DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 8, 2026 has been entered.
Response to Amendment
Applicant’s Amendments, filed May 8, 2026, have been entered. No claims have been amended and claims 1-16 are currently pending.
Response to Arguments
Applicant’s arguments, see Remarks pp. 8-16, filed May 8, 2026, with respect to the rejections of claims 1-16 under 35 U.S.C. 102 and 35 U.S.C. 103 have been fully considered and are persuasive, in part.
Regarding claim 2, Applicant argues that cited prior art Iyer does not teach (Pub. No. US 2022/0301031 A1, hereinafter “Iyer”) does not teach the recited “enhanced text describing the object” (Remarks pp. 13-16). In response, examiner respectfully submits that Iyer teaches that the user interface, in Fig. 5B, includes a table showing the predicted result of the standardized code (Iyer [0078]). Examiner interprets that the Prediction “691104500”, which is the predicted standardized code, discloses the recited “enhanced text describing the object”.
Regarding claim 1, Applicant argues that Misiewicz et al. (Pub. No. US 2022/0138170 A1, hereinafter “Misiewicz”) does not teach amended claim 1, specifically that Misiewicz does not teach the ancestor nodes and descendent nodes (Remarks pp. 8-10). Examiner finds this argument persuasive.
Regarding claims 4-5, Applicant argues that the claims are not taught by Misiewicz, specifically arguing that The Harmonized Commodity Description and Coding System is not non-functional descriptive material (Remarks pp. 10-11). Examiner finds this argument persuasive.
Regarding claim 14, Applicant argues that the claim is not taught by Misiewicz in view of Iyer, based on the arguments presented with respect to claim 1 (Remarks pp. 12-13). Examiner finds this argument persuasive.
Based on the persuasive arguments above, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Liu (Pub. No. US 2017/0177703 A1, hereinafter “Liu”) and Song (Pub. No. US 2015/0161728 A1, hereinafter “Song”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6, 11, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Misiewicz in view of Liu.
Regarding claim 1, Misiewicz teaches:
obtaining a query, wherein the query comprises a text describing the object (Misiewicz - see Fig. 3, 310, where a search query including key words associated with an entity is received [0061].)
transforming, using a first natural language processing model, the query into a query embedding vector (Misiewicz – see Fig. 3, 320, where an embedding vector based on the search query is generated using a neural network [0062]. The neural network may execute natural language processing [0026].)
calculating a set of similarity scores, wherein each similarity score in the set of similarity scores corresponds to a degree of similarity between the query embedding vector and an embedding vector in a set of embedding vectors, wherein each embedding vector in the set of embedding vectors is associated with a location in a hierarchy classification system and a data source of a plurality of data sources, (Misiewicz – see [0063-0064], where the embedding vector associated with the search query is compared to a set of embedding vectors associated with a set of structured data elements related to an entity. The comparison includes scoring each of the structured data elements. The structured data elements are stored in a knowledge graph (i.e. hierarchy classification system) [0011-0012], and the knowledge search system is associated with a plurality of data sources [0014-0015].)
selecting one or more candidate embedding vectors from the set of embedding vectors based on the calculated similarity scores; identifying a set of one or more candidate locations in the hierarchy classification system, wherein each of the one or more candidate locations is associated with at least one of the selected one or more candidate embedding vectors (Misiewicz – see Fig. 3, 340, where, based on the comparison of the embedding vectors and distance-comparison score that is less than the distance threshold level (i.e. selecting one or more candidate embedding vectors), a set of matching structured data elements is identified [0064-0065]. Examiner interprets that a matching structured data elements discloses a candidate location in the knowledge graph (i.e. hierarchy classification system).)
generating an output comprising an indication of the identified one or more candidate locations in the hierarchy classification system, (Misiewicz – see Fig. 3, 350, where a search result is generated in response to the search query that includes at least a portion of the set of matching structured data elements [0067].)
and transmitting the output towards a user device (Misiewicz – see Fig. 3, 360, where the search result is displayed on an interface [0068].)
