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 .
In response to Applicant’s claims filed on September 10, 2025 claims 1, 5-11 are now pending for examination in the application.
Applicant’s arguments:
In regards to claim 1 on Page(s) 9, applicant argues “Without any admissions and solely in an effort to expedite prosecution of the present application, independent claim | is amended, to avoid reciting “identifying” “predicting” and “revising,” features identified as allegedly corresponding to mental processes. Therefore, the claim does not recite a mental process. The claim also does not recite a mathematical concept or certain methods of organizing human activity..
Examiner’s Reply:
The converting and creating steps are performed in the human by using computer as a tool. Error correction in a database is made possible because of the human mind.
Applicant’s arguments:
In regards to claim 1 on Page(s) 10, applicant argues “Applicant respectfully submits these features involve technical complexity that could not be performed as a mental process. Further, these features are also not directed to mathematical concepts or certain methods of organizing human activity.
Examiner’s Reply:
Applicant argues that the claims comprises statutory subject matter. Examiner respectfully disagrees. The examiner notes that the computer as recited in the claims are being used for recognizing and correcting errors in a database (the computer is being used as a generic tool). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application.
Applicant’s arguments:
In regards to claim 1 on Page(s) 10, applicant argues “Moreover, Applicant respectfully submits that even if it is assumed the claim is directed to an abstract idea, which is not conceded, independent claim | recites significantly more than any allegedly abstract idea.”
Examiner’s Reply:
Attribute correction in a database is well-understood, routine, and conventional.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. With respect to claim 1, 10, 11. There is no support for “field attributes ….”.
Dependent claims 2-9 is/are also rejected for inheriting the deficiencies of the independent claims from which they depend on.
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, 5-11 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1, 5-11 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter 2019 PEG.
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the system, method, and medium of claims 1, 5-11 are directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the mathematical concepts and mental processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 10, and 11 are directed towards the Mathematical Concepts and Mental Process Grouping of Abstract Ideas.
Independent claim 1, 10, and 11 recites the following limitations directed towards a Mathematical Concepts and Mental Processes:
converting the plurality of records in the target databased to a data format of another database (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to convert records);
providing an error attribute that is a field including an error in the converted database, from among a plurality of attributes contained in processed data obtained by applying a predetermined process to the target data (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to provide attributes);
creating token sets of words included in each of the correction candidates and the attribute value candidates to determine a revised correction according to the correction (The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by creating token sets).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 8, and 15:
processors (i.e., as a generic processor performing a generic computer function);
obtaining a target database including a plurality of records (recites insignificant extra solution activity that amounts to mere data gathering);
obtaining respective similarities between the error attribute and each field attribute of the target database using schema definitions of the target database and the converted database (recites insignificant extra solution activity that amounts to mere data gathering);
outputting correction candidates for the error attribute in each of the records and first certainty factors of the correction candidates, by inputting feature vectors of the records into a predictive model, wherein the predictive model is a model generated by machine learning using the feature vectors of the records and attribute values of the field attributes as training data (recites insignificant extra solution activity that amounts to outputting data);
obtaining attribute value candidates for a reference attribute and second certainty factors related to each of the attribute value candidates, wherein the reference attributes are selected from among the field attributes of the target database and have similarity equal to or greater than a predetermined threshold (recites insignificant extra solution activity that amounts to mere data gathering).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 10, and 11 are rejected under 35 U.S.C. 101.
With respect to claim(s) 5:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein, the at least one processor id further configured to carry out obtaining a similarity vector between a target record contained in the target data and each standard record contained in standard data (recites insignificant extra solution activity that amounts to mere data gathering), and
outputting, as a correction candidate, an attribute value contained in a standard record having a greater matching probability obtained based on the calculated similarity vectors (recites insignificant extra solution activity that amounts to outputting attribute data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the at least one processor is further configured to carry out obtaining a reference attribute that is an attribute similar to the error attribute semantically or linguistically and contained in the target data (recites insignificant extra solution activity that amounts to mere data gathering).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7:
Step 2A, prong one of the 2019 PEG:
The predetermined process comprises an initialization process to the target data,
The initialization process, comprises referring to correspondence information indicating a correspondence between at least one of the plurality of attributes contained in the target data and at least one of a plurality of attributes contained in standard data (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to initializing attribute data); and,
from the target data, generating, as the processed data, data containing attributes identical to the plurality of attributes contained in the standard data (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate attribute data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8:
Step 2A, prong one of the 2019 PEG:
determining, for each of the plurality of attributes contained in the processed data, whether or not an attribute value of the attribute satisfies a predetermined condition (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determine attribute data).
