DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 July 29th, 2026. Claim 1 and 16 is amended and claims 4, 8, 15, and 19 is/are cancelled. Claims 1-3, 5-7, 9-14, 16-18, and 20 have been examined in this application.
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 1-3, 5-7, 9-14, 16-18, and 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 1-3, 5-7, 9-14 is/are drawn to method (i.e., a process) and claims 16-18, and 20 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:
generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms into a first artificial intelligence (AI) model, wherein the first AI model generalizes from the small-scale samples to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions;
identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content;
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, wherein the processing comprises:
processing the original textual content via an AI large-scale parameter model to generate target textual content by replacing keywords in the original textual content with corresponding local common terms queried from the pre-established knowledge base,
and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information;
and 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.
(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, independent claims 1 and 16 recite tailoring and presenting product information to a target user based on characteristics of the target user, including national or regional attribute information. More specifically, the claims recite identifying a target product object and descriptive information associated with the product object, determining national or regional attribute information of a target user, adapting the product descriptive information to local expression corresponding to the target user's national or regional attribute information, and providing the resulting localized product descriptive information to the target user for presentation on a designated webpage. These limitations recite a certain method of organizing human activity, namely a commercial interaction involving marketing and/or sales activity through localized or personalized presentation of product information to a consumer based on characteristics of the consumer. The claimed localization and presentation of product information according to the target user's national or regional attributes correspond to tailoring customer-facing product information for a particular market or consumer group. Claims 1 and 16 further recite generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms into a first AI model, wherein the first AI model generalizes from the small-scale samples to populate the knowledge base, and subsequently processing textual content using an AI large-scale parameter model and the previously generated knowledge base. These limitations are not included within the identified abstract idea merely because they are used in connection with localized product presentation. Rather, they are treated as additional elements and are evaluated, individually and in combination with the other claim limitations, under Step 2A, Prong Two.
Dependent claims 2-3, 5-7, 9-14, 17-18, and 20 incorporate the abstract idea identified above through their dependency from claims 1 or 16 and further limit the manner in which product information is localized, selected, prioritized, transformed, or presented. Certain dependent limitations further refine the identified commercial interaction. For example, claims 3 and 18 apply localized product information to product variants/SKUs; claim 6 adapts product-title content according to attribute preferences of a demographic corresponding to national or regional information; claim 7 prioritizes user reviews according to geographic or regional information; and claim 10 prioritizes product variants according to locally relevant attribute values. These limitations further describe tailoring customer-facing product information according to characteristics or preferences associated with a particular consumer or geographic market and therefore further relate to the identified commercial interaction involving marketing and/or sales activity.
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 client device, processors, electronic device, and one or more computer-readable memories, etc. (Claims 1 and 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 client device, processors, electronic device, and one or more computer-readable memories, etc. (Claims 1 and 16, and dependent claims 2-3, 5-7, 9-14, 17-18, and 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 client device, processors, electronic device, 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 element(s) of identifying the target product object and its original descriptive information, and determining the national or regional attribute information of the target user, are considered additional elements rather than part of the identified judicial exception, these limitations merely gather or obtain the information used as input to the subsequent localized product-information processing. The product descriptive information supplies the content to be processed, while the national or regional attribute information supplies the criterion according to which that content is localized. Such limitations constitute necessary pre-solution data gathering and do not, by themselves, impose a meaningful limit on the identified abstract idea. Likewise, to the extent the limitation of providing the resulting target descriptive information to a client device for presentation on a designated webpage is considered an additional element, the limitation merely communicates or presents the result of the preceding localization process. The claim does not recite a technological improvement to transmission, webpage rendering, or client-device operation through this output step. Accordingly, the providing and webpage-presentation limitations constitute insignificant post-solution activity. Thus, these input-gathering and output/presentation limitations, considered individually and in combination, merely obtain information necessary to perform the claimed analysis and communicate the resulting information, and therefore do not meaningfully integrate the judicial exception into a practical application. 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 2-3, 5-7, 9-14, 17-18, and 20 likewise do not become integrated into a practical application merely through limitations that specify additional information used as input to the localized product-information process or additional manners of presenting the resulting information. To the extent the dependent claims recite identifying or using SKU information and associated attribute values, demographic or regional preferences, user-review information, product attribute/parameter information, or national or regional information as criteria for performing the claimed localization, prioritization, or customization, such limitations merely specify the type of information gathered or used in carrying out the identified commercial interaction. For example, the recited SKU and product-attribute information supplies product data to which the localized presentation is applied; demographic and regional-preference information supplies criteria for tailoring product-title or product-variant information; and geographic information associated with reviews supplies the criterion used to determine which reviews are prioritized. To the extent such limitations are considered additional elements rather than part of the identified abstract commercial interaction, they amount to input selection or necessary data gathering used in the subsequent personalization/localization analysis and therefore do not, by themselves, impose a meaningful technological limit on the judicial exception. 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 16, and dependent claims 2-3, 5-7, 9-14, 17-18, and 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 elements of independent claims 1 and 16, including identifying at least one target product object and its original descriptive information, determining national or regional attribute information of the target user, and providing the resulting target descriptive information to a client device for presentation on a designated webpage, do not amount to significantly more than the identified judicial exception. To the extent these limitations are considered additional elements rather than part of the judicial exception, identifying or obtaining the product descriptive information and user regional information merely gathers the information used in carrying out the claimed localized commercial-content processing, while providing the resulting target descriptive information to the client device and designated webpage merely transmits, communicates, or presents the result of that processing (Independent Claims 1, and 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). These limitations additionally and/or alternatively append insignificant pre-solution data gathering and post-solution transmission or presentation activity to the judicial exception. Such generic receiving, retrieving, storing, transmitting, and presenting of information 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 2-3, 5-7, 9-14, 17-18, and 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 and 17 further recite utilizing the AI large-scale parameter model to comprehend the original descriptive information before processing the information according to national or regional attribute information. The recited AI model is used functionally to perform semantic or contextual processing of product information for the claimed localization task. The claims do not recite through these added limitations a particular improvement in the operation of the AI large-scale parameter model itself, but instead use the model as a tool to carry out the underlying product-information localization. The Federal Circuit has held that claims that merely apply established machine-learning techniques to a new data environment, without a claimed improvement to the machine-learning technology, do not obtain an inventive concept merely from the use of machine learning. Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). Claims 3 and 18 further recite associating a target product object with multiple SKUs and providing different localized descriptive information for the same SKU to users having different national or regional attribute information. These limitations further specify the product identifiers and product variants to which the localized presentation is applied. The use of SKU information does not alter the underlying computer or localization technology, but instead identifies the particular commercial product information to be processed and presented differently according to the consumer's locale. Thus, the added limitations further particularize the subject matter and data to which the localized commercial-information process is applied rather than supplying a separate inventive concept. Claims 5 and 20 further recite converting keywords associated with a product name and/or adjectives into local common terms corresponding to national or regional attribute information. These limitations further define the claimed localization itself by specifying which portions of the customer-facing product text are replaced and the regional criterion controlling the replacement. They therefore further particularize the underlying localized product-information activity and do not recite an additional technological implementation that changes how the computer, knowledge base, or AI model operates. Claim 6 further recites processing original product-title text into text expressing product attributes according to attribute preferences of a demographic corresponding to the national or regional information for a category of products. This limitation further refines the identified commercial interaction by tailoring customer-facing product information according to demographic or regional preferences associated with the relevant product category. The added limitation specifies the information and consumer preference criteria used to generate the personalized product content, rather than reciting a technological improvement to the mechanism that performs the personalization. Claim 7 further recites reordering user-review textual content based on national or regional information in order to prioritize display of local user reviews. The limitation therefore specifies a geographic criterion for selecting, ranking, and presenting customer-facing information. The additional limitation further implements the identified commercial-information tailoring by determining which review information is presented with greater priority to a particular consumer, rather than reciting an improvement to computer, database, ranking, or display technology. Claim 9 further recites transforming an original product attribute/parameter description according to the target user's national or regional information to generate a localized target attribute/parameter description for display. This limitation further specifies the particular category of product information subject to the localization process and therefore further particularizes the identified localized commercial presentation. Claim 10 further recites, where a product is associated with multiple SKUs having different attribute/parameter values, reordering the SKUs to prioritize display of SKUs having locally commonly used attribute/parameter values corresponding to national or regional information. These limitations further specify regional product-preference information as a criterion for ranking and presenting product variants. The claimed prioritization therefore further implements localized merchandising and presentation of product information according to consumer-region characteristics rather than reciting a technological improvement to the ranking or computer system itself. Claim 11 further recites inputting product attribute/parameter description information and national or regional attribute information into the AI large-scale parameter model so that the model performs the transformation according to the regional information. The limitation thus applies the recited AI model to product-description and user-region data to perform the underlying localization task. It does not recite, through this added limitation, a particular modification to the machine-learning model or a specific improved machine-learning technique; rather, the AI model is employed to obtain the desired localized product-description result. This is analogous to the distinction recognized in Recentive, where applying machine learning to a particular information environment, without a claimed improvement to the machine-learning technology, was insufficient to supply an inventive concept. Claim 12 further recites that the descriptive information includes rich-media information and that the rich-media information is transformed according to the target user's national or regional information to generate target rich-media information for display on the designated webpage. Although this limitation recites computerized media processing and is therefore considered as an additional element rather than merely characterized as part of the abstract idea, the limitation is recited in terms of the result to be achieved, generating regionally adapted rich-media information, without, in the added limitation itself, specifying a particular media-processing technique that improves the operation of the computer or media-processing technology. The limitation therefore applies the same regional content customization objective to another type of customer-facing product information. Claim 13 further recites transforming the composition style, model type, and/or atmospheric elements of original image information according to national or regional information to generate target image information aligned with local preferences. The added limitation specifies the visual characteristics to be adapted and the consumer-preference criterion controlling the adaptation. However, the claim recites the desired localized image result at a functional level rather than a particular improved image-generation or image-processing mechanism. Claim 14 further recites transforming original audio information according to national or regional information to generate target audio information aligned with local preferences. As with claim 13, the limitation recites the desired regionally adapted media result without reciting, in the added limitation, a particular improved audio-processing, speech-generation, signal-processing, or acoustic technique. The additional limitation therefore applies the localized product-information objective to audio content rather than reciting a technological improvement to the underlying audio-processing technology.
