DETAILED ACTION
Response to Amendment
The amendment filed on 10 April 2026 has been entered.
Claims 1-20 are pending.
Claims 4, 12, 20 are cancelled.
Claims 1-3, 5-11, 13-19 are amended.
Claims 21-23 are new.
Claims 1-3, 5-11, 13-19, 21-23 will be pending.
Applicant’s amendments to the Claims have overcome each and every objection and rejection under 35 USC 112(b) and 35 USC 102, previously set forth in the Non-Final Office Action, mailed 19 February 2026.
Response to Arguments
Applicant’s remarks, regarding the rejections of claims under 35 USC 101, have been fully considered.
Examiner notes Applicant’s remarks, regarding the rejections of claims under 35 USC 101, filed 10 April 2026, are directed to newly amended claim limitations for which Examiner has not yet made a prima facie case for, rendering Applicant’s arguments moot.
Applicant’s remarks, regarding the rejections of claims under 35 USC 103, have been fully considered.
Applicant submits Zhao fails to anticipate or render obvious the features of Claim 1. Applicant submits the combination of Kaczynski and Nothaft fails to cure the deficiencies of Zhao with respect to Claim 1.
Applicant’s arguments have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 21 recites the limitation "the vectors" in line 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the vectors" has been construed to be “the set of vectors” in line 5 of claim 1.
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, 5-11, 13-19, 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, abstract idea, without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory
category. MPEP 2106.03:
According to the first part of the Alice analysis, in the instant case, the claims were determined
to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 9-11, 13-16), a machine/system/product (Claims 1-3, 5-8, 17-19, 21-23), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a
judicial exception.
Regarding independent claims 1, 9, 17, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below:
Claims 1, 9, 17:
“identify a subset of vectors stored in the vector database which contain numerical representations of data associated with the query parameter, based on a comparison of the query parameter to the metadata” (mental process of judgement)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“processor configured to receive a query parameter input via an interface of a software application”
“generate metadata of a set of vectors based on extraction of attributes from data used to generate the set of vectors”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“execute a machine learning model on the subset of vectors to update at least one parameter of the machine learning model based on the subset of vectors”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“a storage device comprising a vector database”
“store the metadata with the set of vectors in the vector database”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 9, 17 do not recite what the courts have identified as "significantly more".
Furthermore, regarding dependent claims 2-3, 5-8, 21-23, which depend from claim 1, claims 10-11, 13-16, which depend from claim 9, claims 18-19, which depend from claim 17, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B:
Claims 2, 10, 18:
Incorporates the rejections of claims 1, 9, 17, respectively.
“wherein the processor is configured to detect a selection of a category via a drop-down menu of the interface”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“identify the subset of vectors based on a comparison of the category to respective keywords mapped to the subset of vectors via the metadata.” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 3, 11, 19:
Incorporates the rejections of claims 1, 9, 17, respectively.
“wherein the processor is further configured to detect a selection of a period of time via a drop-down menu of the interface”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“identify the subset of vectors based on a comparison of the period of time to respective keywords mapped to the subset of vectors via the metadata” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 5, 13:
Incorporates the rejections of claims 1, 9, respectively.
“processor is further configured to receive a plurality of strings corresponding to a plurality of merchants”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“execute a second machine learning model on the plurality of strings to generate a plurality of merchant vectors”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“store the plurality of merchant vectors in the vector database”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 6, 14:
Incorporates the rejections of claims 5, 13, respectively.
“store the keywords within metadata of a merchant vector of the merchant within the vector database”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“wherein the processor is further configured to identify keywords associated with a merchant from among the plurality of merchants” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 7, 15:
Incorporates the rejections of claims 1, 9, respectively.
“wherein the processor is configured to query the vector database via an integrated development environment (IDE)”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 8, 16:
Incorporates the rejections of claims 1, 9, respectively.