Misiewicz does not appear to teach:
wherein the hierarchy classification system comprises ancestor nodes and descendent nodes identifying categories and subcategories, respectively, of objects
and a node in the hierarchy classification system
wherein each of the one or more candidate locations indicates a classification of the object
However, Liu teaches:
wherein the hierarchy classification system comprises ancestor nodes and descendent nodes identifying categories and subcategories, respectively, of objects (Liu – in Fig. 3, at operation 310, the access component receives a first data structure. The first data structure may include a first set of elements associated with one or more first categories. The first data structure may be associated with a database or a database system. The access component may receive the first data in response to a query, in response to initiation by the database or database system contacting the publication system, in response to an update operation, or any other suitable manner [0041]. The current category information for the publication may include one or more category levels and leaf categories or category nodes. The category levels and category leaves or nodes may include a word or words for each category level which are interrelated to one another in a semantic relationship [0067]. Also see [0050], where the target sequence represents a category tree path (root-to-leaf) used by the publication system.)
and a node in the hierarchy classification system (Liu – in Fig. 3, at operation 310, the access component receives a first data structure. The first data structure may include a first set of elements associated with one or more first categories. The first data structure may be associated with a database or a database system. The access component may receive the first data in response to a query, in response to initiation by the database or database system contacting the publication system, in response to an update operation, or any other suitable manner [0041]. The current category information for the publication may include one or more category levels and leaf categories or category nodes. The category levels and category leaves or nodes may include a word or words for each category level which are interrelated to one another in a semantic relationship [0067]. Also see [0050], where the target sequence represents a category tree path (root-to-leaf) used by the publication system.)
wherein each of the one or more candidate locations indicates a classification of the object (Liu – in Fig. 3, at operation 310, the access component receives a first data structure. The first data structure may include a first set of elements associated with one or more first categories. The first data structure may be associated with a database or a database system. The access component may receive the first data in response to a query, in response to initiation by the database or database system contacting the publication system, in response to an update operation, or any other suitable manner [0041]. The current category information for the publication may include one or more category levels and leaf categories or category nodes. The category levels and category leaves or nodes may include a word or words for each category level which are interrelated to one another in a semantic relationship [0067]. Also see [0050], where the target sequence represents a category tree path (root-to-leaf) used by the publication system.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz and Liu before them, to modify the system of Misiewicz with the teachings of Liu, as indicated above. One would have been motivated to make such a modification to make relevant recommendations in response to a user’s query (Liu [0003]).
Claim 15 corresponds to claim 1 and is rejected accordingly.
Regarding claim 6, Misiewicz teaches:
wherein the first natural language processing model comprises a large language model trained to determine a similarity of meaning between the text describing the object and texts of the plurality of data sources (Misiewicz – see [0026-0027], where the neural network execute a neural network-based process for natural language processing of the structured data elements to generate associated embedding vectors, and [0038], where the neural network can include BERT, GPT or GPT-2 (i.e. large language model) and are trained to be used by the embedding generator for both structured data element and query embedding. The training process includes selecting example training queries from search logs and generating a label for each. These labeled queries are used to train the neural network to predict relationships between queries and documents.)
Regarding claim 11, Misiewicz teaches:
wherein the output further comprises an indication of a data source associated with the identified one or more candidate locations (Misiewicz – see [0041], where the knowledge search system can provide a universal or “home” search page that collects search result data from multiple different vertical sources to generate an aggregated search results page or interface, and in addition to the universal search page, there are individual vertical source search pages (i.e. indication of a data source).)
Claims 2, 3, 7-9, 12-14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Misiewicz in view of Liu in view of Iyer.
Regarding claim 2, Misiewicz modified by Liu does not appear to teach:
providing the text describing the object to a second natural language processing model
obtaining, from the second natural language processing model, an enhanced text describing the object, wherein the enhanced text comprises a second set of text; and updating the query with the enhanced text
However, Iyer teaches:
providing the text describing the object to a second natural language processing model (Iyer – see [0078], where the user interface includes a model selection drop down menu 503 where a user may select a machine learning model to load for scoring prediction.)
obtaining, from the second natural language processing model, an enhanced text describing the object, wherein the enhanced text comprises a second set of text; and updating the query with the enhanced text (Iyer – see [0078], where in Fig. 5B the user interface includes a table showing the predicted result of the standardize code, where the selected model is passed product attributes used for generating a prediction of a standardized code for product classification. Also see [0029], where machine learning server is configured to send and receive data (i.e. query) and analytics from a plurality of data sources.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Iyer before them, to modify the system of Misiewicz and Liu with the teachings of Iyer, as indicated above. One would have been motivated to make such a modification to classifying an item using machine learning to reduce human error (Iyer [0002-0003]).
