DETAILED ACTION
The Action is responsive to Applicant’s Application filed March 31, 2025.
Please note claims 21-40 are pending.
Terminal Disclaimer
The Terminal Disclaimer has been disapproved. The person who signed the terminal disclaimer is not the applicant, the patentee or an attorney or agent of record. See 37 CFR 1.321(a) and (b). Please file a POA that gives power to the attorney and/or agent of record who is signing the TD, along with another copy of the TD form. Or file a TD form that is signed by the applicant. No Additional fee is required with the resubmission.
Double Patenting
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 21, 39-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 16, 20 of U.S. Patent No. 12,235,882. Although the claims at issue are not identical, they are not patentably distinct from each other because:
"A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus)." ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
"Claim 12 and Claim 13 are generic to the species of invention covered by claim 3 of the patent. Thus, the generic invention is "anticipated" by the species of the patented invention. Cf., Titanium Metals Corp. v. Banner, 778 F.2d 775, 227 USPQ 773 (Fed. Cir. 1985) (holding that an earlier species disclosure in the prior art defeats any generic claim). This court’s predecessor has held that, without a terminal disclaimer, the species claims preclude issuance of the generic application. In re Van Ornum, 686 F.2d 937, 944, 214 USPQ 761, 767 (CCPA 1982); Schneller, 397 F.2d at 354. Accordingly, absent a terminal disclaimer, claims 12 and 13 were properly rejected under the doctrine of obviosuness-type double patenting" (In re Goodman (CA FC) 29 USPQ2d 2010 (12/3/1993).
Claims 21, 39-40 of the current application are generic to the species of invention covered by claims 1, 16, 20 of U.S. Patent No. 12,235,882. Therefore the current application is anticipated by the species of U.S. Patent No. 12,235,882.
Instant Application 19/063208
US Patent No. 12,235,882
21. A method for controlling execution of artificial intelligence (AI) pipelines for semantic data retrieval through client applications, comprising:
receiving, at a user device from a server, a first AI endpoint key and a second AI endpoint key;
receiving an input at the user device;
determining, by an application executing on the user device, a first status of the user device, wherein the application selects a first AI endpoint from a plurality of AI endpoints based on the first status of the device, wherein the selected first AI endpoint is local to the user device;
authorizing access to the first AI endpoint using the first AI endpoint key;
determining whether access to a local dataset is authorized for a user submitting the input;
causing, based on the input, generation of input vectors for comparison to dataset vectors of a vector database, the dataset vectors corresponding to data chunks of the local dataset;
causing identification of similar vectors based on comparing the input vectors to the dataset vectors of the vector database;
identifying the data chunks that correspond to the identified similar vectors;
identifying, by the application, a local AI model for use with the input based on an object selection rule;
identifying first prompts for use with the local AI model, wherein at least one of the identified prompts relates to formatting specific to a graphical user interface ("GUI") at the user device;
transmitting the first prompts and the identified data chunks to the local AI model; and
causing results of the local AI model to be displayed on the GUI at the user device.
1. A method for controlling execution of artificial intelligence (AI) pipelines for semantic data retrieval through client applications, comprising:
receiving, at an AI pipeline endpoint, an AI endpoint key and an AI pipeline input from a client application that executes on a user device, the user device being associated with a user;
causing an AI pipeline execution engine to perform stages including:
identifying a dataset associated with the AI pipeline input;
based on a management policy, determining whether access to the dataset is authorized for a user submitting the AI pipeline input;
causing, based on the AI pipeline input, generation of input vectors with an embedding model, wherein dataset vectors in a vector database were also generated with the embedding model, and wherein the dataset vectors correspond to data chunks of the identified dataset;
causing identification of similar vectors based on comparing the input vectors to the dataset vectors of the vector database;
identifying the data chunks that correspond to the identified similar vectors;
identifying a first AI model for use with the AI pipeline input based on an object selection rule;
generating prompts for use with the first AI model, wherein at least one of the generated prompts relates to formatting specific to the client application;
transmitting the generated prompts and the identified data chunks to the first AI model; and
receiving, from the first Al model, search results that correspond to the AI pipeline input;
adding a hyperlink to the search results, the hyperlink being generated based at least in part on a location of text that corresponds to the identified data chunks; and
causing the search results to be displayed on a user interface provided by the client application.
39. (New) A non-transitory, computer-readable medium containing instructions that, when executed by a hardware-based processor, causes the processor to perform stages for controlling execution of artificial intelligence ("AI") pipelines for semantic data retrieval through client applications, the stages comprising:
receiving, at a user device from a server, a first AI endpoint key and a second AI
endpoint key;
receiving an input at the user device;
determining, by an application executing on the user device, a first status of the user device, wherein the application selects a first AI endpoint from a plurality of AI endpoints based on the first status of the device, wherein the selected first AI
endpoint is local to the user device;
validating access to the first Al endpoint using the first Al endpoint key;
determining whether access to a local dataset is authorized for a user submitting the input;
causing, based on the input, generation of input vectors for comparison to dataset vectors of a vector database, the dataset vectors corresponding to data chunks of the local dataset;
causing identification of similar vectors based on comparing the input vectors to the dataset vectors of the vector database;
identifying the data chunks that correspond to the identified similar vectors;
identifying, by the application, a local AI model for use with the input based on an object selection rule;
identifying first prompts for use with the local AI model, wherein at least one of the
identified prompts relates to formatting specific to a graphical user interface ("GUI") at the user device;
transmitting the first prompts and the identified data chunks to the local AI model; and
causing results of the local AI model to be displayed on the GUI at the user device.
