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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/25/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 21, 23-35, and 38-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,236,202. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious variations of each other.
Regarding Claim 21 (drawn to a method):
Current Application
Claim 21:
A method for maintaining consistent results in an artificial intelligence (“AI”) pipeline, comprising:
submitting, by a prompt engine in a first instance, a first sequence of content queries to a pipeline engine for the AI pipeline, wherein the AI pipeline includes a prompt package and a first language model, and wherein the prompt package and at least a portion of the first sequence of content queries are inputs to the first language model;
receiving first results from the AI pipeline in response to the first sequence;
in a second instance subsequent to receiving the results from the AI pipeline, submitting, by the prompt engine, the first sequence of content queries to the AI pipeline;
receiving second results from the AI pipeline;
vectorizing the first and second results;
semantically comparing the vectorized first results and the vectorized second results to one another, including determining that the vectorized first and second results exceed a threshold difference in at least one of angle and distance; and
in response to the determination, performing a corrective action.
‘202
Claim 1:
A method for maintaining consistent results in an artificial intelligence (“AI”) pipeline, comprising:
submitting, by a prompt engine, a first sequence of content queries to a pipeline engine for the AI pipeline, wherein the AI pipeline includes a prompt package and a language model, and wherein the prompt package and at least a portion of the first sequence of content queries are inputs to the language model;
receiving first results from the AI pipeline in response to the first sequence;
in an instance subsequent to receiving the results from the AI pipeline, submitting, by the prompt engine, the first sequence of content queries to the pipeline;
receiving second results from the AI pipeline;
vectorizing the first and second results;
semantically comparing the vectorized first results and the vectorized second results;
identifying a semantic divergence by determining that the first results and the second results differ semantically more than a predetermined threshold amount; and
in response to the determination, performing a corrective action.
Regarding Claim 38 (drawn to a non-transitory CRM):
Current Application
Claim 38:
A non-transitory, computer-readable medium containing instructions that, when executed by a hardware-based processor, causes the processor to perform stages for maintaining consistent results in an artificial intelligence (“AI”) pipeline, comprising:
submitting, by a prompt engine in a first instance, a first sequence of content queries to a pipeline engine for the AI pipeline, wherein the AI pipeline includes a prompt package and a first language model, and wherein the prompt package and at least a portion of the first sequence of content queries are inputs to the first language model;
receiving first results from the AI pipeline in response to the first sequence;
in a second instance subsequent to receiving the results from the AI pipeline, submitting, by the prompt engine, the first sequence of content queries to the AI pipeline;
receiving second results from the AI pipeline;
vectorizing the first and second results;
semantically comparing the vectorized first results and the vectorized second results to one another, including determining that the vectorized first and second results exceed a threshold difference in at least one of angle and distance; and
in response to the determination, performing a corrective action.
‘202
Claim 8:
A non-transitory, computer-readable medium containing instructions that, when executed by a hardware-based processor, causes the processor to perform stages for maintaining consistent results in an artificial intelligence (“AI”) pipeline, comprising:
submitting, by a prompt engine, a first sequence of content queries to a pipeline engine for the AI pipeline, wherein the AI pipeline includes a prompt package and a language model, and wherein the prompt package and at least a portion of the first sequence of content queries are inputs to the language model;
receiving first results from the AI pipeline in response to the first sequence;
in an instance subsequent to receiving the first results, submitting, by the prompt engine, the first sequence of content queries to the pipeline;
receiving second results from the AI pipeline;
vectorizing the first results and the second results;
semantically comparing the vectorized first results and the vectorized second results;
identifying a semantic divergence by determining that the first results and the second results differ semantically more than a predetermined threshold amount; and
in response to the determination, performing a corrective action.
Regarding Claim 40 (drawn to a system):
Current Application
Claim 40:
A system for maintaining consistent results in an artificial intelligence (“AI”) pipeline, 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:
submitting, by a prompt engine in a first instance, a first sequence of content queries to a pipeline engine for the AI pipeline, wherein the AI pipeline includes a prompt package and a first language model, and wherein the prompt package and at least a portion of the first sequence of content queries are inputs to the first language model;
receiving first results from the AI pipeline in response to the first sequence;
in a second instance subsequent to receiving the results from the AI pipeline, submitting, by the prompt engine, the first sequence of content queries to the AI pipeline;
receiving second results from the AI pipeline;
vectorizing the first and second results;
semantically comparing the vectorized first results and the vectorized second results to one another, including determining that the vectorized first and second results exceed a threshold difference in at least one of angle and distance; and
in response to the determination, performing a corrective action.
