DETAILED ACTION
This action is in response to a filing filed on August 7th, 2024. Claims 1-20 have been examined in this application. The Information Disclosure Statement (IDS) filed on August 9th, May 29th, August 21st, 2024 has been acknowledged.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 1-15 is/are drawn to method (i.e., a process) and claims 13-14 is/are drawn to electronic device (i.e. system) (i.e., a manufacture). (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 1: A method for providing product objects information comprising:
identifying at least one target product object and its original descriptive information to be provided to a target user;
determining national or regional attribute information of the target user;
processing the original descriptive information to adapt to local expression based on the target user’s national or regional attribute information to generate target descriptive information;
providing the target descriptive information corresponding to the at least one target product object to a client device of the target user to provide the target descriptive information on a designated webpage.
Claim 15: A method for providing product object information, comprising:
receiving target descriptive information of at least one target product object provided for a target user from a server, the target descriptive information is generated by transforming original descriptive information of the target product object based on national or regional attribute information of the target user to adapt to the local expression;
displaying the target descriptive information of at least one target product object on a designated webpage.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
Under their broadest reasonable interpretation, the claims recite a method of organizing and presenting product information by identifying a target product and its descriptive information, determining national or regional attributes of a user, and processing the descriptive information to adapt it to a local expression before providing the adapted description to the users for display on a designated webpage. As claimed, the focus is on tailoring content based on user attributes and communicating the tailored information for presentation i.e., collecting information (user region), analyzing/processing information to produce a localized version, and presenting the results, which constitutes an abstract idea in the form of mental processes and/or certain methods of organizing human activity (marketing/personalized content presentation). Similarly, claim 15 recites receiving target descriptive information from a server, where that information was generated by transforming original product descriptive information based on a user’s national or regional attributes to adapt to local expression, and then displaying the transformed descriptive information on a webpage. The claim’s focus remains on content adaptation and communication/presentation of information according to user attributes i.e., receiving, transforming, and displaying information, which amounts to an abstract idea involving information processing and presentation that can be performed as a mental process (conceptually deciding how to rewrite product descriptions for a locale presentation) and/or as a method of organizing activity such as localized merchandising/marketing and customer-facing information presentation. From applicant’s specification, the claimed invention is implemented a product information service system, “customized experience for each individual” typically refers to the strategy of tailoring different product recommendations for different consumers to achieve better sales outcomes. Traditional product recommendations are based on the attributes of the products themselves, such as category, price, sales volume, and other factors. (see at least [0003] of instant specification). The steps under its broadest reasonable interpretation specifically directed to an abstract idea implemented on generic computer infrastructure, which is an instance of certain methods of organizing human activity or mental processes. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
And the dependent claims 2-14 and 17-20 recites an abstract idea, including verifying users, converting documents or collateral into digital tokens, receiving repayment inputs, selecting payment forms such as fiat or cryptocurrency, and recording financial information on a blockchain, which constitute specifically fundamental economic practices such as financing, collateral management, loan repayment, risk mitigation, supply-chain payment coordination, and identifying participants in financial transactions and fall within the judicial exception of “certain methods of organizing human activity”. These claims also recite mental processes, including verifying users, converting documents or collateral into digital tokens, receiving repayment inputs, selecting payment forms such as fiat or cryptocurrency, and recording financial information on a blockchain, which constitute the mere collection, analysis, and storage of data. Such features represent routine financial or commercial operations and generic information-processing steps that have been held abstract in cases such as Alice, Bilski, buySAFE, Electric Power Group, and SAP v. InvestPic., i.e. which constitute the mere collection, analysis, and storage of data, which fall under the abstract idea of Mental Processes.
