DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1 recites a series of steps and therefore, is a process. As such, claim 1 is one of the statutory categories. The claim recites “identify a set of fields within the data record; annotate each one of the set of fields by generating for each field in the set of fields a field metadata, the field metadata comprising: information identifying each field within the data record; and a label for that field; for each field: generate a non-fungible token (NFT); generate an NFT attribute record including information obtained from the data record metadata and the field metadata”, which is merely a concept can be performed in the human mind. The claim involves identifying fields in a data record, labeling them, and creating metadata using generic computer components. Human mind through observation and evaluation can perform the above steps with a pen and paper. These limitations, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Thus, the claim recites a mental process and are directed to a judicial exception.
This judicial exception is not integrated into a practical application because the claim recites the additional element: “store in the memory the NFT attribute record”, which further indicates the element that describe well-understood, routine and conventional functions. The courts have recognized the computer function “Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93” 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)(II). The claim further recites “A system, the system comprising: a memory configured to store a data record and metadata associated with the data record; and a processor operably coupled to the memory, the processor configured to”. However, use of a computer or other machinery in its ordinary capacity for economic or other tasks or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). See MPEP 2106.05(f). The addition of insignificant extra-solution activity does not amount to an inventive concept. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP 2016.05(h).
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Thus, claim 1 is directed to the abstract idea.
As such, the independent claims 18 and 20 are also directed to the abstract idea for the same reason as the claim 1 above.
Regarding claims 2, 7-8, 13, 15 and 19, claims further recite the limitations which indicate the limitations amount to no more than mere instructions to apply the exception using a generic computer component. Use of a computer or other machinery in its ordinary capacity for economic or other tasks or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Therefore, claims include no additional elements that would integrate the judicial exception into a practical application or would amount to significantly more than the abstract idea. Thus, the claims are ineligible.
Regarding claims 3-5, 9 and 11-12, claims further recite the limitations which indicate mere data gathering activity that the courts have found to be insignificant extra-solution activity. The addition of insignificant extra-solution activity does not amount to an inventive concept. Furthermore, claims include no additional elements that would integrate the judicial exception into a practical application or would amount to significantly more than the abstract idea. Thus, the claims are ineligible.
Regarding claims 6, 10, 14 and 16-17, claims further recite the limitations which further recite mental processes and are directed to perform mental processes that fall into the “Mental Processes” groupings of abstract ideas and are directed to a judicial exception. Furthermore, claims include no additional elements that would integrate the judicial exception into a practical application or would amount to significantly more than the abstract idea. Thus, the claims are ineligible.
Claim Rejections - 35 USC § 103
3. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
4. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over JURAT et al. (US 2024/0070306 A1) hereinafter JURAT, in view of Kendapadi et al. (US 2026/0228767 A1) hereinafter Kendapadi.
As to claim 1, JURAT discloses a system, the system comprising: a memory configured to store a data record and metadata associated with the data record; and a processor operably coupled to the memory (Para. 77), the processor configured to:
identify a set of fields within the data record (Fig. 1, Para. 55, authentication platform 113 may process the images and/or the videos to detect biometric data unique to user 101. In one embodiment, the biometric data includes iris patterns, eye color, facial details, hand geometry, a fingerprint, or a combination thereof, i.e., a set of fields. In one example embodiment, authentication platform 113 may process the captured image to detect facial details unique to user 101. In another example embodiment, authentication platform 113 may process the received biometric data to detect fingerprint (s) unique to user 101.);
annotate each one of the set of fields by generating for each field in the set of fields a field metadata (Para. 45, “user interface module 211 may enable a presentation of a graphical user interface (GUI) in UE 103. User interface module 211 may employ various application programming interfaces (APIs) or other function calls corresponding to application 105 on UE 103, thus enabling the display of graphics primitives such as icons, menus, buttons, data entry fields, etc. In another embodiment, user interface module 211 may cause interfacing of guidance information with user 101 to include, at least in part, one or more annotations, audio messages, video messages, or a combination thereof.”. Para. 58, “authentication platform 113 may update metadata associated with at least one dynamic NFT based, at least in part, on the monitoring. Authentication platform 113 may generate a new dynamic NFT based, at least in part, on the updated metadata.”. Thus, annotate each one of the set of fields by generating for each field in the set of fields a field metadata.),
for each field: generate a non-fungible token (NFT) (Para. 33, “NFTs may be generated when blockchain 115 string records of cryptographic hash, a set of characters that verifies a set of data to be unique, onto previous records, therefore, creating a chain of identifiable data blocks. This cryptographic transaction process ensures the authentication of each digital file by providing a digital signature that is used to track NFT ownership.”. Para. 43, “training data may include user credentials, e.g., sample biometric data, sample image data, sample video data, sample credential data, etc. Each set of training data may thus include sample biometric data, sample image data, sample video data, and sample credential data, for training machine learning module 209 to authenticate user 101 and/or to encode sample data into NFTs for storage into blockchain 115.”. Thus, a non-fungible token (NFT) being generated for each field.);
generate an NFT attribute record including information obtained from the data record metadata and the field metadata (Para. 34, “NFTs are non-fungible cryptographic assets that may be declared in a standard token format and may have a unique set of attributes. In one example embodiment, NFTs may be digital assets with unique identifiers that are stored on blockchain 115 and may not be substituted. In another example embodiment, NFTs may be digital representations of real-world objects or tradable rights of digital assets, e.g., pictures, virtual creations, audios, and other types of digital files, where the ownerships may be recorded in blockchain smart contracts.”. Thus, generate an NFT attribute record including information obtained from the data record metadata and the field metadata.).
JURAT does not explicitly disclose the field metadata comprising: information identifying each field within the data record; and a label for that field; store in the memory the NFT attribute record.
