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 . A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/23/2025 has been entered.
Remarks
This Office Action is responsive to Applicants' Amendment filed on January 23, 2025, in which claims 1-3, 7, 11, 12, and 14-15 are currently amended. Claims 1-20 are currently pending.
Response to Arguments
The rejections to claims 1-20 under 35 U.S.C. § 112(a)/(b) are hereby withdrawn, as necessitated by applicant's amendments and remarks made to the rejections.
Applicant’s arguments with respect to rejection of claims 1-20 under 35 U.S.C. 103 based on amendment have been considered, however, are not persuasive.
With respect to Applicant’s arguments on p. 7 of the Remarks submitted 1/23/2025 that Syed does not teach a neural network, Examiner respectfully disagrees. Syed directly teaches using a neural network (See FIG. 7 on p. 111052).
With respect to Applicant’s arguments on p. 9 of the Remarks submitted 1/23/2025 that the effective filing date of the instant application predates Syed, Examiner respectfully disagrees. The instant EFD is 11/09/2020 and Syed is dated 6/15/2020.
Specification
The disclosure is objected to because of the following informalities:
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-4, and 7-9 are rejected under U.S.C. §103 as being unpatentable over the combination of Wroblewski (US20200133961A1) and Syed (“A Novel Blockchain-Based Framework for Vehicle Life Cycle Tracking: An End-to-End Solution”, 2020).
Regarding claim 1, Wroblewski teaches A method comprising: receiving, by a processor, a plurality of data objects and a taxonomy, wherein at least a portion of the plurality of data objects each comprises metadata comprising authenticated data and verified data;([¶0005] "the sequential processing utilizes the ontology describing motion, which includes allowed motion states for a domain of interest, and wherein the sequential processing provides sequences of motion states with metadata including motion details" [¶0046] "The motion details may include any/all of the following: time, position, velocity, proximity to other entities, and proximity to other regions" [¶0068] "The training set may be balanced for the “fishing” and “not fishing” labels, while ignoring unknown labels" With respect to the instant specification time metadata is interpreted as synonymous with authenticated data. Known labels interpreted as synonymous with verified data.)
generating, by the processor, a training dataset comprising the plurality of data objects and the taxonomy; ([¶0037] "AIS data may include static ata such as ship name and type in addition to the dynamic reports of position and velocity. The ship types may be chosen from the AIS-standard of 30 values plus an “unspecified” (i.e., not filled) field. This static data may be arranged into a taxonomy of (AIS) ship types. FIG. 4 shows a taxonomy in an ontology describing entity classes having unique-entity instances (e.g., AIS ship types). The sizes of the lines reflect the relative population of each class/type. In this example, it can be seen that seven types dominate (see region 540): tugs, passenger vessels, sailing vessels, pleasure craft, tankers, cargo vessels, and fishing vessels." [¶0068] "The training set may be balanced for the “fishing” and “not fishing” labels, while ignoring unknown labels")
the training using the taxonomy and at least a subset of data objects from the training dataset as inputs to the [neural network] during the training;([¶0037] "AIS data may include static ata such as ship name and type in addition to the dynamic reports of position and velocity. The ship types may be chosen from the AIS-standard of 30 values plus an “unspecified” (i.e., not filled) field. This static data may be arranged into a taxonomy of (AIS) ship types. FIG. 4 shows a taxonomy in an ontology describing entity classes having unique-entity instances (e.g., AIS ship types). The sizes of the lines reflect the relative population of each class/type. In this example, it can be seen that seven types dominate (see region 540): tugs, passenger vessels, sailing vessels, pleasure craft, tankers, cargo vessels, and fishing vessels." [¶0068] "The training set may be balanced for the “fishing” and “not fishing” labels, while ignoring unknown labels")
and storing, by the processor, the trained [neural network] in a memory after the training for use in classifying objects in data objects, authenticating data and verifying data received by the trained neural network([¶0026] "a Kalman filter means an algorithm stored in BMS that is used to estimate a set of parameters that may be used to determine the position of the vehicle relative to the background at any given time. The estimated parameters are then stored in a non-volatile memory device included with the BMS").