Regarding claim 3, Misiewicz modified by Liu does not appear to teach:
wherein the second natural language processing model comprises a large language model
However, Iyer teaches:
wherein the second natural language processing model comprises a large language model (Iyer – see [0078], where the user interface includes a model selection drop down menu 503 where a user may select a machine learning model to load for scoring prediction. Also see [0060], where a variety of neural network models may be utilized, including feed forward neural networks and combinations of several neural networks.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Iyer before them, to modify the system of Misiewicz, Liu and Iyer with the teachings of Iyer, as indicated above. One would have been motivated to make such a modification to classifying an item using machine learning to reduce human error (Iyer [0002-0003]).
Regarding claim 7, Misiewicz modified by Liu does not appear to teach:
receiving a candidate location selection transmitted by the user device, wherein the candidate location selection identifies a candidate location selected by the user
generating a secondary output comprising an indication of one or more candidate locations in the hierarchy classification system associated with the candidate location selection; transmitting the secondary output towards the user device
However, Iyer teaches:
receiving a candidate location selection transmitted by the user device, wherein the candidate location selection identifies a candidate location selected by the user (Iyer – see [0080], where the user selects the “reclassify” button (i.e. candidate location selection, where Examiner interprets that the old HTS code indicates a candidate location).)
generating a secondary output comprising an indication of one or more candidate locations in the hierarchy classification system associated with the candidate location selection; transmitting the secondary output towards the user device (Iyer – see [0080], where the user interface includes a table 709 (i.e. secondary output) showing the new HTS code for the product classification and a description of the new HTS code after the “reclassify” button is selected.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Iyer before them, to modify the system of Misiewicz and Liu with the teachings of Iyer, as indicated above. One would have been motivated to make such a modification to classifying an item using machine learning to reduce human error (Iyer [0002-0003]).
Regarding claim 8, Misiewicz modified by Liu does not appear to teach:
receiving a candidate location selection transmitted by the user device, wherein the candidate location selection identifies a candidate location selected by the user
generating a secondary output comprising additional information associated with the candidate location selection; transmitting the secondary output towards the user device
However, Iyer teaches:
receiving a candidate location selection transmitted by the user device, wherein the candidate location selection identifies a candidate location selected by the user (Iyer – see [0080], where the user selects the “reclassify” button (i.e. candidate location selection, where Examiner interprets that the old HTS code indicates a candidate location).)
generating a secondary output comprising additional information associated with the candidate location selection; transmitting the secondary output towards the user device (Iyer – see [0080], where the user interface includes a table 709 (i.e. secondary output) showing the new HTS code for the product classification and a description of the new HTS code after the “reclassify” button is selected.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Iyer before them, to modify the system of Misiewicz and Liu with the teachings of Iyer, as indicated above. One would have been motivated to make such a modification to classifying an item using machine learning to reduce human error (Iyer [0002-0003]).
Regarding claim 9, Misiewicz modified by Liu does not appear to teach:
wherein the additional information comprises text from one of the plurality of data sources associated with the candidate location selection and/or tariff information associated with the candidate location selection
However, Iyer teaches:
wherein the additional information comprises text from one of the plurality of data sources associated with the candidate location selection and/or tariff information associated with the candidate location selection (Iyer – see [0080], where the user interface includes a table 709 (i.e. secondary output) showing the new HTS code for the product classification and a description of the new HTS code after the “reclassify” button is selected.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Iyer before them, to modify the system of Misiewicz, Liu and Iyer with the teachings of Iyer, as indicated above. One would have been motivated to make such a modification to classifying an item using machine learning to reduce human error (Iyer [0002-0003]).
