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 .
DETAILED ACTION
This Office Action corresponds to application 19/203,348 which was filed on 05/09/2025 and claims priority of INDIA 202441036759 filed 05/09/2024 and INDIA 202441036745 filed 05/09/2024.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/16/2026 has been entered.
Response to Amendment
In the reply filed 1/16/2026, claims 1, 4, 11, 14, and 19 have been amended. No additional claims have been added or canceled. Accordingly claims 1-20 stand pending.
Response to Arguments
Applicant's arguments filed 1/16/2026 have been fully considered but are moot in view of new grounds of rejection.
The applicant argues that the cited references do not teach “determining whether a respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing”. The examiner respectfully disagrees. Rom teaches, in paragraphs 38-44 and 148-151, determining at least one difference from the incoming file and the standard schema and using that for the mapping and when importing data, determining if there is no data for an imported field. Garera teaches, in paragraphs 27-29, 40-44, and 55-57, identifying attribute-value pairs in the import records that do not map to the attribute labels in the native schema. Therefore, the examiner is not persuaded.
The applicant also argues that the cited references do not teach “in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing, determining whether the respective one of the text-based fields does not match to any of the normalized text-based data samples for the third-party marketplace”. The examiner respectfully disagrees. Rom teaches, in paragraphs 38-44 and 148-151, determining at least one difference from the incoming file and the standard schema and using that for the mapping and when importing data, determining if there is no data for an imported field, and determining if there is no default value for the specific fields of the template after it is determined that there is no imported field for that field. Garera teaches, in paragraphs 27-29, 40-44, and 55-57, identifying attribute-value pairs in the import records that do not map to the attribute labels in the native schema. Since the native schema and product templates may be used by a third party marketplace, determining that the fields of the imported records that do not map to fields in the native schema is interpreted as also determining that they do not map to text-based samples for the third-party marketplace, e.g., in response to the determining. Therefore, the examiner is not persuaded.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 recites the limitation “the normalized text-based data samples for the third-party marketplace" in claims 1, 11, and 19. While those claim introduce normalized text-based data samples that pertain to the product listing, it does not introduce normalized text-based data samples for the third-party marketplace. There is insufficient antecedent basis for this limitation in the claim.
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) 1-7, 10-16, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rom et al. (US2005/0193029, previously presented in ‘892), hereinafter Rom, in view of Garera et al. (US2014/0358931, previously presented in ‘892), hereinafter Garera, and Llaurency (US2009/0201945).
Regarding Claim 1:
Rom teaches:
A computer-implemented method for providing data syndication across multiple platforms, the method comprising: receiving, from a user of a data syndication service, a request to import a product listing onto a webpage of a third-party marketplace (Rom, figures 1 and 5, [0012-0014, 0030, 0034, 0062, 0105], note importing data, such as product data (e.g., product listings), to normalize/standardize/syndicate for marketplaces);
retrieving, via a management portal of the third-party marketplace, a template of text-based fields specific to the third-party marketplace, wherein the template of text-based fields specific to the third-party marketplace provides an indication of text-based fields to be completed prior to importing the product listing onto the webpage of the third-party marketplace (Rom, figures 1, 5-8 and 18, [0038-0044, 0062-0064, 0104, 0113-0114, 0150-0157], note providing language and structure infrastructure including a common language for defining a standard schema and a standard database structure defined using the standard schema using the common language; note the desired schema and mandatory user-defined fields are interpreted as an indication of the fields to be completed to import the data; note schemas and mapping templates; note selecting a template that specifies fields, including text-based fields, specific to a user’s rich-content repository; note a third party can host a rich-content repository, e.g., a third-party marketplace; note that the template specifies mandatory fields, e.g., an indication of text-based fields to be completed prior to importation);
retrieving, from an internal data storage system of the data syndication service, normalized text-based data samples that pertain to the product listing, wherein the normalized text-based data samples comprise marketplace-agnostic labels and wherein at least one marketplace-agnostic label corresponds to at least one specific text-based field of at least one of (i) the template of text-based field specific to the third-party marketplace and (ii) one or more other templates of text-based fields specific to one or more other third-party marketplaces (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, e.g., normalized text-based data samples that pertain to the product listing, to the desired fields, which means the data comprises marketplace-agnostic labels; note the mapping may be for a third party repository, e.g., a third party marketplace),
generating an initial mapping between respective ones of the text-based fields and respective ones of the normalized text-based data samples (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note mapping the imported file to the desired schema and user defined fields) wherein the generating the initial mapping between the respective ones of the text-based fields and the respective ones of the normalized text-based data samples comprises: determining whether a respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing (Rom, figures 1, 5-7, and 17-18, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema and using that for the mapping; note determining if there is no data for an imported field);
in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing, determining whether the respective one of the text-based fields does not match to any of the normalized text-based data samples for the third-party marketplace (Rom, figures 1, 5-7, and 17-18, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema; note determining if there is no default value for the specific fields of the template; note determining the default value after it is determined that there is no imported field for that field), and
determining whether a given text-based field does not match any of the normalized text-based data samples (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema);
in response to a determination that the given text-based fields does not match any of the normalized text-based data samples, generating an additional mapping between a given normalized text-based data sample and the given text-based field (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note when a mandatory mapping is not defined and no default value is provided the item is placed on a rework list for the user to take action and correct, such as providing an additional mapping);
in response to generating the additional mapping: providing the initial mapping and the additional mapping to the user (Rom, figure 1, [0151, 0164], note all modifications to the data are submitted for approval); and
responsive to receiving a confirmation from the user regarding an inclusion of the additional mapping to complete the text-based fields, providing the initial mapping and the additional mapping to the management portal for importation of the product listing onto the webpage of the third-party marketplace (Rom, figure 1, [0012-0014, 0030, 0034, 0062, 0151, 0164], note after approval the data is committed to the rich-content repository; note the rich-content repository is used for webpages of third-party marketplaces).