Independent claim(s) 16 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a server, client device, processors; and one or more computer-readable memories, etc. (Claims 1 and 15-16) is/are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of using a server, client device, processors; and one or more computer-readable memories, etc. (Claims 1 and 15-16, and dependent claims 2-14 and 17-20) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., receive, identify, generate, determine, display, etc. steps performed by a server, client device, processors; and one or more computer-readable memories, etc.). This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element(s) of identifying at least one target product object and its original descriptive information to be provided to a target user providing the target descriptive information corresponding to the at least one target product object to a client device of the target user to provide the target descriptive information on a designated webpage, receiving target descriptive information of at least one target product object provided for a target user from a server, the target descriptive information is generated by transforming original descriptive information of the target product object based on national or regional attribute information of the target user to adapt to the local expression; displaying the target descriptive information of at least one target product object on a designated webpage (Independent Claims 1 and 15-16), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claims 2-14 and 17-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent Claim(s) 1 and 15-16, and dependent claims 2-14 and 17-20 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional element(s) of identifying at least one target product object and its original descriptive information to be provided to a target user providing the target descriptive information corresponding to the at least one target product object to a client device of the target user to provide the target descriptive information on a designated webpage, receiving target descriptive information of at least one target product object provided for a target user from a server, the target descriptive information is generated by transforming original descriptive information of the target product object based on national or regional attribute information of the target user to adapt to the local expression; displaying the target descriptive information of at least one target product object on a designated webpage (Independent Claims 1, and 15-16), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea) i.e. receiving and providing the target descriptive information to a client device, receiving it from a server, and displaying it on a designated webpage is similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), “Storing and retrieving information in memory”, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; “Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93, Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own disclosure at paragraph [0104, 0196] acknowledges that “target product is localized based on the national or reginal attributes of the target user to generate the target text to displayed on the target page” and “the description of the above embodiments, it can be understood by those skilled in the art that the present application can be implemented by means of software combined with the necessary general hardware platform” (i.e., conventional nature of receiving and transmitting data/messages over a network). This additional element therefore do not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-14 and 17-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
Claims 2-14 and 17-20 merely further limit the abstract idea by reciting the use of an “artificial intelligence (AI) large-scale parameter model” to comprehend, transform, or adapt original descriptive information based on national or regional attribute information, the target product object is associated with multiple Stock Keeping Units (SKUs), and that descriptive information or SKUs are processed or reordered to prioritize locally common expressions or attribute values, the original descriptive information comprises textual content, including titles, keywords, adjectives, and user reviews, and that such textual content is transformed, converted, or reordered based on national or regional attribute information, transforming original attribute or parameter descriptions of a product object based on national or regional attribute information to generate localized attribute or parameter descriptions for display, the original descriptive information comprises rich media information, including image and audio information, and that such media is transformed based on national or regional attribute information to align with local preferences. Collectively, these dependent claims add only broadly claim transforming or selecting media content based on user region, which is a form of content presentation and customization, all of which constitute well-understood, routine, and conventional activities performed by generic computer components and therefore fail to integrate the abstract concept into a practical application and it is recited at a high level of generality and does not integrate the judicial exception into a practical application.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-20 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status:
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-6, 9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. 9684653 (“Bhagat”) in view U.S. Pub. 20150206189 (“Norwood”).
As per claims 1 and 16, Bhagat discloses, identifying at least one target product object and its original descriptive information to be provided to a target user (“product information provided by sellers might include values for various attributes of a product, such as an identifier for a product, like a stock keeping unit (“SKU”) number; a title or description of the product; technical specifications for the product; the purchase price; product availability, such as whether the product is “in stock”; shipping parameters and costs; the geographic region in which the product is available to customers; the locale of the seller's fulfillment center; and other types of product details. The product information may then be utilized to generate product listings in a product catalog” and “a Web server module 602 is a software application that is configured to receive and respond for requests for Web pages and other resources. The resources may be stored in the Web site resources database 608 or generated dynamically.”) (Col. 1 Ln. 18-28 and Col. 13 Ln. 40-45):
determining national or regional attribute information of the target user (“the merchant system 108 might be geographically distributed in order to provide the e-commerce marketplace 118 to customers located around the world. For example, a merchant system 108A might be located in the United States in order to provide an English language version of the e-commerce marketplace to customers located in the U.S. Similarly, a merchant system 108B might be located in Europe to serve customers located in that region. Likewise, merchant systems 108C, 108D, and 108E might be located in China, Japan, and Australia, respectively, to serve customers located in those areas …”) (Col. 8 Ln. 37-47);
processing the original descriptive information to adapt to local expression based on the target user’s national or regional attribute information to generate target descriptive information (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
Bhagat discloses, generating a response to the Web page request that includes the translated resources dynamically in response to a request for the resources generated dynamically by the online shopping retrieve a product listing for a particular product offered for sale by the online merchant or another seller from a product catalog, but specifically doesn’t discloses, providing the target descriptive information corresponding to the at least one target product object to a client device of the target user to provide the target descriptive information on a designated webpage, however Norwood discloses, providing the target descriptive information corresponding to the at least one target product object to a client device of the target user to provide the target descriptive information on a designated webpage (“a selection of one or more locales in which target offer listings associated with the merchant client 106 are to be translated and localized for customers in the selected locales. The item information 147 retrieved by the merchant interface application 121 from the merchant client 106 includes such information as a specific item(s) and a stock keeping unit (SKU) or other identifier associated with a merchant, where the merchant SKU identifies particular inventory from which transactions are to be fulfilled. The merchant SKU may be associated, for example, with inventory in a particular location. The item information 147 also includes pricing information relating to the specified item(s) …”) (0017-0020, 0029).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for identifying at least one target product object and its original descriptive information to be provided to a target user; determining national or regional attribute information of the target user; processing the original descriptive information to adapt to local expression based on the target user’s national or regional attribute information to generate target descriptive information, as disclosed by Bhagat, providing the target descriptive information corresponding to the at least one target product object to a client device of the target user to provide the target descriptive information on a designated webpage, as taught by Norwood for the purpose for providing localization architecture for selecting and delivering localized product pages.