However, in the same field of endeavor, Kendapadi discloses the field metadata comprising: information identifying each field within the data record (Para. 61, “The valuation machine learning framework 300 may process the NFT to generate the plurality of contribution features, which may include the NFT artist, the NFT collection, the date the NFT was minted, or the like. The plurality of contribution features may further include contribution features related to the image data and objects depicted in the image, such as the buildings (e.g., with attributes including the number of buildings, and for each building, the color, number of windows, height, etc.), plants (e.g., with attribute including the number of plants and for each plant, the color, plant type, etc.), the clouds (e.g., with attributes including the number of clouds, the height of the clouds in the sky, the color, etc.), and/or the like.”. Thus, the field metadata comprising: information identifying each field within the data record.); and
a label for that field (Para. 25, The term "NFT" or "blockchain token" may refer to a record on a blockchain that may be transferred or traded, and typically comprises unique metadata. NFTs may be associated with a particular asset, either digital or physical. An NFT may comprise a metadata structure, containing information such as the date and time of the token's creation, a name and description of the blockchain token, i.e., a label for that field, and links or other data relating the NFT to an asset.).
store in the memory the NFT attribute record (Para. 60, the NFT valuation engine 208 may use the valuation machine learning framework 300 to extract, process, and/or analyze attributes of the NFT such as the NFT smart contract, the NFT metadata, any multimedia information linked to or associated with the NFT, blockchain data related to transactions of the NFT or related NFTs found on blockchain network 112, or any other related data. The attributes of the NFT may be categorized by one or more models of the valuation machine learning framework 300 into feature types, such as transaction data, creator data, image data, sound data, video data, or the like. The valuation machine learning framework 300 may analyze the various feature data pertaining to the NFT and produce as output the plurality of contribution features. The generated plurality of contribution features may be stored in memory 204, i.e. store in the memory the NFT attribute record, or other storage for later use.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of JURAT by processing the image of JURAT in order to identify the contribution features such as the set of fields related to the image data as training data which can be used to train the machine learning model as disclosed by Kendapadi (Para. 61-62). The historical NFT transaction data may be partitioned into one or more of the categories of training data which may be used to initially train the one or more machine learning models and fit model parameters, validation data which may be used to tune the one or more machine learning model hyperparameters, and testing data, which may be used to evaluate the particular machine learning model (Kendapadi, Para. 62). One of the ordinary skills in the art would have motivated to make this modification in order to prepare the data as input for a machine learning model by formatting, normalizing, cleaning, infilling, or performing other operations to prepare valid machine learning training data as suggested by Kendapadi (Para. 62).
As to claim 2, the claim is rejected for the same reasons as claim 1 above. In addition, JURAT discloses wherein the processor is further configured to transmit the NFT to be recorded on a blockchain (Para. 56, “authentication platform 113 may mint, on the distributed blockchain 115, the dynamic NFTs. In one embodiment, minting an NFT may include validating the NFT, creating a new block, and recording the NFT into blockchain 115. The NFT may be recorded onto the blockchain through a "proof of stake" protocol. Proof of stake is a blockchain consensus mechanism used to validate online transactions, e.g., cryptocurrency transactions.”. Thus, the processor is further configured to transmit the NFT to be recorded on a blockchain.).
As to claim 3, the claim is rejected for the same reasons as claim 1 above. In addition, Kendapadi discloses wherein the data record metadata includes at least one of a data record source information, a data record identifier (ID), a data record file size, or a date and time when the data record was created (Para. 25, “The term "NFT" or "blockchain token" may refer to a record on a blockchain that may be transferred or traded, and typically comprises unique metadata. NFTs may be associated with a particular asset, either digital or physical. An NFT may comprise a metadata structure, containing information such as the date and time of the token's creation, a name and description of the blockchain token, and links or other data relating the NFT to an asset.”. Thus, the data record metadata includes at least one of a data record source information, a data record identifier (ID), a data record file size, or a date and time when the data record was created.).
As to claim 4, the claim is rejected for the same reasons as claim 2 above. In addition, Kendapadi discloses wherein the data record is an image (Para. 27, “an example NFT is associated with image data, a contribution feature may be related to the image data, such as certain artistic styles, image attributes, colors, or the like.”. Thus, the data record is an image.), and the data record metadata includes at least one of an image format, an image resolution, a color depth, a location description corresponding to the image, image creator details, or keywords associated with the image (Para. 27, “The term "contribution feature" may refer to a data construct that is a feature associated with an NFT that may have an effect on the price of the NFT. For example, in an instance where an example NFT is associated with image data, a contribution feature may be related to the image data, such as certain artistic styles, image attributes, colors, or the like. Additionally, a contribution feature may be related to other metadata of the example NFT such as an identity of a previous owner, an identity of a user that created the NFT, an identity of an artist that created the image data associated with the NFT, and/or the like.”. Para. 57, “a contribution feature may be related to other metadata of the example NFT such as an identity of a previous owner, an identity of a user that created the NFT, an identity of an artist that created the image data associated with the NFT, and/or the like.”. Thus, the data record metadata includes at least one of an image format, an image resolution, a color depth, a location description corresponding to the image, image creator details, or keywords associated with the image.).
As to claim 5, the claim is rejected for the same reasons as claim 1 above. In addition, Kendapadi discloses wherein the field metadata further comprises a developer ID, a time of annotation, a system ID, and a target machine learning model ID (Para. 25, “The term "NFT" or "blockchain token" may refer to a record on a blockchain that may be transferred or traded, and typically comprises unique metadata. NFTs may be associated with a particular asset, either digital or physical. An NFT may comprise a metadata structure, containing information such as the date and time of the token's creation, a name and description of the blockchain token, and links or other data relating the NFT to an asset”. Para. 60, “the NFT valuation engine 208 may use the valuation machine learning framework 300 to extract, process, and/or analyze attributes of the NFT such as the NFT smart contract, the NFT metadata, any multimedia information linked to or associated with the NFT, blockchain data related to transactions of the NFT or related NFTs found on blockchain network 112, or any other related data. The attributes of the NFT may be categorized by one or more models of the valuation machine learning framework 300 into feature types, such as transaction data, creator data, image data, sound data, video data, or the like.”. Thus, the field metadata further comprises a developer ID, a time of annotation, a system ID, and a target machine learning model ID.).