“wherein the processor is configured to compare attributes of the set of vectors to a predefined criteria within vector space via execution of the machine learning model on the set of vectors” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 21:
Incorporates the rejection of claim 1.
“wherein the processor is further configured to filter the vectors to remove an additional subset of vectors that comprise metadata that does not satisfy the query parameter prior to executing the machine learning model on the subset of vectors” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 22:
Incorporates the rejection of claim 1.
“wherein the processor is further configured to associate keywords with the set of vectors, store the keywords within the metadata, and match the query parameter to the keywords within the metadata” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 23:
Incorporates the rejection of claim 1.
“wherein the processor is further configured to iteratively execute the machine learning model on the subset of vectors to update the at least one parameter based on execution of a plurality of iterations”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2-3, 5-8, 10-11, 13-16, 18-19, 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more" than the recited judicial exception.
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 1, 8-9, 16-17, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (U.S. Pre-Grant Publication No. 20230035639, hereinafter 'Zhao'), in view of Chao et al. (U.S. Pre-Grant Publication No. 20200159773, hereinafter 'Chao').
Regarding claim 1 and analogous claims 9, 17, Zhao teaches An apparatus comprising ([0064] Embodiments of the invention may be implemented on a computing system. Any combination of mobile, desktop, server, router, switch, embedded device, or other types of hardware may be used. For example, as shown in FIG. 5A, the computing system (500) may include one or more computer processors (502), non-persistent storage (504) (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent storage (506) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory, etc.), a communication interface (512) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), and numerous other elements and functionalities.; [0065] The computer processor(s) (502) may be an integrated circuit for processing instructions. For example, the computer processor(s) may be one or more cores or micro-cores of a processor. The computing system (500) may also include one or more input devices (510), such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device.):
a processor configured to receive a query parameter input via an interface of a software application ([0080] The computing system in FIG. 5A may implement and/or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured for ease of data retrieval, modification, reorganization, and deletion. via an interface of a software application Database Management System (DBMS) is a software application that provides an interface for users to define, create, query, update, or administer databases.; [0081] The a processor configured to receive a query parameter input user, or software application, may submit a statement or query into the DBMS. Then the DBMS interprets the statement. The statement may be a select statement to request information, update statement, create statement, delete statement, etc. Moreover, the statement may include parameters that specify data, or data container (database, table, record, column, view, etc.), ID(s), conditions (comparison operators), functions (e.g. join, full join, count, average, etc.), sort (e.g. ascending, descending), or others. The DBMS may execute the statement. For example, the DBMS may access a memory buffer, a reference or index a file for read, write, deletion, or any combination thereof, for responding to the statement. The DBMS may load the data from persistent or non-persistent storage and perform computations to respond to the query. The DBMS may return the result(s) to the user or software application.),
Zhao fails to teach a storage device comprising a vector database; and
generate metadata of a set of vectors based on extraction of attributes from data used to generate the set of vectors and store the metadata with the set of vectors in the vector database,
identify a subset of vectors stored in the vector database which contain numerical representations of data associated with the query parameter, based on a comparison of the query parameter to the metadata, and execute a machine learning model on the subset of vectors to update at least one parameter of the machine learning model based on the subset of vectors.
