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 .
Detailed Action
The instant application having Application No. 18/413,431 has claims 1-4, 6-10, 12, 14, and 16-20 pending filed on 01/16/2024; there are 3 independent claims and 13 dependent claims, all of which are ready for examination by the examiner. The applicant canceled the original claims 5, 11, 13 and 15 (dated 07/07/2025)
Response to Arguments
This Office Action is in response to applicant’s communication filed on July 7, 2025 in response to PTO Office Action dated May 1, 2025. The Applicant’s remarks and amendments to the claims and/or specification were considered with the results that follow.
Claim Rejections
Claim Rejections - 35 USC § 101
In view of the applicant’s amendment to the independent claims 1, 12 and 17 (dated 07/07/2025), the claim rejection under 35 U.S.C. § 101 for judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more of the claims 1-4, 6-10, 12, 14, and 16-20 is withdrawn.
Claim Rejections - 35 USC § 103
35 USC § 103 Rejection of claims 1-4, 6-10, 12, 14, and 16-20
Applicant's arguments filed on 07/07/2025 with respect to the claims 1-4, 6-10, 12, 14, and 16-20 have been fully considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6-10, 12, 14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Selly Roger (US PGPUB 20230229950) in view of Yaghi et al (US PGPUB 20240202571) and in further view of Jahanbakhsh Kazem (US PGPUB 20180336202).
As per claim 1:
Selly teaches:
“A method of real-time searching using quantum machine learning, comprising” (Paragraph [0040] (a quantum information state of the Programmable Quantum Computer comprises a target to be searched includes))
“receiving a first input data set, wherein the first input data set comprises at least one of a video file, an audio file, or social media content” (Paragraph [0138] (receiving data regarding a target to be searched, a probe (i.e., query) to use in conducting this search and the target may be two-dimensional sequence of pixels regarding target image (a video file))).
Selly does not EXPLICITLY teach: comprising real-time data from a live information feed; creating a first vector representation of the first input data set; accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object; identifying, based on querying the database using a set of operations between the first vector representation and a plurality of vector representations of the plurality of digital objects, a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input data set; and returning the identified subset of digital objects and at least a portion of the first input data set causing a simultaneous display via a user interface.
However, in an analogous art, Yaghi teaches:
“comprising real-time data from a live information feed” (Paragraph [0012] (the system may receive real-time information and determine whether the data stream has completed or not))
“creating a first vector representation of the first input data set” (Paragraph [0014] (the system may generate a first vector representation of the first real-time processed data for use))
“identifying, based on querying the database using a set of operations between the first vector representation and a plurality of vector representations of the plurality of digital objects, a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input data set” (Paragraph [0126] and Paragraph [0127] (the system may retrieve (identifying, based on querying the database) , for each textual dataset in the plurality of textual datasets, a plurality of data, the system may generate a vector representation of the first text data and a plurality of vector representations for each datum in the first plurality of textual data and the system may compare the first text data and the first plurality of textual data by inputting the relevant data in a neural network model and generating text distances to determine similarity metrics (a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input data set))).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Yaghi and apply them on teachings of Selly for the method of real-time searching “comprising real-time data from a live information feed; creating a first vector representation of the first input data set; identifying, based on querying the database using a set of operations between the first vector representation and a plurality of vector representations of the plurality of digital objects, a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input data set”. One would be motivated as it provides historical and contextual data that enables labelers of machine learning input data to aid in labeling decisions, including model evaluation data and label modification data which may aid in resolving labeling inconsistencies between multiple-user labeling tasks. (Yaghi, Paragraph [0002]).
Selly and Yaghi do not EXPLICITLY teach: accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object; and returning the identified subset of digital objects and at least a portion of the first input data set causing a simultaneous display via a user interface.
However, in an analogous art, Jahanbakhsh teaches:
“accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object” (Paragraph [0006] , Paragraph [0007], Paragraph [0114] and Paragraph [0139] (identifying a plurality of first data objects to identify document objects (digital objects) connected to one of the identified first data objects, the document object is preferably provided as a hash of the original document, the index including lists of organization identifiers, each organization identifier associated with at least one document identifier and the set of features of each document may be stored as a feature vector, comprising a count of the number of occurrences of each feature in the document along a pre-ordered set of features (corresponding to a set of content parameters of the corresponding digital object)))
“and returning the identified subset of digital objects and at least a portion of the first input data set causing a simultaneous display via a user interface” (Paragraph [0094], Paragraph [0142] and Paragraph [0143] (The search engine may identify data objects connected to the Buyer object in the database and add these objects or their attributes to the user-specified search features, The system receives queries and communicates results to users via a user interface on the user's computing device and every data object has a visual representation to be displayed to the user)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Jahanbakhsh and apply them on teachings of Selly and Yaghi for the method of real-time searching “accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object; and returning the identified subset of digital objects and at least a portion of the first input data set causing a simultaneous display via a user interface”. One would be motivated as it enables a search of data objects and rank them by their connection to certain other data objects that are relevant to the search query and the method employs a database and algorithms particularly suited to capture and search relationship between the data objects. (Jahanbakhsh, Paragraph [0027]).