When viewed as an ordered combination, the additional elements of claims 2-3, 5-7, 9-14, 17-18, and 20 merely instruct to implement the abstract idea using generic computer components to collect, store, represent, and display information. The claims do not recite any unconventional arrangement of elements, nor do they effect an improvement to computer functionality or another technical field 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 1-3, 5-7, 9-14, 16-18, and 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, 5, 9, 11-14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. 7225199 (“Green”) in view U.S. Pub. 20230259692 (“Wright”) in view U.S. Pub. 20210081411 (“Begun”).
As per claims 1 and 16, Green discloses, method for providing product objects information comprising (Examiner interprets an item/product description as information concerning the claimed product object. Green discloses processing and localization of product/item descriptions. Green's system analyzes product descriptions in terms of product properties, attributes, terminology, and structure) (“The initial step in either a normalization or translation process is to access legacy content 1710 that is associated with the firms' various legacy systems 1712. The legacy content 1710 may be provided as level 1 commerce data consisting of short descriptive phrases delivered as flat file structures that are used as input into the NorTran Workbench 1702 … There are a number of external product and part classification schemas 1714, both proprietary and public. These schemas 1714 relate one class of part in terms of a larger or more general family, a taxonomy of parts for example. These schemas 1714 define the attributes that differentiate one part class from another. For example, in bolts, head style is an attribute for various types of heads such as hex, fillister, Phillips, etc. Using this knowledge in the development of the grammar rules will drastically shorten the time to normalize large quantities of data. Further, it provides a reference to identify many of the synonyms and abbreviations that are used to describe the content …”) (col. 36, ll. 48-65; col. 38, ll. 20-65):
generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base (Examiner interprets Green expressly teaches an “off-line” mode in which knowledge concerning content transformation is captured and collectively defines a knowledge base. After the KB is constructed, it is used to transform later content. Green states that its traditional e-business translation process is essentially off-line, becoming real-time when new content is subsequently added, and further states that the SOLX “off-line” mode captures knowledge concerning the intended transformation, which “collectively defines a knowledge base”; once that knowledge base has been constructed, the system subsequently uses it to transform content) (“In a traditional e-business environment, this translation process essentially is offline. It becomes real-time and online when new content is added to the system. In this case, assuming well-developed special-purpose dictionaries and linguistic information already exists, the process can proceed in an automatic fashion. Content, once translated is stored in a specially indexed look-up database. This database functions as a memory translation repository. With this type of storage environment, the translated content can be scaled to virtually any size and be directly accessed in the e-business process … The SOLx system operates in two distinct modes. The "off-line" mode is used to capture knowledge from the SME/translator and knowledge about the intended transformation of the content. This collectively defines a knowledge base. The off-line mode includes implementation of the configuration and translation processes described above. Once the knowledge base has been constructed, the SOLx system can be used in a file in/file out manner to transform content …”) (col. 34, ll. 55-65 - col. 35, ll. 1-10) by inputting small-scale samples of regional terms into a first artificial intelligence (AI) model (Examiner interprets Green expressly selects 100 item descriptions from a larger content set and analyzes the sample for common items, attribute/value information, vocabulary-adjustment rules, and related linguistic information; Examiner further interprets the terms identified from Green's sampled product-description content, when used in Green's expressly disclosed locale-specific localization environment, as corresponding to regional/local terminology. Green expressly performs localization for multiple registered locales) (“Referring first to FIG. 11, a new SOLx normalization process (1000) is initiated by importing (1102) the content of a source database or portion thereof to be normalized and selecting a quantify of text from a source database. For example, a sample of 100 item descriptions may be selected from the source content "denoted content.txt file." A text editor may be used to select the 100 lines. These 100 lines are then saved to a file named samplecontent.txt for purposes of this discussion …”) (col. 33, ll. 5-20; col. 33, ll. 44–65 - col. 34, ll. 1-5),
wherein the first Al model generalizes from the small-scale samples to populate the pre-established knowledge base with local common terms (Examiner interprets Green's sample processing leads to vocabulary rules and then a domain-specific dictionary containing words and phrases; Green also expressly describes changes to its normalization/translation knowledge bases, i.e. Green's sample-driven KB construction process, such that Green's sample-derived terminology/rules populate Green's KB) (“To build a domain specific dictionary, the SME can run a translation dictionary creation utility. This runs using the rule files created above as input, and produces the initial translation dictionary file. This translation dictionary file contains the words and phrases that were found in the rules. The words and phrases found in the translation dictionary file can then be manually and/or machine translated (1218). This involves extracting a list of all word types using a text editor and then translating the normalized forms manually or through a machine tool such as SYSTRAN. The translated forms can then be inserted into the dictionary file that was previously output. …”) (col. 34, ll. 1-20, Col. 40, ll. 15-40) and local grammatical expression habits for multiple regions (Examiner interprets that Green teaches grammar/normalization rules, statistical language structure, dictionaries, and multiple locale localization. Examiner interprets recurring grammar/syntax rules applicable to a locale as “local grammatical expression habits.” Green's broader localization system supplies the multiple-locale/region context) (“The discovery of structure by N-Gram Analysis is parallel to the discovery of structure by parsing in the Natural Language Engine. The two components are complementary, because each can serve where the other is weak. For example, in the example above, the NLE parser could discover the structure of the decimal number, "[number] (99.5)", saving NGA the task of modeling the grammar of decimal fractions. The statistical model of grammar in NGA can make it unnecessary for human experts to write extensive grammars for NLE to extract a diverse larger-scale grammar. By balancing the expenditure of effort in NGA and NLE, people can minimize the work necessary to analyze the structure of texts …” and “A primary B2B model of the present invention focuses on a Source/Seller managing all transformation/localization. The Seller will communicate with other Integration Servers (such as WebMethods) and bare applications in a "Point to Point" fashion, therefore, all locales and data are registered and all localization is done on the seller side. However, all or some of the localization may be managed by the buyer or on a third party platform such as the global platform”) (col. 39, ll. 31-65 and col. 35, ll. 13-31, Col. 41, ll. 1-10);
identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content (Examiner interprets that Green processes commerce/product-description content comprising textual descriptive phrases. Examiner interprets the underlying item/product as the target product object and the item's textual description as original descriptive information) (“The initial step in either a normalization or translation process is to access legacy content 1710 that is associated with the firms' various legacy systems 1712. The legacy content 1710 may be provided as level 1 commerce data consisting of short descriptive phrases delivered as flat file structures that are used as input into the NorTran Workbench 1702 …”) (col. 36, ll. 55-65, col. 38, ll. 25-65);
determining national or regional attribute information of the target user (Examiner interprets Green teaches target platforms/locales and localization between source and target locales. Examiner interprets the target locale associated with the intended recipient/target environment as regional attribute information) (“A primary B2B model of the present invention focuses on a Source/Seller managing all transformation/localization. The Seller will communicate with other Integration Servers (such as WebMethods) and bare applications in a "Point to Point" fashion, therefore, all locales and data are registered and all localization is done on the seller side. However, all or some of the localization may be managed by the buyer or on a third party platform such as the global platform”) (col. 35, ll. 13-31, Col. 43, ll. 55-65 - col. 44, ll. 1-5);
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 (Examiner interprets Green's LCS receives source text plus source locale and target locale and supplies target-locale content. Its runtime architecture uses the existing vocabulary/content-structure KB to process localized content. Examiner interprets this as adapting the original descriptive information to local expression based on the target locale) (“The LCS 1752 is a fast lookup translation cache. There are two parts to the LCS 1752: an API that is called by Java clients (such as a JSP server process) to retrieve translations, and an user interface 1754 that allows the user 1756 to manage and maintain translations in the LCS database 1752 … the translation memory foundation of the SOLx system 1700, the LCS 1752 is also intended to be used as a standalone product that can be integrated into legacy customer servers to provide translation lookups … SOLx Server 1708 provides the customer with a mechanism for run-time access to the previously cached, normalized and translated data. The SOLx Server 1708 also uses a pipeline processing mechanism that not only permits access to the cached data, but also allows true on-the-fly processing of previously unprocessed content. When the SOLx Server encounters content that has not been cached, it then performs the normalization and/or translation on the fly. The existing knowledge base of the content structure and vocabulary is used to do the on-the-fly processing …”) (Col. 43, ll. 55-65 – col. 44, ll. 1-15).
