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 .
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 a mental process without significantly more.
Representative claim 1 recites:
“A system comprising:
a memory that stores instructions; and
one or more processors coupled to the memory and configured to execute the instructions to perform operations comprising:
generating, based on a JavaScript Object Notation (JSON) resource that comprises metadata for element properties of a data model, a structured representation of the metadata that groups nodes based on type;
generating an output of a generative artificial intelligence (AI); and
generating, based on a template, a document that includes the output of the generative AI and describes the element properties of the data model.”
Independent claims 13 and 18 recited similar subject matter.
The claims recite mental process steps of “generating, based on a JavaScript Object Notation (JSON) resource that comprises metadata for element properties of a data model, a structured representation of the metadata that groups nodes based on type” and “generating, based on a template, a document that includes the output of the generative AI and describes the element properties of the data model.”
Each of these “generation” steps represent data analysis steps that produce a result based on a data analysis. A human being equipped with a generic computer would be capable of performing the data analysis step and the generating a document step.
The independent claims contain additional elements in the form of a generative artificial intelligence. Independent claim 1 additionally contains a memory and one or more processors, independent claim 13 contains a non-transitory computer-readable medium and one or more processors.
This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
The memory, non-transitory computer-readable medium, and one or more processors are recited at a high level of generality. They appear to be generic computing hardware elements. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
The “generative artificial intelligence (AI)” appears to be merely applying a generic machine learning process to a new information analysis context. As noted in Recentive Analytics v. Fox. Corp., No. 23-2437 (Fed. Cir. 2025), merely applying a generic machine learning system to a different context does not provide a practical application.
It is noted that none of the additional elements appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. As such, none of the additional elements appear to integrate the judicial exception into a practical application.
None of the additional elements are sufficient to amount to significantly more than the judicial exception, in part or in whole.
The recitation of generic hardware of the memory, non-transitory computer-readable medium, and one or more processors is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
As noted in Recentive Analytics v. Fox. Corp., No. 23-2437 (Fed. Cir. 2025), merely applying a generic generative artificial intelligence system to a different context does not provide significantly more than an abstract idea.
None of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional. As such, none of the additional elements appears to be, in part or in whole, significantly more than the judicial exception.
Dependent claims 2-12, 14-17, and 19-20 are merely directed towards additional limitations that further define data types or further describe analyses that will occur. It is noted that the claimed data definitions and data analysis steps do not appear to include additional elements that incorporate the claimed subject matter into a practical application. They additionally just appear to be outputs of a data analysis that exist in a document. The dependent claims also do not include additional elements that, in part or in whole, appear to be significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 13-15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Goyal et al. (US Pre-Grant Publication 2018/0037840) as modified by Poirier et al. (US Pre-Grant Publication 2024/0202539).
As to claim 1, Goyal teaches a system comprising:
a memory that stores instructions (see Goyal paragraph [0159]); and
one or more processors coupled to the memory (see Goyal paragraph [0159]) and configured to execute the instructions to perform operations comprising:
generating, based on a JavaScript Object Notation (JSON) resource that comprises metadata for element properties of a data model, a structured representation of the metadata that groups nodes based on type (see Goyal paragraphs [0145]-[0148]. Goyal teaches to generate a structured representation of a JSON document and store the structured representation in a relational database. The structured format classifies, and thus groups, the nodes based on data type);
…
Linder does not teach:
generating an output of a generative artificial intelligence (AI); and
generating, based on a template, a document that includes the output of the generative AI and describes the element properties of the data model.
Poirier teaches:
generating an output of a generative artificial intelligence (AI) (see paragraph [0127]. Poirier accepts a database datable as input. This is processed by “a query and rational generator” that produces an output of a natural language description. It is noted that the “comprehension module” is part of a generative artificial intelligence system, see paragraph [0071]); and
generating, based on a template, a document that includes the output of the generative AI and describes the element properties of the data model (see Poirier paragraph [0127]. The natural language description is then provided to and processed by another model. This includes converting “a database table” into “text format.” Also see paragraph [0085], which discusses converting a database table into natural language. Also see paragraph [0189], which discusses how a natural language summary and visualization may be generated based on a first natural language summary and additional data. It is noted that natural language descriptions of data records can be included in contextual information, see paragraph [0038]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Poirier because Poirier provides improved data record identification and retrieval to the users of a system with disparate data records. This will provide to users of Goyal a method to access and query data records using additional techniques.