Chao teaches a storage device comprising a vector database ([0033] An aspect provides a content access and a storage device storage system comprising a store of metadata vectors in accordance with the method as defined, and a computer program which when executed by a processor performs the method steps as defined.; [0244] Thus there is provided comprising a vector database in the database an established set of metadata vectors each associated with one piece of content, these metadata vectors having a plurality of classified text objects, each classified text object having a respective assigned weighting.); and
generate metadata of a set of vectors based on extraction of attributes from data used to generate the set of vectors and store the metadata with the set of vectors in the vector database
([0104] Each generate metadata content identifier, ID1, ID2 is associated with a respective set of metadata 30 1 30 2 which is a sum set of all metadata which has been provided for that particular piece of content. This metadata can be in natural language form, abbreviated form or any other form. However, each content identifier is also associated with text objects of a set of vectors derived from at least one vector 40 [representing text objects shown in FIG. 4] based on extraction of attributes from data used to generate the set of vectors which has been derived from input metadata. When an incoming natural language description is vectorised, it may be vectorised at different levels (layers). For example, it may be vectorised into a document vector 40, or broken down into paragraphs where each paragraph is converted into paragraph vectors 40 b or split into words where each word is vectorised, 40 c.; [0244] Thus there is provided store the metadata with the set of vectors in the vector database in the database an established set of metadata vectors each associated with one piece of content, these metadata vectors having a plurality of classified text objects, each classified text object having a respective assigned weighting.),
identify a subset of vectors stored in the vector database which contain numerical representations of data associated with the query parameter, based on a comparison of the query parameter to the metadata ([0082] In the first example, a associated with the query parameter, based on a comparison of the query parameter to the metadata query resulting from this translation process allows the computer system to match new metadata with existing metadata; while in the second, the result allows this metadata to be retrieved for some other purpose, such as to offer a range of content to watch.; [0087] A identify a subset of vectors stored in the vector database which contain numerical representations of data machine learning process (MLP) 18 within the translator 14 uses this vectorised version of the input to compare it to known content metadata vectors to identify a list of matches. This is denoted by step S24 in FIG. 2.), and
execute a machine learning model on the subset of vectors to update at least one parameter of the machine learning model based on the subset of vectors ([0084] The system described herein takes a different approach by execute a machine learning model using a machine learning approach to translate the input rather than relying on predetermined rules.; [0256] The system used to generate matches for users that are providing queries can also be used to enhance training. The underlying process is the same. In the sense of training, metadata is provided with the asset with which it is associated. When a query is made, metadata is provided and a number of matches are returned. If a user then provides confirmation of a particular correct match, then this on the subset of vectors to update at least one parameter of the machine learning model based on the subset of vectors feedback can be used to associate the input metadata with the matched content item and this can be incorporated in further training to enhance the weights and associations. Thus, the system may be constantly learning either through updating of the content database with metadata and known assets, or by queries and feedback from those queries confirming a particular asset associated with the metadata.).
Zhao and Chao are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Zhao, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Chao to Zhao before the effective filing date of the claimed invention in order to transform inputs by a translation process without requiring item-by-item manual adaptation by a human operator acting to transform the data, so that it is useful within a computer domain (cf. Chao, [0079] These objectives are addressed herein by providing a translation interface 14 onto a data store 2 that can accept spoken or free entry requests in natural language and/or with abbreviated information such as “what's the war movie with that guy from Friends in it?” and match that with an existing content item.; [0080] The process described herein transforms such inputs by a translation process without requiring item-by-item manual adaptation by a human operator acting to transform the data, so that it is useful within a computer domain—i.e., a machine can perform the matching.).
Regarding claim 8 and analogous claim 16, Zhao, as modified by Chao, teaches The apparatus of claim 1, The method of claim 9, respectively.
Zhao teaches wherein the processor is configured to compare attributes of the set of vectors to a predefined criteria within vector space via execution of the machine learning model on the set of vectors ([0015] An embedding model is applied to the unstructured data of an untransformed transaction to generate a vector. A cluster ID is assigned to the vector by matching the vector with a cluster of vectors. The cluster ID may identify a cluster of vectors that are within a threshold distance of a centroid. For example, a cluster ID corresponding to “invoice memo” unstructured data may correspond to “utilities,” indicating that the untransformed transaction including the invoice memo corresponds to a utilities expense.; [0034] The cluster models (160, 161) include functionality to assign cluster IDs (IDs) (162, 163) to vectors (152, 153). The cluster models (160, 161) may correspond to different unstructured data (141, 142). For example, cluster model (160) may correspond to unstructured data (141) and cluster model (161) may correspond to unstructured data (142). A cluster ID (162) configured to compare attributes of the set of vectors to a predefined criteria within vector space via execution of the machine learning model on the set of vectors identifies a cluster of vectors that are within a threshold distance of a centroid (e.g., center point) of the cluster of vectors. For example, the distance may be based on a cosine similarity or Euclidean distance between vectors. Continuing this example, the centroid may be a point (e.g., a vector) that represents an average of the vectors in the cluster. The cluster models (160, 161) may group vectors (152, 153) into clusters using various techniques, such as k-means clustering and Density-Based Spatial Clustering of Applications with Noise (DBSCAN).).