wherein the processing comprises: processing the original textual content via an AI large-scale parameter model to generate target textual content by replacing keywords in the original textual content with corresponding local common terms queried from the pre-established knowledge base (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets Green teaches transformation according to a normalization KB, including lookup-table-based text replacement, and later use of its existing vocabulary/content-structure KB during runtime localization. Examiner interprets Green's lookup-based replacement using locale vocabulary/dictionary information as querying the pre-established KB for corresponding terms) (“Output of the chart parser and the scoring algorithm is the set of alternative high scoring parse trees. Each parse tree object includes methods for transforming itself according to a knowledge base of normalization rules. Each parse tree object may also emit a String corresponding to text contained by the parse tree or such a String together with a string tag. Most such transformation or emission methods traverse the parse tree in post-order, being applied to a parse tree's children first, then being applied to the tree itself. For example, a toString( ) method collects the results of toString( ) for each child and only then concatenates them, returning the parse tree's String representation. Thus, normalization and output is accomplished as a set of traversal methods inherent in each parse tree. Normalization includes parse tree transformation and traversal methods for replacing or reordering children (rewrite rules), for unconditional or lookup table based text replacement, for decimal punctuation changes, for joining constituents together with specified delimiters or without white space, and for changing tag labels”) (col. 41, ll. 38-55, col. 43, ll. 55-65 – col. 44, ll. 1-15);
and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information (Examiner interprets that Green's runtime SOLX system uses its grammar rules and custom/standard glossaries in localized content processing. Examiner interprets application of those grammar rules/glossaries in the selected target locale as conforming the target text to local grammatical-expression habits. Green generates normalized/translated product-description content using its localization pipeline) (“The Translation Quality Estimation Analyzer (TQA) 1746 merges the structural information from conditioning with the translations from repair, producing a list of translation pairs. If any phrases bypassed machine translation, this merging process gets their translations from the dictionary … As shown in FIG. 1700, the primary NorTran Workbench engines are also used in the SOLx Server 1708. These include: N-Gram Analyzer 1722, Machine Translation Server 1742, Natural Language Engine 1718, Candidate Search Engine 1720, and Translation Quality Analyzer 1746. The SOLx server 1708 also uses the grammar rules 1754 and custom and standard glossaries 1756 from the Workbench 1702. Integration of the SOLx server 1708 for managing communication between the source/legacy system 1712 and targets via the Web 1758 is managed by an integration server 1758 and a workflow control system 1760 …”) (col. 43, ll. 1-15 - col. 44, ll. 20-30, col. 39, ll. 31-65, Col. 42, ll.25-45);
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 (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets delivery of the localized/generated product description through the Web to the requesting device for webpage presentation as satisfying this limitation; i.e. Green teaches delivery of localized content in a Web environment and transmission through application/HTTP servers) (“FIG. 16 shows the modification of such a system that allows the TCS 1600 containing translated content to be accessed in a Web environment. In this figure, original content from the source system 1602 is translated by the NorTran Server 1604 and passed to a TCS repository 1606. A transaction request, whether requested from a foreign system or the source system 1602, will pass into the TCS 1600 through the Document Processing Engine 1608. From there, a communication can be transmitted across the Web 1610 via integration server adaptors 1612, an integration server 1614, an optional application server 1616 and HTTP servers 1618 …”) (col. 36, ll. 30-42).
Green specifically doesn’t express, processing the original textual content via an AI large-scale parameter model to generate target textual content, to provide the target descriptive information on a designated webpage, however Wright discloses, wherein the processing comprises: processing the original textual content via an AI large-scale parameter model to generate target textual content (Examiner interprets Wright's BERT/GPT transformer generative language model as an AI large-scale parameter model, and its generated product description as target textual content that a merchant may provide one or more example product title and product description pairs as input, and that the examples may be selected because the merchant prefers their wording, grammar, tone, length, flow, or other characteristics. Wright further teaches that the generative language model may generate a product description taking those examples into consideration, resulting in generated descriptions that are more consistent with the preferred style of the merchant. Wright also discloses that the generative language model may be a natural language processing machine learning or deep learning model, including a recurrent neural network, LSTM, gated recurrent neural network, transformer model, BERT model, or GPT model) (“a merchant may wish to be provided with computer-generated product descriptions for their products. For example, a natural language processing model such as a generative language model may be used to generate product descriptions for the merchant … Once the merchant provides the input 502 via the user interface 428, the input may be delivered to the generative language model 510, e.g. by transmitting the input from the merchant device 420 to the product description generator 410, which executes the generative language model 510. The generative language model 510 may be any type of natural language processing machine learning or deep learning model, for example: a recurrent neural network (RNN) model such as the long short-term memory (LSTM) model or the gated recurrent neural network, or a transformer model such as the Bidirectional Encoder Representations from Transformers (BERT) or the Generative Pre-trained Transformer (GPT) model.”) (0077-0082, 0089-0090);
to provide the target descriptive information on a designated webpage (Examiner interprets Wright expressly teaches a webpage transmitted to a device that displays a product catalog and a corresponding product description for each product) (“FIG. 7 illustrates a web page 700 which may be provided to a merchant via the user interface 428 of the merchant device 420. The content of the web page 700 may be generated and transmitted to the merchant device 420. The web page 700 displays the product catalog of the merchant's online store. The product catalog may include a product title and a corresponding photo for each product sold through the merchant's online store. The product catalog may also include a product description for each product. A product's product description may be displayed on web page 700, or may be accessible by clicking on or hovering over the respective product title or photo on web page 700. The web page 700 may be accessible by the merchant by pressing a button having an icon and the text: “Products” in a navigation pane 702. Navigation pane 702 may be used by the merchant to access other web pages containing information about the merchant's online store, such as a home page which may provide information about tasks needed to be completed or an orders page which may show fulfilled and unfulfilled orders. The web page 700 may further include a clickable button 710 having the text: “Add Product”. When the button 710 is selected, the merchant may be directed to a web page that allows for the merchant to add a new product to the product catalog …”) (0115).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, processing the original textual content via an AI large-scale parameter model to generate target textual content, to provide the target descriptive information on a designated webpage, as taught by Wright for the purpose using generative NLP models, including BERT/GPT transformer models, to automatically generate product descriptions and to provide the resulting product-description content on a webpage.
Green specifically does not express, a first artificial intelligence (AI) model, wherein the first Al model generalizes from the small-scale samples, however Begun discloses, into a first artificial intelligence (AI) model (Examiner interprets that Begun expressly teaches few-shot learning that generalizes from a small number of labelled instances to a more widely applicable rule or adjustment of learned parameters; it also describes pretrained/fine-tuned ML models) (“technologies disclosed herein use machine learning, artificial intelligence, and other computer-implemented methods to identify various semantically important chunks in documents, automatically provide them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document; and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). “Nearby content” includes content which is horizontally close, such as preceding and following in the reading sequence of text; but also vertically close, such as within the same container structures like lists and sections along with their respective markers, headings, levels, etc.”) (0019), wherein the first Al model generalizes from the small-scale samples (Examiner interprets Begun's machine-learning model operated according to a few-shot learning principle as the recited first AI model. Begun expressly describes its disclosed technologies as employing machine learning and artificial intelligence and further teaches few-shot structure learning that creates a machine-learning model based on limited user feedback. Begun teaches pretraining a machine-translation model, generating a fine-tuning data set from the feedback, and further training the pretrained model according to a few-shot learning principle. Begun further expressly teaches that the system uses few-shot learning techniques to generalize from a small number of labelled instances to a more widely applicable rule or adjustment of learned parameters.) (“system uses few-shot learning techniques to generalize from a small number of labelled instances (for example, selective user feedback), to a more widely applicable rule or adjustment of learned parameters. This greatly reduces the number of times users must be asked for feedback, and more rapidly improves the system's performance” and “Few-shot structure learning takes care of creating a machine learning model relying on feedback provided by the user, as described in steps (14)-(15). This model is then used to generate a structure that combines the user feedback on structure with the one already produced by the system (and perhaps iteratively enhanced by prior feedback) … process takes place in different phases or steps: [0113] (a) First, a machine translation model is pretrained using a publicly available dataset. [0114] (b) The “dispatcher” (see Section ‘Feedback Response’ for a description) filters the user feedback. [0115] (c) New structure files are generated from the user feedback and a fine-tuning machine translation data set generated. [0116] (d) The pretrained model is further trained using the few-shot learning principle”) (0151 and 0110-0116).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, a first artificial intelligence (AI) model, wherein the first Al model generalizes from the small-scale samples, as taught by Begun for the purpose to employ few-shot machine-learning technique to generalize from a small number of labelled instances, thereby reducing the amount of feedback required and more rapidly improving system performance.