Step 2A Prong Two Analysis:
obtaining the error attribute based on a result of the determination (recites insignificant extra solution activity that amounts to mere data gathering).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 9:
Step 2A, prong one of the 2019 PEG:
generating converted data corresponding to the target data, using the correction revised in the revision process (The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by generating converted data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
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) 1, 5-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jagota et al. (US Pub. No. 20200250576) in view of Gao et al. (US Pub. No. 20200151252).
With respect to claim 1, Jagota et al. teaches an information processing apparatus comprising at least one processor, the at least one processor carrying out:
obtaining a target database including a plurality of records (Paragraph 141 discloses on-demand database services may store information from one or more tenants stored into tables of a common database image to form a multi-tenant database system (MTS) and Paragraph 19 discloses the customer resolution engine accesses a retail record that stores Al Jones, 1 Wall Street, New York City, N.Y., 10005, United States, and applies the first name length function to the first name field's value Al by identifying the frequency of 2 character-long first names in the sample first name data set as 0.030, and by calculating the logarithm of 0.030 as the first name length score of negative 1.52 for Al);
providing an error attribute that is a field including an error in the converted database, from among a plurality of attributes contained in processed data obtained by applying a predetermined process to the target data (Paragraph 72 discloses the customer resolution engine captures and leverages data validation and enrichment attributes as part of the record fields to determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco);
obtaining respective similarities between the error attribute and each field attribute of the target database using schema definitions of the target database and the converted database (Paragraph 59 discloses the machine learning framework learns b.sub.i=0.80 and θ.sub.i=0.90 for the city field. The bias value is interpreted as saying that any match on city that scores less than 0.80 (such as 80% similarity of the strings) influences a record-level match negatively, all the way down to negative 0.80 and 149 discloses database object described herein may be implemented as single databases, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and might include a distributed database or storage network and associated processing intelligence);
outputting correction candidates for the error attribute in each of the records and first certainty factors of the correction candidates, by inputting feature vectors of the records into a predictive model, wherein the predictive model is a model generated by machine learning using the feature vectors of the records and attribute values of the field attributes as training data (Paragraph 72 discloses determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco and Paragraph 93 discloses Features and/or differential features are extracted from (u, v), thereby resulting in a vector of features, and a data set of (feature vector, y) pairs. Any standard machine learning algorithm may use the vector of features and the data set to learn a predictive model. The machine learning framework uses the learned model to predict, on a new instance (u, v), the likelihood that the value u is better than the value v, and then converts this likelihood into a suitable score);
obtaining attribute value candidates for a reference attribute and second certainty factors related to each of the attribute value candidates, wherein the reference attributes are selected from among the field attributes of the target database and have similarity equal to or greater than a predetermined threshold (Paragraph 72 discloses determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco and Paragraph 93 discloses Features and/or differential features are extracted from (u, v), thereby resulting in a vector of features, and a data set of (feature vector, y) pairs. Any standard machine learning algorithm may use the vector of features and the data set to learn a predictive model. The machine learning framework uses the learned model to predict, on a new instance (u, v), the likelihood that the value u is better than the value v, and then converts this likelihood into a suitable score). Jagota et al. does not disclose converting the plurality of records in the target databased to a data format of another database.
