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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/21//2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are not persuasive.
Applicant has amended the claims 1, 8 and 15 to include: “one or more previously trained language models possessing knowledge related to language understanding, and training data that only includes the plurality of unstructured text strings and the identified one or more structural elements associated with the at least some of the plurality of unstructured text strings, wherein the identified one or more structural elements serve as ground truth to train the conversion model, wherein the trained conversion model has the knowledge possessed by the trained language models and learns to convert the new input unstructured text string into the structured data record based on the training data.” Upon further consideration of the Awadalla reference it has been determined that these features are taught in par. [0075] of Awadalla which teaches because of the dual training and subsequent coupling of the models, the coupled machine learning model 220 is able to generalize patterns that it previously learned to understand, encode and decode new source instances that it encounters, see par. [0075]. In some embodiments, a previously learned aligned word embedding (e.g., “C” for Woodbridge) is used in subsequent alignments, see par. [0090]. Additionally Awadalla teaches regarding FIG. 5, which illustrates an example embodiment of a process to combine separately trained models into a coupled machine learning model. For these reasons the examiner believes the claims are still taught by the cited rejection.
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.
Claim(s) 1, 3, 4, 6-8, 10, 11, 13-15, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sirangimoorthy U.S. PAP 2021/0248153 A1 in view of Iyer U.S. PAP 2020/0159855 A1 further in view of Awadalla U.S. PAP 2022/0050955 A1.
Regarding claim 1 Sirangimoorthy teaches a method implemented on at least one machine including at least one processor, memory, and communication platform capable of connecting to a network for text processing (a system for extracting information from an unstructured document. The system generally includes a processor and a memory having instructions which, when executed by the processor, performs an operation for extracting information from an unstructured document, see par. [0004-0005]), the method comprising:
receiving a plurality of unstructured text strings (the operation generally includes receiving a source document from which information is to be extracted, see par. [0005]);
receiving, with respect to at least some of the plurality of unstructured text strings, an input from a user identifying one or more structural elements in the unstructured text string (user interaction with the navigation structure may allow for navigation to the location of various elements in the unstructured document, see par. [0017]; software application 122 may also provide a mechanism through which a user can correct the extracted information displayed in the navigation pane and identify additional relevant information to be included in the navigation pane. The corrected information and additional identified information may be added to a structured document e.g., stored in document data store 150 specifying the extracted information and location of the extracted information, and the updated structured document may be used in future viewing of the source unstructured document from which the information was extracted, see par. [0021]);
However Sirangimoorthy does not teach inferring, by the conversion model based on the new input unstructured text string, text content that is incorporated in the structured data record and is indicative of a category and/or sentiment of an entity included in the new input unstructured text string, wherein the inferred text content is not present in the plurality of unstructured text strings included in the training data but is present in the one or more structural elements included in the training data.
IN the same field of endeavor Iyer teaches actions in response to receiving unstructured data from one of a plurality of action request applications. A centralized service is implemented that provides for consistent and reliable action results regardless of which application initiated the request and/or which entity the request is associated with. Results consistency is further realized by structuring the unstructured data and extracting data elements therefrom that provide for determination of a predetermined action categories, see abstract. The unstructured data may take various different formats and the different action request channels may implement one or more diverse applications/tools for receiving the action requests. Once ingested, the unstructured data is converted to structured data and data elements from the structured data are extracted to determine one or more action categories from amongst a plurality of predetermined action categories, see par. [0008]. In specific embodiments of the system, the end-to-end action performance service is further configured to implement sentiment analytics to determine, from at least one of the unstructured data or the structured data, an expressive state exhibited by a user at a time when the action request channel received the user input, see par. [0012]. In specific embodiments of the system, the unstructured data includes at least one text data, see par. [0013]. Once the data has been properly structured, data elements from the structured data are extracted to determine one or more action categories/classifications from amongst a plurality of predetermined action categories/classifications, see par. [0045].
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy invention with the teachings of Iyer for the benefit of creating result consistency from structuring the unstructured text, see abstract.