However, Wroblewski does not explicitly teach training, by the processor, a neural network for classifying at least one object in a data object comprising at least a portion of the object to be classified,
authenticating a data object received by the neural network
and verifying the data object received by the neural network,.
Syed, in the same field of endeavor, teaches training, by the processor, a neural network for classifying at least one object in a data object comprising at least a portion of the object to be classified,([p. 111053] "For training CNN, a vehicle dataset is loaded and fed through CNN for classification of the vehicle into original or modified. For training, all the features of a dataset are made identical or of the same size by padding 00s and equivalent integer values after converting to one-hot arrays and then organizing them")
authenticating a data object received by the neural network ([p. 111054] "Each transaction on the network is executed on a channel, where each party must be authenticated and authorized to transact on that channel. It is a kind of transaction privacy between different ledgers")
and verifying the data object received by the neural network,([p. 111054] "The CA is used for verifying ownership in the network. Each CA is tied to an organization. The cryptographic keys are used by the sender and receiver over the network to cipher messages and to make all the negotiations more reliable").
Wroblewski as well as Syed are directed towards using machine learning for vehicle classification. Therefore, Wroblewski as well as Syed are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Wroblewski with the teachings of Syed by applying the taxonomy based system in Wroblewski to the blockchain system of Syed, both of which use machine learning. Syed provides as additional motivation for combination ([p. 111042 §1] “Blockchain enables participants to store and/or record transactions pertaining to business deals, and its resilience to alteration attempts prevents these transactions from being changed or manipulated. This resilience is important in developing trust in computing and cryptography technologies as participants of the distributed network utilize cryptographic keys to digitally sign transactions”. This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 2, the combination of Wroblewski, and Syed teaches The method of claim 1, wherein the plurality of data objects further comprises one or more of non-published data, published data, images, videos, text data, geographical location data or metadata(Syed [p. 111052] "For damaged vehicles, their images are examined and models are trained over these modified pixelated values").
Regarding claim 3, the combination of Wroblewski, and Syed teaches The method of claim 1, wherein authentication data comprises one or more of a provenance authentication assertion by a content creator or a custodian, a date of copyright registration, authorship information, object information, date of data, date of object, location of object, or data from a copyright registered database(Wroblewski [¶0005] "the kinematic data comprises sets of data each including an entity tag, a timestamp, and a position vector").
Regarding claim 4, the combination of Wroblewski, and Syed teaches The method of claim 1, wherein verification data comprises one or more of a unique digital object identifier; a hash of the metadata together with a signature or a claim(Syed [p. 111048 §IVA] "The status of the newly purchased vehicle will also be reflected and changed from dealer ownership to the buyer in the asset database of the blockchain world state. A blockchain ledger will also be updated for each transaction with a unique cryptographic hash value").
Regarding claim 7, the combination of Wroblewski, and Syed teaches The method of claim 1, wherein at least a portion of the data objects form a class related by an element of the taxonomy, and wherein, during the training, the at least a portion of the data objects in the class are input to the [neural network](Wroblewski [¶0037] "AIS data may include static ata such as ship name and type in addition to the dynamic reports of position and velocity. The ship types may be chosen from the AIS-standard of 30 values plus an “unspecified” (i.e., not filled) field. This static data may be arranged into a taxonomy of (AIS) ship types. FIG. 4 shows a taxonomy in an ontology describing entity classes having unique-entity instances (e.g., AIS ship types). The sizes of the lines reflect the relative population of each class/type. In this example, it can be seen that seven types dominate (see region 540): tugs, passenger vessels, sailing vessels, pleasure craft, tankers, cargo vessels, and fishing vessels." [¶0068] "The training set may be balanced for the “fishing” and “not fishing” labels, while ignoring unknown labels").
