular columns of data, such as when the user types “nation” to refer to a “nation” data column. But a user may use an alternative word, such as “country” in reference to the “nation” column. In this case, natural language processing helps to compute semantically related words) (col. 7, line 59-col. 8, line 12).
Regarding Claim 6, Setlur discloses the method, wherein the plurality of processed representations includes a plurality of word embeddings corresponding to the set of words of the natural language (Some implementations that use neural network models produce word embeddings. A word or phrase from a vocabulary of words (which can be considered a space with a large number of dimensions) is mapped to, or embedded in, a continuous vector space of real numbers (a space with a much smaller number of dimensions)) (col. 8, lines 16-21).
Regarding Claim 7, Setlur discloses the method, wherein the trained word model is trained to identify the similar words further based on a synonym database (In some implementations, the language processing module 238 uses synonym lookup 314 to generate synonyms. Some implementations use both neural network models 312 and synonym lookup 314 to generate synonyms) (col. 8, lines 6-10).
Regarding Claim 8, Setlur discloses the method, further comprising:
detecting that a new data source has been published (adjusting the vector space representations using additional information) (col. 23, lines 23-39); and
in response to detecting that the new data source has been published, generating semantic annotations for the new data source using the trained word model (Here, using the additional information transforms (622) the original vector space 604 of representations into a new vector space 624 of representations) (col. 23, lines 23-39).
Regarding Claim 9, Setlur discloses the method, wherein:
the plurality of processed representations are generated using one or more trained neural network models (In implementations that use neural network models 312, semantic and syntactic relatedness are computed by initially training a neural network on a large corpus of text (generally computed in advance)) (col. 8, lines 13-16); and
the one or more trained neural network models are trained on a large corpus of text of the natural language (In implementations that use neural network models 312, semantic and syntactic relatedness are computed by initially training a neural network on a large corpus of text (generally computed in advance)) (col. 8, lines 13-16).
Regarding Claim 10, Setlur discloses a computer system for processing natural language commands, comprising:
a display (Fig. 2, user interface 210) (The user interface 210 typically includes a display device 212) (page 5, lines 46-48);
one or more processors (Fig. 2, processing units 202) (The computing device 200 typically includes one or more processing units (processors or cores) 202) (col. 5, lines 38-40); and
memory coupled to the one or more processors, the memory storing one or more programs configured to be executed by the one or more processors (Fig. 2, memory 206) (In some implementations, the memory 206 includes one or more storage devices remotely located from the processor(s) 202) (col. 6, lines 6-9), the one or more programs including instructions for:
receiving a user input to specify a natural language command (The natural language processing region 124 includes an input bar (also referred to herein as a command bar) for receiving natural language commands) (col. 5, lines 5-9);
in response to receiving the user input, generating a semantic interpretation for the natural language command using a trained word model (For example, a pattern such as “boy is to girl as prince is to princess” can be generated through algebraic calculations on the vector representations of the words ‘boy,’ ‘girl,’ ‘prince’ and ‘princess.’ The algebraic expression ‘prince’−‘boy’+‘girl’ produces a result that is closest to the vector representation of ‘princess,’ in this example) (col. 8, lines 24-34), based on semantic annotations for a published data source (Word embeddings capture similarity between words and enable efficient computation of semantic similarity using vector arithmetic) (col. 8, lines 21-24), wherein the trained word model is trained to identify similar words based on a plurality of processed representations corresponding to a set of words of a natural language (In implementations that use neural network models 312, semantic and syntactic relatedness are computed by initially training a neural network on a large corpus of text (generally computed in advance)) (col. 8, lines 13-17);
querying the published data source based on the semantic interpretation, thereby retrieving a dataset (Some implementations use synonym lookup 314, such as by querying a lexical database, to find semantic equivalents or synonyms of query words) (col. 9, line 63-col. 10, line 6); and
generating and displaying a data visualization based on the retrieved dataset (In response to receiving the user associations, the application 230 queries the database using a set of queries corresponding to the received user associations, and then displays one or more data visualizations 408) (col. 12, lines 1-17).
Regarding Claim 11, Setlur discloses the computer system, the one or more programs including instructions for:
indexing the published data source to identify data fields from the published data source and data values of the data fields (The schema information region 110 provides named data elements (e.g., field names) that may be selected and used to build a data visualization) (col. 4, lines 51-60); and
enriching the data fields and/or the data values with metadata (Some implementations also include a list of parameters. When the Analytics tab 116 is selected, the user interface displays a list of analytic functions instead of data elements (not shown)) (col. 4, lines 50-60).
Claim 12 and 17 are rejected for the same reason as claim 2.
Claim 13 is rejected for the same reason as claim 4.
Regarding Claim 14, Setlur discloses the computer system, the one or more programs including instructions for: generating the semantic annotations for the published data source using the trained word model, based on the plurality of processed representations corresponding to the set of words of the natural language ( In implementations that use neural network models 312, semantic and syntactic relatedness are computed by initially training a neural network on a large corpus of text (generally computed in advance). Some implementations that use neural network models produce word embeddings. A word or phrase from a vocabulary of words (which can be considered a space with a large number of dimensions) is mapped to, or embedded in, a continuous vector space of real numbers (a space with a much smaller number of dimensions). Word embeddings capture similarity between words and enable efficient computation of semantic similarity using vector arithmetic) (col. 8, lines 13-24).
Regarding Claim 15, Setlur discloses the computer system, the one or more programs further including instructions for performing the generating the semantic annotations concurrently for a plurality of data entity names of the published data source, using a distributed, multitenant-capable text search engine (Word2vec implements two model architectures--continuous bag of words (CBOW) and skip-gram-- for computing continuous vector representations of words (sometimes referred to as word vectors) from very large data sets) (col. 8, lines 44-48).
Claim 16 is rejected for the same reason as claim 1.
Clams 18 is rejected for the same reason as claim 5.
Claim 19 is rejected for the same reason as claim 8.
Claim 20 is rejected for the same reason as claim 9.
Allowable Subject Matter
Claims 4 and 5 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the Double Patenting Rejections and 35 USC 101 rejections are overcome.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Setlur et al (US 10,515,121) discloses using natural language processing for visual analysis of a data set includes displaying a data visualization based on a dataset retrieved from a database using a set of one or more queries and receiving a user input to specify a natural language command related to the displayed data visualization.
He et al. (US 10,133,729 discloses semantically-relevant discovery of solutions.
Hancock (US 2020/0050638) discloses determining the validity or infringement of a patent claim using natural language processing and information retrieval techniques.
Ock et al (US 2019/0188263) discloses word semantic embedding.
Leal et al (US 2018/0300315) discloses automated document analysis and processing using machine learning techniques.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATWANT K SINGH whose telephone number is (571)272-7468. The examiner can normally be reached Monday thru Friday 9:00 AM to 6:00 PM EST.
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/SATWANT K SINGH/Primary Examiner, Art Unit 2653
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