As per claims 3 and 18, Bhagat discloses, the target product object is associated with multiple Stock Keeping Units (SKUs) (“sellers might include values for various attributes of a product, such as an identifier for a product, like a stock keeping unit (“SKU”) number; a title or description of the product; technical specifications for the product; the purchase price; product availability, such as whether the product is “in stock”; shipping parameters and costs; the geographic region in which the product is available to customers; the locale of the seller's fulfillment center; and other types of product details. The product information may then be utilized to generate product listings in a product catalog. The product listings are made available through the online e-commerce marketplace for searching and browsing by customers wishing to purchase the corresponding products from the sellers”) (Col. 1 Ln. 18-32);
and wherein the processing the original descriptive information to adapt to the local expression based on the target user’s national or regional attribute information to generate the target descriptive information comprises (“one embodiment the translation dictionary creation module 302 iteratively applies an Expectation-Maximization (“EM”) algorithm to determine the probability that words in the product information 306A are translations of words in the product information 306B. As known to those skilled in the art, the EM algorithm may be used for the unsupervised learning of bilingual translation dictionaries for statistical machine translation systems. The open source tools GIZA and GIZA++ are implementations of the EM algorithm that may be utilized in embodiments to create the translation dictionary 304. Other tools and/or components might be utilized to implement the EM algorithm in other embodiments. Other mechanisms might also be utilized to create the translation dictionary 304 from the bilingual product information 306”) (Col. 10 Ln. 7-21, Col. 3 Ln. 25-29):
processing the original descriptive information corresponding to the SKUs to adapt to local expression to generate the target descriptive information, thereby to provide different target descriptive information for the same SKU to users with different national or regional attribute information, based on various local expression (“multi-lingual product information provided by sellers may be utilized to provide versions of the e-commerce marketplace 118 in various languages. For instance, product information provided in Spanish may be utilized to provide a Spanish language version of the e-commerce marketplace 118 to customers in Spain and other Spanish-speaking countries. Product information provided in other languages might be utilized to provide versions of the e-commerce marketplace 118 in languages appropriate for different geographic regions”) (Col. 8 Ln. 61-67).
As per claims 4 and 19, Bhagat discloses, the original descriptive information comprises original textual content of the target product object (“generate a translation dictionary from product information that has been received in multiple languages. In particular, a statistical analysis may be performed on product information that has been provided in multiple languages in order to generate the translation dictionary”) (Col. 3 Ln. 25-29, Col. 10 Ln. 7-21);
and wherein the processing the original descriptive information to adapt to the local expression based on the target user’s national or regional attribute information to generate the target descriptive information comprises (“one embodiment the translation dictionary creation module 302 iteratively applies an Expectation-Maximization (“EM”) algorithm to determine the probability that words in the product information 306A are translations of words in the product information 306B. As known to those skilled in the art, the EM algorithm may be used for the unsupervised learning of bilingual translation dictionaries for statistical machine translation systems. The open source tools GIZA and GIZA++ are implementations of the EM algorithm that may be utilized in embodiments to create the translation dictionary 304. Other tools and/or components might be utilized to implement the EM algorithm in other embodiments. Other mechanisms might also be utilized to create the translation dictionary 304 from the bilingual product information 306”) (Col. 10 Ln. 7-21, Col. 3 Ln. 25-29):
transforming the original textual content of the target product object based on the national or regional attribute information of the target user to adapt to the local expression to generate target textual content for display on the designated webpage (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
As per claims 5 and 20, Bhagat discloses, wherein the transforming the original textual content of the target product object based on the national or regional attribute information of the target user to adapt to the local expression comprises (“one embodiment the translation dictionary creation module 302 iteratively applies an Expectation-Maximization (“EM”) algorithm to determine the probability that words in the product information 306A are translations of words in the product information 306B. As known to those skilled in the art, the EM algorithm may be used for the unsupervised learning of bilingual translation dictionaries for statistical machine translation systems. The open source tools GIZA and GIZA++ are implementations of the EM algorithm that may be utilized in embodiments to create the translation dictionary 304. Other tools and/or components might be utilized to implement the EM algorithm in other embodiments. Other mechanisms might also be utilized to create the translation dictionary 304 from the bilingual product information 306”) (Col. 10 Ln. 7-21, Col. 3 Ln. 25-29):
converting keywords related to a product name and/or adjectives included in the original textual content into local common terms corresponding to the national or regional attribute information () (“a seller might provide a description for a product in English and provide the same product description for the product in Spanish. The merchant system might store and utilize this information to provide localized versions of the online e-commerce marketplace in languages appropriate for a particular locale. For example, Spanish language product information might be utilized to provide a Spanish version of the online e-commerce marketplace to customers located in Spain and/or other Spanish speaking countries”) (Col. 3 Ln.8-17).
