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 .
This Office Action is in response to the filing date of 12/27/2023.
Claims 1-20 are pending and have been considered below.
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 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Prong 1, the claim recites “associating the semantic object with the dataset to generate a semantically-annotated dataset” and “performing a semantic aware operation based on the semantically-annotated dataset” as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
Under Prong 2, Step 2A, the judicial exception is not integrated into a practical application. The claim recites the following additional elements “a computing device enabling importation of a library into a computer program…enabling code of the library to be referenced within the computer program to generate a dataset from data obtained via the data storage interface” are merely instructions to implement the abstract idea on a computer, or merely uses a computer, with instructions, as a tool to perform the abstract idea according to MPEP 2106.05(f), thus, not indicative of an integration into a practical application.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components used as the tools to perform the abstract ideas. See MPEP 2106.05(d). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 2 and 11 recite the additional elements that integrate the abstract idea into a practical application. The additional element “accessing a metadata source from which the semantic object is obtained” the courts have identified that receiving data, gathering data, and displaying/presenting the output of the abstract idea is well-understood, routine, conventional activity. See MPEP 2106.05(d). Therefore, it does not amount to significantly more than the abstract idea.
Claims 3, 12, and 20 recite the additional elements that integrate the abstract idea into a practical application. The additional elements “enabling invocation of a semantic-guided operation of the library that utilizes the semantically-annotated dataset to infer an aspect of at least one of a data manipulation or data analysis operation to be performed on the semantically-annotated dataset” are merely instructions to implement the abstract idea on a computer, or merely uses a computer, with instructions, as a tool to perform the abstract idea according to MPEP 2106.05(f), thus, not indicative of an integration into a practical application.
Claims 4, 5, 13, and 14 recite the additional elements that integrate the abstract idea into a practical application. The additional elements are well-understood, routine, conventional activity. See MPEP 2106.05(d). Therefore, it does not amount to significantly more than the abstract idea.
Claims 6 and 15 are also directed to the abstract idea as a human can perform “associating the semantic function with the semantically-annotated dataset” in the mind including with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
Claims 7 and 16 are also directed to the abstract idea as a human can perform “inferring a set of attributes to which the semantic function is to be applied based on the applicability filter” in the mind including with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
Claims 8 and 17 are also directed to the abstract idea as a human can perform “suggesting at least one of a data manipulation or data analysis operation based on the semantically-annotated dataset” in the mind including with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
Claims 9 and 18 are also directed to the abstract idea as a human can perform “suggesting the semantic function based on the applicability filter” in the mind including with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Prong 1, the claim recites “associating the semantic object with the dataset to generate a semantically-annotated dataset” and “performing a semantic aware operation based on the semantically-annotated dataset” as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
Under Prong 2, Step 2A, the judicial exception is not integrated into a practical application. The claim recites the following additional elements “a computing device enabling importation of a library into a computer program…enabling code of the library to be referenced within the computer program to generate a dataset from data obtained via the data storage interface” are merely instructions to implement the abstract idea on a computer, or merely uses a computer, with instructions, as a tool to perform the abstract idea. The additional elements of “a computer-readable storage medium” and “a processor” merely recite the use of a generic computer or computer components as a tool to perform the abstract idea. According to MPEP 2106.05(f), the additional elements not indicative of an integration into a practical application.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components used as the tools to perform the abstract ideas. See MPEP 2106.05(d). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Prong 1, the claim recites “associating the semantic object with the dataset to generate a semantically-annotated dataset” and “performing a semantic aware operation based on the semantically-annotated dataset” as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
Under Prong 2, Step 2A, the judicial exception is not integrated into a practical application. The claim recites the following additional elements “a computing device enabling importation of a library into a computer program…enabling code of the library to be referenced within the computer program to generate a dataset from data obtained via the data storage interface” are merely instructions to implement the abstract idea on a computer, or merely uses a computer, with instructions, as a tool to perform the abstract idea. The additional elements of “a system comprising a processor and a memory” merely recite the use of a generic computer or computer components as a tool to perform the abstract idea. According to MPEP 2106.05(f), the additional elements not indicative of an integration into a practical application.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components used as the tools to perform the abstract ideas. See MPEP 2106.05(d). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Pub. No. 20220382732 to Tateishi.
