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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/07/2025 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 1-10 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-0 of U.S. Patent No. 11,748,577. 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 1 (drawn to a system):
Current Application
Claim 1:
A system for automatically generating natural language text for a response document that satisfies requirements in a requirement specification, comprising:
one or more processors programmed to:
access a plurality of discrete requirements obtained from a requirement specification, each discrete requirement specifying a respective requirement to be satisfied in the response document that is generated responsive to the requirement specification;
for each discrete requirement:
identify a relevant response section from among previously generated response sections, the previously generated response sections having been identified and labeled from a corpus of previously generated response documents;
generate a language model writing matrix based on the identified relevant response section and the discrete requirement, the language model writing matrix comprising natural language text from the identified relevant response section and the discrete requirement that is used as a basis to automatically generate unique natural language text;
activate, using the language model writing matrix, an artificial intelligence (AI) deep learning language model pretrained with natural language content to automatically generate unique text;
obtain, based on the activated AI deep learning language model, a candidate response portion that is predicted to address the discrete requirement, the candidate response portion comprising natural language text; and
generate the response document based on each candidate response portion obtained for each discrete requirement, wherein the response document is automatically generated to satisfy the plurality of discrete requirements.
‘577
Claim 1:
A system for automatically generating natural language text for a response document that satisfies requirements in a requirement specification, comprising:
a datastore that stores previously generated response sections that are structured from a corpus of previously generated response documents, the previously generated response sections having been identified and labeled from the corpus of previously generated response documents;
one or more processors programmed to:
access a plurality of discrete requirements obtained from a requirement specification, each discrete requirement specifying a respective requirement to be satisfied in the response document that is generated responsive to the requirement specification;
for each discrete requirement:
identify a relevant response section from among the previously generated response sections in the datastore;
generate a language model writing matrix based on the identified relevant response section and the discrete requirement, the language model writing matrix comprising natural language text from the identified relevant response section and the discrete requirement that is used as a basis to automatically generate unique natural language text;
activate, using the language model writing matrix, an artificial intelligence (AI) deep learning language model pretrained with natural language content to automatically generate unique text;
obtain, based on the activated AI deep learning language model, a plurality of candidate response portions that are predicted to address the discrete requirement, each candidate response portion comprising natural language text that is unique with respect to other ones of the candidate response portions and represents an alternative response to the discrete requirement;
receive a selection of a candidate response portion, from among the plurality of candidate response portions, for inclusion into the response document; and
generate the response document based on the selected candidate response portions corresponding to each discrete requirement, wherein the response document is automatically generated to satisfy the requirement specification.
As shown in the tables above, it is clear that all the elements of the application claim 1 are to be found in patent claim 1, as the application claim 1 fully encompasses patent claim 1. The difference between the application claim 1 and the patent claim 1 lies in the fact that the patent claim includes more elements and is thus more specific. Thus the invention of claim 1 of the patent is in effect a “species” of the “generic” invention of the application claim 1. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claim 2 of the current application corresponds to claim 2 of U.S. Patent No. 11,748,577.
Claim 3 of the current application corresponds to claim 3 of U.S. Patent No. 11,748,577.
Claim 4 of the current application corresponds to claim 4 of U.S. Patent No. 11,748,577.
Claim 5 of the current application corresponds to claim 5 of U.S. Patent No. 11,748,577.
Claim 6 of the current application corresponds to claim 6 of U.S. Patent No. 11,748,577.
Claim 7 of the current application corresponds to claim 7 of U.S. Patent No. 11,748,577.
Claim 8 of the current application corresponds to claim 8 of U.S. Patent No. 11,748,577.
Claim 9 of the current application corresponds to claim 9 of U.S. Patent No. 11,748,577.
Claim 10 of the current application corresponds to claim 10 of U.S. Patent No. 11,748,577.
Allowable Subject Matter
Claims 1-40 would be allowed if the double patenting rejection above is overcome.
The following is a statement of reasons for the indication of allowable subject matter: Claim 1 of the current application teach similar subject matter as the prior art of Mapranath (US 10,970,321), Karuppusamy et al. (US 2019/0349320), and Tran (US 2022/0237368). However, the prior art, alone or in combination, fails to teach "generate a language model writing matrix based on the identified relevant response section and the discrete requirement, the language model writing matrix comprising natural language text from the identified relevant response section and the discrete requirement that is used as a basis to automatically generate unique natural language text; activate, using the language model writing matrix, an artificial intelligence (AI) deep learning language model pretrained with natural language content to automatically generate unique text; obtain, based on the activated AI deep learning language model, a candidate response portion that is predicted to address the discrete requirement, the candidate response portion comprising natural language text” as recited in claim 1.
Claims 2-10 would be allowed for being dependent on an allowable base claim.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gardner (US 2024/0296279) discloses implementing confidence enhancement for responses by document-based large language models (“LLMs”) or other AI/ML systems.
Gardner (US 2024/0296219) discloses implementing adverse or malicious input mitigation for large language models (“LLMs”) or other AI/ML systems
Gardner (US 2024/0296177) discloses implementing conversational large language model (“LLM”) or other AI/ML-based user tenant orchestration.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras D Shah can be reached at (571}270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SATWANT K SINGH/Primary Examiner, Art Unit 2653
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