As per claim 2:
Selly, Yaghi and Jahanbakhsh teach the real-time searching method of the claim 1 above.
Jahanbakhsh further teaches:
“wherein returning the identified subset of digital object comprises displaying a combination of a first digital object and at least a portion of the first input data set simultaneously on a user interface device” (Paragraph [0063] (the subset (C′) may comprise organizations with the same industry and location attributes as the buyer attributes (which forms part of the search query), the complete attributes of each member of (C′) are compared to the complete attributes of the buyer to calculate a similarity score and auto select a reduced, ordered subset of the most similar organizations (C″) and the set (C’) are displayed to the user, from which a user-selected subset (C″) is derived)).
As per claim 3:
Selly, Yaghi and Jahanbakhsh teach the real-time searching method of the claim 1 above.
Jahanbakhsh further teaches:
“wherein the first relevancy metric comprises at least one of (a) a same word, (b) a shared metadata tag, and/or (c) a similar visual content of an image” (Paragraph [0006] and Paragraph [0056] (calculating a relevancy score for each identified document object with respect to a second part of the search query using the identified topics and the similarity functions may compare the two object's meta tags, text features, firmographic attributes, or audience/topic vectors)).
As per claim 4:
Selly, Yaghi and Jahanbakhsh teach the real-time searching method of the claim 1 above.
Jahanbakhsh further teaches:
“wherein at least one index comprises one or more of (i) a controlled vocabulary index, (ii) an automated topic model index, and/or (iii) a quantum support vector index” (Paragraph [0006] and Paragraph [0056] (calculating a relevancy score for each identified document object with respect to a second part of the search query using the identified topics and the similarity functions may compare the two object's meta tags, text features, firmographic attributes, or audience/topic vectors)).
As per claim 6:
Selly, Yaghi and Jahanbakhsh teach the real-time searching method of the claim 1 above.
Selly further teaches:
“wherein the set of content parameters comprising at least one of written words recorded in the digital object, spoken words recorded in at least one digital object the identified subset of digital objects, and/or visual images recorded in at least one digital object the identified subset of digital objectsthe digital object, and/or visual images recorded in the digital object” (Paragraph [0138] and Paragraph [0145] (an input device for receiving data regarding a target to be searched and a query to use in conducting this search, the target may be, for example, two-dimensional sequence of pixels regarding target image, three-dimensional sequence of voxels regarding target image, or an n-dimensional sequence of data for any number of dimensions greater than one and may receive an additional input which may be a search parameter that is obtained for the input query or search sequence)).
As per claim 7:
Selly, Yaghi and Jahanbakhsh teach the real-time searching method of the claim 1 above.
Jahanbakhsh further teaches:
“further comprising comparing the associated unique hash of each digital object of the identified subset of digital objects with at least one other digital object to identify duplicate digital objects” (Paragraph [0141] (could verify that the document provided in the website has the same hash as a document that was recorded at a certain date and URL, and countersigned by other parties)).
As per claim 8:
Selly, Yaghi and Jahanbakhsh teach the real-time searching method of the claim 1 above.
Yaghi further teaches:
“deleting at least one digital object of the identified subset of digital objects and the at least one other digital objects to de-duplicate the duplicate digital objects” (Paragraph [0053] and Paragraph [0054] (a natural language processing model may accept vector representations of textual data as input, may categorize data into labels, generate similarity metrics between the vector representations and the system may compare labels that have already been generated for lexical similarity in a manner that enables analysis and reduction (deleting at least one digital object to de-duplicate the duplicate digital objects) of duplicate labels or inconsistencies within labeling)).
As per claim 9:
Selly, Yaghi and Jahanbakhsh teach the real-time searching method of the claim 1 above.
Jahanbakhsh further teaches:
“wherein the associated unique hash is recorded to a blockchain” (Paragraph [0141] (could verify that the document provided in the website has the same hash as a document that was recorded at a certain date and organizations making assertions about past services cannot alter those assertions or deny them once they are stored on the blockchain)).
As per claim 10:
Selly, Yaghi and Jahanbakhsh teach the real-time searching method of the claim 1 above.