As to claim 6, the claim is rejected for the same reasons as claim 1 above. In addition, Kendapadi discloses wherein the processor is further configured to validate the annotation by validating for each field in the set of fields, the field metadata, wherein the validation of the field metadata includes comparing the field metadata with a template field metadata for a template data record (Para. 62, “The historical NFT transaction data may be partitioned into one or more of the categories of training data which may be used to initially train the one or more machine learning models and fit model parameters, validation data which may be used to tune the one or more machine learning model hyperparameters, and/or testing data, which may be used to evaluate the particular machine learning model. The processor 202 may interpret and structure the historical NFT transaction data to prepare the data as input for a machine learning model by formatting, normalizing, cleaning, infilling, or performing other operations to prepare valid machine learning training data.”. Thus, the processor is further configured to validate the annotation by validating for each field in the set of fields, the field metadata, wherein the validation of the field metadata includes comparing the field metadata with a template field metadata for a template data record.).
As to claim 7, the claim is rejected for the same reasons as claim 1 above. In addition, Kendapadi discloses wherein the processor is further configured to validate the annotation, wherein the validation is performed by a machine learning model trained to identify valid annotations within a given set of data records (Para. 29, “The term "valuation machine learning framework" may describe a system architecture which may be used to process an NFT and generate at least a predictive valuation score for the NFT. The valuation machine learning framework may include an extraction machine learning model, one or more contribution feature determination machine learning models, one or more per-feature prediction machine learning models, and/or a predictive valuation machine learning model. The valuation machine learning framework may use the one or more included models to process the NFT, generate a plurality of per-feature prediction contribution sores, and a predictive valuation score for the NFT.”. Para. 31, “the contribution feature determination machine learning model is a machine learning model which is trained to process base contribution features of a particular feature type. In some embodiments, the contribution feature determination machine learning model may be a trained CNN and/or may use one or more NLP techniques to process the one or more base contribution features. The contribution feature determination machine learning model may determine one or more attributes for a base contribution feature (e.g., a color attribute, style attribute, etc.). The contribution feature determination machine learning model may the determine the contribution feature based on the base contribution feature and the one or more attributes for the base contribution feature.”. Thus, the processor is further configured to validate the annotation, wherein the validation is performed by a machine learning model trained to identify valid annotations within a given set of data records.).
As to claim 8, the claim is rejected for the same reasons as claim 1 above. In addition, JURAT discloses wherein the processor is further configured to, for a field from the set of fields, generate a smart contract associated with the NFT for the field, the smart contract comprising rules for using the field for training a machine learning model, wherein the rules include at least one of: a number of times the field can be used for training a target machine learning model having a target machine learning model ID; or an expiration time after which the field cannot be used for training the target machine learning model (Fig. 2, Para. 33, “to expedite transactions, a set of rules, e.g., smart contracts, may be stored on the blockchain and executed automatically. Due to transparency, proof of ownership, and traceable transactions in a blockchain network, NFTs may be created using blockchain technology, e.g., blockchain 115. In one embodiment, NFTs may be generated when blockchain 115 string records of cryptographic hash, a set of characters that verifies a set of data to be unique, onto previous records, therefore, creating a chain of identifiable data blocks.”. Para. 35, “the training database may be routinely updated and/or supplemented based on machine learning methods.”.).
As to claim 9, the claim is rejected for the same reasons as claim 1 above. In addition, Kendapadi discloses wherein the data record is an image (Para. 27, “an example NFT is associated with image data, a contribution feature may be related to the image data, such as certain artistic styles, image attributes, colors, or the like.”. Thus, the data record is an image.), and for a field in the set of fields, the information identifying the field comprises one of a semantic segmentation or a line annotation (Para. 83, “The NFT valuation engine 208 may utilize the predictive valuation machine learning model 308 of the valuation machine learning framework 300 to analyze each pair of contribution features in turn. While analyzing a candidate pair of features, the predictive valuation machine learning model 308 may be trained on historical data for similar NFTs that use a similar or the same combination of contribution features”. Thus, the data record is an image, and for a field in the set of fields, the information identifying the field comprises one of a semantic segmentation or a line annotation.).
As to claim 10, the claim is rejected for the same reasons as claim 1 above. In addition, Kendapadi discloses wherein the data record is an image (Para. 27, “an example NFT is associated with image data, a contribution feature may be related to the image data, such as certain artistic styles, image attributes, colors, or the like.”. Thus, the data record is an image.), and for a field in the set of fields, the information identifying the field comprises a boundary for the field, the boundary configured to separate pixels defining a portion of the image comprising the field from pixels defining a portion of the image not comprising the field (Fig. 7, Para. 92, The image preview 706 may display any image data associated with the NFT when image data is available. The area of the image preview 706, i.e. a boundary for the field, may alternatively include a video player user interface, audio player user interface, or the like for previewing digital data associated with the NFT. Para. 63, “the base contribution feature may be indicative of a feature attribute location (e.g., the pixels corresponding to a feature attribute) within the NFT image, a text sequence, and/or the like”.).