However Sirangimoorthy in view of Iyer does not teach training, via machine learning, a conversion model for converting a new input unstructured text string into a structured data record by identifying at least one structural data element from the new input unstructured text string, based on: one or more previously trained language models possessing knowledge related to language understanding, and training data that only includes the plurality of unstructured text strings and the identified one or more structural elements associated with the at least some of the plurality of unstructured text strings, wherein the identified one or more structural elements serve as ground truth to train the conversion model, wherein the trained conversion model has the knowledge possessed by the trained language models and learns to convert the new input unstructured text string into the structured data record based on the training data.
In the same field of endeavor Awadalla teaches improved systems and methods for generating training data and training models for natural language understanding while maintaining a preferred level of data security associated with the training data, see par. [0008]. Awadalla teaches the training engine 153 is configured to train a model via supervised training (e.g., using annotated data as ground truth). The training engine 153 is beneficially configured to be able to use training data that has not been annotated by a human reader/annotator such that the data security (i.e., privacy) of the training data used is maintained, see par. [0047]. In some embodiments, the training engine 153 is configured to train a model (e.g., source model 144) on an unannotated set of training data comprising unstructured (i.e., unannotated) natural language such that the machine learning model is configured to understand a semantic structure of the unannotated set of training data (structural data). In such embodiments, the training engine is able to train the model via unsupervised training, see par. [0048]. Because of the dual training and subsequent coupling of the models, the coupled machine learning model 220 is able to generalize patterns that it previously learned to understand, encode and decode new source instances that it encounters, see par. [0075]. In some embodiments, a previously learned aligned word embedding (e.g., “C” for Woodbridge) is used in subsequent alignments, see par. [0090].
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy in view of Iyer invention with the teachings of Awadalla for the benefit of improving systems and methods for generating training data and training models for natural language understanding while maintaining a preferred level of data security associated with the training data, see par. [0008].
Regarding claim 3 Sirangimoorthy teaches the method of claim 1, wherein the structured data record is a fixed length with a pre-determined number of structural data element; or a variable length with a variable number of structural data element (Text may be concatenated into a series of flattened text elements, and each text element may be associated with a location in an array of text locations for content in the plaintext file, see par. [0029]).
Regarding claim 4 Sirangimoorthy teaches the method of claim 1, wherein a structural data element is one of an entity, a product, a brand, a manufacturer, a feature (the selected element 422 is associated with information located at page 11 of the unstructured document displayed in first pane 410, references a “Lifetime Limit Individual” property of the plan(s) described in the unstructured document, and has a value of “$2,500”, see par. [0048]), and a sentiment, extracted from the unstructured text string.
Regarding claim 6 Sirangimoorthy teaches the method of claim 1, further comprising receiving the input unstructured text string (unstructured document received for analysis, see par. [0027]);
accessing the conversion model (document converter 142 generates one or more intermediate documents from an unstructured document received for analysis, see par. [0027]);
extracting, based on the conversion model, one or more structural data elements from the input unstructured text string (Generally, the plaintext file may include structured text including text content from tables or other organizational structures parsed from the unstructured document, see par. [0028]);
and generating the structured data record based on the one or more structural data elements (Structured document generator 144 uses the intermediate document(s) generated by document converter 142 to generate a structured document including relevant information in the unstructured document, see par. [0032]).
Regarding claim 7 Sirangimoorthy teaches the method of claim 6, wherein the structured data record further comprises at least one of the input unstructured text string and a data element inferred based on the one or more structural data elements (Generally, the plaintext file may include structured text including text content from tables or other organizational structures parsed from the unstructured document, see par. [0028]).
Regarding claim 8 Sirangimoorthy teaches a non-transitory and machine readable medium having information recorded thereon for text processing (computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device, see par. [0065]), wherein the information, when read by the machine, causes the machine to perform:
receiving a plurality of unstructured text strings (the operation generally includes receiving a source document from which information is to be extracted, see par. [0005]);
receiving, with respect to at least some of the plurality of unstructured text strings, an input from a user identifying one or more structural elements in the unstructured text string (user interaction with the navigation structure may allow for navigation to the location of various elements in the unstructured document, see par. [0017]; software application 122 may also provide a mechanism through which a user can correct the extracted information displayed in the navigation pane and identify additional relevant information to be included in the navigation pane. The corrected information and additional identified information may be added to a structured document e.g., stored in document data store 150 specifying the extracted information and location of the extracted information, and the updated structured document may be used in future viewing of the source unstructured document from which the information was extracted, see par. [0021]).