Jahanbakhsh further teaches:
“associating the associated unique hash with each digital object of the identified subset of digital objects by a quantum support vector machine service executing at least one of a machine learning algorithm and/or a quantum computing algorithm” (Paragraph [0114], Paragraph [0117] and Paragraph [0139] (to reduce storage requirements, the document is preferably provided as a hash of the original document, the set of features of each document may be stored as a feature vector, comprising a count of the number of occurrences of each feature in the document and the Topic Module using a supervised Machine Learning technique to classify a document from its extracted features or topic clusters)).
As per claim 12:
Selly teaches:
“A method of real-time object validation using a quantum computer, comprising” (Paragraph [0039] and Paragraph [0040] (a quantum information state of the Programmable Quantum Computer comprises validating the commands according to rules includes))
“receiving a first input digital object comprising real-time data from a live
information feed, wherein the first input digital object comprises at least one of a video file, an audio file, or social media content” (Paragraph [0138] (receiving data regarding a target to be searched, a probe (i.e., query) to use in conducting this search and the target may be two-dimensional sequence of pixels regarding target image (a video file))).
Selly does not EXPLICITLY teach: comprising real-time data from a live information feed; creating, by the quantum computer, a first vector representation of the first input data set; accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object; identifying, based on querying the database using a set of operations between the first vector representation and a plurality of vector representations of the plurality of digital objects, a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input first input digital object; and returning an output comprising the identified subset of digital objects and at least a portion of the first input digital object causing a simultaneous display via a user interface.
However, in an analogous art, Yaghi teaches:
“comprising real-time data from a live information feed” (Paragraph [0012] (the system may receive real-time information and determine whether the data stream has completed or not))
“creating, by the quantum computer, a first vector representation of the first input digital object” (Paragraph [0014] (the system (quantum computer) may generate a first vector representation of the first real-time processed data for use))
“identifying, based on querying the database using a set of operations between the first vector representation and a plurality of vector representations of the plurality of digital objects, a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input digital object” (Paragraph [0126] and Paragraph [0127] (the system may retrieve (identifying, based on querying the database) , for each textual dataset in the plurality of textual datasets, a plurality of data, the system may generate a vector representation of the first text data and a plurality of vector representations for each datum in the first plurality of textual data and the system may compare the first text data and the first plurality of textual data by inputting the relevant data in a neural network model and generating text distances to determine similarity metrics (a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input data set))).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Yaghi and apply them on teachings of Selly for the method of real-time object validation “comprising real-time data from a live information feed; creating, by the quantum computer, a first vector representation of the first input data set; identifying, based on querying the database using a set of operations between the first vector representation and a plurality of vector representations of the plurality of digital objects, a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input first input digital object”. One would be motivated as it provides historical and contextual data that enables labelers of machine learning input data to aid in labeling decisions, including model evaluation data and label modification data which may aid in resolving labeling inconsistencies between multiple-user labeling tasks. (Yaghi, Paragraph [0002]).
Selly and Yaghi do not EXPLICITLY teach: accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object; and returning an output comprising the identified subset of digital objects and at least a portion of the first input digital object causing a simultaneous display via a user interface.
However, in an analogous art, Jahanbakhsh teaches:
“accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object” (Paragraph [0006] , Paragraph [0007], Paragraph [0114] and Paragraph [0139] (identifying a plurality of first data objects to identify document objects (digital objects) connected to one of the identified first data objects, the document object is preferably provided as a hash of the original document, the index including lists of organization identifiers, each organization identifier associated with at least one document identifier and the set of features of each document may be stored as a feature vector, comprising a count of the number of occurrences of each feature in the document along a pre-ordered set of features (corresponding to a set of content parameters of the corresponding digital object)))
“and returning the identified subset of digital objects and at least a portion of the first input data set causing a simultaneous display via a user interface” (Paragraph [0094], Paragraph [0142] and Paragraph [0143] (The search engine may identify data objects connected to the Buyer object in the database and add these objects or their attributes to the user-specified search features, The system receives queries and communicates results to users via a user interface on the user's computing device and every data object has a visual representation to be displayed to the user)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Jahanbakhsh and apply them on teachings of Selly and Yaghi for the method of real-time object validation “accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object; and returning an output comprising the identified subset of digital objects and at least a portion of the first input digital object causing a simultaneous display via a user interface”. One would be motivated as it enables a search of data objects and rank them by their connection to certain other data objects that are relevant to the search query and the method employs a database and algorithms particularly suited to capture and search relationship between the data objects. (Jahanbakhsh, Paragraph [0027]).
As per claim 14:
Selly, Yaghi and Jahanbakhsh teach the real-time object validation method of the claim 12 above.