As per claims 2 and 17, Green 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 (Examiner interprets Green expressly teaches an “off-line” mode in which knowledge concerning content transformation is captured and collectively defines a knowledge base. After the KB is constructed, it is used to transform later content. Green states that its traditional e-business translation process is essentially off-line, becoming real-time when new content is subsequently added, and further states that the SOLX “off-line” mode captures knowledge concerning the intended transformation, which “collectively defines a knowledge base”; once that knowledge base has been constructed, the system subsequently uses it to transform content) (“In a traditional e-business environment, this translation process essentially is offline. It becomes real-time and online when new content is added to the system. In this case, assuming well-developed special-purpose dictionaries and linguistic information already exists, the process can proceed in an automatic fashion. Content, once translated is stored in a specially indexed look-up database. This database functions as a memory translation repository. With this type of storage environment, the translated content can be scaled to virtually any size and be directly accessed in the e-business process … The SOLx system operates in two distinct modes. The "off-line" mode is used to capture knowledge from the SME/translator and knowledge about the intended transformation of the content. This collectively defines a knowledge base. The off-line mode includes implementation of the configuration and translation processes described above. Once the knowledge base has been constructed, the SOLx system can be used in a file in/file out manner to transform content …”) (col. 34, ll. 55-65 - col. 35, ll. 1-10):
utilizing the (AI) large-scale parameter model to comprehend the original descriptive information, and processing the original descriptive information to adapt to the local expression (Examiner notes that the underlined limitation is disclosed by another reference. Examiner interprets Green's LCS is a lookup/localization system that takes source-language text, source locale, and target locale as inputs and returns target text representing translation into the target locale. It is loaded before runtime, and the SOLX server subsequently accesses or generates localized content at runtime using the existing KB. Examiner interprets transformation from source text into target-locale text as adapting the original description to local expression) (col. 43 ll. 54-65 - col. 44 ll. 1-15) based on the national or regional attribute information of the target user to generate the target descriptive information (Examiner interprets that Green says its system can interface with a Target Platform associated with a target to whom the communication is addressed or consumed, and that all locales and data are registered for localization. Green then uses the target locale as an input to its LCS. Examiner interprets the target locale associated with the intended target/recipient as the claimed national or regional attribute information of the target user; Green expressly states that the LCS output is target text representing the translation from source text/source locale into the target locale. The SOLX server uses the existing KB of content structure and vocabulary for runtime processing of uncached content. Examiner interprets the resulting target-locale product-description text as the claimed target descriptive information) (col. 43, ll. 54-65 – col. 44, ll. 1-15, col. 35, ll. 11-30, col. 43, ll. 61-65 – col. 44, ll. 1-5, col. 43, ll. 61-65 – col. 44, ll. 1-15).
Green specifically doesn’t discloses, utilizing an artificial intelligence (AI) large-scale parameter model to comprehend the original descriptive information, however Wright discloses, utilizing the (AI) large-scale parameter model to comprehend the original descriptive information (Examiner notes that the generative language model 510 may be an NLP machine-learning/deep-learning model, including BERT or GPT. Wright describes transformer model 600 used as the generative language model and identifies GPT-3as an example transformer machine-learning model. Examiner interprets the BERT/GPT transformer as the claimed AI large-scale parameter model and maps each input token into an embedding vector representing the meaning of the token; process those vectors through positional encoding and transformer encoder blocks and that multi-head self-attention determines the relevance of each word to the other words of the input and how each word should attend to the others. Examiner interprets this semantic/contextual analysis as the AI model comprehending the original descriptive information) (0081 and 0089-0093).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, utilizing the (AI) large-scale parameter model to comprehend the original descriptive information, as taught by Wright for the purpose of using generative models in localization system in order to semantically and contextually process the original product-description text, thereby facilitating generation of an appropriate product description.
As per claims 3 and 18, Green discloses, 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 (Green's LCS receives source-language text, source locale, and target locale and produces target-locale text. At runtime, the SOLX server accesses cached localized content or processes previously unprocessed content using the existing KB. Examiner interpretation: transforming descriptive information according to the target locale teaches adapting original descriptive information to local expression according to national/regional information) (col. 43, ll. 54-65 – col. 44, ll. 1-15):
processing the original descriptive information corresponding to the SKUs (Examiner interprets that Green expressly states: the TCS may be accessed by the “SKU or part number of the original item,” or by searching original content or its translated variant) (col. 36, ll. 60-65 – col. 37, ll. 1-5) to adapt to local expression to generate the target descriptive information, thereby to provide different target descriptive information for the same SKU (Green's LCS takes source-language text, source locale, and target locale and returns target text corresponding to the target locale; when content is not cached, the existing content-structure and vocabulary KB is used for on-the-fly processing) (col. 43, ll. 61-65 – col. 44, ll. 1-15) to users with different national or regional attribute information, based on various local expression (Green identifies a Target Platform associated with the target to whom the communication is addressed or consumed, teaches that all locales can be registered, and uses the target locale as an LCS input; Green further learns/stores localized vocabulary and grammar; its statistical grammar model captures recurring linguistic structure, and the runtime SOLX server uses grammar rules and custom/standard glossaries) (col. 35, ll. 11-30, and col. 43, ll. 61-65 – col. 44, ll. 1-5, col. 37, ll. 1-30; col. 39, ll. 31-51; col. 44, ll. 20-30).
Green specifically doesn’t disclose, the target product object is associated with multiple Stock Keeping Units (SKUs), however Wright discloses, the target product object is associated with multiple Stock Keeping Units (SKUs) (Wright teaches that a product may have many attributes/characteristics and many variants, including specific size/color combinations, and that products are provided product identifiers such as stock keeping units (SKUs). Examiner interpretation: the multiple variants associated with a product, together with Wright's express SKU identifiers used for products/product listings, teaches or at least suggests the claimed product object associated with multiple SKUs) (“A product may have many attributes and/or characteristics, like size and color, and many variants that expand the available options into specific combinations of all the attributes, like a variant that is size extra-small and green, or a variant that is size large and blue. Products may have at least one variant (e.g., a “default variant”) created for a product without any options. To facilitate browsing and management, products may be grouped into collections, provided product identifiers (e.g., stock keeping unit (SKU)) and the like. Collections of products may be built by either manually categorizing products into one (e.g., a custom collection), by building rulesets for automatic classification (e.g., a smart collection), and the like”) (0057).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, the target product object is associated with multiple Stock Keeping Units (SKUs), as taught by Wright to incorporate multiple-variant/SKU product structure into localized product-information system in order to distinguish and manage different configurations of the same product and to associate localized descriptive information with the respective product variants and attribute combinations..
As per claims 5 and 20, Green discloses, wherein the processing the original textual content comprises (Green expressly processes item/product descriptions consisting of a core item plus terms describing the item's attributes. It further explains that product descriptions are frequently noun phrases composed of nouns and adjectives. Examiner interprets these product-description noun/adjective phrases as the claimed original textual content) (col. 12, ll. 8-22; col. 12, ll. 48-57):
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 (Examiner interprets that Green identifies the “core item” as the item being sold/described. In the example “Black and Decker 3/8 drill with accessories,” Green identifies “drill” as the core item and the remaining words/phrases as describing the item. Examiner interpretation: Green's “core item” term corresponds to a keyword related to the claimed product name; Green expressly states that product-description noun phrases are typically mixtures of nouns and adjectives, giving examples such as “Large metallic object,” “Variable speed drill,” and “Plastic coated plate.” Green further expressly discusses adjective phrases and translating the constituent groups according to linguistic rules; Green's normalization rules expressly include replacement rules, which “allow the replacement of one kind of text with another kind of text.” Green then teaches unguided and guided replacement rules that replace identified text strings with replacement strings. Examiner interpretation: Green's replacement operation corresponds directly to “converting keywords … into” replacement terms. Further, Green's guided replacement rules perform a lookup of an appropriate replacement term. Green also uses custom translation dictionaries containing words/phrases and produces translated text in desired languages. The LCS subsequently takes source text, source locale and target locale and returns target text corresponding to that locale and LCS expressly takes source locale and target locale as inputs and returns target text representing translation into the target locale. Green also teaches registration/localization for multiple locales. Examiner interpretation: the target locale is the national/regional information controlling which localized replacement terminology is produced) (col. 12, ll. 8-57; col. 13, ll. 1-26, col. 14, ll. 59-65 – col. 15, ll. 1-35, col. 42, ll. 10-25; col. 43, ll. 60–65 – col. 44, ll. 1-15).