However, Gao et al. teaches converting the plurality of records in the target databased to a data format of another database (Paragraph 45 discloses Table 300 (FIG. 3) needs to be converted to another format. In one instance, table 300 in PDF format needs to be converted to a WORD format document. The table conversion takes place and the converted table 400 can include a plurality of errors resulting from the conversion of table 300 (FIG. 3) from, e.g., a PDF document to a WORD document);
creating token sets of words included in each of the correction candidates and the attribute value candidates to determine a revised correction according to the correction candidates, the first certainty factors, the attribute value candidates, the second certainty factors and similarities between the token sets.
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Jagota et al. with Gao et al. to include converting the plurality of records in the target databased to a data format of another database. This would have facilitated error correction in fields of databases. See Gao et al. et al. Paragraphs 3-7.
The Jagota et al. reference as modified by Gao et al. teaches all the limitations of claim 1. With respect to claim 5, Jagota et al. teaches the information processing apparatus according to claim 1, wherein the at least one processor is further configured to carry out obtaining a similarity vector between a target record contained in the target data and each standard record contained in standard data, and outputting, as a correction candidate, an attribute value contained in a standard record having a greater matching probability obtained based on the calculated similarity vectors (Paragraph 93 discloses Features and/or differential features are extracted from (u, v), thereby resulting in a vector of features, and a data set of (feature vector, y) pairs. Any standard machine learning algorithm may use the vector of features and the data set to learn a predictive model. The machine learning framework uses the learned model to predict, on a new instance (u, v), the likelihood that the value u is better than the value v, and then converts this likelihood into a suitable score).
The Jagota et al. reference as modified by Gao et al. teaches all the limitations of claim 1. With respect to claim 6, Jagota et al. teaches the information processing apparatus according to claim 1, wherein the at least one processor is further configured to carry out obtaining a reference attribute that is an attribute similar to the error attribute semantically or linguistically and contained in the target data (Paragraph 72 discloses the customer resolution engine captures and leverages data validation and enrichment attributes as part of the record fields to determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco).
The Jagota et al. reference as modified by Gao et al. teaches all the limitations of claim 1. With respect to claim 7, Jagota et al. teaches the information processing apparatus according to claim 1, wherein the predetermined process comprises an initialization process to the target data,
The initialization process, comprises referring to correspondence information indicating a correspondence between at least one of the plurality of attributes contained in the target data and at least one of a plurality of attributes contained in standard data (Paragraph 108 discloses A score can be a rating or a grade. A function can be a process or a relation that associates one element in one set of elements with another element in another set of elements. A feature can be a distinctive attribute or aspect of something); and,
from the target data, generates, as the processed data, data containing attributes identical to the plurality of attributes contained in the standard data (Paragraph 108 discloses A score can be a rating or a grade. A function can be a process or a relation that associates one element in one set of elements with another element in another set of elements. A feature can be a distinctive attribute or aspect of something).
The Jagota et al. reference as modified by Gao et al. teaches all the limitations of claim 7. With respect to claim 8, Jagota et al. teaches the information processing apparatus according to claim 7, wherein the at least one processor further carries out determining, for each of the plurality of attributes contained in the processed data, whether or not an attribute value of the attribute satisfies a predetermined condition, and obtaining the error attribute based on a result of the determination (Paragraph 72 discloses the customer resolution engine captures and leverages data validation and enrichment attributes as part of the record fields to determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco).
` The Jagota et al. reference as modified by Gao et al. teaches all the limitations of claim 7. With respect to claim 9, Gao et al. teaches the information processing apparatus according to claim 1, wherein the at least one processor further carries out generating converted data corresponding to the target data, using the correction revised in the revision process (Paragraph 45 discloses Table 300 (FIG. 3) needs to be converted to another format. In one instance, table 300 in PDF format needs to be converted to a WORD format document. The table conversion takes place and the converted table 400 can include a plurality of errors resulting from the conversion of table 300 (FIG. 3) from, e.g., a PDF document to a WORD document). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Jagota et al. reference and the Gao et al. reference is applicable to dependent claim 9.