While Rom teaches importing data using desired schemas, Rom doesn’t specifically teach in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples, determining whether the respective one of the text-based fields matches to one of the normalized text-based data samples for another third-party marketplace; and using natural language processing to generate additional mappings. However, Garera is in the same field of endeavor, data management, and Garera teaches:
A computer-implemented method for providing data syndication across multiple platforms, the method comprising: receiving, from a user of a data syndication service, a request to import a product listing onto a webpage of a third-party marketplace (Garera, abstract, figures 1 and 3-5, [0027-0029, 0058], note generating normalized records from import records of product listings is interpreted as a request to import product listings; note the normalized records may be used the same way native records are used; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace);
retrieving, via a management portal of the third-party marketplace, a template of text-based fields specific to the third-party marketplace, wherein the template of text-based fields specific to the third-party marketplace provides an indication of text-based fields to be completed prior to importing the product listing onto the webpage of the third-party marketplace (Garera, abstract, figures 1 and 3, [0027-0029, 0040-0045, 0058, 0064, 0068-0069], note relating attribute labels of the import records to the attribute labels in a native schema. The native schema is interpreted as the indication of text-based fields to be completed prior to importing the product listing; note the native schema may comprise a product template for a product type and classifying an import record as a product type is interpreted as retrieving a template of text-based fields; note the product templates may be used by third party marketplaces and when combined with the previous references would be used by the marketplace as taught by Rom);
retrieving, from an internal data storage system of the data syndication service, normalized text-based data samples that pertain to the product listing, wherein the normalized text-based data samples comprise marketplace-agnostic labels and wherein at least one marketplace-agnostic label corresponds to at least one specific text-based field of at least one of (i) the template of text-based field specific to the third-party marketplace and (ii) one or more other templates of text-based fields specific to one or more other third-party marketplaces (Garera, abstract, figures 3-5 and 7-8, [0027-0029, 0040-0046, 0064, 0068-0069], note classifying import records per taxonomies, such as a product type in the taxonomy; note the attribute labels may be text-based fields; note comparing the native schema of that product type to the import record, which is interpreted as retrieving normalized text-based data samples that pertain to the product listing; note the native schema comprise marketplace-agnostic labels since they are for a product, such as a product type/category; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace; note a template for a product type or category may be updated based on the imported records and native records which means the marketplace-agnostic labels correspond to a text-based field of a template; note the product templates may be used by third party marketplaces and when combined with the previous references would be used by the marketplace as taught by Rom);
generating an initial mapping between respective ones of the text-based fields and respective ones of the normalized text-based data samples (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels of the import records to the native schema), wherein the generating the initial mapping between the respective ones of the text-based fields and the respective ones of the normalized text-based data samples comprises: determining whether a respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing (Garera, abstract, figures 1 and 3-5, [0027-0029, 0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema);
in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing, determining whether the respective one of the text-based fields does not match to any of the normalized text-based data samples for the third-party marketplace (Garera, abstract, figures 1 and 3-5, [0027-0029, 0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema. Since the native schema and product templates may be used by a third party marketplace, determining that the fields of the imported records that do not map to fields in the native schema is interpreted as also determining that they do not map to text-based samples for the third-party marketplace, e.g., in response to the determining), and
determining whether the respective one of the text-based fields matches to one of the normalized text-based data samples for another third-party marketplace (Garera, abstract, figures 1 and 6-7, [0040-0043, 0062-0064], note mapping attribute labels/attribute-value pairs of the import records to the native schema; note identifying product attributes may be identified in any schema, dictionary, or other reference corpus, which is interpreted to mean another third-party marketplace);
determining whether a given text-based field does not match any of the normalized text-based data samples (Garera, abstract, figures 1 and 3-5, [0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema);
in response to a determination that the given text-based fields does not match any of the normalized text-based data samples, generating, via natural language processing, an additional mapping between a given normalized text-based data sample and the given text-based field (Garera, abstract, figures 1 and 3-5, [0040-0045, 0055-0057], note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note determining overlap between the unmapped values and the native values to propose a normalization rule is interpreted as a form of natural language processing);
in response to generating the additional mapping: providing the initial mapping and the additional mapping to the user (Garera, figures 3-5, [0048], note providing the mappings to a crowdsourcing forum, e.g., the user); and
responsive to receiving a confirmation from the user regarding an inclusion of the additional mapping to complete the text-based fields, providing the initial mapping and the additional mapping to the management portal for importation of the product listing onto the webpage of the third-party marketplace (Garera, figures 3-5, [0027-0029, 0048-0058], note responsive to the mapping being validated, adding the mapping rule to the rule set; note applying the normalized rules to the import record to generate a normalized record; note the normalized records may be used the same way native records are used; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
While Rom as modified teaches importing data using desired schemas from multiple sources, Rom as modified doesn’t specifically teach using another third party template is in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples. However, Llaurency is in the same field of endeavor, data management, and Llaurency teaches:
in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples, determining whether the respective one of the text-based fields matches to one of the normalized text-based data samples for another third-party marketplace (Llaurency, figure 5 and 8, abstract, 0040-0041, note comparing the first data structure to multiple templates to find a match. When combined with the previously cited references, this would be for the mapping and the additional third-party marketplace templates as taught by Rom and Garera).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Llaurency because all references are directed towards data management and because Llaurency would expand upon the teachings of the previously cited references in data integration which would improve the usability by effectively handling various data structures adapted for distinct systems (Llaurency, abstract, [0009]).