As per claims 9, Green discloses, wherein the original descriptive information comprises an original attribute/parameter description of the target product object (Green expressly defines the terms describing an item as its attributes and the contents or quantity of an attribute as its value. Examples include package, container type, and material. Green further explains that an item description consists of the core item and terms describing its various attributes. Examiner interprets Green's textual attribute/value description as the claimed attribute/parameter description.) (col. 9, ll. 34-65; col. 12, ll. 8-47);
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 (Green's LCS receives source-language text, source locale and target locale and outputs target text corresponding to the target locale. It also performs un-cached normalization/translation at runtime using the existing KB. Examiner interprets transformation of the textual product attributes/values contained in the source description as transforming the original attribute/parameter description) (col. 43, ll. 60-65 - col. 44, ll. 1-15):
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 that Green registers multiple locales and uses the target locale to determine the localized target text. Examiner interprets the target locale associated with the recipient as the claimed national/regional attribute information) (col. 35, ll. 13-31; col. 43, ll. 60-65 - col. 44, ll. 1-5) to adapt to the local expression to generate target attribute/parameter description, for display on a designated webpage (Examiner notes that the underlined limitation is disclose by another prior art. Green translates structured texts such as product descriptions, including their constituents, and outputs target-locale text. Examiner interprets the localized product-attribute/value content as the target attribute/parameter description adapted to local expression) (col. 42, ll. 10-40; col. 43, ll. 60-65 - col. 44, ll. 1-15).
Green specifically doesn’t express, for display on a designated webpage, however Wright discloses, for display on a designated webpage (Examiner interprets Wright expressly teaches a web page displaying a product catalog, including a product title and corresponding product description for each product. Green independently teaches access to translated content in a Web environment and transmission through application/HTTP servers) (“FIG. 7 illustrates a web page 700 which may be provided to a merchant via the user interface 428 of the merchant device 420. The content of the web page 700 may be generated and transmitted to the merchant device 420. The web page 700 displays the product catalog of the merchant's online store. The product catalog may include a product title and a corresponding photo for each product sold through the merchant's online store. The product catalog may also include a product description for each product. A product's product description may be displayed on web page 700, or may be accessible by clicking on or hovering over the respective product title or photo on web page 700. The web page 700 may be accessible by the merchant by pressing a button having an icon and the text: “Products” in a navigation pane 702. Navigation pane 702 may be used by the merchant to access other web pages containing information about the merchant's online store, such as a home page which may provide information about tasks needed to be completed or an orders page which may show fulfilled and unfulfilled orders. The web page 700 may further include a clickable button 710 having the text: “Add Product”. When the button 710 is selected, the merchant may be directed to a web page that allows for the merchant to add a new product to the product catalog …”) (0115).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, for display on a designated webpage, as taught by Wright for the purpose using generative NLP models, including BERT/GPT transformer models, to automatically generate product descriptions and to provide the resulting product-description content on a webpage.
Claims 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. 9684653 (“Green”) in view U.S. Pub. 20230259692 (“Wright”) in view U.S. Pub. 20210081411 (“Begum”) in further view of U.S. Pub. 20210398183 (“Jain”)
As per claims 6, Green specifically doesn’t disclose, the target product object is associated with multiple Stock Keeping Units (SKUs), however Wright discloses, wherein the original textual content comprises original title textual content (Examiner interprets that Wright expressly teaches that input 502 may comprise a product title for which a product description is to be generated, e.g., “ribbed crop tank top.” Examiner interpretation: Wright's product-title input is the claimed original title textual content) (0080-0082);
and wherein the processing the original textual content comprises: processing the original title textual content to adapt to a textual content related to an expression of product attributes (Wright inputs the product title into generative language model 510. The model processes input 502 and generates first output 512, which may be a product description describing the product title of input 502. Examiner interpretation: processing the input product title through Wright's generative model corresponds to processing the original title textual content i.e. Wright transforms a product title into descriptive product text) (0080-0082) 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 (The underlined limitation is disclosed by another prior art).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, the target product object is associated with multiple Stock Keeping Units (SKUs), as taught by Wright to incorporate product-title-based generative processing into localized product-information system in order to automatically generate descriptive product text from product-title information..
Green specifically doesn’t disclose, 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, however Jain discloses, 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 the claim should be corrected to “a category” instead of “an category”. Examiner interpretation: preferences for color/style are preferences concerning product attributes, corresponding to the claimed attribute preferences i.e. Jain expressly says the user query may contain preferences and traits, and gives product-attribute preferences such as particular colors or styles of furniture, including “pinewood in cherry color.” At para. 0067, expressly uses a user's location identifier, e.g. North America versus Asia, and states that product specifications and preferences may differ between the geographic regions, such that different product features are emphasized. It also identifies user attributes including gender, age, age range, occupation, first language, and user preference. And further at para. 0070, defines a target consumer group by age range, gender, geographical location, occupation, first language, and preferences i.e. model selection may depend on the reference-product category and user attributes; and para. 0084 expressly includes product category and target consumer group as product-attribute-space variables; Also see para. 0066 determines a category for the reference product and retrieves a deep-learning model for that specific product category. Jain's furniture model emphasizes colors, dimensions, and weight, whereas a baby-food model emphasizes the ingredient list) (0066-0067, 0070, 0083-0084).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, 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, as taught by Jain for the purpose to generate product-description content that emphasizes product attributes more relevant to the particular target consumer group and geographic region.
Claims 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. 9684653 (“Green”) in view U.S. Pub. 20230259692 (“Wright”) in view U.S. Pub. 20210081411 (“Begum”) in further view of U.S. Pat. 9817907 (“Sharifi”).
As per claims 7, Green specifically doesn’t disclose, wherein the original textual content comprises an original user review textual content; and wherein the processing the original textual content comprises: 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 Sharifi discloses, wherein the original textual content comprises an original user review textual content (Examiner interpretation: the content of an individual user-submitted review corresponds to the claimed original user review textual content i.e. a place page containing one or more reviews, including a review supplied by a user, and a review database storing a plurality of reviews associated with points of interest and identifying the user who contributed each review) (“User interface 252 can provide the identity 252 of the point of interest (e.g. a title or nickname). User interface 252 also includes a review score 256 for the point of interest and an annotation 258 that indicates that the review score 256 corresponds to an average review score from users that stayed at the same hotel as the user performing the search. Another annotation 260 can be included that identifies the number of reviews that are available from users that stayed at the same hotel as the user viewing the information”) (col. 7, ll. 55-65; col. 11, ll. 25-45);
and wherein the processing the original textual content comprises: reordering the original user review textual content (Examiner interpretation: changing the presentation order by promoting selected reviews ahead of other reviews constitutes the claimed reordering of user review content i.e. Sharifi expressly states that information associated with users staying at the identified accommodation can influence “an ordering of reviews” on a place page. It further teaches selecting reviews for display and promoting certain reviews for display in favor of other reviews) (col. 4, ll. 23-30; col. 5, ll. 49-65 - col. 6, ll. 1-3) based on the national or regional attribute information of the target user (Examiner interpretation: the user's geographic location/place of accommodation corresponds to the claimed national or regional attribute information i.e. Sharifi determines the user's location/place of accommodation, including by reverse geocoding the user's geographic location, and uses that geographic context to customize the reviews/results supplied to the user. The user's location may also impact displayed results) (col. 4, ll. 10-30; col. 7, ll. 7-25; col. 8, ll. 55-65), in order to prioritize the display of local user reviews according to the national or regional attribute information (Examiner interprets that Sharifi teaches that reviews supplied by reviewers associated with the same place of accommodation can be displayed in favor of reviews from other reviewers so that the user receives the most relevant reviews. It later expressly teaches identifying those reviews and promoting them for display in favor of other reviews; The review promotion is specifically controlled by the geographic/travel context associated with the target user - e.g., the user's identified place of accommodation/location, and reviews associated with that same geographic context receive favored display i.e. prioritizing reviews associated with the same geographic context as the target user is prioritizing local reviews according to the user's regional attribute information.) (col. 3, ll. 20-35; col. 4, ll. 10-30; col. 5, ll. 49-65 - col. 6, ll. 1-10).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, wherein the original textual content comprises an original user review textual content; and wherein the processing the original textual content comprises: 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 Sharifi for the purpose to apply ordering and prioritizing user-review content based on geographic information associated with the target user, in order to present reviews that are more relevant to the user’s particular geographic context.
Claims 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. 9684653 (“Green”) in view U.S. Pub. 20230259692 (“Wright”) in view U.S. Pub. 20210081411 (“Begum”) in further view of U.S. Pub. 20210398183 (“Jain”) in further view of U.S. Pat. 20210342915 (“Shivaswamy”).