Regarding claim 8, the combination of Wroblewski, and Syed teaches The method of claim 1, wherein the neural network comprises a convolutional neural network (CNN), a recurrent neural network (RNN) or both a CNN and a RNN, the CNN being trained using the authenticated and verified data, and the CNN training being characterized in part by pre-processing one or more image data of the authenticated data by isolating one or more vehicles from image background elements.(Wroblewski [¶0015] "FIGS. 11A-11B show experimental results based on path shape and supervised RNNs").
Regarding claim 9, the combination of Wroblewski, and Syed teaches The method of claim 1, wherein the taxonomy comprises elements comprising an action, a concept, an emotion, an event, a geographic city, a geographic country, a geographic place, a geographic state, a vehicle model age, a vehicle model attribute, a vehicle model ethnicity, a vehicle model, a vehicle model quantity, a vehicle model relationship and role, a vehicle museum collection, a person, an image environment, an image orientation, an image setting, an image technique, an image view, a sign, a topic, a vehicle coachbuilder and/or designer, a vehicle color, a vehicle condition, a vehicle manufacturer, a vehicle model, a vehicle part, a vehicle quantity, a vehicle serial number, a vehicle type or a vehicle year of manufacture(Wroblewski [¶0037] "This static data may be arranged into a taxonomy of (AIS) ship types").
Claim 5 is rejected under U.S.C. §103 as being unpatentable over the combination of Wroblewski and Syed and in further view of Oberhofer (US 20200341951 A1).
Regarding claim 5, the combination of Wroblewski, and Syed teaches The method of claim 1.
However, the combination of Wroblewski, and Syed doesn't explicitly teach wherein the metadata is structured using a schema.
Oberhofer, in the same field of endeavor, teaches the metadata is structured using a schema ([¶0063] "The metadata as well as the payload data, e.g., assets being created, modified and/or deleted, may be tagged using the event schemas, such that appropriate transformations may be applied in a later step").
The combination of Wroblewski and Syed as well as Oberhofer are directed towards systems using machine learning and blockchain together. Therefore, the combination of Wroblewski and Syed as well as Oberhofer are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Wroblewski and Syed with the teachings of Oberhofer by using a blockchain metadata schema. Oberhofer provides as additional motivation for combination ([¶0098] "the transforming of the extracted event data comprises adding one or more structural IDs according to the data model to the extracted event data. Embodiments may have the beneficial effect that the transformed data may better resemble the logical structure of the external data structure defined by the data model. The transformed data may thus be provided in a more compact, e.g., normalized way. For example, message duplication may thus be suppressed to prevent double sending.").
Claim 6 is rejected under U.S.C. §103 as being unpatentable over the combination of Wroblewski, Syed, and Newman (US 20200074300 A1).
Regarding claim 6, the combination of Wroblewski, and Syed teaches The method of claim 1.
However, the combination of Wroblewski, and Syed doesn't explicitly teach further comprising: generating a registry comprising the training dataset;
and storing the registry in the memory.
Newman, in the same field of endeavor, teaches generating a registry comprising the training dataset; ([¶0130] "As shown in FIG. 7, after the NN trainer module 210 starts (step 342), the NN trainer module 210 queries the database 206 using SQL language to collect and format a set of training data" Registry interpreted as synonymous with database.)
and storing the registry in the memory([¶0101] "a portion of the memory 126 (denoted as storage memory herein) may be used for long-term data storing, for example, storing files or databases").
The combination of Wroblewski and Syed as well as Newman are directed towards machine learning. Therefore, the combination of Wroblewski and Syed as well as Newman are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Wroblewski and Syed with the teachings of Newman by using a SQL database. Newman provides as additional motivation for combination ([¶0127] "The database 206 is defined as a normalized set of tables, and thus in practice there is a separate table for each of the key pieces of information to be stored, thereby allowing great flexibility in the use of the data when it is assembled into AI training sets and on generating data analytics."). This motivation for combination also applies to the remaining claims which depend on this combination.