Zhao and Chao are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 22, Zhao, as modified by Chao, teaches The apparatus of claim 1.
Chao teaches wherein the processor is further configured to associate keywords with the set of vectors, store the keywords within the metadata, and match the query parameter to the keywords within the metadata ([0244] Thus there is provided in the database an established set of store the keywords within the metadata metadata vectors each associated with one piece of content, wherein the processor is further configured to associate keywords with the set of vectors these metadata vectors having a plurality of classified text objects, each classified text object having a respective assigned weighting.; [0253] In a fourth step, the metadata vector for the input query is compared to the metadata vectors stored in the database. Each input classified text component of the metadata vector for the input query is compared to each metadata classified text component in the metadata vectors stored in the database.; [0254] In a fifth step, any matched are scored. Specifically, the match the query parameter to the keywords within the metadata metadata vector for the input query is compared to each metadata vector within the database (the established set) to generate matching scores based on their similarity.).
Zhao and Chao are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 23, Zhao, as modified by Chao, teaches The apparatus of claim 1.
Chao teaches wherein the processor is further configured to iteratively execute the machine learning model on the subset of vectors to update the at least one parameter based on execution of a plurality of iterations ([0256] The system used to generate matches for users that are providing queries can also be used to enhance training. The underlying process is the same. In the sense of training, metadata is provided with the asset with which it is associated. When a query is made, metadata is provided and a number of matches are returned. If a user then provides confirmation of a particular correct match, then this feedback can be used to associate the input metadata with the matched content item and this can be incorporated in to update the at least one parameter based on execution of a plurality of iterations further training to enhance the weights and associations. Thus, the wherein the processor is further configured to iteratively system may be constantly execute the machine learning model on the subset of vectors learning either through updating of the content database with metadata and known assets, or by queries and feedback from those queries confirming a particular asset associated with the metadata.).
Zhao and Chao are combinable for the same rationale as set forth above with respect to claim 1.
Claims 5, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao, Chao, and further in view of Miller et al. (U.S. Pre-Grant Publication No. 20240095242, hereinafter 'Miller').
Regarding claim 5 and analogous claim 13, Zhao, as modified by Chao, teaches The apparatus of claim 1, The method of claim 9, respectively.
Zhao teaches wherein the processor is further configured to receive a plurality of strings corresponding to a plurality of merchants ([0035] The receive a plurality of strings corresponding to a plurality of merchants cluster ID may be a unique ID (e.g., an integer or alphanumeric string). Each cluster ID may correspond to an intent (e.g., a pattern, or a purpose) of unstructured data corresponding to the vectors in the cluster identified by the cluster ID (162).),
Zhao, as modified by Chao, fails to teach execute a second machine learning model on the plurality of strings to generate a plurality of merchant vectors, and store the plurality of merchant vectors in the vector database.