Regarding Claim 2:
Rom as modified shows the method as disclosed above;
Rom as modified further teaches:
wherein the generating the initial mapping between the respective ones of the text-based fields and the respective ones of the normalized text-based data samples comprises: determining that a given one of the text-based fields matches to a given one of the normalized text-based data samples for the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, e.g., normalized text-based data samples that pertain to the product listing, to the desired fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema); and
providing, in the initial mapping, an indication of a match score of one-hundred percent for the given one of the text-based fields that matches to the given one of the normalized text-based data samples for the product listing (Rom, figure 1, [0050, 0151, 0106, 0164], note pattern matching rules; note validation ensures the data matches which is interpreted as an indication of a match score of one-hundred percent) (samples (Garera, abstract, figures 1 and 3-5, [0040-0043, 0050], note mapping attribute labels of the import records to the native schema; note a validated threshold percent may be one-hundred percent, which is interpreted to mean a validated mapping is an indication of a match score of one-hundred percent).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 3:
Rom as modified shows the method as disclosed above;
Rom as modified further teaches:
wherein the generating the initial mapping between the respective ones of the text-based fields and the respective ones of the normalized text-based data samples comprises: determining that a given one of the text-based fields does not match to any of the normalized text-based data samples for the product listing (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema) (Garera, abstract, figures 1 and 3-5, [0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema);
determining that the given one of the text-based fields matches to a given one of the normalized text-based data samples for the third-party marketplace (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note mapping the imported file to the desired schema and user defined fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels of the import records to the native schema); and
providing, in the initial mapping, an indication of a match score of less than one- hundred percent for the given one of the text-based fields that matches to the given one of the normalized text-based data samples for the third-party marketplace (Garera, abstract, figures 1 and 3-5, [0040-0043, 0050], note mapping attribute labels of the import records to the native schema; note a validated threshold percent may be less than one-hundred percent, which is interpreted to mean a use case of a validated mapping as an indication of a match score of less than one-hundred percent).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 4:
Rom as modified shows the method as disclosed above;
Rom as modified further teaches:
providing, in the initial mapping, an indication of a match score of less than one- hundred percent for the given one of the text-based fields that matches to the given one of the normalized text-based data samples for the other third-party marketplace (Garera, abstract, figures 1 and 7-9, [0040-0043, 0050, 0062-0064, 0084], note mapping attribute labels of the import records to the native schema; note a validated threshold percent may be less than one-hundred percent, which is interepted to mean a use case of a validated mapping as an indication of a match score of less than one-hundred percent; note attribute-value pairs with scores above a threshold may be selected for mapping, which is also interpreted as an indication of a match score).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 5:
Rom as modified shows the method as disclosed above;
Rom as modified further teaches:
wherein the determining that the given text-based field does not match any of the normalized text-based data samples comprises: determining that a given one of the text-based fields does not match to any of the normalized text-based data samples for the product listing (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema) (Garera, abstract, figures 1 and 3-5, [0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema);
determining that the given one of the text-based fields does not match to any of the normalized text-based data samples for the third-party marketplace (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema) (Garera, abstract, figures 1 and 3-5, [0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema); and
determining that the given one of the text-based fields does not match to any of the normalized text-based data samples for another third-party marketplace (Garera, abstract, figures 1 and 6-7, [0040-0043, 0062-0064], note identifying product attributes may be identified in any schema, dictionary, or other reference corpus, which is interpreted to mean another third-party marketplace; note scoring those attributes and the ones below a threshold are not selected, e.g., determined to not be a match).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 6:
Rom as modified shows the method as disclosed above;
Rom as modified further teaches:
responsive to receiving the confirmation from the user regarding the inclusion of the additional mapping to complete the text-based fields, causing the additional mapping to be stored in the internal data storage system of the data syndication service (Rom, figure 1, [0012-0014, 0030, 0034, 0062, 0151, 0164], note after approval the data is committed to the rich-content repository; note the rich-content repository is used for webpages of third-party marketplaces) (Garera, figures 3-5, [0027-0029, 0048-0058], note responsive to the mapping being validated, adding the mapping rule to the rule set; note applying the normalized rules to the import record to generate a normalized record; note the normalized records may be used the same way native records are used; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 7:
Rom as modified shows the method as disclosed above;
Rom as modified further teaches:
responsive to receiving the confirmation from the user regarding the inclusion of the additional mapping to complete the text-based fields, determining that the additional mapping relates to an existing one of the normalized text-based fields (Garera, figures 3-5, [0027-0029, 0048-0058], note responsive to the mapping being validated, adding the mapping rule to the rule set; note applying the normalized rules to the import record to generate a normalized record, applying the new normalized rule is interpreted as determining that the additional mapping relates to an existing one of the normalized text-based fields; note the normalized records may be used the same way native records are used; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace); and
causing the existing one of the normalized text-based fields to be updated according to the additional mapping (Garera, figures 3-5, [0027-0029, 0048-0058], note responsive to the mapping being validated, adding the mapping rule to the rule set; note applying the normalized rules to the import record to generate a normalized record, applying the new normalized rule is interpreted as determining that the additional mapping relates to an existing one of the normalized text-based fields; note the normalized records may be used the same way native records are used; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 10:
Rom as modified shows the method as disclosed above;
Rom as modified further teaches:
at a moment in time after the importation of the product listing onto the webpage of the third-party marketplace, receiving, via the webpage of the third-party marketplace, a first set of text-based data samples that pertain to the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, which means a first and second set of samples have been retrieved) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema, which means a first and second set of samples have been retrieved);
receiving, via the management portal, a second set of text-based data samples that pertain to the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, which means a first and second set of samples have been retrieved) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema, which means a first and second set of samples have been retrieved);
retrieving, from the internal data storage system, a third set of text-based data samples that pertain to the product listing (Garera, abstract, figures 1 and 6-7, [0040-0043, 0062-0064], note identifying product attributes may be identified in any schema, dictionary, or other reference corpus, which is interpreted to mean another set of samples pertaining to the product listing were retrieved);
extracting attributes from the first, second, and third sets of text-based data samples based on predetermined comparison criteria (Garera, abstract, figures 1 and 6- 9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note attribute extraction from product sources; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement);
comparing the attributes with respect to one another and outputting additional binary results based, at least in part, on agreement, or disagreement, of the respective ones of the attributes (Garera, abstract, figures 1 and 6- 9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note attribute extraction from product sources; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement); and
executing another re-importation of the product listing onto the webpage of the third-party marketplace, based on at least one disagreement of the compared attributes (Garera, abstract, figures 1, 4, and 6-9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement. When combined with the binary hashing as taught by Liu below, this would be for image binary hashes) (Rom, figure 1, [0012-0014, 0030, 0034, 0062, 0151, 0164], note after approval the data is committed to the rich-content repository, which means a disagreement would result in executing another re-importation for that data; note the rich-content repository is used for webpages of third-party marketplaces. When combined with the binary hashing as taught by Liu below, this would be for image binary hashes).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 11:
Rom teaches:
A data syndication system, comprising: a computing device configured to implement a data syndication service, wherein, to implement the data syndication service, the computing device is further configured to: receive, from a user of the data syndication service, a request to import a product listing onto a webpage of a third-party marketplace (Rom, figures 1 and 5, [0012-0014, 0030, 0034, 0062, 0105], note importing data, such as product data (e.g., product listings), to normalize/standardize/syndicate for marketplaces);
retrieve, via an Application Programming Interface (API), a template of text-based fields specific to the third-party marketplace, wherein the template of text-based fields specific to the third-party marketplace provides an indication of text-based fields to be completed prior to importing the product listing onto a webpage of a third-party marketplace (Rom, figures 1, 5-8 and 18, [0038-0044, 0062-0064, 0104, 0113-0114, 0150-0157], note providing language and structure infrastructure including a common language for defining a standard schema and a standard database structure defined using the standard schema using the common language; note the desired schema and mandatory user-defined fields are interpreted as an indication of the fields to be completed to import the data; note schemas and mapping templates; note selecting a template that specifies fields, including text-based fields, specific to a user’s rich-content repository; note a third party can host a rich-content repository, e.g., a third-party marketplace; note that the template specifies mandatory fields, e.g., an indication of text-based fields to be completed prior to importation);
retrieve, from a Product Information Management and Digital Asset Management (PIM-DAM) service of the data syndication service, normalized text- based data samples that pertain to the product listing, wherein the normalized text-based data samples comprise marketplace-agnostic labels and wherein at least one marketplace-agnostic label corresponds to at least one specific text-based field of at least one of (i) the template of text-based field specific to the third-party marketplace and (ii) one or more other templates of text-based fields specific to one or more other third-party marketplaces (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, e.g., normalized text-based data samples that pertain to the product listing, to the desired fields, which means the data comprises marketplace-agnostic labels; note the mapping may be for a third party repository, e.g., a third party marketplace);
generate an initial mapping between respective ones of the text-based fields and respective ones of the normalized text-based data samples (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note mapping the imported file to the desired schema and user defined fields), wherein the generating the initial mapping between the respective ones of the text-based fields and the respective ones of the normalized text-based data samples comprises: determining whether a respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing (Rom, figures 1, 5-7, and 17-18, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema and using that for the mapping; note determining if there is no data for an imported field);
in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing, determining whether the respective one of the text-based fields does not match to any of the normalized text-based data samples for the third-party marketplace (Rom, figures 1, 5-7, and 17-18, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema; note determining if there is no default value for the specific fields of the template; note determining the default value after it is determined that there is no imported field for that field), and
determine whether a given text-based field does not match any of the normalized text-based data samples (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema);
in response to a determination that the given text-based field does not match any of the normalized text-based data samples, generate an additional mapping between a given normalized text-based data sample and the given text-based field (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note when a mandatory mapping is not defined and no default value is provided the item is placed on a rework list for the user to take action and correct, such as providing an additional mapping);
in response to generating the additional mapping: provide, via a user interface of the PIM-DAM service, the initial mapping and the additional mapping to the user (Rom, figure 1, [0151, 0164], note all modifications to the data are submitted for approval); and
responsive to reception of a confirmation from the user regarding an inclusion of the additional mapping to complete the text-based fields, provide, via the API, the initial mapping and the additional mapping for importation of the product listing onto the webpage of the third-party marketplace (Rom, figure 1, [0012-0014, 0030, 0034, 0062, 0151, 0164], note after approval the data is committed to the rich-content repository; note the rich-content repository is used for webpages of third-party marketplaces); and
a database configured to implement the PIM-DAM service (Rom, figure 1A, note data syndication and databases).