Claim 10 is rejected under U.S.C. §103 as being unpatentable over the combination of Wroblewski and Syed and Gupta (US20200065848A1).
Regarding claim 10, the combination of Wroblewski, and Syed teaches The method of claim 1, wherein the neural network comprises a first neural network and a second neural network, wherein the first neural network is the trained neural network,(Syed See FIG. 8)
wherein the first neural network is a CNN (Syed See FIG. 8).
However, the combination of Wroblewski, and Syed doesn't explicitly teach the method further comprising: secondary training, with a processor, the second neural network for performing natural language processing of voice data, wherein the voice data comprises a query, the secondary training using at least a subset of data from the training dataset as inputs to the second neural network during the secondary training,
and the second neural network is a RNN.
Gupta, in the same field of endeavor, teaches the method further comprising: secondary training, with a processor, the second neural network for performing natural language processing of voice data, wherein the voice data comprises a query, the secondary training using at least a subset of data from the training dataset as inputs to the second neural network during the secondary training, ([¶0022] "the case resolution AI model may be trained by way of converting training set queries to structural formats using a word vectorizer (e.g., a TFIDF vectorizer). In some implementations, the case resolution AI model may be trained using textual data, voice data, or data associated with textual and/or voice data, and may predict domains using the word vectorizer" [¶0033] "the intelligent case management platform may train the customer sentiment model using one or more model training techniques, such as a recurrent neural network technique")
and the second neural network is a RNN([¶0033] "the intelligent case management platform may train the customer sentiment model using one or more model training techniques, such as a recurrent neural network technique").
The combination of Wroblewski and Syed as well as Gupta are directed towards machine learning. Therefore, the combination of Wroblewski and Syed as well as Gupta are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Wroblewski and Syed with the teachings of Gupta by using an RNN for querying voice data. Gupta provides as additional motivation for combination ([¶0014] "the customer data may be intelligently analyzed to predict future spending patterns, by which an agent may gain insight into the customer's preferences and/or behaviors to provide more precisely targeted offers and promotions, which further improves customer retention").
Claims 11, 14, 15, 16, and 17 are rejected under U.S.C. §103 as being unpatentable over the combination of Syed and Tang (US10410182B1).
Regarding claim 11, Syed teaches A method comprising: receiving, by a processor, a data object comprising an image of at least a portion of a data object;([p. 111052] "For damaged vehicles, their images are examined and models are trained over these modified pixelated values")
processing, by the processor,([p. 111059] "The prototype was also evaluated for CPU utilization, and it was observed that with an increase in the sub-policies, both CPU utilization and VSCC latency increased. The CPU utilization is presented in Figure 14")
an input comprising data from the data objects using a trained neural network that has been trained to classify the object, ([p. 111053] "For training CNN, a vehicle dataset is loaded and fed through CNN for classification of the vehicle into original or modified. For training, all the features of a dataset are made identical or of the same size by padding 00s and equivalent integer values after converting to one-hot arrays and then organizing them" [p. 111051] "The prediction is based on an extensive training set and the output of the model serves as the estimated value of the vehicle based on its history maintained relative to prices")
authenticate the data objects ([p. 111054] "Each transaction on the network is executed on a channel, where each party must be authenticated and authorized to transact on that channel. It is a kind of transaction privacy between different ledgers")
and verify the data objects;([p. 111054] "The CA is used for verifying ownership in the network. Each CA is tied to an organization. The cryptographic keys are used by the sender and receiver over the network to cipher messages and to make all the negotiations more reliable")
authenticating the data objects using the trained neural network; ([p. 111054] "Each transaction on the network is executed on a channel, where each party must be authenticated and authorized to transact on that channel. It is a kind of transaction privacy between different ledgers")
verifying the data objects using the trained neural network; ([p. 111054] "The CA is used for verifying ownership in the network. Each CA is tied to an organization. The cryptographic keys are used by the sender and receiver over the network to cipher messages and to make all the negotiations more reliable")
determining, using the trained neural network, that the object belongs to a class of objects; ([p. 111053] "The training data is composed of features and labels, where features mean the intensity of various pixilated values and labels mean the class of vehicle. CNN classify values into original or modified. CNN training is elaborated in Figure9").