As per claims 6, Bhagat discloses, wherein the original textual content comprises original title textual content user (”product information provided by sellers might include values for various attributes of a product, such as an identifier for a product, like a stock keeping unit (“SKU”) number; a title or description of the product; technical specifications for the product; the purchase price; product availability, such as whether the product is “in stock”; shipping parameters and costs; the geographic region in which the product is available to customers; the locale of the seller's fulfillment center; and other types of product details. The product information may then be utilized to generate product listings in a product catalog” and “a Web server module 602 is a software application that is configured to receive and respond for requests for Web pages and other resources. The resources may be stored in the Web site resources database 608 or generated dynamically.”) (Col. 1 Ln. 18-28 and Col. 13 Ln. 40-45);
and wherein the transforming the original textual content of the target product object based on the national or regional attribute information of the target user to adapt to the local expression comprises (Col. 10 Ln. 7-21, Col. 3 Ln. 25-29):
processing the original title textual content to adapt to a textual content related to an expression of product attributes based on attribute preferences of a demographic corresponding to the national or regional attribute information for an category of products to which the target product object belongs (Examiner notes that attribute preferences of a demographic broadly as locale-based preferences (e.g., what details are emphasized in a region/country), which is consistent with localization systems) (“one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
As per claims 9, Bhagat discloses, wherein the original descriptive information comprises an original attribute/parameter description of the target product object (“product information provided by sellers might include values for various attributes of a product, such as an identifier for a product, like a stock keeping unit (“SKU”) number; a title or description of the product; technical specifications for the product; the purchase price; product availability, such as whether the product is “in stock”; shipping parameters and costs; the geographic region in which the product is available to customers; the locale of the seller's fulfillment center; and other types of product details. The product information may then be utilized to generate product listings in a product catalog” and “a Web server module 602 is a software application that is configured to receive and respond for requests for Web pages and other resources. The resources may be stored in the Web site resources database 608 or generated dynamically.”) (Col. 1 Ln. 18-28 and Col. 13 Ln. 40-45);
and wherein the processing the original descriptive information to adapt to the local expression based on the target user’s national or regional attribute information to generate the target descriptive information comprises (“generate a translation dictionary from product information that has been received in multiple languages. In particular, a statistical analysis may be performed on product information that has been provided in multiple languages in order to generate the translation dictionary” and “one embodiment the translation dictionary creation module 302 iteratively applies an Expectation-Maximization (“EM”) algorithm to determine the probability that words in the product information 306A are translations of words in the product information 306B. As known to those skilled in the art, the EM algorithm may be used for the unsupervised learning of bilingual translation dictionaries for statistical machine translation systems. The open source tools GIZA and GIZA++ are implementations of the EM algorithm that may be utilized in embodiments to create the translation dictionary 304. Other tools and/or components might be utilized to implement the EM algorithm in other embodiments. Other mechanisms might also be utilized to create the translation dictionary 304 from the bilingual product information 306”) (Col. 3 Ln. 25-29 and Col. 10 Ln. 7-21):
transforming the original attribute/parameter description of the target product object based on the national or regional attribute information of the target user to adapt to the local expression to generate target attribute/parameter description, for display on a designated webpage (Examiner notes, translation dictionary is text-centric) (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
As per claims 11, Bhagat discloses, wherein the transforming the original attribute/parameter description of the target product object based on the national or regional attribute information of the target user to adapt to the local expression comprises (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27);
inputting the original attribute/parameter description information and the national or regional attribute information of the target user into an AI large-scale parameter model, so that the AI large-scale parameter model performs the transforming the original attribute/parameter description of the target product object based on the national or regional attribute information of the target user to adapt to into the local expression (“As known to those skilled in the art, the EM algorithm may be used for the unsupervised learning of bilingual translation dictionaries for statistical machine translation systems. The open source tools GIZA and GIZA++ are implementations of the EM algorithm that may be utilized in embodiments to create the translation dictionary 304. Other tools and/or components might be utilized to implement the EM algorithm in other embodiments. Other mechanisms might also be utilized to create the translation dictionary 304 from the bilingual product information 306”) (Col. 10 Ln. 11-21).