However Sirangimoorthy does not teach inferring, by the conversion model based on the new input unstructured text string, text content that is incorporated in the structured data record and is indicative of a category and/or sentiment of an entity included in the new input unstructured text string, wherein the inferred text content is not present in the plurality of unstructured text strings included in the training data but is present in the one or more structural elements included in the training data.
IN the same field of endeavor Iyer teaches actions in response to receiving unstructured data from one of a plurality of action request applications. A centralized service is implemented that provides for consistent and reliable action results regardless of which application initiated the request and/or which entity the request is associated with. Results consistency is further realized by structuring the unstructured data and extracting data elements therefrom that provide for determination of a predetermined action categories, see abstract. The unstructured data may take various different formats and the different action request channels may implement one or more diverse applications/tools for receiving the action requests. Once ingested, the unstructured data is converted to structured data and data elements from the structured data are extracted to determine one or more action categories from amongst a plurality of predetermined action categories, see par. [0008]. In specific embodiments of the system, the end-to-end action performance service is further configured to implement sentiment analytics to determine, from at least one of the unstructured data or the structured data, an expressive state exhibited by a user at a time when the action request channel received the user input, see par. [0012]. In specific embodiments of the system, the unstructured data includes at least one text data, see par. [0013]. Once the data has been properly structured, data elements from the structured data are extracted to determine one or more action categories/classifications from amongst a plurality of predetermined action categories/classifications, see par. [0045].
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy invention with the teachings of Iyer for the benefit of creating result consistency from structuring the unstructured text, see abstract.
However Sirangimoorthy in view of Iyer does not teach training, via machine learning, a conversion model for converting a new input unstructured text string into a structured data record by identifying at least one structural data element from the new input unstructured text string, based on: one or more previously trained language models possessing knowledge related to language understanding, and training data that only includes the plurality of unstructured text strings and the identified one or more structural elements associated with the at least some of the plurality of unstructured text strings, wherein the identified one or more structural elements serve as ground truth to train the conversion model, wherein the trained conversion model has the knowledge possessed by the trained language models and learns to convert the new input unstructured text string into the structured data record based on the training data.
In the same field of endeavor Awadalla teaches improved systems and methods for generating training data and training models for natural language understanding while maintaining a preferred level of data security associated with the training data, see par. [0008]. Awadalla teaches the training engine 153 is configured to train a model via supervised training (e.g., using annotated data as ground truth). The training engine 153 is beneficially configured to be able to use training data that has not been annotated by a human reader/annotator such that the data security (i.e., privacy) of the training data used is maintained, see par. [0047]. In some embodiments, the training engine 153 is configured to train a model (e.g., source model 144) on an unannotated set of training data comprising unstructured (i.e., unannotated) natural language such that the machine learning model is configured to understand a semantic structure of the unannotated set of training data (structural data). In such embodiments, the training engine is able to train the model via unsupervised training, see par. [0048]. Because of the dual training and subsequent coupling of the models, the coupled machine learning model 220 is able to generalize patterns that it previously learned to understand, encode and decode new source instances that it encounters, see par. [0075]. In some embodiments, a previously learned aligned word embedding (e.g., “C” for Woodbridge) is used in subsequent alignments, see par. [0090].
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy in view of Iyer invention with the teachings of Awadalla for the benefit of improving systems and methods for generating training data and training models for natural language understanding while maintaining a preferred level of data security associated with the training data, see par. [0008].
Regarding claim 10 Sirangimoorthy teaches the medium of claim 8, wherein the structured data record is a fixed length with a pre-determined number of structural data element; or a variable length with a variable number of structural data element (Text may be concatenated into a series of flattened text elements, and each text element may be associated with a location in an array of text locations for content in the plaintext file, see par. [0029]).
Regarding claim 11 Sirangimoorthy teaches the medium of claim 8, wherein a structural data element is one of an entity, a product, a brand, a manufacturer, a feature (the selected element 422 is associated with information located at page 11 of the unstructured document displayed in first pane 410, references a “Lifetime Limit Individual” property of the plan(s) described in the unstructured document, and has a value of “$2,500”, see par. [0048]), and a sentiment, extracted from the unstructured text string.