Regarding claim 12, Misiewicz modified by Liu does not appear to teach:
receiving a candidate location selection from the user device
determining that a data source associated with the candidate location selection is modified; and transmitting a modification update towards the user device
However, Iyer teaches:
receiving a candidate location selection from the user device (Iyer – see [0080], where the user selects the “reclassify” button (i.e. candidate location selection, where Examiner interprets that the old HTS code indicates a candidate location).)
determining that a data source associated with the candidate location selection is modified; and transmitting a modification update towards the user device (Iyer – see [0077], where re-classification engine receives an existing or old standardized code for product classification and converts it (i.e. updates) to a new standardized code. The re-classification may maintain and update the mapping rules based on one or more business requirements, user input, and updates from regulatory agencies with regard to the changes in the product classification or numbering scheme. Also see [0080], where the user interface includes a table 709 showing the new HTS code (i.e. modification update) for the product classification and a description of the new HTS code after the “reclassify” button is selected.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Iyer before them, to modify the system of Misiewicz and Liu with the teachings of Iyer, as indicated above. One would have been motivated to make such a modification to classifying an item using machine learning to reduce human error (Iyer [0002-0003]).
Regarding claim 13, Misiewicz does not appear to teach:
calculating a confidence score associated with the set of one or more candidate locations; determining that the confidence score is below a confidence threshold; and in response to the determining, generating the output comprises including an indication that the identified one or more candidate locations was identified as a result of the confidence score being below the confidence threshold
However, Iyer teaches:
calculating a confidence score associated with the set of one or more candidate locations; determining that the confidence score is below a confidence threshold; and in response to the determining, generating the output comprises including an indication that the identified one or more candidate locations was identified as a result of the confidence score being below the confidence threshold (Iyer – see [0007-0008], where a first prediction of a standardized code and a first confidence score for the first prediction in association with a classification of the product is determined (i.e. calculating a confidence score). Where the first confidence score fails to satisfy the threshold (i.e. below a confidence threshold), feedback from a user and the first prediction of the standardize code to the classification of the product is assigned based on the feedback.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Iyer before them, to modify the system of Misiewicz and Liu with the teachings of Iyer, as indicated above. One would have been motivated to make such a modification to classifying an item using machine learning to reduce human error (Iyer [0002-0003]).
Regarding claim 14, Misiewicz teaches:
rendering a graphical user interface (GUI); submitting a query with the GUI, wherein the query comprises a text describing the object (Misiewicz – see [0021], where the end user may initiate a search by entering an input query via an interface of a webpage associated with the entity.)
obtaining a response to the query, the response comprising a first set of one or more candidate locations in a hierarchy classification system, (Misiewicz - see Fig. 3, 340, where, based on the comparison of the embedding vectors and distance-comparison score that is less than the distance threshold level, a set of matching structured data elements is identified (i.e. first set of one or more candidate locations) [0064-0065]. Examiner interprets that a matching structured data elements discloses a candidate location in the knowledge graph (i.e. hierarchy classification system). Also see Fig. 3, 350, where a search result is generated in response to the search query that includes at least a portion of the set of matching structured data elements [0067].)
displaying a first candidate locations section in the GUI, the first candidate locations section identifying the first set of one or more candidate locations in the hierarchy classification system, (Misiewicz – see [0041], where the knowledge search system can provide a universal or “home” search page that collects search result data from multiple different vertical sources to generate an aggregated search results page or interface for provisioning to the end user system.)
displaying an additional description section in the GUI, the additional description section identifying an additional description of the object based on the first set of one or more candidate locations (Misiewicz – see [0041], where in addition to the universal search page, there are individual vertical source (i.e. additional description) search pages that generate search results.)
Misiewicz does not appear to teach:
wherein the hierarchy classification system comprises ancestor nodes and descendent nodes identifying categories and subcategories, respectively, of objects
wherein each candidate location of the first set of one or more candidate locations is associated with a node in the hierarchy classification system and indicates a classification of the object
obtaining an indication of a selection of the additional description
displaying a second candidate locations section in the GUI, the second candidate locations section identifying a second set of one or more candidate locations in the hierarchy classification system based on the selection of the additional description,
wherein each candidate location of the second set of one or more candidate locations is associated with a node in the hierarchy classification system and indicates a classification of the object
obtaining an indication of a selection of a candidate location in the second set of one or more candidate location, wherein the selected candidate location is a complete classification in the hierarchy classification section
and displaying an additional information section in the GUI, the additional information section identifying additional information associated with the selected candidate location
However, Liu teaches:
wherein the hierarchy classification system comprises ancestor nodes and descendent nodes identifying categories and subcategories, respectively, of objects (Liu – in Fig. 3, at operation 310, the access component receives a first data structure. The first data structure may include a first set of elements associated with one or more first categories. The first data structure may be associated with a database or a database system. The access component may receive the first data in response to a query, in response to initiation by the database or database system contacting the publication system, in response to an update operation, or any other suitable manner [0041]. The current category information for the publication may include one or more category levels and leaf categories or category nodes. The category levels and category leaves or nodes may include a word or words for each category level which are interrelated to one another in a semantic relationship [0067]. Also see [0050], where the target sequence represents a category tree path (root-to-leaf) used by the publication system.)