As per claims 12, Bhagat discloses, wherein the original descriptive information comprises original rich media information of the target product object (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27);
and wherein the processing the original descriptive information to adapt to the local expression based on the target user’s national or regional attribute information to generate the target descriptive information comprises (Col. 3 Ln. 25-29 and Col. 10 Ln. 7-21):
transforming the original rich media information of the target product object based on the national or regional attribute information of the target user to adapt to the local expression to generate target rich media information, for display on the designated webpage (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
As per claims 13, Bhagat discloses, wherein the original rich media information comprises original image information (“online shopping module 112 might utilize pre-stored or dynamically created resources to generate the e-commerce marketplace 118. For instance, Web pages, images, text files, program code for generating Web pages, metadata, scripts, executable code, and other types of data utilized to create and/or provide a Web page might be stored or dynamically generated. Other types of resources might also be stored or generated dynamically by the online shopping module 112 to provide the e-commerce marketplace 118”) (Col. 6 Ln. 60-67);
and wherein the transforming the original rich media information of the target product object based on the national or regional attribute information of the target user to adapt to the local expression comprises (Col. 3 Ln. 25-29 and Col. 10 Ln. 7-21):
transforming composition style, model type, and/or atmospheric elements of the original image information based on the national or regional attribute information of the target user, to generate target image information that aligns with local preferences corresponding to the national or regional attribute information (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
As per claims 14, Bhagat discloses, and wherein the transforming the original rich media information of the target product object based on the national or regional attribute information of the target user to adapt to the local expression comprises (Col. 3 Ln. 25-29 and Col. 10 Ln. 7-21): transforming the original audio information based on the national or regional attribute information of the target user, to generate target audio information that aligns with local preferences corresponding to the national or regional attribute information (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
Bhagat discloses, generating a response to the Web page request that includes the translated resources dynamically in response to a request for the resources generated dynamically by the online shopping retrieve a product listing for a particular product offered for sale by the online merchant or another seller from a product catalog, but specifically doesn’t discloses, wherein the original rich media information comprises original audio information, however Norwood discloses, wherein the original rich media information comprises original audio information (“network page server 127 is executed to serve up localized offer listings to the customer clients 116 and operates in conjunction with the electronic commerce system 129 to perform purchase transactions. The localized offer listings may include such offer listing source data as hypertext markup language (HTML), extensible markup language (XML), extensible HTML (XHTML), mathematical markup language (MathML), scalable vector graphics (SVG), cascading style sheets (CSS), images, audio, video, graphics, text, and/or any other data that may be used in serving up or generating the offer listings”) (0023).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for identifying at least one target product object and its original descriptive information to be provided to a target user; determining national or regional attribute information of the target user; processing the original descriptive information to adapt to local expression based on the target user’s national or regional attribute information to generate target descriptive information, as disclosed by Bhagat, wherein the original rich media information comprises original audio information, as taught by Norwood for the purpose for providing localization architecture for selecting and delivering localized product pages.
As per claims 15, Bhagat discloses, receiving target descriptive information of at least one target product object provided for a target user from a server (”product information provided by sellers might include values for various attributes of a product, such as an identifier for a product, like a stock keeping unit (“SKU”) number; a title or description of the product; technical specifications for the product; the purchase price; product availability, such as whether the product is “in stock”; shipping parameters and costs; the geographic region in which the product is available to customers; the locale of the seller's fulfillment center; and other types of product details. The product information may then be utilized to generate product listings in a product catalog” and “a Web server module 602 is a software application that is configured to receive and respond for requests for Web pages and other resources. The resources may be stored in the Web site resources database 608 or generated dynamically.”) (Col. 1 Ln. 18-28 and Col. 13 Ln. 40-45), the target descriptive information is generated by transforming original descriptive information of the target product object based on national or regional attribute information of the target user to adapt to the local expression (“he merchant system 108 might be geographically distributed in order to provide the e-commerce marketplace 118 to customers located around the world. For example, a merchant system 108A might be located in the United States in order to provide an English language version of the e-commerce marketplace to customers located in the U.S. Similarly, a merchant system 108B might be located in Europe to serve customers located in that region. Likewise, merchant systems 108C, 108D, and 108E might be located in China, Japan, and Australia, respectively, to serve customers located in those areas …”) (Col. 8 Ln. 37-47) Also refer to: (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
Bhagat discloses, displaying the target descriptive information of at least one target product object on a designated webpage, however Norwood discloses, displaying the target descriptive information of at least one target product object on a designated webpage (“a selection of one or more locales in which target offer listings associated with the merchant client 106 are to be translated and localized for customers in the selected locales. The item information 147 retrieved by the merchant interface application 121 from the merchant client 106 includes such information as a specific item(s) and a stock keeping unit (SKU) or other identifier associated with a merchant, where the merchant SKU identifies particular inventory from which transactions are to be fulfilled. The merchant SKU may be associated, for example, with inventory in a particular location. The item information 147 also includes pricing information relating to the specified item(s) …”) (0017-0020, 0029).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for identifying at least one target product object and its original descriptive information to be provided to a target user; determining national or regional attribute information of the target user; processing the original descriptive information to adapt to local expression based on the target user’s national or regional attribute information to generate target descriptive information, as disclosed by Bhagat, displaying the target descriptive information of at least one target product object on a designated webpage, as taught by Norwood for the purpose for providing localization architecture for selecting and delivering localized product pages.
Claims 2, 7-8, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. 9684653 (“Bhagat”) in view U.S. Pub. 20150206189 (“Norwood”) in further view of U.S. Pub. 20240346233 (“Paulino”).
