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 .
Drawings
The drawings are objected to because:
In Figure 5, the arrow labels and the circled text are not legible.
In Figure 7, the arrow labels and the legend are not legible.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a computer-implemented method comprising: identifying, by one or more processors and from a document data store, an initial document subset for a generative text request; generating, by the one or more processors and using a machine learning classifier model, a contextual classification for the one or more request text fields; and providing, by the one or more processors, the contextual classification.
The claim 1 limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “one or more processors” and “a machine learning classifier model”, nothing in the claim elements preclude the actions from practically being performed in the mind. For example, “identifying” in the context of this claim encompasses a person identifying a subset of documents from a set of documents to be used for a generative text request, “generating” in the context of this claim encompasses a person determining a contextual classification for a request text field, and “providing” in the context of this claim encompasses a person stating or writing a contextual classification. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements “one or more processors” and “a machine learning classifier model”. The additional elements amount to no more than mere instructions to apply the exception using generic computer components. There are no details about a particular machine learning classifier model or how the machine learning classifier model operates to generate a contextual classification. The machine learning classifier model is used to generally apply an abstract idea (generating a contextual classification) without placing any limitation on how the machine learning classifier model operate to generate a contextual classification. The claim omits any details as to how the machine learning classifier model solves a technical problem, and instead recites only the idea of a solution or outcome. The claim invokes a machine learning classifier model merely as a tool for generating a contextual classification rather than purporting to improve the technology or a computer (See MPEP 2106.05(f)). Therefore, the limitation represents no more than mere instructions to apply the judicial exception on a computer. Examples of generic computer components can be found in paragraph 0034 of the specification, “For example, the processing element 205 may be embodied as one or more complex programmable logic devices (CPLDs), microprocessors, multi-core processors, coprocessing entities, application-specific instruction-set processors (ASIPs), microcontrollers, and/or controllers. Further, the processing element 205 may be embodied as one or more other processing devices or circuitry. The term circuitry may refer to an entirely hardware embodiment or a combination of hardware and computer program products. Thus, the processing element 205 may be embodied as integrated circuits, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), hardware accelerators, other circuitry, and/or the like.”. Accordingly, the 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.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Lazaridou et al. ("Internet-augmented language models through few-shot prompting for open-domain question answering"), hereinafter Lazaridou, in view of He et al. (US Patent No. 7,603,348), hereinafter He.
Regarding claim 1, Lazaridou discloses a computer-implemented method comprising:
identifying, by one or more processors and from a document data store, an initial document subset for a generative text request (Section 3, lines 1-6, "In this section, we describe our approach for improving the performance of pre-trained LMs in the task of open-domain question answering. Specifically, we propose to use few-shot prompting as a flexible and robust way to condition any pre-trained LSLM on external evidence, allowing for better grounding to factual and up-to-date information. Our approach consists of 3 steps (see Appendix A.1 for an illustration of the method). First, given a question we retrieve a set of relevant documents from the web using a search engine (§3.1)."; Section 3.1, lines 1-7, "Given a question q, we need to obtain a set of relevant documents D which would allow us to extend the model’s knowledge to factual and (potentially) new information not already present in its weights. With a view to more realistic and open-ended user interactions, we retrieve documents using an off-the-shelf search engine, i.e., Google Search. Specifically, we use each question q verbatim as a query and issue a call to Google Search via the Google Search API.1 For each question, we retrieve the top 20 urls and parse their HTML content to extract clean text, resulting in a set of documents D per question q."; Retrieving a set of documents from the internet, where the set of documents is relevant to a question, reads on identifying an initial document subset for a generative text request from a document data store.).
Lazaridou does not specifically disclose: generating, by the one or more processors and using a machine learning classifier model, a contextual classification for the one or more request text fields; and providing, by the one or more processors, the contextual classification.
He teaches:
generating, by the one or more processors and using a machine learning classifier model, a contextual classification for the one or more request text fields (Column 2, lines 28-41, "The disclosed embodiments provide a system 100 for classifying a search query. The system 100 analyzes a plurality of queries that are manually and/or automatically categorized within a query taxonomy. The system 100 submits the queries, or a subset thereof, to a search engine and identifies one or more of the top returned web pages to represent the queries. One or more of the terms in the web pages are extracted and combined to form one or more term vectors that provide context to the query. The term vectors may be combined to represent the queries as points in a high dimensional vector space. The system 100 uses the term vectors and manual categorizations as training data to "train" a machine learning classifier function that can automatically associate an un-categorized query with a category within the taxonomy."; Column 2, lines 61-65, "The embodiments disclosed herein may be implemented in one or more computer programs executing on one or more programmable systems comprising at least one processor and at least one data storage system."; Automatically associating an un-categorized query with a category within a taxonomy using a machine learning classifier to provide context to the query reads on generating a contextual classification for a request text field using a machine learning classifier model.);
and providing, by the one or more processors, the contextual classification (Column 2, lines 7-16, "By way of introduction, information sought by a user from a search engine, i.e. the intent of the search query, may not always be readily determinable from the user's query and/or context surrounding the query. The difficulties in understanding the query may stem from the fact that queries often comprise very little information, e.g. a query typically has less than three terms. By automatically associating a category label, herein referred to as a category index number, to the query, its meaning, e.g. context information and/or user intent of the query, may be better understood."; Column 5, lines 52-58, "The classifier function processor 108 is coupled with the vector space processor 106 and creates a machine learning classifier function. The classifier function outputs a value that may correspond to a category index number. For example, the value may equal a category index number or approximately equal a category index number, in which case the query may be assumed to belong to the corresponding category."; The classifier function outputting a value that corresponds to a category index number reads on providing the contextual classification.).
He is considered to be analogous to the claimed invention because it is in the same field of text response systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lazaridou to incorporate the teachings of He to automatically associate an un-categorized query with a category within a taxonomy using a machine learning classifier to provide context to the query. Doing so would allow for improving web search applications and recommendation systems by identifying similar and related queries that provide a user with additional and alternative search results (He; Column 2, lines 13-24).
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/775,727 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other.
Regarding claim 1, claim 1 of copending Application No. 18/775,727 claims all the limitations set forth in the application claim 1:
US Application No. 18/589,179 Claim 1
US Application No. 18/775,727 Claim 1
A computer-implemented method comprising:
A computer-implemented method comprising:
identifying, by one or more processors and from a document data store, an initial document subset for a generative text request;
identifying, by one or more processors and from a document data store, an initial document subset for a generative text request that comprises a request to generate a generative text document based on one or more request text fields;
generating, by the one or more processors and using a machine learning classifier model, a contextual classification for the one or more request text fields;
generating, by the one or more processors and using a machine learning classifier model, a contextual classification for the one or more request text fields;
and providing, by the one or more processors, the contextual classification.
identifying, by the one or more processors and from the initial document subset, a refined document subset based on the contextual classification;
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
UzZaman et al. (US Patent Application Publication No 2024/0249081)
Ram et al. ("In-Context Retrieval-Augmented Language Models")
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James Boggs whose telephone number is (571)272-2968. The examiner can normally be reached M-F 8:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at (571)272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES BOGGS/Examiner, Art Unit 2657