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 .
Response to Amendment
Applicant’s Amendments, filed December 22, 2025, have been entered. No claims have been amended, and claims 1-16 are currently pending.
Response to Arguments
Applicant's arguments filed December 22, 2025 have been fully considered but they are not persuasive. Regarding claim 1, Applicant argues that the knowledge graph in cited prior art Misiewicz et al. (Pub. No. US 2022/0138170 A1, hereinafter “Misiewicz”) does not disclose the recited “hierarchy classification system” (Remarks pp. 7-8). In response, examiner respectfully submits that Misiewicz teaches an index manager includes an indexing service configured to index multiple structured elements associated with an entity (such as a merchant’s website), and that the structured data elements represent “knowledge” or facts associated with the entities and are maintained in an indexed data store (also referred to as a knowledge graph) [0024], where the structured data elements may be various FAQs associated with an entity [0026]. The knowledge search system employs the vector generation and comparison process to rank candidate search results within a vertical or search result category (e.g., ranking candidate FAQs within an FAQ vertical). Each respective search result candidate within a vertical (e.g., also referred to a “vertical category”) can be assigned a score or ranking based on the corresponding comparison of the embedding vectors associated with the candidate search result and the search terms of the search query [0013]. The knowledge search system employs the vector generation and comparison process to rank set of multiple vertical sources or search result category (e.g., ranking candidate FAQs within an FAQ vertical) in addition to the universal search page, there are individual vertical source search pages [0015]. In other words, the knowledge graph discloses a hierarchical classification system where a vertical is at the top of the hierarchy, vertical categories are at a lower level of the hierarchy, and structured data elements such as FAQs are another level down the hierarchy.
Applicant argues that the Office Action does not allege that Misiewicz’s embedding vector is associated with a plurality of data sources (Remarks p. 8). In response, examiner respectfully submits that the knowledge engine search platform search results are based on multiple data sets (e.g., multiple vertical sources) including a native data set (e.g., data maintained by the entity system) and third-party data sets (e.g., data acquired from one or more third party search providers) [0014]. The knowledge search system employs the vector generation and comparison process (see [0063-0064] for the comparison and scoring process that includes the teaching that embedding vectors are associated with a set of structured data elements related to an entity) to rank a set of multiple vertical sources or search result category [0015]. In other words, since the structured data elements are hierarchically located in vertical sources, and the embedding vectors are associated with a set of structured data elements, the embedding vectors are associated with a data source of a plurality of data sources.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-6, 11, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Misiewicz.
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].)
Claim 15 corresponds to claim 1 and is rejected accordingly.
Regarding claim 4, Misiewicz teaches:
wherein the hierarchy classification system is The Harmonized Commodity Description and Coding System (Misiewicz - the structured data elements are stored in a knowledge graph (i.e. hierarchy classification system) [0011-0012]. Examiner interprets that “The Harmonized Commodity Description and Coding System” represents non-functional descriptive material, see MPEP 2111.05, and also does not limit the scope of the claim with reference to MPEP 2111.04.)
Regarding claim 5, Misiewicz 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 (Misiewicz - 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]. Examiner interprets that the recited plurality of data sources represent non-functional descriptive material, see MPEP 2111.05, and also does not limit the scope of the claim with reference to MPEP 2111.04.)
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).)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 2, 3, 7-9, 12-14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Misiewicz in view of Iyer (Pub. No. US 2022/0301031 A1, hereinafter “Iyer”).
Regarding claim 2, Misiewicz 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 and Iyer before them, to modify the system of Misiewicz with the teachings of Iyer of 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. 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 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 and Iyer before them, to modify the system of Misiewicz and Iyer with the teachings of Iyer of wherein the second natural language processing model comprises a large language model. 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 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 and Iyer before them, to modify the system of Misiewicz and Iyer with the teachings of Iyer of 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. 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 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 and Iyer before them, to modify the system of Misiewicz and Iyer with the teachings of Iyer of 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. 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 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 and Iyer before them, to modify the system of Misiewicz and Iyer with the teachings of Iyer of 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. 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 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 and Iyer before them, to modify the system of Misiewicz and Iyer with the teachings of Iyer of 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. 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 and Iyer before them, to modify the system of Misiewicz and Iyer with the teachings of Iyer of 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. 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:
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 and Iyer before them, to modify the system of Misiewicz and Iyer with the teachings of Iyer of 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. 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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Misiewicz 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 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 and Huang before them, to modify the system of Misiewicz with the teachings of Iyer of 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. 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RANJIT P DORAISWAMY/ Examiner, Art Unit 2166
/SANJIV SHAH/ Supervisory Patent Examiner, Art Unit 2166