Regarding claim 13 Sirangimoorthy teaches the medium of claim 8, wherein the information, when read by the machine, further causes the machine to perform:
receiving the input unstructured text string (unstructured document received for analysis, see par. [0027]);
accessing the conversion model (document converter 142 generates one or more intermediate documents from an unstructured document received for analysis, see par. [0027]);
extracting, based on the conversion model, one or more structural data elements from the input unstructured text string (Generally, the plaintext file may include structured text including text content from tables or other organizational structures parsed from the unstructured document, see par. [0028]);
and generating the structured data record based on the one or more structural data elements (Structured document generator 144 uses the intermediate document(s) generated by document converter 142 to generate a structured document including relevant information in the unstructured document, see par. [0032]).
Regarding claim 14 Sirangimoorthy teaches the medium of claim 13, wherein the structured data record further comprises at least one of the input unstructured text string and a data element inferred based on the one or more structural data elements (Generally, the plaintext file may include structured text including text content from tables or other organizational structures parsed from the unstructured document, see par. [0028]).
Regarding claim 15 Sirangimoorthy teaches a system for text processing (a system for extracting information from an unstructured document. The system generally includes a processor and a memory having instructions which, when executed by the processor, performs an operation for extracting information from an unstructured document, see par. [0005]), comprising:
a user conversion input interface implemented on a processor and configured for receiving a plurality of unstructured text strings (the operation generally includes receiving a source document from which information is to be extracted, see par. [0005]),
receiving, with respect to at least some of the plurality of unstructured text strings, an input from a user identifying one or more structural elements in the unstructured text string (user interaction with the navigation structure may allow for navigation to the location of various elements in the unstructured document, see par. [0017]; software application 122 may also provide a mechanism through which a user can correct the extracted information displayed in the navigation pane and identify additional relevant information to be included in the navigation pane. The corrected information and additional identified information may be added to a structured document e.g., stored in document data store 150 specifying the extracted information and location of the extracted information, and the updated structured document may be used in future viewing of the source unstructured document from which the information was extracted, see par. [0021]).
However Sirangimoorthy does not teach inferring, by the conversion model based on the new input unstructured text string, text content that is incorporated in the structured data record and is indicative of a category and/or sentiment of an entity included in the new input unstructured text string, wherein the inferred text content is not present in the plurality of unstructured text strings included in the training data but is present in the one or more structural elements included in the training data.
IN the same field of endeavor Iyer teaches actions in response to receiving unstructured data from one of a plurality of action request applications. A centralized service is implemented that provides for consistent and reliable action results regardless of which application initiated the request and/or which entity the request is associated with. Results consistency is further realized by structuring the unstructured data and extracting data elements therefrom that provide for determination of a predetermined action categories, see abstract. The unstructured data may take various different formats and the different action request channels may implement one or more diverse applications/tools for receiving the action requests. Once ingested, the unstructured data is converted to structured data and data elements from the structured data are extracted to determine one or more action categories from amongst a plurality of predetermined action categories, see par. [0008]. In specific embodiments of the system, the end-to-end action performance service is further configured to implement sentiment analytics to determine, from at least one of the unstructured data or the structured data, an expressive state exhibited by a user at a time when the action request channel received the user input, see par. [0012]. In specific embodiments of the system, the unstructured data includes at least one text data, see par. [0013]. Once the data has been properly structured, data elements from the structured data are extracted to determine one or more action categories/classifications from amongst a plurality of predetermined action categories/classifications, see par. [0045].
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy invention with the teachings of Iyer for the benefit of creating result consistency from structuring the unstructured text, see abstract.
However Sirangimoorthy in view of Iyer does not teach training, via machine learning, a conversion model for converting a new input unstructured text string into a structured data record by identifying at least one structural data element from the new input unstructured text string, based on: one or more previously trained language models possessing knowledge related to language understanding, and training data that only includes the plurality of unstructured text strings and the identified one or more structural elements associated with the at least some of the plurality of unstructured text strings, wherein the identified one or more structural elements serve as ground truth to train the conversion model, wherein the trained conversion model has the knowledge possessed by the trained language models and learns to convert the new input unstructured text string into the structured data record based on the training data.