Miller teaches execute a second machine learning model on the plurality of strings to generate a plurality of merchant vectors, and store the plurality of merchant vectors in the vector database ([0049] Beginning with NLP engine 212, a text embedding for language modeling and feature learning techniques can include mapping words or phrases to vectors of real numbers, for example. Through word embeddings, words or phrases associated with an item listing having semantic similarity to other item listings can become detectable by the NLP engine 212 when comparing vector representations. As such, an to generate a plurality of merchant vectors embedding determined by NLP engine 212 for the seed search selection 204 or the modifier 206 may correspond to a semantic representation of one or more data objects, such that the embedding represents a word, a phrase, a sentence, or other portions of textual content associated with both the search query and the user's true or ideal search intent. The representation may be a vector of real numbers, thereby providing various advantages over other approaches by improving dimensionality reduction and context similarities, for instance.; [0050] In some embodiments, NLP engine 212 may receive one or more text characters (e.g., a Latin alphabet letter or number, an Arabic alphabet letter or number, a Roman numeral, another symbol) or text strings comprising more than one text character, from the seed search selection 204, from an item listing associated with the seed search selection 204, or from the modifier 206. The execute a second machine learning model on the plurality of strings NLP engine 212 may process the one or more text characters or the one or more text strings as needed, and store the plurality of merchant vectors in the vector database store the processed one or more text characters or the processed one or more text strings (e.g., within the search index 232) in data store 230. The item listing associated with the seed search selection 204 may be received from an entity, such as a third-party seller, a consumer, one or more online marketplaces, a manufacturer, a retailer, a collector, an item expert, a website, another entity, or the like, or a combination thereof. In addition, the item listing associated with the seed search selection 204 may be stored within the search index 232, for example.).
Zhao, Chao, and Miller are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Zhao and Chao, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Miller to Zhao before the effective filing date of the claimed invention in order to allow users to navigate through multiple modalities to identify search results more closely related to the user's intent (cf. Miller, [0017] Unlike these two existing search methods, the technology disclosed herein allows users to navigate through multiple modalities to identify search results more closely related to the user's intent. For example, related items may be identified using a modifier (e.g., a free-text input or audio input) that does not restrict users to predefined filters that solely, and linearly, narrow a first set of search results further. The technology disclosed herein allows users to navigate search results by modifying searches via various modalities for an item (e.g., by pivoting the search based on the title, description, other metadata attributes, and/or the image of one or more search results). In embodiments, search results may be modified to exclude an item attribute, to include an additional item attribute, to amplify a quality of an item, to aggregate an attribute across multiple items (e.g., red leather for couches and sectionals), and to identify different items with a similar attribute (e.g., posters and pillows having the same pattern).).
Claims 2, 10, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao, Chao, and further in view of Kharbanda et al. (U.S. Pre-Grant Publication No. 20240202795, hereinafter 'Kharbanda').
Regarding claim 2 and analogous claims 10, 18, Zhao, as modified by Chao, teaches The apparatus of claim 1, The method of claim 9, and The non-transitory computer-readable storage medium of claim 17.
Chao teaches identify the subset of vectors based on a comparison of the category to respective keywords mapped to the subset of vectors via the metadata ([0062] A second challenge is referred to herein as “querying”. When a user 8 wishes to interrogate the content database 2 for information, he may wish to identify content items which match certain criteria. The user may not know precisely by what mapped to the subset of vectors via the metadata metadata the content item is already identified, and may therefore wish to pose a query in a style that he finds most convenient, for example the spoken word or typed natural language text. based on a comparison of the category to respective keywords Such a query is labelled 10, and is supplied via a human interface 12 into the system. The system should be capable of receiving such a query and to return matched content items which fit most closely the users' query. Such matches are referenced by reference numeral 16.; [0098] After the machine learning training phase, when the MLP 18 is presented with a fresh vectorised input, it can use the patterns, features and weightings that it has learned identify the subset of vectors to determine which existing content item(s) may be similar to the one described by the input. If an input can be resolved into more than one vector, the outputs are further weighted (ordered) according to the weights given to each vectorisation.; [0099] When this process is used to query the database for content, the system may return a list of matches ordered by probability.).