As to claim 11, the claim is rejected for the same reasons as claim 10 above. In addition, Kendapadi discloses wherein the boundary is one of a rectangle, a cuboid, a polygon, or a closed curve (Fig. 7, Para. 92, “The image preview 706 may display any image data associated with the NFT when image data is available. The area of the image preview 706, may alternatively include a video player user interface, audio player user interface, or the like for previewing digital data associated with the NFT.”. Para. 63, “the base contribution feature may be indicative of a feature attribute location (e.g., the pixels corresponding to a feature attribute) within the NFT image, a text sequence, and/or the like”.).
As to claim 12, the claim is rejected for the same reasons as claim 10 above. In addition, Kendapadi discloses wherein for a field in the set of fields, the NFT attribute record for the field includes at least one of a data record source information, a data record ID, a developer ID, a time of annotation, a system ID, a target machine learning model ID, the label for the field, the boundary for the field, a link to a location in the memory storing the data record; or a smart contract associated with the NFT (Para. 25, “The term "NFT" or "blockchain token" may refer to a record on a blockchain that may be transferred or traded, and typically comprises unique metadata. NFTs may be associated with a particular asset, either digital or physical. An NFT may comprise a metadata structure, containing information such as the date and time of the token's creation, a name and description of the blockchain token, and links or other data relating the NFT to an asset.”. Para. 61, “The valuation machine learning framework 300 may process the NFT to generate the plurality of contribution features, which may include the NFT artist, the NFT collection, the date the NFT was minted, or the like. The plurality of contribution features may further include contribution features related to the image data and objects depicted in the image, such as the buildings (e.g., with attributes including the number of buildings, and for each building, the color, number of windows, height, etc.), plants (e.g., with attribute including the number of plants and for each plant, the color, plant type, etc.), the clouds (e.g., with attributes including the number of clouds, the height of the clouds in the sky, the color, etc.), and/or the like.”. Thus, the NFT attribute record for the field includes at least one of a data record source information, a data record ID, a developer ID, a time of annotation, a system ID, a target machine learning model ID, the label for the field, the boundary for the field, a link to a location in the memory storing the data record; or a smart contract associated with the NFT.).
As to claim 13, the claim is rejected for the same reasons as claim 12 above. In addition, JURAT discloses wherein for a field in the set of fields the NFT attribute record further includes a digital signature for a developer using a hash of image data for the field (Para. 33, “Due to transparency, proof of ownership, and traceable transactions in a blockchain network, NFTs may be created using blockchain technology, e.g., blockchain 115. In one embodiment, NFTs may be generated when blockchain 115 string records of cryptographic hash, a set of characters that verifies a set of data to be unique, onto previous records, therefore, creating a chain of identifiable data blocks. This cryptographic transaction process ensures the authentication of each digital file by providing a digital signature that is used to track NFT ownership.”. Thus, for a field in the set of fields the NFT attribute record further includes a digital signature for a developer using a hash of image data for the field.).
As to claim 14, the claim is rejected for the same reasons as claim 1 above. In addition, JURAT discloses wherein the processor is further configured to: receive a target machine learning model ID for a target machine learning model (Para. 75, The training data 612 and a training algorithm 620 (e.g., one or more of the modules implemented using the machine learning model and/or may be used to train the machine learning model) may be provided to a training component 630 that may apply the training data 612 to the training algorithm 620 to generate the machine learning model. According to an implementation, the training component 630 may be provided comparison results 616 that compare a previous output of the corresponding machine learning model, i.e, a target machine learning model ID, to apply the previous result to re-train the machine learning model. The comparison results 616 may be used by the training component 630 to update the corresponding machine learning model.); and for a field from the set of fields, determine, based on the NFT associated with the field, whether the field and the field metadata are authorized to be used for training the target machine learning model (Para. 43, “training module 207 may provide supervised learning to machine learning module 209 by providing training data that contains input and correct output, to allow machine learning module 209 to learn over time. The training may be performed based on the deviation of a processed result from a documented result when the inputs are fed into machine learning module 209, e.g., an algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized. In one embodiment, training data may include user credentials, e.g., sample biometric data, sample image data, sample video data, sample credential data, etc. Each set of training data may thus include sample biometric data, sample image data, sample video data, and sample credential data, for training machine learning module 209 to authenticate user 101 and/or to encode sample data into NFTs for storage into blockchain 115.”. Thus, for a field from the set of fields, determine, based on the NFT associated with the field, whether the field and the field metadata are authorized to be used for training the target machine learning model.).
As to claim 15, the claim is rejected for the same reasons as claim 1 above. In addition, JURAT discloses wherein the set of fields comprises a plurality of fields, and wherein the processor is further configured to: generate, for the data record, a field mapping of the plurality of fields having associated NFT and NFT attribute records; and store the field mapping in the memory (Para. 35, “database 117 may be any type of database, such as relational, hierarchical, object-oriented, and/or the like, wherein data are organized in any suitable manner, including as data tables or lookup tables. In one embodiment, database 117 may store and manage multiple types of information that can provide means for aiding in the content provisioning and sharing process. In an embodiment, database 117 may include a machine-learning based training database with pre-defined mapping defining a relationship between various input parameters and output parameters based on various statistical methods. In an embodiment, the training database may include machine learning algorithms to learn mappings between input parameters related to the user such as but not limited to financial transaction information, online activity information, historical user information and interests, contextual information, etc.”. Thus, the set of fields comprises a plurality of fields, and wherein the processor is further configured to: generate, for the data record, a field mapping of the plurality of fields having associated NFT and NFT attribute records; and store the field mapping in the memory.).