wherein each candidate location of the first set of one or more candidate locations is associated with a node in the hierarchy classification system and indicates a classification of the object (Liu – in Fig. 3, at operation 310, the access component receives a first data structure. The first data structure may include a first set of elements associated with one or more first categories. The first data structure may be associated with a database or a database system. The access component may receive the first data in response to a query, in response to initiation by the database or database system contacting the publication system, in response to an update operation, or any other suitable manner [0041]. The current category information for the publication may include one or more category levels and leaf categories or category nodes. The category levels and category leaves or nodes may include a word or words for each category level which are interrelated to one another in a semantic relationship [0067]. Also see [0050], where the target sequence represents a category tree path (root-to-leaf) used by the publication system.)
wherein each candidate location of the second set of one or more candidate locations is associated with a node in the hierarchy classification system and indicates a classification of the object (Liu – in Fig. 3, at operation 310, the access component receives a first data structure. The first data structure may include a first set of elements associated with one or more first categories. The first data structure may be associated with a database or a database system. The access component may receive the first data in response to a query, in response to initiation by the database or database system contacting the publication system, in response to an update operation, or any other suitable manner [0041]. The current category information for the publication may include one or more category levels and leaf categories or category nodes. The category levels and category leaves or nodes may include a word or words for each category level which are interrelated to one another in a semantic relationship [0067]. Also see [0050], where the target sequence represents a category tree path (root-to-leaf) used by the publication system.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz and Liu before them, to modify the system of Misiewicz with the teachings of Liu, as indicated above. One would have been motivated to make such a modification to make relevant recommendations in response to a user’s query (Liu [0003]).
Misiewicz modified by Liu does not appear to teach:
obtaining an indication of a selection of the additional description
displaying a second candidate locations section in the GUI, the second candidate locations section identifying a second set of one or more candidate locations in the hierarchy classification system based on the selection of the additional description,
obtaining an indication of a selection of a candidate location in the second set of one or more candidate location, wherein the selected candidate location is a complete classification in the hierarchy classification section
and displaying an additional information section in the GUI, the additional information section identifying additional information associated with the selected candidate location
However, Iyer teaches:
obtaining an indication of a selection of the additional description (Iyer – see [0080], where the user selects (i.e. obtain an indication of a selection) a re-classification model 705 (i.e. additional description) in the drop down menu.)
displaying a second candidate locations section in the GUI, the second candidate locations section identifying a second set of one or more candidate locations in the hierarchy classification system based on the selection of the additional description, (Iyer – see [0080], where table 709 shows the new HTS code for the product classification and a description of the new HTS code. Examiner interprets that the section of the GUI where 709 is eventually displayed discloses a second candidate locations section.)
obtaining an indication of a selection of a candidate location in the second set of one or more candidate location, wherein the selected candidate location is a complete classification in the hierarchy classification section (Iyer – see [0080], where the user selects the “reclassify” button.)
and displaying an additional information section in the GUI, the additional information section identifying additional information associated with the selected candidate location (Iyer – see [0080], where the user interface includes a table 709 showing the new HTS code for the product classification and a description of the new HTS code after the “reclassify” button is selected.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Iyer before them, to modify the system of Misiewicz and Liu with the teachings of Iyer, as indicated above. One would have been motivated to make such a modification to classifying an item using machine learning to reduce human error (Iyer [0002-0003]).
Claim 16 corresponds to claim 14 and is rejected accordingly.
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Misiewicz in view of Liu in view of Song.