However, Syed does not explicitly teach generating a result, by the trained neural network, wherein the result comprises a closest match to the object and a plurality of data objects related to the closest match; and displaying the result on a device, wherein the result comprises at least one image comprising a matching object.
Tang, in the same field of endeavor, teaches generating a result, by the trained neural network, wherein the result comprises a closest match to the object and a plurality of data objects related to the closest match; and displaying the result on a device, wherein the result comprises at least one image comprising a matching object([Col. 1 l. 48-Col. 2 l. 6] "According to some implementations, an extended reality device may comprise a memory and one or more processors operatively coupled to the memory. The memory and the one or more processors may be configured to obtain image data corresponding to one or more parts of a vehicle that are visible in a field of view of the extended reality device; identify one or more anchor points in a coordinate space corresponding to the field of view of the extended reality device, wherein the one or more anchor points correspond to the one or more parts of the vehicle in the coordinate space corresponding to the field of view of the extended reality device; obtain, from one or more machine learning models, an output that provides a representation of an expected visual appearance of the one or more parts of the vehicle; identify, based on the image data and the representation provided by the output from the one or more machine learning models, at least one discrepancy between an actual visual appearance of the one or more parts of the vehicle and the expected visual appearance of the one or more parts of the vehicle; obtain digital content based on the at least one discrepancy; and render the digital content on a display of the extended reality device, wherein the one or more processors, when rendering the digital content, are further configured to place the digital content in the coordinate space using the one or more anchor points that correspond to the one or more parts of the vehicle.").
Syed as well as Tang are directed towards using machine learning for vehicle services. Therefore, Syed as well as Tang are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Syed with the teachings of Tang by generating and displaying data including an image of a matching object. Tang provides as additional motivation for combination (Col. 12 l. 65-Col. 13 l. 16] "even if the vehicle history report does not list any records indicating that the tires were replaced, the one or more artificial intelligence techniques may detect that the tires were replaced based on the tires appearing shinier, having less tread wear, and/or the like compared to what would be expected given the mileage on the vehicle. Relatedly, where one or more tires appear shinier, have better tread, and/or the like relative to other tires, this may indicate that a blowout occurred such that the user should check for possible alignment issues. In this way, the visual inspection based on the one or more artificial intelligence techniques can detect issues that may be in need of repair or maintenance in addition to identifying parts of the vehicle that have been repaired or replaced. In this way, substantial computing and/or network resources may be conserved"). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 14, the combination of Syed, and Tang teaches The method of claim 11, wherein the data objects comprising the image of at least a portion of the data object is received from a registered user(Tang [Col. 2 l. 7-27] "The one or more instructions, when executed by one or more processors of an extended reality device, may cause the one or more processors to obtain image data corresponding to a set of parts of a vehicle visible in a field of view of the extended reality device" [Col. 8 l. 62-Col. 9 l. 4] "The extended reality device may render digital content indicating the expected current odometer reading, which can be visually compared to an actual odometer reading (e.g., by the user, using a computer vision technique").
Regarding claim 15, the combination of Syed, and Tang teaches The method of claim 11, wherein authenticating the data using the trained neural network comprises: processing, by the neural network, the data objects and checking for authentication data embedded in the data objects, wherein the authentication data comprises one or more of a provenance authentication assertion by a content creator or a custodian, a date of copyright registration, authorship information, data object information, date of data, date of the data object, location of data object or location of data object in the data object or data object information from a copyright registered database, optionally, wherein if the authentication data is present and complete, then the neural network classifies the data objects as authenticated and if the authentication data is not present or is incomplete, then the neural network classifies the data objects as not authenticated(Syed [p. 111057] "The SC has given the interface of RESTful to directly query the vehicle and record the violation along with its proofs. For violation report, SC transaction updates the couchDB with following records associated with its vehicle ID, such as vehicleID, time-of-violation, location-of-violation, amount-of-violation, violation type, and proofs of the violation").