As per claims 10, Green specifically doesn’t 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: if the target product object is associated with multiple Stock Keeping Units (SKUs) having different attribute values/parameter values, however Wright 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: if the target product object is associated with multiple Stock Keeping Units (SKUs) having different attribute values/parameter values (Wright teaches that a product may have many variants corresponding to different combinations of product attributes, for example, an extra-small green product versus a large blue product, and further identifies SKU as a product identifier. Examiner interprets Wright's multiple product variants having different attribute combinations, in combination with its express disclosure of SKU identifiers, as teaching or suggesting a product object associated with multiple SKUs having different attribute values/parameter values) (0057).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, 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: if the target product object is associated with multiple Stock Keeping Units (SKUs) having different attribute values/parameter values, as taught by Wright to incorporate multiple-variant/SKU product structure into localized product-information system so that localized product descriptive information could be associated with and generated for the particular product variants and their respective attribute combinations.
Green specifically doesn’t discloses, of the SKUs with locally commonly used attribute values/parameter values, corresponding to the national or regional attribute information, however Jain discloses, 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 that the underlined limitation is disclosed by another prior art. Jain discloses that product specifications and preferences may differ significantly according to geographic region and that geographically trained models may emphasize different product features. Examiner interprets Jain's region-dependent product preferences as teaching or suggesting identification of product attribute values that are more commonly preferred or relevant within the particular geographic region.) (“the user attribute may be a location identifier specifying whether the user is located in North America or in Asia. As product specifications and preferences may differ significantly between the two continents, the PRICE M4 product matching model trained on data classified into different geographical regions may emphasize on different features of the product. Similarly, the user attribute may comprise a gender of a human user, an age, an age range, occupation, first language, and/or other user characteristics. In another example, the user attribute may be a user preference. For example, the user may be looking for a product under a particular brand, or may be looking for a used or rental product rather than a new product. In some embodiments, depending on how training data sets are organized, different M4 models may be trained respectively to match products across different type or condition categories, such as new-to-new, new-to-used, and used-to-rental.”) (0067).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, of the SKUs with locally commonly used attribute values/parameter values, corresponding to the national or regional attribute information, as taught by Jain in order to identify product variants having attribute values that are more relevant to, or commonly preferred by, users in the target user's geographic region, thereby tailoring the product information presented to the regional preferences of the target user.
Green specifically doesn’t discloses, of the SKUs with locally commonly used attribute values/parameter values, corresponding to the national or regional attribute information, however Shivaswamy discloses, reordering the SKUs to prioritize the display (Shivaswamy teaches ranking a first search result associated with a first product variant higher than a second search result associated with a second product variant based on relative user-activity data, including quantities of sales, views, and/or impressions associated with the respective variants. Examiner interprets Shivaswamy's ranking of product variants according to variant-specific popularity/user-activity information as teaching reordering product variants to prioritize their display) (“a first search result associated with the first variant is ranked based at least in part on the first user activity data. In some embodiments, the ranking engine 160 performs the ranking and at least some of its functionality as described with respect to the ranking engine 160 is included in block 509. Some embodiments rank a first search result associated with the first variant higher than a second search result associated with the second variant based at least in part on the first user activity data relative to the second user activity data of the same first listing … the ranking at block 509 is based on the quantity of sales of the first variant being higher relative to the second variant, the quantity of views of the first variant being higher relative to the second variant, and/or the quantity of impressions of the first variant being higher relative to the second variant”) (0071-0076).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, reordering the SKUs to prioritize the display, as taught by Shivaswamy for the purpose to apply variant-ranking technique to the region-sensitive product variants in order to prioritize for display those product variants having attribute values determined to be more commonly used, preferred, or relevant within the target user's geographic region.
Claims 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. 9684653 (“Green”) in view U.S. Pub. 20230259692 (“Wright”) in view U.S. Pub. 20210081411 (“Begum”) in further view of U.S. Pub. 20210398183 (“Jain).
As per claims 11, Green discloses, to adapt to into the local expression (Green's LCS takes source-language text, source locale and target locale and produces target text corresponding to the target locale. Green's runtime system also uses its existing content/vocabulary KB together with grammar rules and custom/standard glossaries. Examiner interprets target-locale transformation using locale-specific vocabulary and grammar as adapting the description into the claimed local expression) (col. 43, ll. 60-65 - col. 44, ll. 1-15).
Green does not expressly disclose inputting the original attribute/parameter description information into an AI large-scale parameter model such that the model transforms the product description, however Wright 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: inputting the original attribute/parameter description information and the national or regional attribute information of the target user into the Al large-scale parameter model (Wright teaches an input/prompt containing product textual information, including a product title and optionally example product-title/product-description pairs. The input is delivered to generative language model 510, which processes it and produces a product description. Examiner interprets Wright discloses inputting product textual information into a generative language model, including a BERT/GPT transformer, and generating corresponding product-description text) (0080-0082m, 0089-0093), so that the Al large-scale parameter model performs the transforming the original attribute/parameter description of the target product object (Wright's generative model receives product textual input and itself generates corresponding product-description text. Examiner interprets the resulting generation/modification of product descriptive text as the claimed transformation of the original attribute/parameter description) (“prompt may include text corresponding to the product for which a modifiable product description is to be generated. For example, input 502 may be or include a product title for which the merchant wants a modifiable product description generated, or it may be or include one or more example product title and product description pairs followed by a product title for which the merchant wants a modifiable product description generated. For example, if the merchant wanted a product description for a ribbed crop tank top product, the merchant may simply enter the words “ribbed crop tank top” as input 502. Alternatively, the merchant may enter one or more example product title and product description pairs, such as “linen halter top” and its corresponding product description, “corduroy cargo pants” and its corresponding product description, “embroidered poplin top” and its corresponding product description, followed by the words “ribbed crop tank top”, as input 502. The one or more example product title and product description pairs may be chosen by the merchant to form part of input 502 because the merchant favours the example product descriptions over other product descriptions … generative language model 510 may process the input 502 and return a first output 512. In some embodiments, the first output 512 may be an unmodifiable product description describing the product title of input 502. If the merchant entered one or more example product title and product description pairs as part of input 502, the generative language model 510 may analyze the example pairs such that the first output 512 resembles the one or more example descriptions in terms of wording, or grammar, or tone, or length, or flow, or any other characteristic possessed by the example product descriptions”) (0080-0082, 0089-0093).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, 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: inputting the original attribute/parameter description information and the national or regional attribute information of the target user into the Al large-scale parameter model, so that the Al large-scale parameter model performs the transforming the original attribute/parameter description of the target product object, as taught by Wright to incorporate multiple-variant/SKU product structure into localized product-information system so that localized product descriptive information could be associated with and generated for the particular product variants and their respective attribute combinations.
Green specifically doesn’t discloses, based on the national or regional attribute information of the target user, however Jain discloses, 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 Primary prior art. Jain supplies the teaching that geographic user information influences model processing and which product features are emphasized i.e. Jain teaches that retrieval/selection of a previously trained deep-learning model may be based on the user's geographic attribute and that models trained using different geographical regions may emphasize different product features because product specifications and preferences differ geographically) (“the user attribute may be a location identifier specifying whether the user is located in North America or in Asia. As product specifications and preferences may differ significantly between the two continents, the PRICE M4 product matching model trained on data classified into different geographical regions may emphasize on different features of the product. Similarly, the user attribute may comprise a gender of a human user, an age, an age range, occupation, first language, and/or other user characteristics. In another example, the user attribute may be a user preference. For example, the user may be looking for a product under a particular brand, or may be looking for a used or rental product rather than a new product. In some embodiments, depending on how training data sets are organized, different M4 models may be trained respectively to match products across different type or condition categories, such as new-to-new, new-to-used, and used-to-rental.”) (0067-0070).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, based on the national or regional attribute information of the target user, as taught by Jain in order to generate product-description content emphasizing product attributes more relevant to the preferences associated with the target user's geographic region, with Green's target-locale localization further adapting the resulting content to local expression.
Claims 12 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. 9684653 (“Green”) in view U.S. Pub. 20230259692 (“Wright”) in view U.S. Pub. 20210081411 (“Begum”) in further view of U.S. Pub. 20150206189 (“Norwood”) in further view of U.S. Pat. 10930263 (“Mahyar”).
