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 .
DETAIL ACTION
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 2/4/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant invention encompasses three sets of claims: a method in claims 1-7 (i.e., a process), a system in claims 8-14 (i.e., a manufacture) and a non-transitory computer readable storage medium in claims 15-20 (i.e., a manufacture). All claims are directed to one of the four statutory categories and meet the requirements of step 1.
Step 2A
Prong One
The claimed invention is directed to an abstract idea without significant more. The instant invention is broadly directed to “generating an output of the targeted information from the LLM”. Claim 1 recites the following (with emphasis added):
Claim 1: A method of retrieving targeted information from a document by a large language model, the method being implemented by at least one processor, the method comprising:
receiving a document having a token length that is greater than a predetermined token length and receiving a query for targeted information associated with the document;
tagging the document with a plurality of sentence tags;
splitting the tagged document into a plurality of segments comprising a first segment and a second segment;
implementing a large language model (LLM);
assigning the first segment and the second segment to the LLM in a chronological order;
instructing the LLM to identify and select a first set of relevant tokens within the first segment and then further instructing the LLM to identify and select a second set of relevant tokens within the second segment;
performing at least one from among a prompt-based approach and an attention-based approach that highlights relevant tokens by the LLM from the first set of relevant tokens and from the second set of relevant tokens; and
providing an output of the targeted information from the LLM based on an extraction of the highlighted relevant tokens that is responsive to the query.
The bold portions of claim 1 encompass the abstract idea, which is also encompassed by the dependent claims 2-7, and substantially also encompassed by claims 8-14 and 15-20.
Claims 1, 8, and 15 recite the steps to generate an output of the targeted information from a document by a natural language model including a natural language processing. These limitations, when given their broadest reasonable interpretation, are directed to certain performing of organizing human activity and mental processes, which is abstract idea.
Prong Two
This judicial exception is not integrated into a practical application because mere instruction to implement on computers (i.e. storage medium or processors in claim 1 and 8) or a computer model (language model here in claim 1), or merely using computers as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment for field of use is not considered integration into a practical application. Claim 1 recites using large language prompt to generate output data of the trained language model. Using input data to a trained machine-learning or language model is a generic feature of natural language process, which does not represent a technological improvement. The using of the computer and natural language process does not add improvement to the functioning of a computer or to any other technology field, which failed to enable the abstract idea to integrate into a practical application. The claims are drafted in a result-oriented fashion, without the requisite specificity needed to provide a nonabstract technological solution. The computing system and natural language process are directed to the components of a system amount to merely field of use type limitations and/or extra solution activity to implement the abstract idea as presented.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires 'more than simply stat[ing] the [abstract idea] while adding the words 'apply it."' Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include 'additional features' to ensure 'that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].'" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims include the additional elements other than the abstract idea which include a processor, storage medium, language model and a display with user interface (in claim 1). These additional elements are merely conventional computer and computer model. Any potentially technical aspects of the claims are well-known generic computer components performing conventional functions (e.g., a processor performing a mental process). The present claims have been analyzed both individually and in combination and, the instant claims do not provide any improvement of the functioning of the computer or improvement to computer technology or any other technical field. There do not appear to be any meaningful limitations other than those that are well-understood, routine and conventional in the field. Thus, the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claims 1-7 are not patent eligible.
Claims 8-14 and 15-10 recite similar limitations of claims 1-7, thus are abstract idea and not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over DeFoor et al (US 11972223 B1) in view of Venkateshwaran et al (US 20260105259 A1) and Phukan et al (US 20260119809 A1).
Regarding claim 1, DeFoor discloses a method of retrieving targeted information from a document by a large language model [e.g. FIG. 1-2, 4 and 11; retrieve one or more text portion from documents by a large language model], the method being implemented by at least one processor [e.g. FIG. 7; processors], the method comprising:
receiving a document [e.g. FIG. 10-12; relevant document] having a token length [e.g. FIG. 1, 5 and 10; a number of tokens (e.g. words)] that is greater than a predetermined token length [e.g. a threshold of the number of tokens] and receiving a query for targeted information associated with the document [e.g. natural language query];
splitting the tagged document into a plurality of segments comprising a first segment and a second segment [e.g. FIG. 12-13; split document to document portions (pages or paragraphs) and chunks];
implementing a large language model (LLM) [e.g. FIG. 1-2 and 4-5; implementing a large language model];
assigning the first segment and the second segment to the LLM in a chronological order [e.g. FIG. 9];
instructing the LLM to identify and select a first set of relevant tokens within the first segment and then further instructing the LLM to identify and select a second set of relevant tokens within the second segment [e.g. FIG. 1-2, 4, 9 and 12-13]; determining relevance score for chunk level for different portions of the documents; chunks including tokens (e.g. words)];
performing at least one from among a prompt-based approach [e.g. determining document relevance scores based on the document relevance prompts] by the LLM from the first set of relevant tokens and from the second set of relevant tokens [e.g. FIG. 1-2, 4, 9 and 12-13]; and
providing an output [e.g. FIG. 17; an answer to the user] of the targeted information from the LLM based on an extraction of the relevant tokens that is responsive to the query [e.g. FIG. 1-2, 4, 6, 9, 12-13, and 17].