Regarding claim 16, the combination of Syed, and Tang teaches The method of claim 11, wherein verifying the data objects using the trained neural network comprises: processing, by the trained neural network, the data objects and checking for verification data,(Syed [p. 111053] "For training CNN, a vehicle dataset is loaded and fed through CNN for classification of the vehicle into original or modified. For training, all the features of a dataset are made identical or of the same size by padding 00s and equivalent integer values after converting to one-hot arrays and then organizing them" [p. 111051] "The prediction is based on an extensive training set and the output of the model serves as the estimated value of the vehicle based on its history maintained relative to prices")
wherein the verification data comprises one or more of a unique digital object identifier, a hash of the metadata together with a signature, or a claim(Syed [p. 111048 §IVA] "The status of the newly purchased vehicle will also be reflected and changed from dealer ownership to the buyer in the asset database of the blockchain world state. A blockchain ledger will also be updated for each transaction with a unique cryptographic hash value").
Regarding claim 17, the combination of Syed, and Tang teaches The method of claim 16, wherein if the verification data is present, the neural network processes the verification data using a signature algorithm and compares the verification data to an output of the signature algorithm(Syed [p. 111059] "The three major CPU intensive operations during the policy validation phase include de-serialization of identity (i.e., x.509 certificate), validation of identity with organization MSP, and verification of signature on the transaction data. The block bytes increased with an increase in the number of endorsements due to a number of x.509 certificates encoded in each transaction").
Claims 12 and 13 are rejected under U.S.C. §103 as being unpatentable over the combination of Syed and Tang and Shpalensky (US20190259136A1).
Regarding claim 12, the combination of Syed, and Tang teaches wherein the first image class represents an environment, the environment being other than the object,(Syed [p. 111050] "The owner can write a policy to restrict the vehicles to staying within a certain geographic area using GPS and similarly").
However, the combination of Syed, and Tang doesn't explicitly teach The method of claim 11, further comprising: wherein the trained neural network outputs a probability comprising, for each pixel in the image of at least a portion of the object, a first probability that the pixel belongs to a first image class and a second probability that the pixel belongs to a second image class,
determining, based on the probability, one or more pixels in the image that are classified as the object; and determining, based on the probability, one or more pixels in the image that are classified as environment.
Shpalensky, in the same field of endeavor, teaches The method of claim 11, further comprising: wherein the trained neural network outputs a probability comprising, for each pixel in the image of at least a portion of the object, a first probability that the pixel belongs to a first image class and a second probability that the pixel belongs to a second image class, ([0064] "FIG. 9 illustrates another example image or image channel 902 representative of a particular fully body part from pose data 124 corresponding to example coarse super resolution image 123, arranged in accordance with at least some implementations of the present disclosure. As shown in FIG. 9, a particular image or image channel of images or image channels 602 may be generated for right shoulder 411 (e.g., a particular full body part) via a generate image channel operation 901. As shown, in some embodiments, for particular full body parts, pose data 124 may include probability values indicating the probability a pixel of coarse super resolution image 123 corresponds to the particular full body part. In the illustrated embodiment, a first region 911 of pose data 124 has a first probability the pixels therein correspond to a right shoulder and a second region 912 having a second probability the pixels therein correspond to the right shoulder such that the first probability is greater than the second probability. Although illustrated with first and second regions 911, 912 that are concentric and have first and second values, any particular shapes and numbers of values may be implemented.")