As to claim 16, the claim is rejected for the same reasons as claim 15 above. In addition, JURAT discloses wherein the data record is a first data record, and the field mapping is a first field mapping, the processor is further configured to:
receive at least a second data record, the second data record including associated second data record metadata (Fig. 1, Para. 55, authentication platform 113 may process the images and/or the videos to detect biometric data unique to user 101. In one embodiment, the biometric data includes iris patterns, eye color, facial details, hand geometry, a fingerprint, or a combination thereof, i.e., a second data record. In one example embodiment, authentication platform 113 may process the captured image to detect facial details unique to user 101. In another example embodiment, authentication platform 113 may process the received biometric data to detect fingerprint (s) unique to user 101. Para. 58, “authentication platform 113 may update metadata associated with at least one dynamic NFT based, at least in part, on the monitoring. Authentication platform 113 may generate a new dynamic NFT based, at least in part, on the updated metadata”.);
for each field in the second set of fields:
generate an NFT (Para. 33, “NFTs may be generated when blockchain 115 string records of cryptographic hash, a set of characters that verifies a set of data to be unique, onto previous records, therefore, creating a chain of identifiable data blocks. This cryptographic transaction process ensures the authentication of each digital file by providing a digital signature that is used to track NFT ownership.”. Para. 43, “training data may include user credentials, e.g., sample biometric data, sample image data, sample video data, sample credential data, etc. Each set of training data may thus include sample biometric data, sample image data, sample video data, and sample credential data, for training machine learning module 209 to authenticate user 101 and/or to encode sample data into NFTs for storage into blockchain 115.”. Thus, a non-fungible token (NFT) being generated for each field.);
generate an NFT attribute record including information obtained from the second data record metadata and the field metadata (Para. 34, “NFTs are non-fungible cryptographic assets that may be declared in a standard token format and may have a unique set of attributes. In one example embodiment, NFTs may be digital assets with unique identifiers that are stored on blockchain 115 and may not be substituted. In another example embodiment, NFTs may be digital representations of real-world objects or tradable rights of digital assets, e.g., pictures, virtual creations, audios, and other types of digital files, where the ownerships may be recorded in blockchain smart contracts.”. Thus, generate an NFT attribute record including information obtained from the second data record metadata and the field metadata.);
transmit the NFT to be recorded on a blockchain (Para. 56, “authentication platform 113 may mint, on the distributed blockchain 115, the dynamic NFTs. In one embodiment, minting an NFT may include validating the NFT, creating a new block, and recording the NFT into blockchain 115. The NFT may be recorded onto the blockchain through a "proof of stake" protocol. Proof of stake is a blockchain consensus mechanism used to validate online transactions, e.g., cryptocurrency transactions.”. Thus, the processor is further configured to transmit the NFT to be recorded on a blockchain.);
generate, for the second data record, a second field mapping of the second set of fields having associated NFTs and NFT attribute records; and generate, a training data mapping including information about: the first data record and the associated first field mapping, and the second data record, and the associated second field mapping (Para. 35, “database 117 may be any type of database, such as relational, hierarchical, object-oriented, and/or the like, wherein data are organized in any suitable manner, including as data tables or lookup tables. In one embodiment, database 117 may store and manage multiple types of information that can provide means for aiding in the content provisioning and sharing process. In an embodiment, database 117 may include a machine-learning based training database with pre-defined mapping defining a relationship between various input parameters and output parameters based on various statistical methods. In an embodiment, the training database may include machine learning algorithms to learn mappings between input parameters related to the user such as but not limited to financial transaction information, online activity information, historical user information and interests, contextual information, etc.”.).
JURAT does not explicitly disclose store the second data record in the memory; identify a second set of fields within the second data record; annotate each one of the second set of fields by generating for each field in the second set of fields a field metadata, the field metadata including information identifying each field within the second data record, and a label for that field; and store in the memory the NFT attribute record; and store the training data mapping in the memory.
However, in the same field of endeavor, Kendapadi discloses store the second data record in the memory (Para. 60, the NFT valuation engine 208 may use the valuation machine learning framework 300 to extract, process, and/or analyze attributes of the NFT such as the NFT smart contract, the NFT metadata, any multimedia information linked to or associated with the NFT, blockchain data related to transactions of the NFT or related NFTs found on blockchain network 112, or any other related data. The attributes of the NFT may be categorized by one or more models of the valuation machine learning framework 300 into feature types, such as transaction data, creator data, image data, sound data, video data, or the like. The valuation machine learning framework 300 may analyze the various feature data pertaining to the NFT and produce as output the plurality of contribution features. The generated plurality of contribution features may be stored in memory 204, i.e. store in the memory the NFT attribute record, or other storage for later use.);
identify a second set of fields within the second data record (Para. 61, “The valuation machine learning framework 300 may process the NFT to generate the plurality of contribution features, which may include the NFT artist, the NFT collection, the date the NFT was minted, or the like. The plurality of contribution features may further include contribution features related to the image data and objects depicted in the image, such as the buildings (e.g., with attributes including the number of buildings, and for each building, the color, number of windows, height, etc.), plants (e.g., with attribute including the number of plants and for each plant, the color, plant type, etc.), the clouds (e.g., with attributes including the number of clouds, the height of the clouds in the sky, the color, etc.), and/or the like.”. Thus, identify a second set of fields within the second data record.);