While Rom teaches importing data using desired schemas, Rom doesn’t specifically teach in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples, determining whether the respective one of the text-based fields matches to one of the normalized text-based data samples for another third-party marketplace; and using natural language processing to generate additional mappings. However, Garera is in the same field of endeavor, data management, and Garera teaches:
A data syndication system, comprising: a computing device configured to implement a data syndication service, wherein, to implement the data syndication service, the computing device is further configured to: receive, from a user of the data syndication service, a request to import a product listing onto a webpage of a third-party marketplace (Garera, abstract, figures 1 and 3-5, [0027-0029, 0058], note generating normalized records from import records of product listings is interpreted as a request to import product listings; note the normalized records may be used the same way native records are used; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace);
retrieve, via an Application Programming Interface (API), a template of text-based fields specific to the third-party marketplace, wherein the template of text-based fields specific to the third-party marketplace provides an indication of text-based fields to be completed prior to importing the product listing onto a webpage of a third-party marketplace (Garera, abstract, figures 1 and 3, [0027-0029, 0040-0045, 0058, 0064, 0068-0069], note relating attribute labels of the import records to the attribute labels in a native schema. The native schema is interpreted as the indication of text-based fields to be completed prior to importing the product listing; note server system or some other system providing an interface to the service system, e.g., an API, may execute the methods; note the native schema may comprise a product template for a product type and classifying an import record as a product type is interpreted as retrieving a template of text-based fields; note the product templates may be used by third party marketplaces and when combined with the previous references would be used by the marketplace as taught by Rom);
retrieve, from a Product Information Management and Digital Asset Management (PIM-DAM) service of the data syndication service, normalized text- based data samples that pertain to the product listing, wherein the normalized text-based data samples comprise marketplace-agnostic labels and wherein at least one marketplace-agnostic label corresponds to at least one specific text-based field of at least one of (i) the template of text-based field specific to the third-party marketplace and (ii) one or more other templates of text-based fields specific to one or more other third-party marketplaces (Garera, abstract, figures 3-5 and 7-8, [0027-0029, 0040-0046, 0064, 0068-0069], note classifying import records per taxonomies, such as a product type in the taxonomy; note the attribute labels may be text-based fields; note comparing the native schema of that product type to the import record, which is interpreted as retrieving normalized text-based data samples that pertain to the product listing; note the native schema comprise marketplace-agnostic labels since they are for a product, such as a product type/category; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace; note a template for a product type or category may be updated based on the imported records and native records which means the marketplace-agnostic labels correspond to a text-based field of a template; note the product templates may be used by third party marketplaces and when combined with the previous references would be used by the marketplace as taught by Rom);
generate an initial mapping between respective ones of the text-based fields and respective ones of the normalized text-based data samples (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels of the import records to the native schema), wherein the generating the initial mapping between the respective ones of the text-based fields and the respective ones of the normalized text-based data samples comprises: determining whether a respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing (Garera, abstract, figures 1 and 3-5, [0027-0029, 0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema);
in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples for the product listing, determining whether the respective one of the text-based fields does not match to any of the normalized text-based data samples for the third-party marketplace (Garera, abstract, figures 1 and 3-5, [0027-0029, 0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema. Since the native schema and product templates may be used by a third party marketplace, determining that the fields of the imported records that do not map to fields in the native schema is interpreted as also determining that they do not map to text-based samples for the third-party marketplace, e.g., in response to the determining), and
determining whether the respective one of the text-based fields matches to one of the normalized text-based data samples for another third-party marketplace (Garera, abstract, figures 1 and 6-7, [0040-0043, 0062-0064], note mapping attribute labels/attribute-value pairs of the import records to the native schema; note identifying product attributes may be identified in any schema, dictionary, or other reference corpus, which is interpreted to mean another third-party marketplace);
determine whether a given text-based field does not match any of the normalized text-based data samples (Garera, abstract, figures 1 and 3-5, [0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema);
in response to a determination that the given text-based field does not match any of the normalized text-based data samples, generate, via natural language processing, an additional mapping between a given normalized text-based data sample and the given text-based field (Garera, abstract, figures 1 and 3-5, [0040-0045, 0055-0057], note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note determining overlap between the unmapped values and the native values to propose a normalization rule is interpreted as a form of natural language processing);
in response to generating the additional mapping: provide, via a user interface of the PIM-DAM service, the initial mapping and the additional mapping to the user (Garera, figures 3-5, [0048], note providing the mappings to a crowdsourcing forum, e.g., the user); and
responsive to reception of a confirmation from the user regarding an inclusion of the additional mapping to complete the text-based fields, provide, via the API, the initial mapping and the additional mapping for importation of the product listing onto the webpage of the third-party marketplace (Garera, figures 3-5, [0027-0029, 0048-0058], note responsive to the mapping being validated, adding the mapping rule to the rule set; note applying the normalized rules to the import record to generate a normalized record; note the normalized records may be used the same way native records are used; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace); and
a database configured to implement the PIM-DAM service (Garera, figures 1-2, note databases and memory).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
While Rom as modified teaches importing data using desired schemas from multiple sources, Rom as modified doesn’t specifically teach using another third party template is in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples. However, Llaurency is in the same field of endeavor, data management, and Llaurency teaches:
in response to a determination that the respective one of the text-based fields does not match to any of the normalized text-based data samples, determining whether the respective one of the text-based fields matches to one of the normalized text-based data samples for another third-party marketplace (Llaurency, figure 5 and 8, abstract, 0040-0041, note comparing the first data structure to multiple templates to find a match. When combined with the previously cited references, this would be for the mapping and the additional third-party marketplace templates as taught by Rom and Garera).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Llaurency because all references are directed towards data management and because Llaurency would expand upon the teachings of the previously cited references in data integration which would improve the usability by effectively handling various data structures adapted for distinct systems (Llaurency, abstract, [0009]).