Regarding claim 4, Misiewicz modified by Liu does not appear to teach:
wherein the hierarchy classification system is The Harmonized Commodity Description and Coding System
However, Song teaches:
wherein the hierarchy classification system is The Harmonized Commodity Description and Coding System (Song – the heuristic algorithm of the present invention does not use a ready-made formula, but means an art of good guessing or a procedure thereof which allows a user to discover and know a knowledge through an effort of minimizing trial and error of rule of thumb through an experience in a situation of incomplete information (for example, a good commodity classification expert determines a HS commodity classification code of a corresponding commodity through question and answer and fact data about some core contents. The expert determines a HS commodity classification code without a difficulty in the determination as much as possible based on a lot of experiences on commodity classification) [0061]. HS commodity classification is classifying a variety of products traded worldwide into one HS commodity classification code on the basis of the Harmonized Commodity Description and Coding System (Harmonized System in short or HS) enacted by the World Customs Organization (WCO) [0004]. The database that the HS commodity classification code determination unit searches includes at least any one of general rules for commodity classification, rules for inclusion/exclusion of commodity classification, definitions of commodities, and essential specifications for commodity classification [0032].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Song before them, to modify the system of Misiewicz and Liu with the teachings of Song, as indicated above. One would have been motivated to make such a modification to correctly classify a HS commodity (Song [0005]).
Regarding claim 5, Misiewicz modified by Liu does not appear to teach:
wherein the plurality of data sources comprise one or more of the Harmonized Traffic Schedule for the United States (HTSUS), World Customs Organization Explanatory Notes and Harmonized System, a set of United States Customs Rulings, or a list of human expert classifications
However, Song teaches:
wherein the plurality of data sources comprise one or more of the Harmonized Traffic Schedule for the United States (HTSUS), World Customs Organization Explanatory Notes and Harmonized System, a set of United States Customs Rulings, or a list of human expert classifications (Song – the heuristic algorithm of the present invention does not use a ready-made formula, but means an art of good guessing or a procedure thereof which allows a user to discover and know a knowledge through an effort of minimizing trial and error of rule of thumb through an experience in a situation of incomplete information (for example, a good commodity classification expert determines a HS commodity classification code of a corresponding commodity through question and answer and fact data about some core contents. The expert determines a HS commodity classification code without a difficulty in the determination as much as possible based on a lot of experiences on commodity classification) [0061]. The database that the HS commodity classification code determination unit searches includes at least any one of general rules for commodity classification, rules for inclusion/exclusion of commodity classification, definitions of commodities, and essential specifications for commodity classification (i.e. human expert classification) [0032].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Song before them, to modify the system of Misiewicz and Liu with the teachings of Song, as indicated above. One would have been motivated to make such a modification to correctly classify a HS commodity (Song [0005]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Misiewicz in view of Liu in view of Huang (Patent No. US 12,259,895 B1, hereinafter “Huang”).
Regarding claim 10, Misiewicz teaches:
prior to selecting the one or more candidate embedding vectors, selecting one or more initial candidate embedding vectors from the set of embedding vectors based on the calculated similarity scores (Misiewicz – see [0063], where the comparison of the embedding vector associated with the search query to a set of embedding vectors associated with a set of structured data elements relating to the entity includes a matching determination based on relative similarity in a numerical vector space.)
Misiewicz modified by Liu does not appear to teach:
providing the one or more initial candidate embedding vectors and their respective data sources to a third natural language processing model; and obtaining, from the third natural language processing model, a second similarity score, wherein selecting the one or more candidate embedding vectors from the set of embedding vectors is further based on the second similarity score
However, Huang teaches:
providing the one or more initial candidate embedding vectors and their respective data sources to a third natural language processing model; and obtaining, from the third natural language processing model, a second similarity score, wherein selecting the one or more candidate embedding vectors from the set of embedding vectors is further based on the second similarity score (Huang – see Col. 12 lines 62-67, Col. 13 lines 1-20, where a large language model which may be a representation-based model which can be trained to receive an input query and output a first embedding, receive a candidate query and output a second embedding, and training service 116 can generate a similarity score from the first and second embeddings.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Misiewicz, Liu and Huang before them, to modify the system of Misiewicz and Liu with the teachings of Iyer, as indicated above. One would have been motivated to make such a modification to generate useful features for ranking the search results (Huang [Col. 1 lines 7-24]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RANJIT P DORAISWAMY whose telephone number is (571)270-5759. The examiner can normally be reached Monday-Friday 9:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571) 272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RANJIT P DORAISWAMY/ Examiner, Art Unit 2166
/SANJIV SHAH/ Supervisory Patent Examiner, Art Unit 2166