In the same field of endeavor Awadalla teaches improved systems and methods for generating training data and training models for natural language understanding while maintaining a preferred level of data security associated with the training data, see par. [0008]. Awadalla teaches the training engine 153 is configured to train a model via supervised training (e.g., using annotated data as ground truth). The training engine 153 is beneficially configured to be able to use training data that has not been annotated by a human reader/annotator such that the data security (i.e., privacy) of the training data used is maintained, see par. [0047]. In some embodiments, the training engine 153 is configured to train a model (e.g., source model 144) on an unannotated set of training data comprising unstructured (i.e., unannotated) natural language such that the machine learning model is configured to understand a semantic structure of the unannotated set of training data (structural data). In such embodiments, the training engine is able to train the model via unsupervised training, see par. [0048]. Because of the dual training and subsequent coupling of the models, the coupled machine learning model 220 is able to generalize patterns that it previously learned to understand, encode and decode new source instances that it encounters, see par. [0075]. In some embodiments, a previously learned aligned word embedding (e.g., “C” for Woodbridge) is used in subsequent alignments, see par. [0090].
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy in view of Iyer invention with the teachings of Awadalla for the benefit of improving systems and methods for generating training data and training models for natural language understanding while maintaining a preferred level of data security associated with the training data, see par. [0008].
Regarding claim 17 Sirangimoorthy teaches the system of claim 15, wherein the structured data record is a fixed length with a pre-determined number of structural data element; or a variable length with a variable number of structural data element (Text may be concatenated into a series of flattened text elements, and each text element may be associated with a location in an array of text locations for content in the plaintext file, see par. [0029]).
Regarding claim 18 Sirangimoorthy teaches the system of claim 15, wherein a structural data element is one of an entity, a product, a brand, a manufacturer, a feature (the selected element 422 is associated with information located at page 11 of the unstructured document displayed in first pane 410, references a “Lifetime Limit Individual” property of the plan(s) described in the unstructured document, and has a value of “$2,500”, see par. [0048]), and a sentiment, extracted from the unstructured text string.
Regarding claim 19 Sirangimoorthy teaches the system of claim 15, wherein the structured data generation engine is further configured for:
receiving the input unstructured text string (unstructured document received for analysis, see par. [0027]);
accessing the conversion model (document converter 142 generates one or more intermediate documents from an unstructured document received for analysis, see par. [0027]);
extracting, based on the conversion model, one or more structural data elements from the input unstructured text string (Generally, the plaintext file may include structured text including text content from tables or other organizational structures parsed from the unstructured document, see par. [0028]);
and generating the structured data record based on the one or more structural data elements (Structured document generator 144 uses the intermediate document(s) generated by document converter 142 to generate a structured document including relevant information in the unstructured document, see par. [0032]).
Regarding claim 20 Sirangimoorthy teaches the system of claim 19, wherein the structured data record further comprises at least one of the input unstructured text string and a data element inferred based on the one or more structural data elements (Generally, the plaintext file may include structured text including text content from tables or other organizational structures parsed from the unstructured document, see par. [0028]).
Claim(s) 2, 5, 9, 12 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sirangimoorthy U.S. PAP 2021/0248153 A1, in view of Iyer U.S. PAP 2020/0159855, in view of Awadalla U.S. PAP 2022/0050955 A1, further in view of Kannan U.S. PAP 2011/0289026 A1.
Regarding claim 2 Sirangimoorthy in view of Iyer does not teach the method of claim 1, wherein each of the unstructured text string corresponds to a description related to a product; and the structured data record converted from an unstructured text string describing the product includes at least one of a product name, a brand name for the product, and one or more features of the product.
IN a similar field of endeavor Kannan teaches a method and apparatus for electronically matching an electronic offer to structured data for a product offering, see abstract. This allows vendors to easily match descriptions to stored structured data from unstructured data, see par. [0002].
wherein each of the unstructured text string corresponds to a description related to a product (Attributes in unstructured text may be determined. The unstructured text may be submitted to a parsing system where the parsing system parses the unstructured text, see par. [0005]; Unstructured data 400 may be data that comes from a vendor that describes a product for sale but is not in a structured formant, see par. [0018]);
and the structured data record converted from an unstructured text string describing the product includes at least one of a product name, a brand name for the product, and one or more features of the product (The structured record 300 may be created from the product specifications provided by a reputed supplier of information on consumer electronic goods and may be added to the category Digital camera by a classifier used for this purpose, see par. [0018]).