Regarding Claim 12:
Rom as modified shows the system as disclosed above;
Rom as modified further teaches:
wherein, to generate the initial mapping between the respective ones of the text-based fields and the respective ones of the normalized text-based data samples, the computing device is further configured to: determine that a given one of the text-based fields matches to a given one of the normalized text-based data samples for the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, e.g., normalized text-based data samples that pertain to the product listing, to the desired fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema); and
provide, in the initial mapping, an indication of a match score of one-hundred percent for the given one of the text-based fields that matches to the given one of the normalized text-based data samples for the product listing (Rom, figure 1, [0050, 0151, 0106, 0164], note pattern matching rules; note validation ensures the data matches which is interpreted as an indication of a match score of one-hundred percent) (samples (Garera, abstract, figures 1 and 3-5, [0040-0043, 0050], note mapping attribute labels of the import records to the native schema; note a validated threshold percent may be one-hundred percent, which is interepted to mean a validated mapping is an indication of a match score of one-hundred percent).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 13:
Rom as modified shows the system as disclosed above;
Rom as modified further teaches:
wherein, to generate the initial mapping between the respective ones of the text-based fields and the respective ones of the normalized text-based data samples, the computing device is further configured to: determine that a given one of the text-based fields does not match to any of the normalized text-based data samples for the product listing (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema) (Garera, abstract, figures 1 and 3-5, [0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema);
determine that the given one of the text-based fields matches to a given one of the normalized text-based data samples for the third-party marketplace (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note mapping the imported file to the desired schema and user defined fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels of the import records to the native schema); and
provide, in the initial mapping, an indication of a match score of less than one- hundred percent for the given one of the text-based fields that matches to the given one of the normalized text-based data samples for the third-party marketplace (Garera, abstract, figures 1 and 3-5, [0040-0043, 0050], note mapping attribute labels of the import records to the native schema; note a validated threshold percent may be less than one-hundred percent, which is interepted to mean a use case of a validated mapping as an indication of a match score of less than one-hundred percent).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 14:
Rom as modified shows the system as disclosed above;
Rom as modified further teaches:
provide, in the initial mapping, an indication of a match score of less than one- hundred percent for the given one of the text-based fields that matches to the given one of the normalized text-based data samples for the other third-party marketplace (Garera, abstract, figures 1 and 7-9, [0040-0043, 0050, 0062-0064, 0084], note mapping attribute labels of the import records to the native schema; note a validated threshold percent may be less than one-hundred percent, which is interepted to mean a use case of a validated mapping as an indication of a match score of less than one-hundred percent; note attribute-value pairs with scores above a threshold may be selected for mapping, which is also interpreted as an indication of a match score).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 15:
Rom as modified shows the system as disclosed above;
Rom as modified further teaches:
wherein, to determine that the given text-based field does not match any of the normalized text-based data samples, the computing device is further configured to: determine that a given one of the text-based fields does not match to any of the normalized text-based data samples for the product listing (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema) (Garera, abstract, figures 1 and 3-5, [0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema);
determine that the given one of the text-based fields does not match to any of the normalized text-based data samples for the third-party marketplace (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema) (Garera, abstract, figures 1 and 3-5, [0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema); and
determine that the given one of the text-based fields does not match to any of the normalized text-based data samples for another third-party marketplace (Garera, abstract, figures 1 and 6-7, [0040-0043, 0062-0064], note identifying product attributes may be identified in any schema, dictionary, or other reference corpus, which is interpreted to mean another third-party marketplace; note scoring those attributes and the ones below a threshold are not selected, e.g., determined to not be a match).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 16:
Rom as modified shows the system as disclosed above;
Rom as modified further teaches:
wherein the database is further configured to implement a data comparison service and a data resolution service (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note mapping the imported file to the desired schema and user defined fields; note determining at least one difference from the incoming file and the standard schema; note when a mandatory mapping is not defined and no default value is provided the item is placed on a rework list for the user to take action and correct, such as providing an additional mapping) (Garera, abstract, figures 1 and 3-5, [0040-0043, 0055-0057], note mapping attribute labels of the import records to the native schema; note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema; note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note determining overlap between the unmapped values and the native values to propose a normalization rule is interpreted as a form of natural language processing);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Regarding Claim 18:
Rom as modified shows the system as disclosed above;
Rom as modified further teaches:
wherein, to implement the data comparison service, the database is further configured to: at a moment in time after the importation of the product listing onto the webpage of the third-party marketplace, receive, via the webpage of the third-party marketplace, a first set of text-based data samples that pertain to the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, which means a first and second set of samples have been retrieved) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema, which means a first and second set of samples have been retrieved);
receiving, via the API, a second set of text-based data samples that pertain to the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, which means a first and second set of samples have been retrieved) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema, which means a first and second set of samples have been retrieved);
retrieve, from the PIM-DAM service, a third set of text-based data samples that pertain to the product listing (Garera, abstract, figures 1 and 6-7, [0040-0043, 0062-0064], note identifying product attributes may be identified in any schema, dictionary, or other reference corpus, which is interpreted to mean another set of samples pertaining to the product listing were retrieved);
extract attributes from the first, second, and third sets of text-based data samples based on predetermined comparison criteria (Garera, abstract, figures 1 and 6- 9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note attribute extraction from product sources; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement);
compare the attributes with respect to one another and outputting additional binary results based, at least in part, on agreement, or disagreement, of the respective ones of the attributes (Garera, abstract, figures 1 and 6- 9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note attribute extraction from product sources; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement); and
execute another re-importation of the product listing onto the webpage of the third-party marketplace, based on at least one disagreement of the compared attributes(Garera, abstract, figures 1, 4, and 6-9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement) (Rom, figure 1, [0012-0014, 0030, 0034, 0062, 0151, 0164], note after approval the data is committed to the rich-content repository, which means a disagreement would result in executing another re-importation for that data; note the rich-content repository is used for webpages of third-party marketplaces).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
Claim 19 discloses substantially the same limitations as claim 1 respectively, except claim 19 is directed to a non-transitory, computer-readable medium comprising at least one processor (Rom, claim 15, note processor) while claim 1 is directed to a method. Therefore claim 19 is rejected under the same rationale set forth for claim 1.
Claim Rejections - 35 USC § 103
Claim(s) 8 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rom in view of Garera, Llaurency, and Wu et al. (US2008/0313165, previously presented in ‘892), hereinafter Wu.