As per claims 12, Green specifically doesn’t express, wherein the original descriptive information comprises original rich media information of the target product object, however Norwood discloses, wherein the original descriptive information comprises original rich media information of the target product object (Norwood's localized e-commerce offer listings expressly may contain images, audio, video, graphics, text, SVG, and other media. Examiner interprets the non-text media components, particularly images, audio, video, and graphics, as the claimed rich media information associated with the target product object/offer listing) (“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. In some embodiments, the offer listing source data may be distributed across multiple data stores”) (0023);
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: 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 (Examiner notes that the underlined limitation is disclosed by another prior art. Norwood provides the e-commerce context: offer-listing content includes rich media and is localized. Norwood further expressly says localized offer listings are translated based at least partly on the “locale preferred” by users of the customer client, and that the particular locales may be associated with those users i.e. Examiner interprets Norwood's user-preferred locale as the claimed national or regional attribute information of the target user) (“localization engine 124 performs the localization of the content contained in one or more offer listings specified by the merchant client 106 using the user interface generated by the merchant interface application 121. Localization of the content is performed using the information obtained by the merchant interface application 121, wherein the localization engine 124 may generate one or more localized offer listings. The network page server 127 serves up the localized offer listings to one or more customer clients 116 in the form of localized network pages or other forms of network content, where the localized offer listings have been translated into one or more languages based at least in part on the locale preferred by one or more users of the customer client 116. Other content in the localized offer listings such as, for example, pricing information, is also localized according to selections made by the merchant client 106 …”) (0014-0015, 0013).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, based on the national or regional attribute information of the target user, as taught by Norwood to utilize locale-specific e-commerce offer-listing system in order to provide localized e-commerce content, including rich-media components, appropriate for customers associated with the target locale, thereby improving the accessibility and relevance of the product presentation for users in different locales.
Green specifically doesn’t express, to adapt to the local expression to generate target rich media information, however Mahyar discloses, to adapt to the local expression to generate target rich media information (Examiner interprets Mahyar teaches automatic generation of localized media in multiple languages; an English source is used to produce predicted audio in Mandarin, French, Swedish, etc. The generated audio waveform is in a target language, and post-processing includes local accent matching. Examiner interprets the resulting localized media as target rich-media information adapted to local expression. Mahyar further teaches target-language audio generation and post-processing, including local accent matching. Examiner interprets the resulting localized audio/video content as the claimed target rich media information adapted to local expression) (“The disclosed techniques have the practical application of enabling automatic generation of dubbed video content for multiple languages, with particular speakers in each dubbing having the same voice characteristics as the corresponding speakers in the original version of the video content. For example, for the English language movie The Last Samurai starring the actor Tom Cruise playing the captured US Army Capt. Nathan Algren, a Japanese dubbing can be automatically generated, where the Japanese vocalization of the dialogue spoken by the Nathan Algren character replicates the speech characteristics of the actor Tom Cruise …” and “Such information includes content for rendering and display on display 306(1) including, for example, any type of video content. In some implementations, a portion of device memory 320 may be distributed across one or more other devices including servers, network attached storage devices, and so forth) (col. 1, ll. 48-65 - col. 2, ll. 1-25; col. 2, ll. 40-63; col. 4, ll. 62-67).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, to adapt to the local expression to generate target rich media informatio, as taught by Mahyar to incorporate automatic media-localization technique in order to transform rich-media components into localized target media appropriate for the user's locale, thereby improving the accessibility and usefulness of localized product content for users in different geographic regions.
As per claims 14, Green doesn’t expressly disclose, wherein the original rich media information comprises original audio information; 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: to generate target audio information that aligns with local preferences corresponding to the national or regional attribute information, however Norwood discloses, wherein the original rich media information comprises original audio information (Norwood expressly states that localized product offer listings may contain audio) (“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. In some embodiments, the offer listing source data may be distributed across multiple data stores”) (0023);
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: transforming the original audio information based on the national or regional attribute information of the target user (Examiner notes that the underlined limitation is disclosed by another prior art. Norwood provides the e-commerce context: offer-listing content includes rich media and is localized. Norwood further expressly says localized offer listings are translated based at least partly on the “locale preferred” by users of the customer client, and that the particular locales may be associated with those users i.e. Examiner interprets Norwood's user-preferred locale as the claimed national or regional attribute information of the target user) (“localization engine 124 performs the localization of the content contained in one or more offer listings specified by the merchant client 106 using the user interface generated by the merchant interface application 121. Localization of the content is performed using the information obtained by the merchant interface application 121, wherein the localization engine 124 may generate one or more localized offer listings. The network page server 127 serves up the localized offer listings to one or more customer clients 116 in the form of localized network pages or other forms of network content, where the localized offer listings have been translated into one or more languages based at least in part on the locale preferred by one or more users of the customer client 116. Other content in the localized offer listings such as, for example, pricing information, is also localized according to selections made by the merchant client 106 …”) (0014-0015, 0013).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, based on the national or regional attribute information of the target user, as taught by Norwood to utilize locale-specific e-commerce offer-listing system in order to provide localized e-commerce content, including rich-media components, appropriate for customers associated with the target locale, thereby improving the accessibility and relevance of the product presentation for users in different locales.
Green doesn’t expressly disclose, transforming the original audio information to generate target audio information that aligns with local preferences corresponding to the national or regional attribute information, however Mahyar discloses, transforming the original rich media information of the target product object (Mahyar's automatic media-localization system takes source-language content and produces predicted audio waveforms in a target language, e.g. English source content localized into Mandarin, French or Swedish audio) (col. 1, ll. 48-65 - col. 2, ll. 1-25),
transforming the original audio information (Mahyar's automatic media-localization system takes source-language content and produces predicted audio waveforms in a target language, e.g. English source content localized into Mandarin, French or Swedish audio) (col. 1, ll. 48-65 - col. 2, ll. 1-25),
to generate target audio information that aligns with local preferences corresponding to the national or regional attribute information (Examiner interprets Mahyar generates predicted target-language audio waveforms and selects/tunes the generated audio during the localization process; Mahyar expressly teaches post-processing the localized audio for “local accent matching”; Examiner interprets local-accent matching for a target localization language/region as adapting the target audio to a locally appropriate audio expression/preference) (“The disclosed techniques have the practical application of enabling automatic generation of dubbed video content for multiple languages, with particular speakers in each dubbing having the same voice characteristics as the corresponding speakers in the original version of the video content. For example, for the English language movie The Last Samurai starring the actor Tom Cruise playing the captured US Army Capt. Nathan Algren, a Japanese dubbing can be automatically generated, where the Japanese vocalization of the dialogue spoken by the Nathan Algren character replicates the speech characteristics of the actor Tom Cruise …” and “Such information includes content for rendering and display on display 306(1) including, for example, any type of video content. In some implementations, a portion of device memory 320 may be distributed across one or more other devices including servers, network attached storage devices, and so forth) (col. 1, ll. 48-65 - col. 2, ll. 1-25; col. 2, ll. 40-63; col. 4, ll. 62-67).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, transforming the original audio information to generate target audio information that aligns with local preferences corresponding to the national or regional attribute information, as taught by Mahyar to incorporate automatic media-localization technique in order to transform rich-media components into localized target media appropriate for the user's locale, thereby improving the accessibility and usefulness of localized product content for users in different geographic regions.
Claims 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. 9684653 (“Green”) in view U.S. Pub. 20230259692 (“Wright”) in view U.S. Pub. 20210081411 (“Begum”) in further view of U.S. Pub. 20150206189 (“Norwood”) in further view of U.S. Pat. 10930263 (“Mahyar”) in further view of U.S. Pub. 20190251612 (“Fang”) in further view of U.S. Pub. 20210398183 (“Jain).
As per claims 13, Green specifically doesn’t express, wherein the original rich media information comprises original image information 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: transforming composition style, model type, and/or atmospheric elements of the original image information, to generate target image information that aligns with local preferences corresponding to the national or regional attribute information, however Fang discloses, wherein the original rich media information comprises original image information (Fang obtains an existing fashion-item query image and uses it as the starting point for generating a modified version. This directly supplies original image information) (“synthesizing new designs and fashions personalized for a user, the personalized fashion generation system can also modify existing fashion items to better match a user's tastes and preferences. For instance, the personalized fashion generation system uses the trained GAN and the personalized preference network to modify existing fashion items to better align with a user's preferences … optimized to the user's preferences is identified, the personalized fashion generation system feeds the optimized latent code used as input into the generator of the trained GAN to create a modified version of fashion item shown in the query image. Indeed, by employing latent user features in connection with the trained GAN and the personalized preference network, the personalized fashion generation system can modify an existing fashion item to design a tailored version of the item for the user”) (0032-0034);
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: 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 (Examiner notes that the underlined limitation is taught by another prior art. Fang modifies an existing query image based on preferences and expressly applies different modifications and designs. For the same original pants image, different generated outputs comprise long pants, capris, or shorts and vary in color; expressly discusses modified styles and designs) (“the personalized fashion generation system applies different modifications to the query image 502 and subsequent modified synthesized images based on each user's individual personal visual preferences. To illustrate, as mentioned above, the query image 502 of the men's shirt (e.g., top three rows) is the same. However, the user-generated modified synthesized image 510, as well as images with fewer iterations, are distinct between the three corresponding users. Indeed, the personalized fashion generation system employs the visually-aware personalized image generation network 500 to uniquely apply modifications and designs that are uniquely tailored to each user's preferences …”) (0127-0129), to generate target image information that aligns with local preferences corresponding to the national or regional attribute information (Examiner notes that the underlined limitation is taught by another prior art. Fang generates a modified synthesized image from the original/query image, optimized according to user preference. Fang establishes image modification according to visual preferences; Jain supplies the missing relationship that preferences differ geographically. Thus, using regional preference information to control Fang's image modification teaches/suggests producing an image aligned with local preferences) (0033-0034, 0111-0118, 0010).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, wherein the original rich media information comprises original image information 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: transforming composition style, model type, and/or atmospheric elements of the original image information, to generate target image information that aligns with local preferences corresponding to the national or regional attribute information, as taught by Fang so that the visual style/design of the product image would be adapted to product preferences associated with the target user's geographic region, thereby presenting product imagery that is more relevant to users in that locale.