As per claims 2 and 17, Bhagat discloses, wherein the processing the original descriptive information to adapt to the local expression based on the target user’s national or regional attribute information to generate the target descriptive information comprises (“generate a translation dictionary from product information that has been received in multiple languages. In particular, a statistical analysis may be performed on product information that has been provided in multiple languages in order to generate the translation dictionary” and “one embodiment the translation dictionary creation module 302 iteratively applies an Expectation-Maximization (“EM”) algorithm to determine the probability that words in the product information 306A are translations of words in the product information 306B. As known to those skilled in the art, the EM algorithm may be used for the unsupervised learning of bilingual translation dictionaries for statistical machine translation systems. The open source tools GIZA and GIZA++ are implementations of the EM algorithm that may be utilized in embodiments to create the translation dictionary 304. Other tools and/or components might be utilized to implement the EM algorithm in other embodiments. Other mechanisms might also be utilized to create the translation dictionary 304 from the bilingual product information 306”) (Col. 3 Ln. 25-29 and Col. 10 Ln. 7-21):
utilizing an artificial intelligence (AI) large-scale parameter model to comprehend the original descriptive information, and processing the original descriptive information to adapt to the local expression based on the national or regional attribute information of the target user to generate the target descriptive information (Examiner notes that the underlined limitation is disclose by another reference) (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
Bhagat discloses, allowing the model to focus on the most contextually relevant parts of the input for the task for handling sequence data and their capacity for parallel computation, often serve as foundational elements in constructing large generative AI models (LXM), but specifically doesn’t discloses, utilizing an artificial intelligence (AI) large-scale parameter model to comprehend the original descriptive information, however Paulino discloses, utilizing an artificial intelligence (AI) large-scale parameter model to comprehend the original descriptive information (Examiner notes that AI large-scale parameter model can encompass trained statistical models used in machine translation including probabilistic models to process and generate localized descriptions)(“a review summary that summarizes the set of reviews associated with the item is generated, using the trained large language model. In embodiments, the review summary is generated in accordance with the set of weights that correspond with the set of reviews. At block 706, the review summary is provided to a data store for subsequent presentation. In some cases, the review summary is subsequently provided for display based on a request to view the review summary for the item”) (0091).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for identifying at least one target product object and its original descriptive information to be provided to a target user; determining national or regional attribute information of the target user; processing the original descriptive information to adapt to local expression based on the target user’s national or regional attribute information to generate target descriptive information, as disclosed by Bhagat, utilizing an artificial intelligence (AI) large-scale parameter model to comprehend the original descriptive information, as taught by Paulino for the purpose to have more influence or provide more constructive insight to potential consumers and item providers.
As per claims 7, Bhagat discloses, wherein the original textual content comprises an original user review textual content (”product information provided by sellers might include values for various attributes of a product, such as an identifier for a product, like a stock keeping unit (“SKU”) number; a title or description of the product; technical specifications for the product; the purchase price; product availability, such as whether the product is “in stock”; shipping parameters and costs; the geographic region in which the product is available to customers; the locale of the seller's fulfillment center; and other types of product details. The product information may then be utilized to generate product listings in a product catalog” and “a Web server module 602 is a software application that is configured to receive and respond for requests for Web pages and other resources. The resources may be stored in the Web site resources database 608 or generated dynamically.”) (Col. 1 Ln. 18-28 and Col. 13 Ln. 40-45);
and wherein the transforming the original textual content of the target product object based on the national or regional attribute information of the target user to adapt to the local expression comprises (“one embodiment the translation dictionary creation module 302 iteratively applies an Expectation-Maximization (“EM”) algorithm to determine the probability that words in the product information 306A are translations of words in the product information 306B. As known to those skilled in the art, the EM algorithm may be used for the unsupervised learning of bilingual translation dictionaries for statistical machine translation systems. The open source tools GIZA and GIZA++ are implementations of the EM algorithm that may be utilized in embodiments to create the translation dictionary 304. Other tools and/or components might be utilized to implement the EM algorithm in other embodiments. Other mechanisms might also be utilized to create the translation dictionary 304 from the bilingual product information 306”) (Col. 10 Ln. 7-21, Col. 3 Ln. 25-29).