determining, based on the probability, one or more pixels in the image that are classified as the object; and determining, based on the probability, one or more pixels in the image that are classified as environment([0064] "FIG. 9 illustrates another example image or image channel 902 representative of a particular fully body part from pose data 124 corresponding to example coarse super resolution image 123, arranged in accordance with at least some implementations of the present disclosure. As shown in FIG. 9, a particular image or image channel of images or image channels 602 may be generated for right shoulder 411 (e.g., a particular full body part) via a generate image channel operation 901. As shown, in some embodiments, for particular full body parts, pose data 124 may include probability values indicating the probability a pixel of coarse super resolution image 123 corresponds to the particular full body part. In the illustrated embodiment, a first region 911 of pose data 124 has a first probability the pixels therein correspond to a right shoulder and a second region 912 having a second probability the pixels therein correspond to the right shoulder such that the first probability is greater than the second probability. Although illustrated with first and second regions 911, 912 that are concentric and have first and second values, any particular shapes and numbers of values may be implemented.").
The combination of Syed and Tang as well as Shpalensky are directed towards machine learning. Therefore, The combination of Syed and Tang as well as Shpalensky are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of The combination of Syed and Tang with the teachings of Shpalensky by performing per-pixel predictions in an image super-resolution method. Shpalensky provides as additional motivation for combination ([¶0002] "It may be advantageous to improve the resolution of low resolution images of people to super resolution images such that, for example, virtual views of a scene are improved for enhanced user experience. It is with respect to these and other considerations that the present improvements have been needed. Such improvements may become critical as the desire to provide immersive user experiences in scenes attained by multiple cameras such as professional sporting events becomes more widespread"). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 13, the combination of Syed, Tang, and Shpalensky teaches The method of claim 12, further comprising: generating a [geographical] result, by the trained neural network, wherein the [geographical] result comprises a closest match to the environment; and displaying the [geographical] result on the device, wherein the geographical result comprises at least one image of a matching environment(Tang [Col. 1 l. 48-Col. 2 l. 6] "According to some implementations, an extended reality device may comprise a memory and one or more processors operatively coupled to the memory. The memory and the one or more processors may be configured to obtain image data corresponding to one or more parts of a vehicle that are visible in a field of view of the extended reality device; identify one or more anchor points in a coordinate space corresponding to the field of view of the extended reality device, wherein the one or more anchor points correspond to the one or more parts of the vehicle in the coordinate space corresponding to the field of view of the extended reality device; obtain, from one or more machine learning models, an output that provides a representation of an expected visual appearance of the one or more parts of the vehicle; identify, based on the image data and the representation provided by the output from the one or more machine learning models, at least one discrepancy between an actual visual appearance of the one or more parts of the vehicle and the expected visual appearance of the one or more parts of the vehicle; obtain digital content based on the at least one discrepancy; and render the digital content on a display of the extended reality device, wherein the one or more processors, when rendering the digital content, are further configured to place the digital content in the coordinate space using the one or more anchor points that correspond to the one or more parts of the vehicle." Environment interpreted as synonymous with mixed reality environment which is explicitly taught in Tang. Interpretation supported by instant specification at [¶0047]. While Tang does not explicitly teach a geographical result it would be obvious in view of the combination of Tang and Syed where Syed explicitly teaches using a neural network for a geographically restricted result.).
Claim 18 is rejected under U.S.C. §103 as being unpatentable over the combination of Syed and Tang and in further view of Hassani (US20200406859A1).
Regarding claim 18, the combination of Syed, and Tang teaches The method of claim 17.
However, the combination of Syed, and Tang doesn't explicitly teach The method of claim 17, wherein if the verification data matches the output of the signature algorithm, then the trained neural network verifies the data objects, and if the verification data does not match the output of the signature algorithm, then the trained neural network does not verify the data objects.
Hassani, in the same field of endeavor, teaches if the verification data matches the output of the signature algorithm, then the trained neural network verifies the data objects, and if the verification data does not match the output of the signature algorithm, then the trained neural network does not verify the data objects([¶0020] "recognition neural network may compare user authentication data received from biometric readers in real-time with stored user template data that is stored on the private blockchain to verify or determine an identity of a user").