With respect to claim 10, Jagota et al. teaches an information processing method comprising:
obtaining a target database including a plurality of records (Paragraph 141 discloses on-demand database services may store information from one or more tenants stored into tables of a common database image to form a multi-tenant database system (MTS) and Paragraph 19 discloses the customer resolution engine accesses a retail record that stores Al Jones, 1 Wall Street, New York City, N.Y., 10005, United States, and applies the first name length function to the first name field's value Al by identifying the frequency of 2 character-long first names in the sample first name data set as 0.030, and by calculating the logarithm of 0.030 as the first name length score of negative 1.52 for Al);
providing an error attribute that is a field including an error in the converted database, from among a plurality of attributes contained in processed data obtained by applying a predetermined process to the target data (Paragraph 72 discloses the customer resolution engine captures and leverages data validation and enrichment attributes as part of the record fields to determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco);
obtaining respective similarities between the error attribute and each field attribute of the target database using schema definitions of the target database and the converted database (Paragraph 59 discloses the machine learning framework learns b.sub.i=0.80 and θ.sub.i=0.90 for the city field. The bias value is interpreted as saying that any match on city that scores less than 0.80 (such as 80% similarity of the strings) influences a record-level match negatively, all the way down to negative 0.80 and 149 discloses database object described herein may be implemented as single databases, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and might include a distributed database or storage network and associated processing intelligence);
outputting correction candidates for the error attribute in each of the records and first certainty factors of the correction candidates, by inputting feature vectors of the records into a predictive model, wherein the predictive model is a model generated by machine learning using the feature vectors of the records and attribute values of the field attributes as training data (Paragraph 72 discloses determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco and Paragraph 93 discloses Features and/or differential features are extracted from (u, v), thereby resulting in a vector of features, and a data set of (feature vector, y) pairs. Any standard machine learning algorithm may use the vector of features and the data set to learn a predictive model. The machine learning framework uses the learned model to predict, on a new instance (u, v), the likelihood that the value u is better than the value v, and then converts this likelihood into a suitable score);
obtaining attribute value candidates for a reference attribute and second certainty factors related to each of the attribute value candidates, wherein the reference attributes are selected from among the field attributes of the target database and have similarity equal to or greater than a predetermined threshold (Paragraph 72 discloses determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco and Paragraph 93 discloses Features and/or differential features are extracted from (u, v), thereby resulting in a vector of features, and a data set of (feature vector, y) pairs. Any standard machine learning algorithm may use the vector of features and the data set to learn a predictive model. The machine learning framework uses the learned model to predict, on a new instance (u, v), the likelihood that the value u is better than the value v, and then converts this likelihood into a suitable score). Jagota et al. does not disclose converting the plurality of records in the target databased to a data format of another database.
However, Gao et al. teaches converting the plurality of records in the target databased to a data format of another database (Paragraph 45 discloses Table 300 (FIG. 3) needs to be converted to another format. In one instance, table 300 in PDF format needs to be converted to a WORD format document. The table conversion takes place and the converted table 400 can include a plurality of errors resulting from the conversion of table 300 (FIG. 3) from, e.g., a PDF document to a WORD document);
creating token sets of words included in each of the correction candidates and the attribute value candidates to determine a revised correction according to the correction candidates, the first certainty factors, the attribute value candidates, the second certainty factors and similarities between the token sets.
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Jagota et al. with Gao et al. to include converting the plurality of records in the target databased to a data format of another database. This would have facilitated error correction in fields of databases. See Gao et al. et al. Paragraphs 3-7.
