DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1 – 20 are pending in this Office Correspondence.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 (5, 7, 8) are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: The claims 2 recites a “method for receiving a vector database. . ..; processing the vector database to determine . . a first dependency . . . ; processing the vector database to determine first content . . . ; determining a user and an urgency of a first notification . . . ; generating for display . . . ; and retrieving the third native unstructured dataset component. . .” the claim(s) recites a series of steps and, therefore, is a process
Step 2A Prong One:
"processing the vector database to determine " as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0004 & 0009] where one can mentally evaluate to process the vector database to determine, using a first artificial intelligence model, a first dependency of the first native unstructured dataset component.
“processing the vector database to determine first content ” as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraph [0004 & 0009] where one can mentally evaluate to process the vector database to determine, using a first artificial intelligence model, a first dependency of the first native unstructured dataset component.
“determining a user and an urgency of a first notification” as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0009] where one can mentally evaluate to determine first content.
These imitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "database" or "processor", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “process” and “determine”, in the context of this claim encompasses a user mentally, and with the aid of pen and paper, within the plurality of command sets, “ensures data consistency, integrity, and efficient querying and allows users to derive insights, perform spatial analyses, and extract meaningful information from the vector representations in vector database.”
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements " receiving a vector database", display and retrieving. These limitations amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).
The limitations represents an extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data. (See MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The limitations "receiving”, “display” and “retrieve” are recognized by the courts as well-understood, routine , and conventional activities 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)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....; Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Therefore, the claim is not patent eligible.
Accordingly, claims 1 and 15 are rejected for the same rational under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Therefore, claims 1, 2 and 15 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Further the limitations in the dependent claims 3 – 14 and 16 – 20, respectively, merely specify the type of the data gathered and analyzed without adding significantly more. Analysis of the dependent claims is shown below.
Claim 3 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 3 recites the same abstract idea of claim 2. The claim recites the additional limitation of “detecting a first object in the first content; comparing the first object to a listing of temporal identifiers; determining a first temporal identifier in the first content based on comparing the first object to the listing of temporal identifiers; and determining the urgency for transmitting the first notification based on the first temporal identifier”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 16, since they also recite limitations that further elaborate on the abstract idea.
Claim 4 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 4 recites the same abstract idea of claim 2. The claim recites the additional limitation of “detecting a first object in the first content; comparing the first object to a listing of user identifiers; determining a first user identifier in the first content based on comparing the first object to the listing of user identifiers; and determining the user for the first notification based on the first user identifier”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 17, since they also recite limitations that further elaborate on the abstract idea.
Claim 6 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 6 recites the same abstract idea of claim 2. The claim recites the additional limitation of “determining a second native unstructured dataset component in the first native unstructured dataset; generating a second vector representation of the second native unstructured dataset component; and storing the second vector representation in the vector database”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim. Same rationale applies to claim 19, since they also recite limitations that further elaborate on the abstract idea.
Claim 9 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 9 recites the same abstract idea of claim 2. The claim recites the additional limitation of “determining a first task in the first native unstructured dataset component; determining a first task lineage for the first task; and determining a second task based on the first task lineage”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 10 is dependent on claim 9 and includes all the limitations of claim 9. Therefore, claim 10 recites the same abstract idea of claim 9. The claim recites the additional limitation of “determining a first pattern in vector representations; and determining the first task lineage based on the first pattern”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 11 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 11 recites the same abstract idea of claim 2. The claim recites the additional limitation of “retrieving the first vector representation from the vector database; and inputting the first vector representation and the first dependency into the second artificial intelligence model”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 12 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 12 recites the same abstract idea of claim 2. The claim recites the additional limitation of “determining a first pattern in the vector database; and determining the first pattern comprises the first vector representation and a fourth vector representation, wherein the fourth vector representation corresponds to the third native unstructured dataset component.”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 13 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 13 recites the same abstract idea of claim 2. The claim recites the additional limitation of “determining a mapping of the first vector representation to a first word in the first native unstructured dataset component; and updating a first pointer based on the mapping”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Claim 14 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 14 recites the same abstract idea of claim 2. The claim recites the additional limitation of “determining a first token in a first native unstructured dataset; and partitioning the first token into the first native unstructured dataset component.”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Therefore, claims 1 – 4, 6 and 9 – 14, 15 – 17 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more than the abstract idea.