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy in view of Iyer invention with the teachings of Kannan for the benefit of allowing vendors to easily match descriptions to stored structured data from unstructured data, see par. [00002].
Regarding claim 5 Sirangimoorthy in view of Iyer does not teach the method of claim 4, wherein a structural data element is further an inference derived based on content of the unstructured text string.
In the same field of endeavor Kannan teaches a matching function may be defined over a set of automatically selected attributes. It may evaluate the similarity between the semantic inference of unstructured texts and entities, while taking into account both the relative importance of the attributes and the difference between missing values and mismatched values, see par. [0022].
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy in view of Iyer invention with the matching function aught by Kannan for the benefit of using semantic inference to understand the unstructured text while using a small number of matched examples to learn the function, see par. [0022].
Regarding claim 9 Sirangimoorthy in view of Iyer does not teach the medium of claim 8, wherein each of the unstructured text string corresponds to a description related to a product; and the structured data record converted from an unstructured text string describing the product includes at least one of a product name, a brand name for the product, and one or more features of the product.
IN a similar field of endeavor Kannan teaches a method and apparatus for electronically matching an electronic offer to structured data for a product offering, see abstract. This allows vendors to easily match descriptions to stored structured data from unstructured data, see par. [0002].
wherein each of the unstructured text string corresponds to a description related to a product (Attributes in unstructured text may be determined. The unstructured text may be submitted to a parsing system where the parsing system parses the unstructured text, see par. [0005]; Unstructured data 400 may be data that comes from a vendor that describes a product for sale but is not in a structured formant, see par. [0018]);
and the structured data record converted from an unstructured text string describing the product includes at least one of a product name, a brand name for the product, and one or more features of the product (The structured record 300 may be created from the product specifications provided by a reputed supplier of information on consumer electronic goods and may be added to the category Digital camera by a classifier used for this purpose, see par. [0018]).
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy in view of Iyer invention with the teachings of Kannan for the benefit of allowing vendors to easily match descriptions to stored structured data from unstructured data, see par. [00002].
Regarding claim 12 Sirangimoorthy in view of Iyer does not teach the medium of claim 11, wherein a structural data element is further an inference derived based on content of the unstructured text string.
In the same field of endeavor Kannan teaches a matching function may be defined over a set of automatically selected attributes. It may evaluate the similarity between the semantic inference of unstructured texts and entities, while taking into account both the relative importance of the attributes and the difference between missing values and mismatched values, see par. [0022].
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy in view of Iyer invention with the matching function aught by Kannan for the benefit of using semantic inference to understand the unstructured text while using a small number of matched examples to learn the function, see par. [0022].
Regarding claim 16 Sirangimoorthy in view of Iyer does not teach the system of claim 15, wherein each of the unstructured text string corresponds to a description related to a product; and the structured data record converted from an unstructured text string describing the product includes at least one of a product name, a brand name for the product, and one or more features of the product.
IN a similar field of endeavor Kannan teaches a method and apparatus for electronically matching an electronic offer to structured data for a product offering, see abstract. This allows vendors to easily match descriptions to stored structured data from unstructured data, see par. [0002].
wherein each of the unstructured text string corresponds to a description related to a product (Attributes in unstructured text may be determined. The unstructured text may be submitted to a parsing system where the parsing system parses the unstructured text, see par. [0005]; Unstructured data 400 may be data that comes from a vendor that describes a product for sale but is not in a structured formant, see par. [0018]);
and the structured data record converted from an unstructured text string describing the product includes at least one of a product name, a brand name for the product, and one or more features of the product (The structured record 300 may be created from the product specifications provided by a reputed supplier of information on consumer electronic goods and may be added to the category Digital camera by a classifier used for this purpose, see par. [0018]).
It would have been obvious to one of ordinary skill in the art to combine the Sirangimoorthy in view of Iyer invention with the teachings of Kannan for the benefit of allowing vendors to easily match descriptions to stored structured data from unstructured data, see par. [00002].
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 Michael Ortiz-Sanchez whose telephone number is (571)270-3711. The examiner can normally be reached Monday- Friday 9AM-6PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL ORTIZ-SANCHEZ/Primary Examiner, Art Unit 2656
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