Per claims 1, 10, and 19, Tateishi teaches a method performed by a computing device, the method comprising:
enabling importation of a library into a computer program, the library comprising a data storage interface, a data operation, and a semantic object that comprises an instantiation of a class of an object-oriented programming language (see at least paragraph [0052] “…in the described embodiment where the Python is assumed to be employed, the description will be made assuming that ‘Pandas’ library, which is a well-known library written in the Python for data manipulation and analysis, is imported into the target program…”);
enabling code of the library to be referenced within the computer program to generate a dataset from data obtained via the data storage interface (see at least paragraph [0052] “…one or more specific methods in the ‘Pandas’ library are targeted for the redefinition, for reference. A method that returns, creates or manipulates (herein, these operations are collectively referred to as ‘handles’ or ‘handling’) a data structure object such as a dataset and/or a column object is targeted as the specific methods to be redefined…”);
associating the semantic object with the dataset to generate a semantically-annotated dataset (see at least paragraph [0060] “In the embodiment where the Python is used, assuming that the ‘Pandas’ library is imported into the target program 102, the data structure object (i.e. dataset) may correspond to a ‘DataFrame’ object or a ‘Series’ object in the ‘Pandas’ module…”); and
performing a semantic aware operation based on the semantically-annotated dataset (see at least paragraph [0034] “Manipulating the values in the data structure object may correspond to being instructed by an ‘apply’ method of the ‘Series’ object or an ‘applymap’ method of the ‘DataFrame’ object with a designated function (f) defining the specified way to manipulate. Hence, in this case, the ‘Series.apply’ method (or ‘DataFrame.applymap’) method may be redefined such that the synthetic ‘Series’ (or ‘DataFrame’) object is created by using the mixed data object and the designated function (f) is applied to values in the created synthetic ‘Series’ (or ‘DataFrame’) object, instead of applying the designated function to the values in ‘Series’ (or ‘DataFrame’) object in a manner instructed originally in the target program”).
Per claims 2 and 11, Tateishi further teaches
accessing a metadata source, by a metadata interface of the library, from which the semantic object is obtained (see at least paragraph [0032] “Accessing the part of the data structure object may correspond to being instructed by ‘getitem’ method of the ‘DataFrame’ object…”).
Per claims 3, 12, and 20, Tateishi further teaches
wherein said performing a semantic aware operation based on the semantically-annotated dataset comprises: enabling invocation of a semantic-guided operation of the library that utilizes the semantically-annotated dataset to infer an aspect of at least one of a data manipulation or data analysis operation to be performed on the semantically-annotated dataset (see at least paragraph [0034] “Manipulating the values in the data structure object may correspond to being instructed by an ‘apply’ method of the ‘Series’ object or an ‘applymap’ method of the ‘DataFrame’ object with a designated function (f) defining the specified way to manipulate. Hence, in this case, the ‘Series.apply’ method (or ‘DataFrame.applymap’) method may be redefined such that the synthetic ‘Series’ (or ‘DataFrame’) object is created by using the mixed data object and the designated function (f) is applied to values in the created synthetic ‘Series’ (or ‘DataFrame’) object, instead of applying the designated function to the values in ‘Series’ (or ‘DataFrame’) object in a manner instructed originally in the target program”).
Per claims 4 and 13, Tateishi further teaches:
obtaining the data, by a compute interface of the library, via the data storage interface (see at least paragraph [0032] “Accessing the part of the data structure object may correspond to being instructed by a‘_getitem_’ method of the ‘DataFrame’ object…”) ; and accessing a compute resource, by the compute interface, to perform the at least one of the data manipulation or data analysis operation (see at least paragraph [0033] “handling the data structure object may include manipulating values in the data structure object in a specified way…”).