Jahanbakhsh further teaches:
“wherein the plurality of digital objects have the associated unique hash, which may be a plurality of NFTs, each NFT having an associated block chain hash function” (Paragraph [0141] (a browser or third-party plugin could verify that the document provided in the website has the same hash as a document that was recorded at a certain date and URL,countersigned by other parties and . that organizations making assertions about past services cannot alter those assertions or deny them once they are stored on the blockchain)).
As per claim 17:
Selly teaches:
“A method of using one or more databases for storing the output of quantum machine learning systems, the method comprising” (Paragraph [0038] (it provides methods for determining when and how to leverage quantum computing devices when solving computational tasks where it is configured to generate, as output, the method includes))
“receiving a first input digital object comprising real-time data from a live
information feed, wherein the first input digital object comprises at least one of a video file, an audio file, or social media content” (Paragraph [0138] (receiving data regarding a target to be searched, a probe (i.e., query) to use in conducting this search and the target may be two-dimensional sequence of pixels regarding target image (a video file)))
“persistently storing in the database an output comprising the identified subset of digital objects that are results of the query” (Paragraph [0107] and Paragraph [0139] (the computer system may be a Web server used to provide data searching services as an application service provider, search results may be presented on user display as a map showing direct hits and close homologies for a given probe or query and the result of such processing can be stored in any type of memory or other computer-readable medium (persistent storage))).
Selly does not EXPLICITLY teach: comprising real-time data from a live information feed; creating a first vector representation of the first input data set; accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object; identifying, based on querying the database using a set of operations between the first vector representation and a plurality of vector representations of the plurality of digital objects, a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input first input data set; and identifying and delivering, using a quantum support vector machines service, the set identified subset of digital objects to a user interface.
However, in an analogous art, Yaghi teaches:
“comprising real-time data from a live information feed” (Paragraph [0012] (the system may receive real-time information and determine whether the data stream has completed or not))
“creating a first vector representation of the first input data set” (Paragraph [0014] (the system (quantum computer) may generate a first vector representation of the first real-time processed data for use))
“identifying, based on querying the database using a set of operations between the first vector representation and a plurality of vector representations of the plurality of digital objects, a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input first input data set” (Paragraph [0126] and Paragraph [0127] (the system may retrieve (identifying, based on querying the database) , for each textual dataset in the plurality of textual datasets, a plurality of data, the system may generate a vector representation of the first text data and a plurality of vector representations for each datum in the first plurality of textual data and the system may compare the first text data and the first plurality of textual data by inputting the relevant data in a neural network model and generating text distances to determine similarity metrics (a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input data set))).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Yaghi and apply them on teachings of Selly for the method for storing the output of quantum machine learning systems “comprising real-time data from a live information feed; creating a first vector representation of the first input data set; identifying, based on querying the database using a set of operations between the first vector representation and a plurality of vector representations of the plurality of digital objects, a subset of the plurality of digital objects satisfying a first relevancy metric relative to the first input first input data set”. One would be motivated as it provides historical and contextual data that enables labelers of machine learning input data to aid in labeling decisions, including model evaluation data and label modification data which may aid in resolving labeling inconsistencies between multiple-user labeling tasks. (Yaghi, Paragraph [0002]).
Selly and Yaghi do not EXPLICITLY teach: accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object; and identifying and delivering, using a quantum support vector machines service, the set identified subset of digital objects to a user interface.
However, in an analogous art, Jahanbakhsh teaches:
“accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object” (Paragraph [0006] , Paragraph [0007], Paragraph [0114] and Paragraph [0139] (identifying a plurality of first data objects to identify document objects (digital objects) connected to one of the identified first data objects, the document object is preferably provided as a hash of the original document, the index including lists of organization identifiers, each organization identifier associated with at least one document identifier and the set of features of each document may be stored as a feature vector, comprising a count of the number of occurrences of each feature in the document along a pre-ordered set of features (corresponding to a set of content parameters of the corresponding digital object)))
“and identifying and delivering, using a quantum support vector machines service, the set identified subset of digital objects to a user interface” (Paragraph [0094], Paragraph [0142] and Paragraph [0143] (The search engine may identify data objects connected to the Buyer object in the database and add these objects or their attributes to the user-specified search features, The system receives queries and communicates results to users via a user interface on the user's computing device and every data object has a visual representation to be displayed to the user)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Jahanbakhsh and apply them on teachings of Selly and Yaghi for the method for storing the output of quantum machine learning systems “accessing a database of a plurality of digital objects, each digital object comprising an associated unique hash and indexed by an at least one vector representation in the database corresponding to a set of content parameters of the corresponding digital object; and identifying and delivering, using a quantum support vector machines service, the set identified subset of digital objects to a user interface”. One would be motivated as it enables a search of data objects and rank them by their connection to certain other data objects that are relevant to the search query and the method employs a database and algorithms particularly suited to capture and search relationship between the data objects. (Jahanbakhsh, Paragraph [0027]).