16. A non-transitory, computer-readable medium containing instructions that, when executed by a hardware-based processor, causes the processor to perform stages for controlling execution of artificial intelligence (AI) pipelines for semantic data retrieval through client applications, the stages comprising:
receiving, at an Al pipeline endpoint, an Al endpoint key and an Al pipeline input from an application that executes on a user device, the user device being associated with a user;
causing an AI pipeline execution engine to perform search stages including: identifying a dataset associated with the AI pipeline input;
based on a management policy, determining whether access to the dataset is authorized for a user submitting the AI pipeline input;
causing generation of input vectors with an embedding model, wherein the embedding model is selected based on having generated dataset vectors in a vector database, the dataset vectors corresponding to the identified dataset;
causing identification of similar vectors based on comparing the input vectors to
the dataset vectors of the vector database, wherein the similar vectors include dataset vectors that are within a threshold distance or angle measurement to the input vectors;
identifying data chunks that correspond to the identified similar vectors;
identifying a first AI model for use with the AI pipeline input, including determining that user of the first AI model complies with an object selection rule that relates to compute performance cost, memory performance cost, or network component cost for using the first AI model;
generating prompts for use with the first AI model, wherein the prompts are generated based on the application and a user selection from the application;
transmitting the generated prompts and the identified data chunks to the first AI
model; and
receiving, from the first Al model, search results that correspond to the AI pipeline input;
adding a hyperlink to the search results; and
causing display of the search results within the application.
40. (New) A system for controlling execution of artificial intelligence (AI) pipelines for semantic data retrieval through client applications, comprising:
a memory storage including a non-transitory, computer-readable medium comprising instructions; and
at least one hardware-based processor that executes the instructions to carry out stages comprising:
receiving, at a user device from a server, a first Al endpoint key and a second Al
endpoint key;
receiving an input at the user device;
determining, by an application executing on the user device, a first status of the user device, wherein the application selects a first AI endpoint from a plurality of AI endpoints based on the first status of the device, wherein the selected first AI endpoint is local to the user device; validating access to the first AI endpoint using the first AI endpoint key;
determining whether access to a local dataset is authorized for a user submitting the input;
causing, based on the input, generation of input vectors for comparison to dataset
vectors of a vector database, the dataset vectors corresponding to data chunks of
the local dataset;
causing identification of similar vectors based on comparing the input vectors to the dataset vectors of the vector database;
identifying the data chunks that correspond to the identified similar vectors;
identifying, by the application, a local AI model for use with the input based on an
object selection rule;
identifying first prompts for use with the local AI model, wherein at least one of the
identified prompts relates to formatting specific to a graphical user interface
("GUI") at the user device;
transmitting the first prompts and the identified data chunks to the local Al model;
causing results of the local AI model to be displayed on the GUI at the user device.
20. A system for controlling execution of artificial intelligence (AI) pipelines for semantic data retrieval through client applications, comprising:
a memory storage including a non-transitory, computer-readable medium comprising instructions; and
at least one hardware-based processor that executes the instructions to carry out stages comprising:
receiving, at an AI pipeline endpoint, an AI endpoint key and an AI pipeline input
from a client application that executes on a user device, the user device being associated with a user;
identifying a dataset associated with the AI pipeline input; based on a management policy, determining whether access to the dataset is authorized for a user submitting the AI pipeline input;
causing generation of input vectors with an embedding model, wherein the embedding model is selected based on having generated dataset vectors in a vector database, the dataset vectors corresponding to the identified dataset;
causing identification of similar vectors based on comparing the input vectors to the dataset vectors of the vector database, wherein the similar vectors include dataset vectors that are within a threshold distance or angle measurement to the AI pipeline input vectors;
identifying data chunks that correspond to the identified similar vectors;
identifying an AI model for use with the AI pipeline input, including determining that
user of the AI model complies with an object selection rule that is based on user
information and cost or performance thresholds of the AI model;
generating prompts for use with the AI model, wherein the prompts are generatedbased on the application and a user selection from the application;
transmitting the generated prompts and the identified data chunks to the Al model;
and receiving, from the first AI model, search results that correspond to the AI pipeline input;
adding a hyperlink to the search results; and
causing display of the search results within the client application.
Allowable Subject Matter
Claims 22-38 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/VAN H OBERLY/Primary Examiner, Art Unit 2166
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