Bhagat discloses, allowing the model to focus on the most contextually relevant parts of the input for the task for handling sequence data and their capacity for parallel computation, often serve as foundational elements in constructing large generative AI models (LXM), but specifically doesn’t discloses, reordering the original user review textual content based on the national or regional attribute information of the target user, in order to prioritize the display of local user reviews according to the national or regional attribute information, however Paulina discloses, reordering the original user review textual content based on the national or regional attribute information of the target user (Examiner notes that under BRI, “reordering” includes sorting/filtering review display order (e.g., “show most relevant first,” “show local first”), i.e. local user reviews according to national/regional attribute information can be interpreted as reviews written in the user’s language/region, which is a predictable extension of Bhagat’s region/language segmentation when combined with a review-ranking reference) (“existing reviews may be distributed across multiple locations, such as different websites, and also may contain a large volume of reviews that are not helpful for a potential consumer in that these reviews do not provide explanation or supporting detail regarding the features of the product that led to the review. For instance, reviews such “it's good” or “I hated it,” reviews that contain obscenities, or reviews that are not relevant to the product, do not help the potential customer to better understand the product. Further still, many of these reviews can be outdated and regard older versions of the product, thus requiring a user to look at the date of the review and have knowledge regarding the current version of the product. Accordingly, reviewing numerous product reviews to locate those reviews that are relevant and helpful to a current product places a burden on the user that can unnecessarily consume computing resources, as well as consume the user's time”) (0016), in order to prioritize the display of local user reviews according to the national or regional attribute information (“data selection may be based on any of a variety of aspects, such as data of reviews, weights of reviews, rankings associated with reviews, reviewers providing the review, and/or the like. As one example, the prompt generator 224 can first call for the input size constraint of tokens. Responsively, the prompt generator 224 can then tokenize each of the review candidates to generate tokens and, thereafter, responsively and progressively add each data set ranked/weighted from highest to lowest if and until the token threshold (indicating the input size constraint) is met or exceeded, at which point the prompt generator 224 stops”) (0076).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for identifying at least one target product object and its original descriptive information to be provided to a target user; determining national or regional attribute information of the target user; processing the original descriptive information to adapt to local expression based on the target user’s national or regional attribute information to generate target descriptive information, as disclosed by Bhagat, reordering the original user review textual content based on the national or regional attribute information of the target user, in order to prioritize the display of local user reviews according to the national or regional attribute information, as taught by Paulino for the purpose to have more influence or provide more constructive insight to potential consumers and item providers.
As per claims 8, Bhagat discloses, wherein the transforming the original textual content of the target product object based on the national or regional attribute information of the target user to adapt to the local expression comprises (”product information provided by sellers might include values for various attributes of a product, such as an identifier for a product, like a stock keeping unit (“SKU”) number; a title or description of the product; technical specifications for the product; the purchase price; product availability, such as whether the product is “in stock”; shipping parameters and costs; the geographic region in which the product is available to customers; the locale of the seller's fulfillment center; and other types of product details. The product information may then be utilized to generate product listings in a product catalog” and “a Web server module 602 is a software application that is configured to receive and respond for requests for Web pages and other resources. The resources may be stored in the Web site resources database 608 or generated dynamically.”) (Col. 1 Ln. 18-28 and Col. 13 Ln. 40-45):
inputting the original textual content and the national or regional attribute information of the target user into an AI large-scale parameter model, so that the AI large-scale parameter model performs the transforming the original textual content of the target product object based on the national or regional attribute information of the target user to adapt to into the local expression (Examiner notes that the underlined limitation is disclosed by another reference. Further, Examiner notes that under BRI, adapt to conform to local grammatical expression/contextual coherence is inherent to machine translation/localization quality improvement, especially when using an LLM that generates fluent output, where inputs can be implicit model receives locale parameter / language code / region ID alongside text) (“An LLM can also perform machine translation, which includes the process of using machine learning to automatically translate text from one language to another without human involvement. Modern machine translation goes beyond simple word-to-word translation to communicate the full meaning of the original language text in the target language. It analyzes all text elements and recognizes how the words influence one another.”) (0080).
Bhagat discloses, allowing the model to focus on the most contextually relevant parts of the input for the task for handling sequence data and their capacity for parallel computation, often serve as foundational elements in constructing large generative AI models (LXM), but specifically doesn’t discloses, AI large-scale parameter model, so that the AI large-scale parameter model performs, the AI large-scale parameter model is also used to adapt the transformed textual content to conform to local grammatical expression and/or local contextual coherence style based on the local expression corresponding to the national or regional attribute information, to generate the target textual content, however Paulina discloses, AI large-scale parameter model, so that the AI large-scale parameter model performs (“LLMs are GOOGLE's BERT and OpenAI's GPT-2, GPT-3, and GPT-4. For instance, GPT-3 is a large language model with 175 billion parameters trained on 570 gigabytes of text. These models have capabilities ranging from writing a simple essay to generating complex computer codes-all with limited to no supervision. Accordingly, an LLM is a deep neural network that is very large (billions to hundreds of billions of parameters) and understands, processes, and produces human natural language by being trained on massive amounts of text”) (0080),
the AI large-scale parameter model is also used to adapt the transformed textual content to conform to local grammatical expression and/or local contextual coherence style based on the local expression corresponding to the national or regional attribute information, to generate the target textual content (“select the currency to get a community summary by buyer programs 100 trading in similar currencies. Community manager 120 defines the currency of the home page summary and can view the summary in each currency the community 112 is trading in by selecting the currency from a list box of appropriate currencies. Community manager 120 can set the default currency for display when first accessing the home page. Community manager home page 202 allows the user to select the currency for the trading snapshot. The community manager defines the currency of the trading snap shot and views the snap shot in each currency in which the community 112 is trading.”) (0566, 0329).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for identifying at least one target product object and its original descriptive information to be provided to a target user; determining national or regional attribute information of the target user; processing the original descriptive information to adapt to local expression based on the target user’s national or regional attribute information to generate target descriptive information, as disclosed by Bhagat, AI large-scale parameter model, so that the AI large-scale parameter model performs, the AI large-scale parameter model is also used to adapt the transformed textual content to conform to local grammatical expression and/or local contextual coherence style based on the local expression corresponding to the national or regional attribute information, to generate the target textual content, as taught by Paulino for the purpose to have more influence or provide more constructive insight to potential consumers and item providers.