Double Patenting
Claim 1 – 20 of this application is patentably indistinct from claims 1 – 20 of Application No. 18/667,711, now U.S. Patent 12,393,625. Pursuant to 37 CFR 1.78(f) or pre-AIA 37 CFR 1.78(b), when two or more applications filed by the same applicant contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
The subject matter claimed in the instant application is fully disclosed in the co-pending application and is covered by the co-pending application since the co-pending application and the application are claiming common subject matter, as follows:
Instant application 19/304,535
Co-pending Application 18/667,711
USP 12,393,625
1. A system for providing real-time, user-specific notifications of unstructured data by processing the unstructured data without interstitial standardization, the system comprising:
receiving a first native unstructured dataset;
determining a first native unstructured dataset component in the first native unstructured dataset;
generating a first vector representation of the first native unstructured dataset component;
generating a vector database comprising the first vector representation;
generating a first pointer to the first native unstructured dataset component in the first native unstructured dataset;
processing the vector database to determine, using a first artificial intelligence model, a first dependency of the first native unstructured dataset component, wherein the first dependency comprises a third native unstructured dataset component in a second native unstructured dataset, and wherein the first artificial intelligence model is trained to determine dependencies between native unstructured datasets based on historic vector representations of historic unstructured datasets; and
processing the vector database to determine, using a second artificial intelligence model, first content for the first native unstructured dataset component, wherein the second artificial intelligence model uses natural language processing to determine the first content.
2. A method for providing real-time, user-specific notifications of unstructured data by processing the unstructured data without interstitial standardization, the method comprising:
receiving a vector database comprising a first vector representation based on a first native unstructured dataset component;
processing the vector database to determine, using a first artificial intelligence model, a first dependency of the first native unstructured dataset component, wherein the first dependency comprises a third native unstructured dataset component in a second native unstructured dataset, and wherein the first artificial intelligence model is trained to determine dependencies between native unstructured datasets based on historic vector representations of historic unstructured datasets;
processing the vector database to determine, using a second artificial intelligence model, first content for the first native unstructured dataset component, wherein the second artificial intelligence model uses natural language processing to determine the first content;
determining a user and an urgency of a first notification corresponding to the first native unstructured dataset based on the first content; and
generating for display, on a user interface, a first notification based on the user and the urgency, wherein the first notification is populated based on retrieving the first native unstructured dataset component and retrieving the third native unstructured dataset component based on the first dependency.
3. The method of claim 2, wherein determining the urgency of the first notification further comprises: detecting a first object in the first content; comparing the first object to a listing of temporal identifiers; determining a first temporal identifier in the first content based on comparing the first object to the listing of temporal identifiers; and determining the urgency for transmitting the first notification based on the first temporal identifier.
4. The method of claim 2, wherein determining the user of the first notification further comprises: detecting a first object in the first content; comparing the first object to a listing of user identifiers; determining a first user identifier in the first content based on comparing the first object to the listing of user identifiers; and determining the user for the first notification based on the first user identifier.
5. The method of claim 2, further comprising: determining a first relationship between the first native unstructured dataset component and a second native unstructured dataset component in the first native unstructured dataset, using a third artificial intelligence model, wherein the third artificial intelligence model is trained to determine relationships between native content in unstructured datasets based on historic relationships in historic unstructured content; determining a third vector representation of the first relationship; and storing the third vector representation in the vector database.
6. The method of claim 2, further comprising: determining a second native unstructured dataset component in the first native unstructured dataset; generating a second vector representation of the second native unstructured dataset component; and storing the second vector representation in the vector database.
7. The method of claim 2, wherein retrieving the first native unstructured dataset component further comprises: determining a first location of the first native unstructured dataset component in the first native unstructured dataset based on a first pointer; determining a first dimension of the first native unstructured dataset component based on the first pointer; and populating a template for the first notification based on the first location and the first dimension.
8. The method of claim 2, wherein retrieving the third native unstructured dataset component based on the first dependency further comprises: determining a second pointer to the third native unstructured dataset component in the second native unstructured dataset; determining a second location of the third native unstructured dataset component in the second native unstructured dataset based on the second pointer; and determining a second dimension of the third native unstructured dataset component based on the second pointer.
9. The method of claim 2, wherein processing the vector database to determine the first dependency of the first native unstructured dataset component further comprises: determining a first task in the first native unstructured dataset component; determining a first task lineage for the first task; and determining a second task based on the first task lineage.