As to claim 2, Goyal as modified by Poirier teaches a system of claim 1, wherein the operations further comprise:
receiving a prompt for the generative AI (see Poirier paragraph [0127]);
providing at least a subset of the element properties to the generative AI (see Poirier paragraph [0127]); and
providing the prompt to the generative AI (see Poirier paragraph [0127]).
As to claim 3, Goyal as modified by Poirier teaches a system of claim 1, wherein the generating of the structured representation of the metadata that groups nodes based on type comprises:
generating a first portion of the structured representation of the metadata for a first group of nodes of base data (see Goyal paragraphs [0145]-[0148]);
generating a second portion of the structured representation of the metadata for a second group of nodes of restricted data (see Goyal paragraphs [0145]-[0148]);
generating a third portion of the structured representation of the metadata for a third group of nodes of calculated data (see Goyal paragraphs [0145]-[0148]);
generating a fourth portion of the structured representation of the metadata for a fourth group of nodes of filter element data (see Goyal paragraphs [0145]-[0148]); and
generating a fifth portion of the structured representation of the metadata for a fifth group of nodes of variable element data (see Goyal paragraphs [0145]-[0148]).
As to claims 13 and 18, see the rejection of claim 1.
As to claim 14, see the rejection of claim 2.
As to claim 15, see the rejection of claim 3.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Goyal et al. (US Pre-Grant Publication 2018/0037840) as modified by Poirier et al. (US Pre-Grant Publication 2024/0202539), and further in view of Dar et al. (US Pre-Grant Publication 2025/0272507).
As to claim 4, Goyal as modified by Poirier teaches a system of claim 1.
Goyal as modified does not teach wherein the generating of the document comprises generating the document in portable document format (PDF) or hypertext markup language (HTML).
Dar teaches wherein the generating of the document comprises generating the document in portable document format (PDF) or hypertext markup language (HTML) (see Dar paragraph [0050]. Dar teaches to be able to parse and identify table data and produce an answer using an LLM in HTML format).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Dar because Dar provides for improved recognition of tabular structures in a prompt and providing an answer based on the recognition of those structures, which will improve the ability of Goyal as modified to properly analyze tabular structures (see Dar paragraphs [0049] and [0050]).
As to claim 16, see the rejection of claim 4.
Claims 5 and 17 is rejected under 35 U.S.C. 103 as being unpatentable over Goyal et al. (US Pre-Grant Publication 2018/0037840) as modified by Poirier et al. (US Pre-Grant Publication 2024/0202539), and further in view of Bevilacqua-Linn et al. (US Pre-Grant Publication 2014/0281854).
As to claim 5, Goyal as modified teaches a system of claim 1.
Goyal does not teach wherein the operations further comprise:
based on the generated document, duplicating the data model.
Bevilacqua-Linn teaches wherein the operations further comprise:
based on the generated document, duplicating the data model (see paragraphs [0032]-[0033]. Bevilacqua-Linn receives a document containing an object model and converts it into a JSON document. In view of Goyal as modified by Poirier, this will duplicate the original JSON model. Bevilacqua-Linn also then reproduces the object model at a user device, which also duplicates an object model).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Bevilacqua-Linn because Bevilacqua-Linn merely provides tools to convert and manage object models using different formats as needed. This will extend the functionality and flexibility of Goyal as modified to respond to user needs.
As to claim 17, see the rejection and citation of claim 5.
Claims 6 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Goyal et al. (US Pre-Grant Publication 2018/0037840) as modified by Poirier et al. (US Pre-Grant Publication 2024/0202539), and further in view of Balzar et al. (US Pre-Grant Publication 2014/0279839).
As to claim 6, Goyal as modified teaches a system of claim 1.
Goyal does not teach wherein the document describes a calculated measure with exception aggregation.