Zhao, as modified by Chao, fails to teach wherein the processor is configured to detect a selection of a category via a drop-down menu of the interface, and
Kharbanda teaches wherein the processor is configured to detect a selection of a category via a drop-down menu of the interface ([0066] A plurality of descriptor via a drop-down menu of the interface user-interface elements (e.g., a chip, a tile, and/or drop-down elements) can be provided for display in the image-generation interface. wherein the processor is configured to detect a selection of a category Each descriptor user-interface element can be associated with a different descriptor (e.g., an adjective and/or a complementary noun or verb associated with the particular category). The descriptors may be general descriptors for a plurality of different categories. Alternatively and/or additionally the plurality of descriptors may be determined and/or provided based on the selected category (e.g., a clothing material and/or a brand may be provided based on a clothing category being selected).), and
Zhao, Chao, and Kharbanda are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Zhao and Chao, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Kharbanda to Zhao before the effective filing date of the claimed invention in order to enable faster and/or more accurate determination of requested search results (cf. Kharbanda, [0084] Another technical benefit of the systems and methods of the present disclosure is the ability to leverage one or more user interface elements to provide suggested inputs for the machine-learned model. For example, a plurality of category user interface elements can be provided with each user interface element being associated with a different category for dataset generation. A plurality of descriptor user interface elements can be provided to allow for more detailed prompt generation. The plurality of descriptor user interface elements may be provided for display and/or refined based on the selection of a particular category. The different user interface elements may lead to more directed prompt generation based on terms the model may be trained on specifically. Moreover, the increased versatility of the search and retrieval process according to the present disclosure can enable faster and/or more accurate determination of requested search results.).
Claims 3, 11, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao, Chao, and further in view of Younessian et al. (U.S. Pre-Grant Publication No. 20240078240, hereinafter 'Younessian').
Regarding claim 3 and analogous claims 11, 19, Zhao, as modified by Chao, teaches The apparatus of claim 1, The method of claim 9, The non-transitory computer-readable storage medium of claim 17, respectively.
Zhao, as modified by Chao, fails to teach wherein the processor is further configured to detect a selection of a period of time via a drop-down menu of the interface, and in response, identify the subset of vectors based on a comparison of the period of time to respective keywords mapped to the subset of vectors via the metadata.
Younessian teaches wherein the processor is further configured to detect a selection of a period of time via a drop-down menu of the interface, and in response, identify the subset of vectors based on a comparison of the period of time to respective keywords mapped to the subset of vectors via the metadata ([0122] FIG. 6 shows an example table 600 of results indicating frequency value information for multiple content channels. For example, the request from the user device 190 may include the content channels and/or content sources for which frequency value information is requested (e.g., the specific content channels or based on the primary content provided by content channels (e.g., national news, weather, local news, sports, movies, comedy, music, etc.)). The content channels may be selected by a user via a channel drop-down box 605 or another method. The request from the user device 190 may also include the time period to analyze the content of the selected content channels for (e.g., 1 day of content, 2 days of content, 3 days of content or any other time period between and including 1 hour to 1 year). For example, wherein the processor is further configured to detect a selection of a period of time via a drop-down menu of the interface the time period may be selected by a user via a time period drop-down box 610 or another method.; [0223] At 1710, the computing device (e.g., the computing device 110 and/or the keyword engine 170) may determine, for each word or phrase of the plurality of words or phrases, an associated time period. For example, the associated time period may be the time the content segment from which the particular word or phrase of the plurality of words or phrases came from was created or the time the content segment from which the particular word or phrase of the plurality of words or phrases came from was intended to be output. For example, the keyword engine 170 may and in response, identify the subset of vectors determine the time period associated with each word or phrase based on the time value. For example, the based on a comparison of the period of time to respective keywords mapped to the subset of vectors via the metadata time value may be embedded in metadata associated with the particular word or phrase of the plurality of words or phrases. The keyword engine 170 may store (e.g., in the text data storage 139, 171) or otherwise indicate the time period or value associated with each word or phrase of the plurality of words or phrases.).