Per claims 5 and 14, Tateishi further teaches:
wherein performance of at least one of the data manipulation or the data analysis operation on the semantically-annotated dataset comprises: propagating semantics of the semantically-annotated dataset to an output of the at least one of the data manipulation or data analysis operation (see at least paragraph [0083] “In the ‘Pandas’ library, there are (i) a first type method that returns a data structure object (e.g., ‘pandas.read_csv’ method that returns a ‘DataFrame’ object having contents of CSV file), (ii) a second type method that accesses a part of a data structure object (e.g., ‘DataFrame._getitem_’ method that accesses a column in a ‘DataFrame’ object), (iii) a third type method that manipulates values in a data structure object in a specified way”).
Per claims 6 and 15, Tateishi further teaches:
wherein the semantic object includes a semantic function; and wherein said associating the semantic object with the dataset to generate a semantically-annotated dataset comprises: associating the semantic function with the semantically-annotated dataset (see at least paragraph [0034] “…Manipulating the values in the data structure object may correspond to being instructed by an ‘apply’ method of the ‘Series’ object or an ‘applymap’ method of the ‘DataFrame’ object with a designated function (f) defining the specified way to manipulate…”).
Per claims 7 and 16, Tateishi further teaches:
wherein the semantic function includes an applicability filter; and wherein the infer an aspect of at least one of a data manipulation or data analysis operation to be performed on the semantically-annotated dataset comprises: inferring a set of attributes to which the semantic function is to be applied based on the applicability filter (see at least paragraph [0034] “…the data structure object may correspond to a ‘Series’ object or a ‘DataFrame’ object. The 1- or 2-dimensional data structure object may correspond to a ‘Series’ object or a ‘DataFrame’ object. Manipulating the values in the data structure object may correspond to being instructed by an ‘apply’ method of the ‘Series’ object or an ‘applymap’ method of the ‘DataFrame’ object with a designated function (f) defining the specified way to manipulate. Hence, in this case, the ‘Series.apply’ method (or ‘DataFrame.applymap’) method may be redefined such that the synthetic ‘Series’ (or ‘DataFrame’) object is created by using the mixed data object and the designated function (f) is applied to values in the created synthetic ‘Series’ (or ‘DataFrame’) object, instead of applying the designated function to the values in ‘Series’ (or ‘DataFrame’) object in a manner instructed originally in the target program”).
Per claims 8 and 17, Tateishi further teaches:
wherein said performing a semantic aware operation based on the semantically-annotated dataset comprises: suggesting at least one of a data manipulation or data analysis operation based on the semantically-annotated dataset (see at least paragraph [0034] “…Manipulating the values in the data structure object may correspond to being instructed by an ‘apply’ method of the ‘Series’ object or an ‘applymap’ method of the ‘DataFrame’ object with a designated function (f) defining the specified way to manipulate... “).
Per claims 9 and 18, Tateishi further teaches:
wherein the semantic object includes a semantic function that includes an applicability filter; and wherein said suggesting at least one of a data manipulation or data analysis operation based on the semantically-annotated dataset comprises: suggesting the semantic function based on the applicability filter (see at least paragraph [0034] “…the data structure object may correspond to a ‘Series’ object or a ‘DataFrame’ object. The 1- or 2-dimensional data structure object may correspond to a ‘Series’ object or a ‘DataFrame’ object. Manipulating the values in the data structure object may correspond to being instructed by an ‘apply’ method of the ‘Series’ object or an ‘applymap’ method of the ‘DataFrame’ object with a designated function (f) defining the specified way to manipulate. Hence, in this case, the ‘Series.apply’ method (or ‘DataFrame.applymap’) method may be redefined such that the synthetic ‘Series’ (or ‘DataFrame’) object is created by using the mixed data object and the designated function (f) is applied to values in the created synthetic ‘Series’ (or ‘DataFrame’) object, instead of applying the designated function to the values in ‘Series’ (or ‘DataFrame’) object in a manner instructed originally in the target program”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20210209099 relates to data science.
WO2019215713 relates to data scientists.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHILLIP H NGUYEN whose telephone number is (571)270-1070. The examiner can normally be reached Monday-Friday 9:00AM-5:00PM.
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/PHILLIP H NGUYEN/Primary Examiner, Art Unit 2191