Balzar teaches wherein the document describes a calculated measure with exception aggregation (see Balzar paragraphs [0075]-[0078])
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Balzar because Goyal as modified by Poirier analyzes table data to in response to a user query and Balzar merely provides additional analyses that a user may perform on table data. These additional analyses will increase the flexibility of Goyal as modified to respond to user queries.
As to claim 7, Goyal as modified teaches a system of claim 1.
Goyal does not teach wherein the document describes a restricted measure without constant selection.
Balzar teaches wherein the document describes a restricted measure without constant selection (see Balzar paragraph [0088])
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Balzar because Goyal as modified by Poirier analyzes table data to in response to a user query and Balzar merely provides additional analyses that a user may perform on table data. These additional analyses will increase the flexibility of Goyal as modified to respond to user queries.
As to claim 8, Goyal as modified teaches a system of claim 1.
Goyal as modified does not teach wherein the document describes a restricted measure with constant selection of all dimensions.
Balzar teaches wherein the document describes a restricted measure with constant selection of all dimensions (see paragraph [0070], which shows the use of a ConstantDate in a query).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Balzar because Goyal as modified by Poirier analyzes table data to in response to a user query and Balzar merely provides additional analyses that a user may perform on table data. These additional analyses will increase the flexibility of Goyal as modified to respond to user queries.
As to claim 19, see the rejection of claim 6.
As to claim 20, see the rejection of claim 7.
Claims 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Goyal et al. (US Pre-Grant Publication 2018/0037840) as modified by Poirier et al. (US Pre-Grant Publication 2024/0202539), and further in view of Bauchot et al. (US Pre-Grant Publication 2012/0215569).
As to claim 9, Goyal as modified teaches a system of claim 1.
Goyal as modified does not teach wherein the document describes a restricted measure using a restricted variable.
Bauchot teaches wherein the document describes a restricted measure using a restricted variable (see Bauchot paragraphs [0018] and [0023]. “Restrictive” input data may be used to query a system for results).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Bauchot because Bauchot merely provides additional analyses and query parameters that a user may use to identify data. These additional analyses and parameters will increase the flexibility of Goyal as modified to respond to user queries.
As to claim 11, Goyal as modified teaches a system of claim 1.
Goyal as modified does not teach wherein the document describes a restricted measure variable with a filter comprising one or more values.
Bauchot teaches wherein the document describes a restricted measure variable with a filter comprising one or more values (see Bauchot paragraphs [0018] and [0023]. Results are calculated from a restrictive measure and other filters).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Bauchot because Bauchot merely provides additional analyses and query parameters that a user may use to identify data. These additional analyses and parameters will increase the flexibility of Goyal as modified to respond to user queries.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Goyal et al. (US Pre-Grant Publication 2018/0037840) as modified by Poirier et al. (US Pre-Grant Publication 2024/0202539), and further in view of Ailawadi et al. (US Pre-Grant Publication 2024/0330421).
As to claim 10, Goyal as modified teaches a system of claim 1.
Goyal as modified does not teach wherein the document describes a count distinct measure with one or more dimensions.
Ailawadi teaches wherein the document describes a count distinct measure with one or more dimensions (see paragraph [0076]. Ailawadi shows an example of training generative AI using a count distinct measure to produce an output).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Ailawadi because Ailawadi merely provides additional analyses and query parameters that a user may use to identify data. These additional analyses and parameters will increase the flexibility of Goyal as modified to respond to user queries.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Goyal et al. (US Pre-Grant Publication 2018/0037840) as modified by Poirier et al. (US Pre-Grant Publication 2024/0202539), and further in view of Balasubramanyan et al. (US Pre-Grant Publication 2015/0339358).
As to claim 12, Goyal as modified teaches a system of claim 1.
Goyal as modified does not teach wherein the document describes a restricted measure variable with a filter comprising one or more ranges.
Balasubramanyan teaches wherein the document describes a restricted measure variable with a filter comprising one or more ranges (see Balasubramanyan paragraph [0069]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Goyal by the teachings of Ailawadi because Ailawadi merely provides additional analyses and query parameters that a user may use to identify data. These additional analyses and parameters will increase the flexibility of Goyal as modified to respond to user queries.
Conclusion
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/CHARLES D ADAMS/ Primary Examiner, Art Unit 2165
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