Regarding Claim 8:
Rom as modified shows the method as disclosed above;
Rom as modified further teaches:
retrieving, via the management portal of the third-party marketplace, another indication of image-based fields to be completed prior to the importing the product listing onto the webpage of the third-party marketplace (Rom, figures 1 and 5-6, [0038-0044, 0062, 0104, 0151], note providing language and structure infrastructure including a common language for defining a standard schema and a standard database structure defined using the standard schema using the common language; note the desired schema and mandatory user-defined fields are interpreted as an indication of the fields to be completed to import the data; note schemas and mapping templates. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, abstract, figures 1 and 3, [0040, 0058], note relating attribute labels of the import records to the attribute labels in a native schema. The native schema is interpreted as the indication of fields to be completed prior to importing the product listing. When combined with the product schema as taught by Wu below, this would be for image-based fields);
retrieving, from the internal data storage system, normalized image-based data samples that pertain to the product listing, wherein the normalized image-based data samples comprise marketplace-agnostic labels (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, e.g., normalized data samples that pertain to the product listing, to the desired fields, which means the data comprises marketplace-agnostic labels. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema, which is interpreted as retrieving normalized data that pertains to the product listing and they comprise marketplace-agnostic labels. When combined with the product schema as taught by Wu below, this would be for image-based fields);
generating an initial mapping between respective ones of the image-based fields and respective ones of the normalized image-based data samples (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note mapping the imported file to the desired schema and user defined fields. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels of the import records to the native schema. When combined with the product schema as taught by Wu below, this would be for image-based fields);
determining that a given image-based field does not match any of the normalized image-based data samples (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note determining at least one difference from the incoming file and the standard schema. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, abstract, figures 1 and 3-5, [0040-0044, 0055-0057], note identify attribute-value pairs in the import records that do not map to the attribute labels in the native schema. When combined with the product schema as taught by Wu below, this would be for image-based fields);
generating, via the natural language processing, another additional mapping between a given normalized image-based data sample and the given image-based field (Rom, figures 1 and 5-6, [0038-0044, 0148-0151], note when a mandatory mapping is not defined and no default value is provided the item is placed on a rework list for the user to take action and correct, such as providing an additional mapping. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, abstract, figures 1 and 3-5, [0040-0045, 0055-0057], note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note determining overlap between the unmapped values and the native values to propose a normalization rule is interpreted as a form of natural language processing. When combined with the product schema as taught by Wu below, this would be for image-based fields);
providing the initial mapping and the other additional mapping to the user (Rom, figure 1, [0151, 0164], note all modifications to the data are submitted for approval) (Garera, figures 3-5, [0048], note providing the mappings to a crowdsourcing forum, e.g., the user); and
responsive to receiving the confirmation from the user regarding the inclusion of the other additional mapping to complete the image-based fields, providing the initial mapping and the other additional mapping to the management portal for the importation of the product listing onto the webpage of the third-party marketplace (Rom, figure 1, [0012-0014, 0030, 0034, 0062, 0151, 0164], note after approval the data is committed to the rich-content repository; note the rich-content repository is used for webpages of third-party marketplaces. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, figures 3-5, [0027-0029, 0048-0058], note responsive to the mapping being validated, adding the mapping rule to the rule set; note applying the normalized rules to the import record to generate a normalized record; note the normalized records may be used the same way native records are used; note the native records may be used for a site hosted by a merchant to provide access to information about products, e.g., product listings on a third-party marketplace. When combined with the product schema as taught by Wu below, this would be for image-based fields).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
While Rom as modified teaches importing data using desired schemas and mapping product listings, Rom as modified doesn’t specifically teach image-based fields. However, Wu is in the same field of endeavor, data management, and Wu teaches:
retrieving, via the management portal of the third-party marketplace, another indication of image-based fields to be completed prior to the importing the product listing onto the webpage of the third-party marketplace (Wu, [0024], note the product authority includes an authoritative list of products and part of the schema for the products include image-based fields. When combined with the product data mapping as taught previously this would be for attribute/values/labels as taught by Rom and Garera);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Wu because all references are directed towards data management and because Wu would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of matching product listings by normalizing the product information to find a list of potentially matching products (Wu, [0002]).
Claim 20 discloses substantially the same limitations as claim 8 respectively, except claim 20 is directed to a non-transitory, computer-readable medium comprising at least one processor (Rom, claim 15, note processor) while claim 8 is directed to a method. Therefore claim 20 is rejected under the same rationale set forth for claim 8.
Claim Rejections - 35 USC § 103
Claim(s) 9 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rom in view of Garera, Llaurency, Wu, and Liu (US2020/0089808, previously presented in ‘892).
Regarding Claim 9:
Rom as modified shows the method as disclosed above;
Rom as modified further teaches:
at a moment in time after the importation of the product listing onto the webpage of the third-party marketplace, receiving, via the webpage of the third-party marketplace, a first set of image-based data samples that pertain to the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, which means a first and second set of samples have been retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema, which means a first and second set of samples have been retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields);
receiving, via the management portal, a second set of image-based data samples that pertain to the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, which means a first and second set of samples have been retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema, which means a first and second set of samples have been retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields);
retrieving, from the internal data storage system, a third set of image- based data samples that pertain to the product listing (Garera, abstract, figures 1 and 6-7, [0040-0043, 0062-0064], note identifying product attributes may be identified in any schema, dictionary, or other reference corpus, which is interpreted to mean another set of samples pertaining to the product listing were retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields);
comparing the binary hashes with respect to one another and outputting binary results based, at least in part, on agreement, or disagreement, of the binary hashes (Garera, abstract, figures 1 and 3-5, 9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement. When combined with the binary hashing as taught by Liu below, this would be for image binary hashes); and
executing another re-importation of the product listing onto the webpage of the third-party marketplace, based on at least one disagreement of the compared binary hashes (Garera, abstract, figures 1, 4, and 6-9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement. When combined with the binary hashing as taught by Liu below, this would be for image binary hashes) (Rom, figure 1, [0012-0014, 0030, 0034, 0062, 0151, 0164], note after approval the data is committed to the rich-content repository, which means a disagreement would result in executing another re-importation for that data; note the rich-content repository is used for webpages of third-party marketplaces. When combined with the binary hashing as taught by Liu below, this would be for image binary hashes).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
While Rom as modified teaches importing data using desired schemas and mapping product listings, Rom as modified doesn’t specifically teach image-based fields; generating binary hashes of the respective first, second, and third sets of image-based data samples; and comparing the binary hashes. However, Wu is in the same field of endeavor, data management, and Wu teaches:
retrieving image-based fields (Wu, [0024], note the product authority includes an authoritative list of products and part of the schema for the products include image-based fields. When combined with the product data mapping as taught previously this would be for attribute/values/labels as taught by Rom and Garera);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Wu because all references are directed towards data management and because Wu would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of matching product listings by normalizing the product information to find a list of potentially matching products (Wu, [0002]).