10. The method of claim 9, wherein processing the vector database to determine the first dependency of the first native unstructured dataset component further comprises: determining a first pattern in vector representations; and determining the first task lineage based on the first pattern.
11. The method of claim 2, wherein processing the vector database to determine the first content for the first native unstructured dataset component further comprises: retrieving the first vector representation from the vector database; and inputting the first vector representation and the first dependency into the second artificial intelligence model.
12. The method of claim 2, wherein processing the vector database to determine the first dependency of the first native unstructured dataset component further comprises: determining a first pattern in the vector database; and determining the first pattern comprises the first vector representation and a fourth vector representation, wherein the fourth vector representation corresponds to the third native unstructured dataset component.
13. The method of claim 2, wherein generating a vector database further comprises: determining a mapping of the first vector representation to a first word in the first native unstructured dataset component; and updating a first pointer based on the mapping.
14. The method of claim 2, wherein determining the first native unstructured dataset component further comprises: determining a first token in a first native unstructured dataset; and partitioning the first token into the first native unstructured dataset component.
15. One or more non-transitory, computer-readable mediums, comprising instructions that, when executed by one or more processors, cause operations comprising: accessing a vector database comprising a first vector representation based on a first native unstructured dataset component; processing the vector database to determine, using a first artificial intelligence model, a first dependency of the first native unstructured dataset component, wherein the first dependency comprises a third native unstructured dataset component in a second native unstructured dataset, and wherein the first artificial intelligence model is trained to determine dependencies between native unstructured datasets based on historic vector representations of historic unstructured datasets; processing the vector database to determine, using a second artificial intelligence model, first content for the first native unstructured dataset component, wherein the second artificial intelligence model uses natural language processing to determine the first content; and determining a user and an urgency of a first notification corresponding to the first native unstructured dataset based on the first content.
16. The one or more non-transitory, computer-readable mediums of claim 15, wherein determining the urgency of the first notification further comprises: detecting a first object in the first content; comparing the first object to a listing of temporal identifiers; determining a first temporal identifier in the first content based on comparing the first object to the listing of temporal identifiers; and determining the urgency for transmitting the first notification based on the first temporal identifier.
17. The one or more non-transitory, computer-readable mediums of claim 15, wherein determining the user of the first notification further comprises: detecting a first object in the first content; comparing the first object to a listing of user identifiers; determining a first user identifier in the first content based on comparing the first object to the listing of user identifiers; and determining the user for the first notification based on the first user identifier.
18. The one or more non-transitory, computer-readable mediums of claim 15, wherein the instructions further cause operations comprising: determining a first relationship between the first native unstructured dataset component and a second native unstructured dataset component in a first native unstructured dataset, using a third artificial intelligence model, wherein the third artificial intelligence model is trained to determine relationships between native content in unstructured datasets based on historic relationships in historic unstructured content; determining a third vector representation of the first relationship; and storing the third vector representation in the vector database.
19. The one or more non-transitory, computer-readable mediums of claim 15, wherein the instructions further cause operations comprising: determining a second native unstructured dataset component in the first native unstructured dataset; generating a second vector representation of the second native unstructured dataset component; and storing the second vector representation in the vector database.
20. The one or more non-transitory, computer-readable mediums of claim 15, wherein the instructions further cause operations comprising: retrieving the first native unstructured dataset component using a first pointer further comprises: determining a first location of the first native unstructured dataset component in the first native unstructured dataset based on the first pointer; determining a first dimension of the first native unstructured dataset component based on the first pointer; and populating a template for the first notification based on the first location and the first dimension.
1. A system for providing real-time, user-specific notifications of unstructured data by processing the unstructured data without interstitial standardization, the system comprising:
receiving a first native unstructured dataset;
determining a first native unstructured dataset component in the first native unstructured dataset; generating a first vector representation of the first native unstructured dataset component;
generating a vector database comprising the first vector representation;
generating a first pointer to the first native unstructured dataset component in the first native unstructured dataset;
processing the vector database to determine, using a first artificial intelligence model, a first dependency of the first native unstructured dataset component, wherein the first dependency comprises a third native unstructured dataset component in a second native unstructured dataset, and wherein the first artificial intelligence model is trained to determine dependencies between native unstructured datasets based on historic vector representations of historic unstructured datasets; and
processing the vector database to determine, using a second artificial intelligence model, first content for the first native unstructured dataset component, wherein the second artificial intelligence model uses natural language processing to determine the first content.