The combination of Syed and Tang as well as Hassani are directed towards using machine learning and blockchains for vehicle services. Therefore, The combination of Syed and Tang as well as Hassani are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of The combination of Syed and Tang with the teachings of Hassani by processing the authentication data directly through the neural network. Hassani provides as additional motivation for combination ([¶0034] “The rider authentication server 212 is accordingly updated to associate the authentication ID with a particular location within the blockchain database 214 for faster queries in the future.”).
Claims 19 and 20 are rejected under U.S.C. §103 as being unpatentable over the combination of Syed and Tang and in further view of Newman.
Regarding claim 19, the combination of Syed, and Tang teaches The method of claim 11.
However, the combination of Syed, and Tang doesn't explicitly teach wherein the trained neural network outputs a verified data objects to a registry,
wherein the registry is stored in a memory.
Newman, in the same field of endeavor, teaches the trained neural network outputs a verified data objects to a registry, ([¶0130] "As shown in FIG. 7, after the NN trainer module 210 starts (step 342), the NN trainer module 210 queries the database 206 using SQL language to collect and format a set of training data" Registry interpreted as synonymous with database.)
wherein the registry is stored in a memory([¶0101] "a portion of the memory 126 (denoted as storage memory herein) may be used for long-term data storing, for example, storing files or databases").
The combination of Syed and Tang as well as Newman are directed towards machine learning. Therefore, The combination of Syed and Tang as well as Newman are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of The combination of Syed and Tang with the teachings of Newman by using a SQL database. Newman provides as additional motivation for combination ([¶0127] "The database 206 is defined as a normalized set of tables, and thus in practice there is a separate table for each of the key pieces of information to be stored, thereby allowing great flexibility in the use of the data when it is assembled into AI training sets and on generating data analytics."). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 20, the combination of Syed, and Tang teaches wherein at least a portion of the plurality of data objects each comprises metadata comprising authenticated data and verification data(Syed [p. 111055] "The MSP is installed on each channel peer to ensure that transaction requests are issued to the peer originated from an authenticated and authorized user [...] The proposed framework records all transactions that are carried out over the network. They are recorded in an append-only manner and are stored in an immutable fashion such that; Transactions (Txs) are signed by private keys and verified with public keys while hashes of blocks ensure transactions are not altered").
However, the combination of Syed, and Tang doesn't explicitly teach The method of claim 11, wherein determining, using the trained neural network, comprises: searching a registry comprising a plurality of data objects and a taxonomy.
Newman, in the same field of endeavor, teaches determining, using the trained neural network, comprises: searching a registry comprising a plurality of data objects and a taxonomy, ([¶0129] "The trainer module 210 uses data stored in the database 206 for training the data classification module 208. The data in the database 206 is normalized meaning that each “piece” of information is stored in a number of separate tables in the database 206. Such data is assembled and collected in a format that the classification module 208 can operate thereon" [¶0130] "As shown in FIG. 7, after the NN trainer module 210 starts (step 342), the NN trainer module 210 queries the database 206 using SQL language to collect and format a set of training data (step 344). The set of data obtained at step 344 comprises the technical details for each tender in a textual format. Key items such as the purchasing organization, technical description, location, and the like, are appended together to form one corpus or text.").
The combination of Syed and Tang as well as Newman are directed towards machine learning. Therefore, The combination of Syed and Tang as well as Newman are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of The combination of Syed and Tang with the teachings of Newman by using a SQL database. Newman provides as additional motivation for combination ([¶0127] "The database 206 is defined as a normalized set of tables, and thus in practice there is a separate table for each of the key pieces of information to be stored, thereby allowing great flexibility in the use of the data when it is assembled into AI training sets and on generating data analytics."). This motivation for combination also applies to the remaining claims which depend on this combination.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Javaid (“DrivMan: Driving Trust Management and Data Sharing in VANETs with Blockchain and Smart Contracts”, 2019) is directed towards a smart contract system for intelligent vehicles.
Khan (“Smart Contract Centric Inference Engine For Intelligent Electric Vehicle Transportation System”, 2020) is directed towards using a neural network with smart contracts for a vehicle transportation system.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124