Green specifically doesn’t discloses, based on the national or regional attribute information of the target user, however Jain discloses, based on the national or regional attribute information of the target user (Examiner notes that Fang discloses transforming an existing image to generate a modified image corresponding to user visual preferences, but does not expressly disclose that such preferences correspond to the national or regional attribute information of the target user. However, Jain supplies the teaching that geographic user information influences model processing and which product features are emphasized i.e. Jain teaches that retrieval/selection of a previously trained deep-learning model may be based on the user's geographic attribute and that models trained using different geographical regions may emphasize different product features because product specifications and preferences differ geographically) (“the user attribute may be a location identifier specifying whether the user is located in North America or in Asia. As product specifications and preferences may differ significantly between the two continents, the PRICE M4 product matching model trained on data classified into different geographical regions may emphasize on different features of the product. Similarly, the user attribute may comprise a gender of a human user, an age, an age range, occupation, first language, and/or other user characteristics. In another example, the user attribute may be a user preference. For example, the user may be looking for a product under a particular brand, or may be looking for a used or rental product rather than a new product. In some embodiments, depending on how training data sets are organized, different M4 models may be trained respectively to match products across different type or condition categories, such as new-to-new, new-to-used, and used-to-rental.”) (0067-0070).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for generating, in an offline process performed before processing the original descriptive information for the target user, a pre-established knowledge base by inputting small-scale samples of regional terms to populate the pre-established knowledge base with local common terms and local grammatical expression habits for multiple regions, identifying at least one target product object and its original descriptive information to be provided to a target user, wherein the original descriptive information comprises original textual content; 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, with corresponding local common terms queried from the pre-established knowledge base, and adapting the target textual content to conform to the local grammatical expression habits corresponding to the national or regional attribute information, thereby generating the target descriptive information, and providing the target descriptive information corresponding to the at least one target product object to a client device of the target user, as disclosed by Green, based on the national or regional attribute information of the target user, as taught by Jain so that the visual style/design of the generated product image is adapted to preferences associated with the target user's geographic region, thereby presenting product imagery more relevant to users in that locale.
Response to Arguments
With regards to § 101 rejections:
The arguments filed on July 29th, 2026, with respect to the rejection(s) of claims 1-3, 5-7, 9-14, 16-18, and 20 under 35 U.S.C 101 have been fully considered but are unpersuasive. See Remarks 8-12.
Applicant states that the amended Claim 1 Does Not Recite a Mental Process.
Applicant’s arguments have been considered but are not persuasive. Applicant states that amended claims 1 and 16 cannot be characterized as mental processes because the recited first AI model, offline knowledge-base generation, and subsequent AI large-scale parameter-model processing cannot practically be performed in the human mind. The Examiner has reconsidered the rejection in view of the amendment and does not rely on those AI/knowledge-base operations as themselves constituting a mental process. Rather, the claims recite a certain method of organizing human activity, namely a commercial interaction involving tailoring and presenting customer-facing product information according to characteristics of a consumer, including the consumer’s national or regional attributes. Advertising, marketing, and sales activities fall within the recognized commercial-interactions subgroup. MPEP § 2106.04(a)(2)(II). Applicant’s amendment and arguments concerning the AI processing architecture are expressly considered as additional elements under Step 2A, Prong Two and Step 2B.
Applicant state that Claim 1 Integrates Any Alleged Abstract Idea Into a Practical Application. Applicant further states that the ordered offline/runtime arrangement constitutes a technological improvement because regional-term generalization and knowledge-base population occur offline before subsequent user-specific localization, allegedly improving content-production efficiency and real-time performance. This argument is not persuasive. The claims use the first AI model to generate regional linguistic information, store that information in a knowledge base, and subsequently use the stored information through an AI large-scale parameter model to produce localized product content. The claimed arrangement changes how and when information used to perform the localized commercial-content process is generated and accessed, but does not recite an improvement to AI-model operation, database functionality, computer operation, networking, or another technological mechanism. Although a software improvement may reside in a logical process rather than particular hardware, the claim must itself reflect an improvement to computer functionality or another technology or technical field. MPEP § 2106.05(a). The asserted efficiency in generating localized product content therefore does not, on the present claim language, integrate the identified commercial interaction into a practical application.
Applicant further state that Claim 1 Recites Significantly More. Applicant’s arguments have been considered but are not persuasive. The additional elements also do not amount to significantly more under Step 2B. Identifying the target product information and determining the user’s national or regional information merely supply information used in the subsequent localization process, while providing the resulting localized information to a client device for display on a webpage merely communicates the result. To the extent treated as additional elements, these are insignificant pre-solution data gathering and post-solution presentation activities under MPEP § 2106.05(g). The substantive AI-model and knowledge-base limitations are separately considered as an ordered combination and are not characterized merely as extra-solution activity; however, they employ AI and stored linguistic information as tools for carrying out the identified localized product-information activity and do not provide a separate inventive concept sufficient to amount to significantly more than the judicial exception. Accordingly, Applicant’s arguments do not overcome the rejection of claims 1-3, 5-7, 9-14, 16-18, and 20 under 35 U.S.C. § 101. See MPEP §§ 2106.05(a)–(c), (e)– (h).
With regards to § 103 rejections:
Applicant's arguments, see pages 13-14, filed July 29th, 2026, with respect to the rejection(s) of claims 1, 3, 5, 6, 9, and 11-14, 16, 18, and 20 under 35 U.S.C 102/103 have been fully considered but are unpersuasive/moots on new ground of rejection.
Applicant's arguments have been considered but are not persuasive because the present rejection does not rely on Wright's prompt-time examples to populate Bhagat's translation dictionary, nor does the present rejection require the modification criticized by Applicant. The rejection has been reconsidered in view of the amended claims. Green is presently relied upon for the offline construction and subsequent use of a localization knowledge base. Green expressly describes selecting a limited sample of item descriptions, deriving vocabulary/rules and dictionary information from sampled content, operating in an “off-line” mode to capture knowledge concerning the intended transformation that “collectively defines a knowledge base,” and subsequently using the constructed knowledge base to transform later content, including runtime processing using the existing content-structure and vocabulary knowledge base. Green additionally teaches multiple locales, locale-specific vocabulary/grammar, lookup-based text replacement, and subsequent localized processing. Begun, rather than Wright, is relied upon for the particular missing AI-generalization aspect. Begun expressly teaches a machine-learning/AI model using few-shot learning techniques to generalize from a small number of labelled instances to a more widely applicable rule or adjustment of learned parameters. Thus, the present rejection does not depend on treating Wright's example descriptions as the claimed samples or on treating Wright's model output as populating a separate dictionary. Wright is relied upon for the separate limitations concerning processing product textual information through an AI large-scale parameter model, including BERT/GPT transformer models, to generate product-description text, and for presentation of product-description information on a designated webpage. Accordingly, Applicant's statements directed to the previous Bhagat/Wright/Duan/Norwood prior arts do not address the factual basis of the present rejection. The present combination relies on Green's offline sample-driven localization knowledge base, Begun's expressly taught few-shot AI generalization, and Wright's large-scale generative language-model processing. The references are combined for their respective known functions rather than by converting Wright's prompt-time examples into Bhagat's dictionary. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ Begun's known few-shot machine-learning generalization technique in Green's sample-driven knowledge-base construction process so that useful generalized linguistic information could be derived from a relatively small number of samples, thereby reducing the amount of feedback/training information required while improving the ability of the localization system to process additional content. It further would have been obvious to employ Wright's known generative BERT/GPT product-description processing in Green's localized product-information environment to generate localized customer-facing product descriptions using modern generative-language-model processing. Each prior art reference performs its known function in the combination, and the combination represents application of known techniques to a related product-information/localization system for predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US. Pat. 10861276 (“Arora”).
Arora discloses, a system for vending products to a customer that encompasses a group of co-located vending machines managed by a vending company, a database of current inventory of products in the vending machines and customer purchase history; and the use of a personal electronic device by the customer. Embodiments include a single order by a customer from a sorted list of prior purchases, and where a single purchase comprises sub-products from multiple co-located vending machines. Embodiments include the customer selecting either products or vending machines from a list of options provided via the user interface of the personal electronic device, wherein the list of options depends on the actual available inventory in vending machines co-located with the customer, and the customer purchase history.
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/GAUTAM UBALE/Primary Examiner, Art Unit 3689
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