annotate each one of the second set of fields by generating for each field in the second set of fields a field metadata (Para. 25, The term "NFT" or "blockchain token" may refer to a record on a blockchain that may be transferred or traded, and typically comprises unique metadata. NFTs may be associated with a particular asset, either digital or physical. An NFT may comprise a metadata structure, containing information such as the date and time of the token's creation, a name and description of the blockchain token, and links or other data relating the NFT to an asset. Thus, annotate each one of the second set of fields by generating for each field in the second set of fields a field metadata.), the field metadata including information identifying each field within the second data record (Para. 61, “The valuation machine learning framework 300 may process the NFT to generate the plurality of contribution features, which may include the NFT artist, the NFT collection, the date the NFT was minted, or the like. The plurality of contribution features may further include contribution features related to the image data and objects depicted in the image, such as the buildings (e.g., with attributes including the number of buildings, and for each building, the color, number of windows, height, etc.), plants (e.g., with attribute including the number of plants and for each plant, the color, plant type, etc.), the clouds (e.g., with attributes including the number of clouds, the height of the clouds in the sky, the color, etc.), and/or the like.”. Thus, the field metadata comprising: information identifying each field within the data record.), and a label for that field (Para. 25, The term "NFT" or "blockchain token" may refer to a record on a blockchain that may be transferred or traded, and typically comprises unique metadata. NFTs may be associated with a particular asset, either digital or physical. An NFT may comprise a metadata structure, containing information such as the date and time of the token's creation, a name and description of the blockchain token, i.e., a label for that field, and links or other data relating the NFT to an asset.); and
store in the memory the NFT attribute record (Para. 60, the NFT valuation engine 208 may use the valuation machine learning framework 300 to extract, process, and/or analyze attributes of the NFT such as the NFT smart contract, the NFT metadata, any multimedia information linked to or associated with the NFT, blockchain data related to transactions of the NFT or related NFTs found on blockchain network 112, or any other related data. The attributes of the NFT may be categorized by one or more models of the valuation machine learning framework 300 into feature types, such as transaction data, creator data, image data, sound data, video data, or the like. The valuation machine learning framework 300 may analyze the various feature data pertaining to the NFT and produce as output the plurality of contribution features. The generated plurality of contribution features may be stored in memory 204, i.e. store in the memory the NFT attribute record, or other storage for later use.); and
store the training data mapping in the memory (Para. 60, the NFT valuation engine 208 may use the valuation machine learning framework 300 to extract, process, and/or analyze attributes of the NFT such as the NFT smart contract, the NFT metadata, any multimedia information linked to or associated with the NFT, blockchain data related to transactions of the NFT or related NFTs found on blockchain network 112, or any other related data. The attributes of the NFT may be categorized by one or more models of the valuation machine learning framework 300 into feature types, such as transaction data, creator data, image data, sound data, video data, or the like. The valuation machine learning framework 300 may analyze the various feature data pertaining to the NFT and produce as output the plurality of contribution features. The generated plurality of contribution features may be stored in memory 204, i.e. store in the memory the NFT attribute record, or other storage for later use.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of JURAT by processing the image of JURAT in order to identify the contribution features such as the set of fields related to the image data as training data which can be used to train the machine learning model as disclosed by Kendapadi (Para. 61-62). The historical NFT transaction data may be partitioned into one or more of the categories of training data which may be used to initially train the one or more machine learning models and fit model parameters, validation data which may be used to tune the one or more machine learning model hyperparameters, and testing data, which may be used to evaluate the particular machine learning model (Kendapadi, Para. 62). One of the ordinary skills in the art would have motivated to make this modification in order to prepare the data as input for a machine learning model by formatting, normalizing, cleaning, infilling, or performing other operations to prepare valid machine learning training data as suggested by Kendapadi (Para. 62).
As to claim 17, the claim is rejected for the same reasons as claim 16 above. In addition, JURAT discloses wherein the processor is further configured to: receive the training data mapping (Para. 35, “database 117 may be any type of database, such as relational, hierarchical, object-oriented, and/or the like, wherein data are organized in any suitable manner, including as data tables or lookup tables. In one embodiment, database 117 may store and manage multiple types of information that can provide means for aiding in the content provisioning and sharing process. In an embodiment, database 117 may include a machine-learning based training database with pre-defined mapping defining a relationship between various input parameters and output parameters based on various statistical methods. In an embodiment, the training database may include machine learning algorithms to learn mappings between input parameters related to the user such as but not limited to financial transaction information, online activity information, historical user information and interests, contextual information, etc.”.);
receive a target machine learning model ID for a target machine learning model (Para. 75, The training data 612 and a training algorithm 620 (e.g., one or more of the modules implemented using the machine learning model and/or may be used to train the machine learning model) may be provided to a training component 630 that may apply the training data 612 to the training algorithm 620 to generate the machine learning model. According to an implementation, the training component 630 may be provided comparison results 616 that compare a previous output of the corresponding machine learning model, i.e, a target machine learning model ID, to apply the previous result to re-train the machine learning model. The comparison results 616 may be used by the training component 630 to update the corresponding machine learning model.); and
select, based on NFTs associated with a plurality of fields listed within the training data mapping, fields having corresponding field metadata, that are authorized to be used for training the target machine learning model (Para. 43, “training module 207 may provide supervised learning to machine learning module 209 by providing training data that contains input and correct output, to allow machine learning module 209 to learn over time. The training may be performed based on the deviation of a processed result from a documented result when the inputs are fed into machine learning module 209, e.g., an algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized. In one embodiment, training data may include user credentials, e.g., sample biometric data, sample image data, sample video data, sample credential data, etc. Each set of training data may thus include sample biometric data, sample image data, sample video data, and sample credential data, for training machine learning module 209 to authenticate user 101 and/or to encode sample data into NFTs for storage into blockchain 115.”. Thus, select, based on NFTs associated with a plurality of fields listed within the training data mapping, fields having corresponding field metadata, that are authorized to be used for training the target machine learning model.).