“persistently storing in a database a set of digital objects that are the results of a query of a results table” (Paragraph [0045] and Paragraph [0129] (the quantum machine learning system is configured to solve multiple computational tasks including optimization tasks, simulation tasks, arithmetic tasks, and database search tasks etc., and includes computer-readable media suitable for storing digital and/or quantum data in all forms of non-volatile digital and/or quantum memory, media and memory devices (persistently storing))).
As per claim 18:
Selly, Yaghi and Jahanbakhsh teach the method for storing the output of the quantum machine learning systems according to the claim 17 above.
Selly further teaches:
“wherein the results are produced by a set algebra calculation using an index of at least two other collections” (Paragraph [0046] (many different comparison functions may be calculated by the same interference between probe and target wavefunctions in layer encoded superpositions and a correlation sum may be computed by scaling the first encoded probe sequence representation and a first encoded target sequence by separate factors whose product is equal to the reciprocal of the variance of the first encoded probe sequence)).
As per claim 19:
Selly, Yaghi and Jahanbakhsh teach the method for storing the output of the quantum machine learning systems according to the claim 17 above.
Selly further teaches:
“processing, using the quantum support vector machines service, the identified subset of digital objects to derive sets of hashed object IDs and comparing the identified subset of digital objects to other sets of digital objects to identify overlap” (Paragraph [0436] (the type of tag data may include cryptographic hashing codes as unique identifiers where these hashing codes are generated as a signature or digest of a data record and are designed to not match if the data record contains any bit differences)).
As per claim 20:
Selly, Yaghi and Jahanbakhsh teach the method for storing the output of the quantum machine learning systems according to the claim 17 above.
Jahanbakhsh further teaches:
“wherein the quantum support vector machines service creates vectors for language models that can be used to represent the identified subset of digital objects” (Paragraph [0114] and Paragraph [0115] (the set of features of each document may be stored as a feature vector, comprising a count of the number of occurrences of each feature in the document along a pre-ordered set of features, and the Topic Module may process the set of features using a topic model to create a topic vector t, which is a statistical of topics of the document over all topics that make up the topic space in the topic model)).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Selly Roger (US PGPUB 20230229950) in view of Yaghi et al (US PGPUB 20240202571) and in further view of Jahanbakhsh Kazem (US PGPUB 20180336202) and Horowitz Elliot (US PGPUB 20180365114) .
As per claim 16:
Selly, Yaghi and Jahanbakhsh teach the real-time object validation method of the claim 12 above.
Selly, Yaghi and Jahanbakhsh do not EXPLICITLY teach:wherein the quantum computer has a common object request broker architecture (CORBA).
However, in an analogous art, Horowitz teaches:
“wherein the quantum computer has a common object request broker architecture (CORBA)” (Paragraph [0116] and Paragraph [0017]( the processor may be any type of processor, multiprocessor or controller or a quantum computer where the network may use various methods, protocols and standards, including, among others, Fiber Channel, Token Ring, Ethernet, Wireless Ethernet, Bluetooth, IP, IPV6, TCP/IP and CORBA)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Horowitz and apply them on teachings of Selly, Yaghi and Jahanbakhsh for the method of real-time object validation “wherein the quantum computer has a common object request broker architecture (CORBA)”. One would be motivated as a distributed database architected with an eventual consistency model can see significant improvements in reliability and/or consistency based on enhancing failure resolution of write operations. (Horowitz, Paragraph [0004]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lu et al, (US PGPUB 20190272344), system and method of generating an index structure for indexing a plurality of unstructured data objects, including: generating a set of compact feature vectors, the set including a compact feature vector for each of the data objects, the compact feature vector for each data object including a sequence of hashed values that represent the data object; generating a plurality of twisted compact feature vector sets for each of set of compact feature vectors, each of the twisted compact feature vector sets being generated by applying a respective random shuffling permutation to the set of compact feature vectors; and for each twisted compact feature vector set, generating an index for the data objects in which the data objects are slotted based on sequences of hashed values in the twisted compact feature vector set.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAMAL K DEWAN whose telephone number is (571)272-2196. The examiner can normally be reached on Mon-Fri 8:00 AM – 5:00 PM (EST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TONY MAHMOUDI can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Kamal K Dewan/
Examiner, Art Unit 2163
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163