Zhao, Chao, and Younessian are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Zhao and Chao, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Younessian to Zhao before the effective filing date of the claimed invention in order to evaluate words or phrases that were used a certain number of times during the previous period of time (cf. Younessian, [0003] One or more content sources, such as content channels, may be selected and a computing device may determine words or phrases being used in content from each content channel during a recent period of time, such as the previous one to three days. The computing device may determine how often each word or phrase is used during that recent period of time. The computing device may also determine how often each of those words or phrases are used during a previous period of time, such as the last week, two weeks, or month. Those words or phrases that were used a certain number of times during the previous period of time may be removed from the group of words or phrases being evaluated or no further action may be taken with regard to those words or phrases. The remaining words or phrases for the particular content source may be organized based on how often the word or phrase was used during the recent period of time and sent to a user device.).
Claims 6, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao, Chao, Miller, and further in view of Bhowan et al. (U.S. Pre-Grant Publication No. 20190147103, hereinafter 'Bhowan').
Regarding claim 6 and analogous claim 14, Zhao, as modified by Chao and Miller, teaches The apparatus of claim 5, The method of claim 13.
Chao teaches wherein the processor is further configured to identify keywords associated with a merchant from among the plurality of merchants, and store the keywords within metadata of a merchant vector of the merchant within the vector database ([0244] Thus there is provided within the vector database in the database an established set of within metadata of a vector metadata vectors each associated with one piece of content, these metadata vectors having a plurality of store the keywords classified text objects, each classified text object having a respective assigned weighting.).
Zhao, as modified by Chao and Miller, fails to teach wherein the processor is further configured to identify keywords associated with a merchant from among the plurality of merchants, and store the keywords within metadata of a merchant vector of the merchant within the vector database.
Bhowan teaches wherein the processor is further configured to identify keywords associated with a merchant from among the plurality of merchants, and store the keywords within metadata of a merchant vector of the merchant within the vector database ([0010] Electronic documents may originate from various sources and contain disparate contents. An electronic document in the form of, e.g., a document file may further include multiple pages each containing disparate contents. An automatic document recognition, identification, classification, and cataloging task may require identification of relevant pages from a large collection of electronic documents and extract metadata from each of the relevant pages. Such metadata recognition/identification may be achieved using content recognition/identification and/or classifier models developed based on machine learning technologies. Here, the term metadata may broadly refer to an attribute of the electronic document pages. This attribute may not be included in, e.g., headers of the corresponding electronic files. For example, a collection of electronic documents may contain pages of vendor invoices among many other information. In such cases, wherein the processor is further configured to identify keywords associated with a merchant from among the plurality of merchants metadata may refer to vendor names or IDs associated with the invoices contained in the collection of electronic documents. Such metadata of electronic document pages may belong to a predefined set of metadata categories, e.g., a set of vendor names/IDs. The store the keywords within metadata of a merchant vector of the merchant metadata information may be embedded in texts, graphics, or layouts of the electronic documents. Recognition/Identification of such metadata may help better extract information of interest, label, and organize the electronic documents. While the term “electronic document” may be normally used to refer to an electronic file that may contain multiple pages, it will be used, in the disclosure below, synonymously to the term “page” for simplicity of description. As such, each page of a collection of document pages may be referred to as an electronic document and a collection of documents may refer to pages parsed and extracted from electronic document files and other sources.).
Zhao, Chao, Miller, and Bhowan are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Zhao, Chao, and Miller, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Bhowan to Zhao before the effective filing date of the claimed invention in order to improve accuracy, and at the same time, reduce computational requirements during model development, model update, and feature computation (cf. Bhowan, [0012] The disclosure below provides a multi-stage hierarchical approach to the problem of metadata recognition/identification for improving accuracy, and at the same time, for reducing computational requirements during model development, model update, and feature computation for an input electronic document.).
Claims 7, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao, Chao, and further in view of Ahadian et al. (U.S. Pre-Grant Publication No. 20080270983, hereinafter 'Ahadian').
Regarding claim 7 and analogous claim 15, Zhao, as modified by Chao, teaches The apparatus of claim 1, The method of claim 9.