As to claim 18, JURAT discloses a method for authenticating a training data (Para. 35), the method comprising: identifying a set of fields within a data record (Fig. 1, Para. 55, authentication platform 113 may process the images and/or the videos to detect biometric data unique to user 101. In one embodiment, the biometric data includes iris patterns, eye color, facial details, hand geometry, a fingerprint, or a combination thereof, i.e., a set of fields. In one example embodiment, authentication platform 113 may process the captured image to detect facial details unique to user 101. In another example embodiment, authentication platform 113 may process the received biometric data to detect fingerprint (s) unique to user 101.);
annotating each one of the set of fields by generating for each field in the set of fields a field metadata (Para. 45, “user interface module 211 may enable a presentation of a graphical user interface (GUI) in UE 103. User interface module 211 may employ various application programming interfaces (APIs) or other function calls corresponding to application 105 on UE 103, thus enabling the display of graphics primitives such as icons, menus, buttons, data entry fields, etc. In another embodiment, user interface module 211 may cause interfacing of guidance information with user 101 to include, at least in part, one or more annotations, audio messages, video messages, or a combination thereof.”. Para. 58, “authentication platform 113 may update metadata associated with at least one dynamic NFT based, at least in part, on the monitoring. Authentication platform 113 may generate a new dynamic NFT based, at least in part, on the updated metadata.”. Thus, annotate each one of the set of fields by generating for each field in the set of fields a field metadata.),
for each field: generating a non-fungible token (NFT) (Para. 33, “NFTs may be generated when blockchain 115 string records of cryptographic hash, a set of characters that verifies a set of data to be unique, onto previous records, therefore, creating a chain of identifiable data blocks. This cryptographic transaction process ensures the authentication of each digital file by providing a digital signature that is used to track NFT ownership.”. Para. 43, “training data may include user credentials, e.g., sample biometric data, sample image data, sample video data, sample credential data, etc. Each set of training data may thus include sample biometric data, sample image data, sample video data, and sample credential data, for training machine learning module 209 to authenticate user 101 and/or to encode sample data into NFTs for storage into blockchain 115.”. Thus, a non-fungible token (NFT) being generated for each field.);
generating an NFT attribute record including information obtained from data record metadata associated with the data record and the field metadata (Para. 34, “NFTs are non-fungible cryptographic assets that may be declared in a standard token format and may have a unique set of attributes. In one example embodiment, NFTs may be digital assets with unique identifiers that are stored on blockchain 115 and may not be substituted. In another example embodiment, NFTs may be digital representations of real-world objects or tradable rights of digital assets, e.g., pictures, virtual creations, audios, and other types of digital files, where the ownerships may be recorded in blockchain smart contracts.”. Thus, generate an NFT attribute record including information obtained from the data record metadata and the field metadata.).
JURAT does not explicitly disclose the field metadata comprising: information identifying each field within the data record; and a label for that field; and storing in a memory the NFT attribute record.
However, in the same field of endeavor, Kendapadi discloses the field metadata comprising: information identifying each field within the data record (Para. 61, “The valuation machine learning framework 300 may process the NFT to generate the plurality of contribution features, which may include the NFT artist, the NFT collection, the date the NFT was minted, or the like. The plurality of contribution features may further include contribution features related to the image data and objects depicted in the image, such as the buildings (e.g., with attributes including the number of buildings, and for each building, the color, number of windows, height, etc.), plants (e.g., with attribute including the number of plants and for each plant, the color, plant type, etc.), the clouds (e.g., with attributes including the number of clouds, the height of the clouds in the sky, the color, etc.), and/or the like.”. Thus, the field metadata comprising: information identifying each field within the data record.); and
a label for that field (Para. 25, The term "NFT" or "blockchain token" may refer to a record on a blockchain that may be transferred or traded, and typically comprises unique metadata. NFTs may be associated with a particular asset, either digital or physical. An NFT may comprise a metadata structure, containing information such as the date and time of the token's creation, a name and description of the blockchain token, i.e., a label for that field, and links or other data relating the NFT to an asset.); and
storing in a memory the NFT attribute record (Para. 60, the NFT valuation engine 208 may use the valuation machine learning framework 300 to extract, process, and/or analyze attributes of the NFT such as the NFT smart contract, the NFT metadata, any multimedia information linked to or associated with the NFT, blockchain data related to transactions of the NFT or related NFTs found on blockchain network 112, or any other related data. The attributes of the NFT may be categorized by one or more models of the valuation machine learning framework 300 into feature types, such as transaction data, creator data, image data, sound data, video data, or the like. The valuation machine learning framework 300 may analyze the various feature data pertaining to the NFT and produce as output the plurality of contribution features. The generated plurality of contribution features may be stored in memory 204, i.e. store in a memory the NFT attribute record, or other storage for later use.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of JURAT by processing the image of JURAT in order to identify the contribution features such as the set of fields related to the image data as training data which can be used to train the machine learning model as disclosed by Kendapadi (Para. 61-62). The historical NFT transaction data may be partitioned into one or more of the categories of training data which may be used to initially train the one or more machine learning models and fit model parameters, validation data which may be used to tune the one or more machine learning model hyperparameters, and testing data, which may be used to evaluate the particular machine learning model (Kendapadi, Para. 62). One of the ordinary skills in the art would have motivated to make this modification in order to prepare the data as input for a machine learning model by formatting, normalizing, cleaning, infilling, or performing other operations to prepare valid machine learning training data as suggested by Kendapadi (Para. 62).
As to claim 19, the claim is rejected for the same reasons as claim 18 above. In addition, JURAT discloses further comprising transmitting the NFT to be recorded on a blockchain (Para. 56, “authentication platform 113 may mint, on the distributed blockchain 115, the dynamic NFTs. In one embodiment, minting an NFT may include validating the NFT, creating a new block, and recording the NFT into blockchain 115. The NFT may be recorded onto the blockchain through a "proof of stake" protocol. Proof of stake is a blockchain consensus mechanism used to validate online transactions, e.g., cryptocurrency transactions.”. Thus, transmitting the NFT to be recorded on a blockchain.).