‘202
Claim 15:
A system for maintaining consistent results in an artificial intelligence (“AI”) pipeline, 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:
causing, by a prompt engine, a submission of a first sequence of content queries to a pipeline engine for the AI pipeline, wherein the AI pipeline includes a prompt package and a language model, and wherein the prompt package and at least a portion of the first sequence of content queries are inputs to the language model;
receiving first results from the AI pipeline in response to the first sequence;
in an instance subsequent to receiving the first results, causing, by the prompt engine, a second submission of the first sequence of content queries to the pipeline;
receiving second results from the AI pipeline;
causing vectorization of the first and second results;
causing, by the prompt engine, a semantic comparison of the vectorized first results and the vectorized second results, wherein a semantic divergence is identified by determining that the first results and the second results differ semantically more than a predetermined threshold amount; and
in response to the identifying, causing performance of a corrective action.
As shown in the tables above, it is clear that all the elements of the application claims 21, 38, and 40 are to be found in patent claims 1, 8, and 15, as the application claims 21, 38, and 40 fully encompasses patent claims 1, 8, and 15. The difference between the application claims 21, 38, and 40 and the patent claims 1, 8, and 15 lies in the fact that the patent claims includes more elements and is thus more specific. Thus the invention of claims 1, 8, and 15 of the patent is in effect a “species” of the “generic” invention of the application claims 21, 38, and 40. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claim 23 of the current application corresponds to the corresponding portion of claim 2 of U.S. Patent No. 12,236,202.
Claim 24 of the current application corresponds to the corresponding portion of claim 2 of U.S. Patent No. 12,236,202.
Claim 25 of the current application corresponds to the corresponding portion of claim 2 of U.S. Patent No. 12,236,202.
Claim 26 of the current application corresponds to the corresponding portion of claim 3 of U.S. Patent No. 12,236,202.
Claim 27 of the current application corresponds to the corresponding portion of claim 5 of U.S. Patent No. 12,236,202.
Claim 28 of the current application corresponds to the corresponding portion of claim 3 of U.S. Patent No. 12,236,202.
Claim 29 of the current application corresponds to the corresponding portion of claim 3 of U.S. Patent No. 12,236,202.
Claim 30 of the current application corresponds to the corresponding portion of claim 3 of U.S. Patent No. 12,236,202.
Claim 31 of the current application corresponds to the corresponding portion of claim 4 of U.S. Patent No. 12,236,202.
Claim 32 of the current application corresponds to the corresponding portion of claim 4 of U.S. Patent No. 12,236,202.
Claim 33 of the current application corresponds to the corresponding portion of claim 6 of U.S. Patent No. 12,236,202.
Claim 34 of the current application corresponds to the corresponding portion of claim 6 of U.S. Patent No. 12,236,202.
Claim 35 of the current application corresponds to the corresponding portion of claim 7 of U.S. Patent No. 12,236,202.
Claim 39 of the current application corresponds to the corresponding portion of claim 9 of U.S. Patent No. 12,236,202.
Claims 2, 36, and 37 of the current application do not correspond to any claims of U.S. Patent No. 12,236,202.
Allowable Subject Matter
Claims 21, 23-35, and 38-40 would be allowed if the double patenting rejections above were overcome.
The following is a statement of reasons for the indication of allowable subject matter: Claims 21, 38, and 40 of the current application teaches similar subject matter as the prior art of Tunstall- Pedoe et al. (US 11,763,096), Tunstall-Pedoe et al. (US 11,829,725), and Das et al. (US 11,475,053). However, the prior art, alone or in combination fails to teach “semantically comparing the vectorized first results and the vectorized second results to one another, including determining that the vectorized first and second results exceed a threshold difference in at least one of angle and distance; and in response to the determination, performing a corrective action” as recited in claims 21, 38, and 40.
Claims 23-35, and 39 would be allowed for being dependent on an allowable base claim.
Claim 2, 36, and 37 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.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Das et al. (US 11,017,764) discloses predicting follow-on request to a natural language request.
Das et al. (US 11,670,288) discloses predicting follow-on request to a natural language request.
Sriharsha et al. (US 11,663,176) discloses data field extraction model training for a data intake and query system.
Sriharsha et al. (US 11,704,490) discloses data field extraction model training for a data intake and query system.
Sriharsha et al. (US 2022/0036177) discloses data field extraction model training for a data intake and query system.
Bhathena et al. (US 2021/0282296) discloses conditional hierarchical domain routing and intent classification for virtual assistant queries.
Smith Lewis et al. (US 2024/0289863) discloses providing adaptive ai-driven conversational agents.
Cheng et al. (US 2024/0303443) discloses building a customized generative artificial intelligent platform.
Cheng et al. (US 2024/0303473) discloses building a customized generative artificial intelligent platform.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATWANT K SINGH whose telephone number is (571)272-7468. The examiner can normally be reached Monday thru Friday 9:00 AM to 6:00 PM EST.
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/SATWANT K SINGH/Primary Examiner, Art Unit 2653
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