2. A method for providing real-time, user-specific notifications of unstructured data by processing the unstructured data without interstitial standardization, the method comprising:
receiving a vector database comprising a first vector representation based on a first native unstructured dataset component;
processing the vector database to determine, using a first artificial intelligence model, a first dependency of the first native unstructured dataset component, wherein the first dependency comprises a third native unstructured dataset component in a second native unstructured dataset, and wherein the first artificial intelligence model is trained to determine dependencies between native unstructured datasets based on historic vector representations of historic unstructured datasets;
processing the vector database to determine, using a second artificial intelligence model, first content for the first native unstructured dataset component, wherein the second artificial intelligence model uses natural language processing to determine the first content;
determining a user and an urgency of a first notification corresponding to the first native unstructured dataset based on the first content; and generating for display, on a user interface, a first notification based on the user and the urgency, wherein the first notification is populated based on retrieving the first native unstructured dataset component and retrieving the third native unstructured dataset component based on the first dependency.
3. The method of claim 2, wherein determining the urgency of the first notification further comprises: detecting a first object in the first content; comparing the first object to a listing of temporal identifiers; determining a first temporal identifier in the first content based on comparing the first object to the listing of temporal identifiers; and determining the urgency for transmitting the first notification based on the first temporal identifier.
4. The method of claim 2, wherein determining the user of the first notification further comprises: detecting a first object in the first content; comparing the first object to a listing of user identifiers; determining a first user identifier in the first content based on comparing the first object to the listing of user identifiers; and determining the user for the first notification based on the first user identifier.
The method of claim 2, further comprising: determining a first relationship between the first native unstructured dataset component and a second native unstructured dataset component in the first native unstructured dataset, using a third artificial intelligence model, wherein the third artificial intelligence model is trained to determine relationships between native content in unstructured datasets based on historic relationships in historic unstructured content; determining a third vector representation of the first relationship; and storing the third vector representation in the vector database.
6. The method of claim 2, further comprising: determining a second native unstructured dataset component in the first native unstructured dataset; generating a second vector representation of the second native unstructured dataset component; and storing the second vector representation in the vector database.
7. The method of claim 2, wherein retrieving the first native unstructured dataset component further comprises: determining a first location of the first native unstructured dataset component in the first native unstructured dataset based on a first pointer; determining a first dimension of the first native unstructured dataset component based on the first pointer; and populating a template for the first notification based on the first location and the first dimension.
8. The method of claim 2, wherein retrieving the third native unstructured dataset component based on the first dependency further comprises: determining a second pointer to the third native unstructured dataset component in the second native unstructured dataset; determining a second location of the third native unstructured dataset component in the second native unstructured dataset based on the second pointer; and determining a second dimension of the third native unstructured dataset component based on the second pointer.
9. The method of claim 2, wherein processing the vector database to determine the first dependency of the first native unstructured dataset component further comprises: determining a first task in the first native unstructured dataset component; determining a first task lineage for the first task; and determining a second task based on the first task lineage.
10. The method of claim 9, wherein processing the vector database to determine the first dependency of the first native unstructured dataset component further comprises: determining a first pattern in vector representations; and determining the first task lineage based on the first pattern.
11. The method of claim 2, wherein processing the vector database to determine the first content for the first native unstructured dataset component further comprises: retrieving the first vector representation from the vector database; and inputting the first vector representation and the first dependency into the second artificial intelligence model.
12. The method of claim 2, wherein processing the vector database to determine the first dependency of the first native unstructured dataset component further comprises: determining a first pattern in the vector database; and determining the first pattern comprises the first vector representation and a fourth vector representation, wherein the fourth vector representation corresponds to the third native unstructured dataset component.
13. The method of claim 2, wherein generating a vector database further comprises: determining a mapping of the first vector representation to a first word in the first native unstructured dataset component; and updating a first pointer based on the mapping.
14. The method of claim 2, wherein determining the first native unstructured dataset component further comprises: determining a first token in a first native unstructured dataset; and partitioning the first token into the first native unstructured dataset component.