As to claim 20, JURAT discloses a non-transitory computer-readable medium storing instructions that, when executed by one or more processors (Para. 77), cause the one or more processors to:
identify a set of fields within a data record (Fig. 1, Para. 55, authentication platform 113 may process the images and/or the videos to detect biometric data unique to user 101. In one embodiment, the biometric data includes iris patterns, eye color, facial details, hand geometry, a fingerprint, or a combination thereof, i.e., a set of fields. In one example embodiment, authentication platform 113 may process the captured image to detect facial details unique to user 101. In another example embodiment, authentication platform 113 may process the received biometric data to detect fingerprint (s) unique to user 101.);
annotate each one of the set of fields by generating for each field in the set of fields a field metadata (Para. 45, “user interface module 211 may enable a presentation of a graphical user interface (GUI) in UE 103. User interface module 211 may employ various application programming interfaces (APIs) or other function calls corresponding to application 105 on UE 103, thus enabling the display of graphics primitives such as icons, menus, buttons, data entry fields, etc. In another embodiment, user interface module 211 may cause interfacing of guidance information with user 101 to include, at least in part, one or more annotations, audio messages, video messages, or a combination thereof.”. Para. 58, “authentication platform 113 may update metadata associated with at least one dynamic NFT based, at least in part, on the monitoring. Authentication platform 113 may generate a new dynamic NFT based, at least in part, on the updated metadata.”. Thus, annotate each one of the set of fields by generating for each field in the set of fields a field metadata.),
for each field: generate a non-fungible token (NFT) (Para. 33, “NFTs may be generated when blockchain 115 string records of cryptographic hash, a set of characters that verifies a set of data to be unique, onto previous records, therefore, creating a chain of identifiable data blocks. This cryptographic transaction process ensures the authentication of each digital file by providing a digital signature that is used to track NFT ownership.”. Para. 43, “training data may include user credentials, e.g., sample biometric data, sample image data, sample video data, sample credential data, etc. Each set of training data may thus include sample biometric data, sample image data, sample video data, and sample credential data, for training machine learning module 209 to authenticate user 101 and/or to encode sample data into NFTs for storage into blockchain 115.”. Thus, a non-fungible token (NFT) being generated for each field.);
generate an NFT attribute record including information obtained from data record metadata associated with the data record and the field metadata (Para. 34, “NFTs are non-fungible cryptographic assets that may be declared in a standard token format and may have a unique set of attributes. In one example embodiment, NFTs may be digital assets with unique identifiers that are stored on blockchain 115 and may not be substituted. In another example embodiment, NFTs may be digital representations of real-world objects or tradable rights of digital assets, e.g., pictures, virtual creations, audios, and other types of digital files, where the ownerships may be recorded in blockchain smart contracts.”. Thus, generate an NFT attribute record including information obtained from the data record metadata and the field metadata.).
JURAT does not explicitly disclose the field metadata comprising: information identifying each field within the data record; and a label for that field; and store in a memory the NFT attribute record.
However, in the same field of endeavor, Kendapadi discloses the field metadata comprising: information identifying each field within the data record (Para. 61, “The valuation machine learning framework 300 may process the NFT to generate the plurality of contribution features, which may include the NFT artist, the NFT collection, the date the NFT was minted, or the like. The plurality of contribution features may further include contribution features related to the image data and objects depicted in the image, such as the buildings (e.g., with attributes including the number of buildings, and for each building, the color, number of windows, height, etc.), plants (e.g., with attribute including the number of plants and for each plant, the color, plant type, etc.), the clouds (e.g., with attributes including the number of clouds, the height of the clouds in the sky, the color, etc.), and/or the like.”. Thus, the field metadata comprising: information identifying each field within the data record.); and
a label for that field (Para. 25, The term "NFT" or "blockchain token" may refer to a record on a blockchain that may be transferred or traded, and typically comprises unique metadata. NFTs may be associated with a particular asset, either digital or physical. An NFT may comprise a metadata structure, containing information such as the date and time of the token's creation, a name and description of the blockchain token, i.e., a label for that field, and links or other data relating the NFT to an asset.).
store in a memory the NFT attribute record (Para. 60, the NFT valuation engine 208 may use the valuation machine learning framework 300 to extract, process, and/or analyze attributes of the NFT such as the NFT smart contract, the NFT metadata, any multimedia information linked to or associated with the NFT, blockchain data related to transactions of the NFT or related NFTs found on blockchain network 112, or any other related data. The attributes of the NFT may be categorized by one or more models of the valuation machine learning framework 300 into feature types, such as transaction data, creator data, image data, sound data, video data, or the like. The valuation machine learning framework 300 may analyze the various feature data pertaining to the NFT and produce as output the plurality of contribution features. The generated plurality of contribution features may be stored in memory 204, i.e. store in a memory the NFT attribute record, or other storage for later use.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of JURAT by processing the image of JURAT in order to identify the contribution features such as the set of fields related to the image data as training data which can be used to train the machine learning model as disclosed by Kendapadi (Para. 61-62). The historical NFT transaction data may be partitioned into one or more of the categories of training data which may be used to initially train the one or more machine learning models and fit model parameters, validation data which may be used to tune the one or more machine learning model hyperparameters, and testing data, which may be used to evaluate the particular machine learning model (Kendapadi, Para. 62). One of the ordinary skills in the art would have motivated to make this modification in order to prepare the data as input for a machine learning model by formatting, normalizing, cleaning, infilling, or performing other operations to prepare valid machine learning training data as suggested by Kendapadi (Para. 62).
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sultana et al. (US 2021/0374247 A1) teaches a secure ML pipeline to improve the robustness of ML models against poisoning attacks and utilizing data provenance as a tool.
Figge et al. (US 2024/0096070 A1) teaches generating and processing digital images associated with nonfungible tokens.
KANG (US 2024/0039725 A1) teaches editing a custom decorated digital image and issuing them as a non fungible token.
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD SOLAIMAN BHUYAN whose telephone number is (571)272-7843. The examiner can normally be reached on Monday - Friday 9:00am-5:00pm EST.
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, Charles Rones can be reached on 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571 -273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Mohammad S Bhuyan/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
Prosecution Insights