Zhao, as modified by Chao, fails to teach wherein the processor is configured to query the vector database via an integrated development environment (IDE).
Ahadian teaches wherein the processor is configured to query the vector database via an integrated development environment (IDE) ([0028] In one embodiment, the IDE tool may be configured to parse program source code as it is entered into a text editor provided by the IDE. Upon detecting that a text string is embedded database statement, the IDE may pass the query to a query parser configured to evaluate both the syntactic structure and semantic content of the query. For example, semantic validation provides the validation to indicate whether database table names, column names and other such artifacts referred to in an embedded database statement are valid. And syntactic validation provides validation to indicate whether keywords and statement structure are valid, based on a particular query language (e.g., SQL). Any errors identified by the query parser may be returned to the IDE, and displayed to the developer.).
Zhao, Chao, and Ahadian are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Zhao and Chao, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Ahadian to Zhao before the effective filing date of the claimed invention in order to provide development assistance to a computer programmer related to database statements embedded within computer program source code (cf. Ahadian, [0010] Embodiments of the invention provide an intelligent integrated development environment (IDE) tool for database-aware application development. For example, embodiments of the invention may provide development assistance to a computer programmer related to database statements embedded within computer program source code.).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao, Chao, and further in view of Zuo et al. (U.S. Patent No. 11636291, hereinafter 'Zuo').
Regarding claim 21, Zhao, as modified by Chao, teaches The apparatus of claim 1.
Zhao, as modified by Chao, fails to teach wherein the processor is further configured to filter the vectors to remove an additional subset of vectors that comprise metadata that does not satisfy the query parameter prior to executing the machine learning model on the subset of vectors.
Zuo teaches wherein the processor is further configured to filter the vectors to remove an additional subset of vectors that comprise metadata that does not satisfy the query parameter prior to executing the machine learning model on the subset of vectors ([Col. 5, Line 57-Col. 6, Line 9] Post-filtering component 124 may compare that comprise metadata metadata representing attributes of the merged search results with filter data specifying a content attribute. Post-filtering component 124 may wherein the processor is further configured to filter the vectors filter out that does not satisfy the query parameter prior to executing the machine learning model on the subset of vectors search results that do not include the attributes specified for the current similarity-determination task. For example, a similarity determination task may receive input query data 114 corresponding to a particular style of a dress. After generating an additional subset of vectors embedding data representing the dress and performing a search of similarity index 123, a plurality of search results representing other dresses determined to be similar to the input dress may be determined. However, the post-filtering component 124 may specify that all dress have the attribute color=yellow. Accordingly, dresses that do not include such an attribute (e.g., dresses that are not yellow) to remove may be removed from the search results output by the content similarity determination system 100.; As previously described, the output data generated by the content similarity determination system 100 may be used to generate a similarity dataset 130.).
Zhao, Chao, and Zuo are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Zhao and Chao, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Zuo to Zhao before the effective filing date of the claimed invention in order to provide a high degree of scalability and efficiency to handle large-scale data processing and manage computing resource (cf. Zuo, [Col. 2, Line 59-Col. 3, Line 8] The various content similarity determination systems and techniques described herein may provide a high degree of scalability and efficiency to handle large-scale data processing. For example, the systems and/or techniques described herein may be capable of processing large and continuously growing content sets, and may publish refreshed similarity results within controllable time frames. Furthermore, the various systems and techniques described herein may manage the computing resource (e.g., CPU/GPU usage and memory usage) to optimize for cost. The various processing described herein involves efficient and scalable image and text embedding computation from deep neural networks and large item indexing for similarity search. In various examples, the systems and/or techniques described herein may identify and cache pre-computed results to impart result reusability across different applications without duplicated computations.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAGGIE MAIDO whose telephone number is (703) 756-1953. The examiner can normally be reached M-Th: 6am - 4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached on (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
Prosecution Insights