As per claims 10, Bhagat discloses, wherein the transforming the original attribute/parameter description of the target product object based on the national or regional attribute information of the target user to adapt to the local expression comprises (“the translation dictionary includes the words found in the product information in one language (e.g. English). For each of the words in the first language, the translation dictionary also includes one or more words from the foreign language product information (e.g. Spanish), along with a corresponding probability that the word is a translation of the word in the first language. For example, the translation dictionary might include the English word “shoe.” For the word “shoe”, the translation dictionary might also include the Spanish words “zapato” and “herradura.” For the words “zapato” and “herradura”, the translation dictionary might indicate that the probability that the Spanish word “zapato” is a translation of the English word “shoe” is 85%, and that the probability that the Spanish word “herradura” is a translation of the English word “shoe” is 15%” and “The translated search queries can then be utilized to search the marketplace 118 in its native language, thereby likely providing better search results than if the foreign language search query had been processed without translation. Details regarding the structure of and functionality provided by the language translation system 114 will be provided below with respect to FIGS. 3-9”) (Col. 3 Ln. 30-45 and Col. 9 Ln. 21-27).
Bhagat discloses, allowing the model to focus on the most contextually relevant parts of the input for the task for handling sequence data and their capacity for parallel computation, often serve as foundational elements in constructing large generative AI models (LXM), but specifically doesn’t discloses, if the target product object is associated with multiple Stock Keeping Units (SKUs) having different attribute values/parameter values, reordering the SKUs to prioritize the display of the SKUs with locally commonly used attribute values/parameter values, corresponding to the national or regional attribute information, however Paulina discloses, if the target product object is associated with multiple Stock Keeping Units (SKUs) having different attribute values/parameter values, reordering the SKUs to prioritize the display of the SKUs with locally commonly used attribute values/parameter values, corresponding to the national or regional attribute information (Examiner notes (“a user viewing an item (e.g., a potential product to purchase) may select a link or icon to view a review summary associated with the item. In other cases, a user may indirectly or implicitly select to generate or view a review summary(s) related to an item. For instance, a user may navigate to a media store application or website. Based on the navigation to the media store application or website, the user may indirectly indicate to generate or view a review summary. In some cases, such an indication may be based on generally navigating to the application or website. For instance, a review summary may be requested for each item to be presented in the application or website or for a particular item(s) being features or promoted. In other cases, such an indication may be based on selecting a particular product” and “existing reviews may be distributed across multiple locations, such as different websites, and also may contain a large volume of reviews that are not helpful for a potential consumer in that these reviews do not provide explanation or supporting detail regarding the features of the product that led to the review. For instance, reviews such “it's good” or “I hated it,” reviews that contain obscenities, or reviews that are not relevant to the product, do not help the potential customer to better understand the product. Further still, many of these reviews can be outdated and regard older versions of the product, thus requiring a user to look at the date of the review and have knowledge regarding the current version of the product”) (0032 and 0016).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for identifying at least one target product object and its original descriptive information to be provided to a target user; determining national or regional attribute information of the target user; processing the original descriptive information to adapt to local expression based on the target user’s national or regional attribute information to generate target descriptive information, as disclosed by Bhagat, if the target product object is associated with multiple Stock Keeping Units (SKUs) having different attribute values/parameter values, reordering the SKUs to prioritize the display of the SKUs with locally commonly used attribute values/parameter values, corresponding to the national or regional attribute information, as taught by Paulino for the purpose to have more influence or provide more constructive insight to potential consumers and item providers and to increase conversion and reduce browsing time, this is a known merchandising optimization in localized marketplaces.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US. Pat. 11222362 (“Whiteman”).
Whiteman discloses, a method of localizing an element present in a piece of content having a plurality of elements. A cost of localizing an element with respect to each of one or more localization sources is first computed. At least one criterion based on which a localization source for localizing the element is to be determined is obtained. A localization source for localizing the element is then selected based on an assessment with respect to the at least one criterion. The element of the content is then localized using the selected localization source.
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/GAUTAM UBALE/Primary Examiner, Art Unit 3689