15. One or more non-transitory, computer-readable mediums, comprising instructions that, when executed by one or more processors, cause operations comprising: accessing a vector database comprising a first vector representation based on a first native unstructured dataset component; processing the vector database to determine, using a first artificial intelligence model, a first dependency of the first native unstructured dataset component, wherein the first dependency comprises a third native unstructured dataset component in a second native unstructured dataset, and wherein the first artificial intelligence model is trained to determine dependencies between native unstructured datasets based on historic vector representations of historic unstructured datasets; processing the vector database to determine, using a second artificial intelligence model, first content for the first native unstructured dataset component, wherein the second artificial intelligence model uses natural language processing to determine the first content; and determining a user and an urgency of a first notification corresponding to the first native unstructured dataset based on the first content.
16. The one or more non-transitory, computer-readable mediums of claim 15, wherein determining the urgency of the first notification further comprises: detecting a first object in the first content; comparing the first object to a listing of temporal identifiers; determining a first temporal identifier in the first content based on comparing the first object to the listing of temporal identifiers; and determining the urgency for transmitting the first notification based on the first temporal identifier.
17. The one or more non-transitory, computer-readable mediums of claim 15, wherein determining the user of the first notification further comprises: detecting a first object in the first content; comparing the first object to a listing of user identifiers; determining a first user identifier in the first content based on comparing the first object to the listing of user identifiers; and determining the user for the first notification based on the first user identifier.
18. The one or more non-transitory, computer-readable mediums of claim 15, further comprising: determining a first relationship between the first native unstructured dataset component and a second native unstructured dataset component in a first native unstructured dataset, using a third artificial intelligence model, wherein the third artificial intelligence model is trained to determine relationships between native content in unstructured datasets based on historic relationships in historic unstructured content; determining a third vector representation of the first relationship; and storing the third vector representation in the vector database.
19. The one or more non-transitory, computer-readable mediums of claim 15, further comprising: determining a second native unstructured dataset component in the first native unstructured dataset; generating a second vector representation of the second native unstructured dataset component; and storing the second vector representation in the vector database.
20. The one or more non-transitory, computer-readable mediums of claim 15, wherein retrieving the first native unstructured dataset component using a first pointer further comprises: determining a first location of the first native unstructured dataset component in the first native unstructured dataset based on the first pointer; determining a first dimension of the first native unstructured dataset component based on the first pointer; and populating a template for the first notification based on the first location and the first dimension.
Claims 1 – 20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1 – 20 of co-pending application No. 18/667,711, now U.S. Patent 12,393,625. Although the conflicting claims are not identical, they are not patentably distinct from each other because of corresponding language that recites virtually all of the same elements and functions claimed in the claim 1 of instant application and claim 1 of the copending invention, e.g., “processing the vector database to determine, using a first artificial intelligence model, a first dependency of the first native unstructured dataset component, wherein the first dependency comprises a third native unstructured dataset component in a second native unstructured dataset, and wherein the first artificial intelligence model is trained to determine dependencies between native unstructured datasets based on historic vector representations of historic unstructured datasets.”
The claimed differences would be obvious to a programmer of ordinary skill because the instant claims are merely broader and/or alternate variations of the claims recited in the co-pending application.
Because the instant claims merely add/modify the additional elements from the set of elements and functions claimed in the parent application, such modifications would be readily apparent to a programmer of ordinary skill.
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to omit/add/modify the additional elements of claim 1 to arrive at the claim 1 of the instant application because the person would have realized that the remaining element would perform the same functions as before.
It would have been obvious to modify instant claims in order to provides efficient indexing techniques to speed up spatial queries and retrievals of spatial objects based on their geometric properties and minimizes storage requirements for vector data while ensuring quick access and retrieval. The method manages temporal data, allows databases and systems to handle time-related queries efficiently. The system determines the format or structure of the content to accurately locate and extract the user identifier.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sriharsha et al. (U.S. Patent Application Publication 2022/0036177),
Neels et al. (U.S. Patent Application Publication 2015/0019537),
Sriharsha et al. (U.S. Patent Application Publication 2022/0036002),
Al-Shameri et al. (U.S. Patent Application Publication 2010/0223276).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID AL ALAM whose telephone number is (571)272-4030. The examiner can normally be reached on M-F 8:00 AM-5:00 PM.
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June 13, 2026
/SHAHID A ALAM/Primary Examiner, Art Unit 2161
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