While Rom as modified teaches importing data using desired schemas and mapping product listings, Rom as modified doesn’t specifically generating binary hashes of the respective first, second, and third sets of image-based data samples; and comparing the binary hashes. However, Liu is in the same field of endeavor, data management, and Liu teaches:
generating binary hashes of the respective first, second, and third sets of image-based data samples (Liu, [0026], note comparing binary hashes of images for search results. When combined with the previous references this would be for the data samples used for importing data as taught by Rom and Garera);
comparing the binary hashes with respect to one another and outputting results based, at least in part, on agreement, or disagreement, of the binary hashes (Liu, [0026], note comparing binary hashes of images for search results. When combined with the previous references this would be for the data samples used for importing data as taught by Rom and Garera);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Liu because all references are directed towards data management and because Liu would expand upon the teachings of the previously cited references in information retrieval which would improve the accuracy of matching product listings by utilizing semantic comparison based on binary hashing signatures (Liu, [0026]).
Regarding Claim 17:
Rom as modified shows the system as disclosed above;
Rom as modified further teaches:
wherein, to implement the data comparison service, the database is further configured to: at a moment in time after the importation of the product listing onto the webpage of the third-party marketplace, receive, via the webpage of the third-party marketplace, a first set of image-based data samples that pertain to the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, which means a first and second set of samples have been retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema, which means a first and second set of samples have been retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields);
receive, via the API, a second set of image-based data samples that pertain to the product listing (Rom, figures 1 and 5-6, [0038-0044, 0104-0108, 0148-0151], note importing the data using an associated schema and mapping template, or creation of a mapping template, to map the imported file fields, which means a first and second set of samples have been retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields) (Garera, abstract, figures 1 and 3-5, [0040-0043], note mapping attribute labels/attribute-value pairs of the import records to the native schema, which means a first and second set of samples have been retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields);
retrieve, from the PIM-DAM service, a third set of image-based data samples that pertain to the product listing (Garera, abstract, figures 1 and 6-7, [0040-0043, 0062-0064], note identifying product attributes may be identified in any schema, dictionary, or other reference corpus, which is interpreted to mean another set of samples pertaining to the product listing were retrieved. When combined with the product schema as taught by Wu below, this would be for image-based fields);
compare the binary hashes with respect to one another and outputting binary results based, at least in part, on agreement, or disagreement, of the binary hashes (Garera, abstract, figures 1 and 3-5, 9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement. When combined with the binary hashing as taught by Liu below, this would be for image binary hashes); and
execute another re-importation of the product listing onto the webpage of the third-party marketplace, based on at least one disagreement of the compared binary hashes (Garera, abstract, figures 1, 4, and 6-9, [0040-0045, 0055-0057, 0062-0064, 0070-0084], note comparing values of the unmapped labels with values in attribute-value pairs of native records having a product type to determine a degree of overlap; note generating a proposed normalization rule mapping the unmapped label to the native label; note a product record may be ingested from an outside entity and assigning a binary result based on the matching scoring, e.g. agreement or disagreement. When combined with the binary hashing as taught by Liu below, this would be for image binary hashes) (Rom, figure 1, [0012-0014, 0030, 0034, 0062, 0151, 0164], note after approval the data is committed to the rich-content repository, which means a disagreement would result in executing another re-importation for that data; note the rich-content repository is used for webpages of third-party marketplaces. When combined with the binary hashing as taught by Liu below, this would be for image binary hashes).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Garera because all references are directed towards data management and because Garera would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of classifying documents by automatically relating structured data from different schemas as well as generating structured representation of unstructured data, particularly product-related documents (Garera, [0004]).
While Rom as modified teaches importing data using desired schemas and mapping product listings, Rom as modified doesn’t specifically teach image-based fields; generating binary hashes of the respective first, second, and third sets of image-based data samples; and comparing the binary hashes. However, Wu is in the same field of endeavor, data management, and Wu teaches:
retrieving image-based fields (Wu, [0024], note the product authority includes an authoritative list of products and part of the schema for the products include image-based fields. When combined with the product data mapping as taught previously this would be for attribute/values/labels as taught by Rom and Garera);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Wu because all references are directed towards data management and because Wu would expand upon the teachings of the previously cited references in data integration which would improve the efficiency of matching product listings by normalizing the product information to find a list of potentially matching products (Wu, [0002]).
While Rom as modified teaches importing data using desired schemas and mapping product listings, Rom as modified doesn’t specifically generate binary hashes of the respective first, second, and third sets of image-based data samples; and comparing the binary hashes. However, Liu is in the same field of endeavor, data management, and Liu teaches:
generating binary hashes of the respective first, second, and third sets of image-based data samples (Liu, [0026], note comparing binary hashes of images for search results. When combined with the previous references this would be for the data samples used for importing data as taught by Rom and Garera);
compare the binary hashes with respect to one another and outputting results based, at least in part, on agreement, or disagreement, of the binary hashes (Liu, [0026], note comparing binary hashes of images for search results. When combined with the previous references this would be for the data samples used for importing data as taught by Rom and Garera);
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Liu because all references are directed towards data management and because Liu would expand upon the teachings of the previously cited references in information retrieval which would improve the accuracy of matching product listings by utilizing semantic comparison based on binary hashing signatures (Liu, [0026]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mukherjee et al. (US2015/0026304) teaches maintaining common data across multiple platforms such as marketplaces;
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/JOHN J MORRIS/Examiner, Art Unit 2152 2/19/2026
/NEVEEN ABEL JALIL/Supervisory Patent Examiner, Art Unit 2152