With respect to claim 11, Jagota et al. teaches a computer-readable non-transitory storage medium storing therein a program for causing a computer to function as an information processing apparatus and for causing a computer to carry out:
A process of obtaining a target database including a plurality of records (Paragraph 141 discloses on-demand database services may store information from one or more tenants stored into tables of a common database image to form a multi-tenant database system (MTS) and Paragraph 19 discloses the customer resolution engine accesses a retail record that stores Al Jones, 1 Wall Street, New York City, N.Y., 10005, United States, and applies the first name length function to the first name field's value Al by identifying the frequency of 2 character-long first names in the sample first name data set as 0.030, and by calculating the logarithm of 0.030 as the first name length score of negative 1.52 for Al);
A process of providing an error attribute that is a field including an error in the converted database, from among a plurality of attributes contained in processed data obtained by applying a predetermined process to the target data (Paragraph 72 discloses the customer resolution engine captures and leverages data validation and enrichment attributes as part of the record fields to determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco);
A process of obtaining respective similarities between the error attribute and each field attribute of the target database using schema definitions of the target database and the converted database (Paragraph 59 discloses the machine learning framework learns b.sub.i=0.80 and θ.sub.i=0.90 for the city field. The bias value is interpreted as saying that any match on city that scores less than 0.80 (such as 80% similarity of the strings) influences a record-level match negatively, all the way down to negative 0.80 and 149 discloses database object described herein may be implemented as single databases, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and might include a distributed database or storage network and associated processing intelligence);
A process of outputting correction candidates for the error attribute in each of the records and first certainty factors of the correction candidates, by inputting feature vectors of the records into a predictive model, wherein the predictive model is a model generated by machine learning using the feature vectors of the records and attribute values of the field attributes as training data (Paragraph 72 discloses determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco and Paragraph 93 discloses Features and/or differential features are extracted from (u, v), thereby resulting in a vector of features, and a data set of (feature vector, y) pairs. Any standard machine learning algorithm may use the vector of features and the data set to learn a predictive model. The machine learning framework uses the learned model to predict, on a new instance (u, v), the likelihood that the value u is better than the value v, and then converts this likelihood into a suitable score);
A process of obtaining attribute value candidates for a reference attribute and second certainty factors related to each of the attribute value candidates, wherein the reference attributes are selected from among the field attributes of the target database and have similarity equal to or greater than a predetermined threshold (Paragraph 72 discloses determine that “11 Main St” is not a valid street address for “San Francisco, Calif. 94105,” infer a data entry error in the street number, and then identify the nearest string or geo-proximity match as a reliable candidate, thereby identifying only one unique John Smith on Main Street in San Francisco and Paragraph 93 discloses Features and/or differential features are extracted from (u, v), thereby resulting in a vector of features, and a data set of (feature vector, y) pairs. Any standard machine learning algorithm may use the vector of features and the data set to learn a predictive model. The machine learning framework uses the learned model to predict, on a new instance (u, v), the likelihood that the value u is better than the value v, and then converts this likelihood into a suitable score). Jagota et al. does not disclose converting the plurality of records in the target databased to a data format of another database.
However, Gao et al. teaches A process of converting the plurality of records in the target databased to a data format of another database (Paragraph 45 discloses Table 300 (FIG. 3) needs to be converted to another format. In one instance, table 300 in PDF format needs to be converted to a WORD format document. The table conversion takes place and the converted table 400 can include a plurality of errors resulting from the conversion of table 300 (FIG. 3) from, e.g., a PDF document to a WORD document);
A process of creating token sets of words included in each of the correction candidates and the attribute value candidates to determine a revised correction according to the correction candidates, the first certainty factors, the attribute value candidates, the second certainty factors and similarities between the token sets.
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Jagota et al. with Gao et al. to include converting the plurality of records in the target databased to a data format of another database. This would have facilitated error correction in fields of databases. See Gao et al. et al. Paragraphs 3-7.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-Pub. No. 20220138238 is directed to MASSIVE SCALE HETEROGENEOUS DATA INGESTION AND USER RESOLUTION: [0403] the lazy interpretation system may include an Anchoring Entity Resolution (AER) process that corrects tags attached to the previously received data to be associated with the best known anchoring entity. The best known anchoring entity may dynamically change based on information contained in the new incoming data, such as based on the analytics of previously received data, or based on improvements in anchoring entity resolution itself. In some embodiments, the anchoring entity resolution may update the previously attached tags. The anchoring entity resolution process may periodically or continuously run in the background or foreground, may be automatically triggered by the occurrence of a predefined event, and/or initiated by a system overseer, requesting entity, or other user.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154