Although DeFoor discloses splitting a document to paragraphs, sentences, chunks e.g. column 14 lines 4-16], it is noted that DeFoor differs to the present invention in that DeFoor fails to explicitly disclose the concept of sentence tags and the highlighted relevant tokens.
However, Venkateshwaran teaches the well-known concept of tagging the document [e.g. FIG. 19-21; [0010]; tagging sentences included in a document] with a plurality of sentence tags [e.g. FIG. 21; tags for sentences] for extracting domain specific insights from a corpus of files containing large documents by a large language model [e.g. FIG. 3-6; natural language model];
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the retrieving text from a document system disclosed by DeFoor to exploit the well-known tagging sentences in a document technique taught by Venkateshwaran as above, in order to provide improved machine learning models [See Venkateshwaran; [0137]].
Moreover, Phukan teaches the well-known concept of retrieving targeted information from a document by a large language model [e.g. FIG. 1-2; [0029] by performing from an attention-based approach [e.g. FIG. 3; [0098]; attention mechanism] that highlights relevant tokens [e.g. anchor token(s)] by the LLM [e.g. multimodal system] from the first set of relevant tokens and from the second set of relevant tokens [tokens in the document], and providing an output [e.g. FIG. 2; output a response] from the LLM based on an extraction of the highlighted relevant tokens [e.g. FIG. 1-3 and 10].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the retrieving text from a document system disclosed by DeFoor to exploit the well-known tagging sentences in a document technique taught by Venkateshwaran and the well-known concept of performing multimodal attribution within a digital document for a selection of an artificial intelligence generated answer techniques taught by Phukan as above, in order to provide improved machine learning models [See Venkateshwaran; [0137]] and the computational accuracy and efficiency improvements of the multimodal attribution system [See Phukan; [0033]].
Regarding claim 2, DeFoor, Venkateshwaran and Phukan further disclose splitting the tagged document comprises including a sentence from an end portion of a previous segment into a current segment [e.g. DeFoor: a sentence is split across different pages; Venkateshwaran: [0058]; sentence boundary detection].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the retrieving text from a document system disclosed by DeFoor to exploit the well-known tagging sentences in a document technique taught by Venkateshwaran and the well-known concept of performing multimodal attribution within a digital document for a selection of an artificial intelligence generated answer techniques taught by Phukan as above, in order to provide improved machine learning models [See Venkateshwaran; [0137]] and the computational accuracy and efficiency improvements of the multimodal attribution system [See Phukan; [0033]].
Regarding claim 3, DeFoor, Venkateshwaran and Phukan further disclose each of the first segment and the second segment has a predetermined chunk token length [e.g. DeFoor: FIG. 5] .
Regarding claim 4, DeFoor, Venkateshwaran and Phukan further disclose the selecting the first set of the relevant tokens and the second set of the relevant tokens within the first segment and the second segment comprises selecting predetermined top-k relevant tokens [e.g. Phukan: DeFoor: FIG. 1-2 and 5; FIG. 3 and 5-6; [0125]; selecting top-k tokens as anchors].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the retrieving text from a document system disclosed by DeFoor to exploit the well-known tagging sentences in a document technique taught by Venkateshwaran and the well-known concept of performing multimodal attribution within a digital document for a selection of an artificial intelligence generated answer techniques taught by Phukan as above, in order to provide improved machine learning models [See Venkateshwaran; [0137]] and the computational accuracy and efficiency improvements of the multimodal attribution system [See Phukan; [0033]].
Regarding claim 5, DeFoor, Venkateshwaran and Phukan further disclose attaching a predetermined marker to the relevant tokens [e.g. Phukan: anchor token(s)]; and instantiating the LLM to highlight the relevant tokens based on the attached predetermined marker [e.g. DeFoor: FIG. 1-2 and 5; Phukan: FIG. 3 and 5-6; selecting top-k tokens as anchors].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the retrieving text from a document system disclosed by DeFoor to exploit the well-known tagging sentences in a document technique taught by Venkateshwaran and the well-known concept of performing multimodal attribution within a digital document for a selection of an artificial intelligence generated answer techniques taught by Phukan as above, in order to provide improved machine learning models [See Venkateshwaran; [0137]] and the computational accuracy and efficiency improvements of the multimodal attribution system [See Phukan; [0033]].
Regarding claim 6, DeFoor, Venkateshwaran and Phukan further disclose the attention-based approach [e.g. DeFoor: FIG. 1-2 and 5; paying attention to text divisions; Phukan: FIG. 3; [0098]; attention mechanism] comprises: performing a multi-head attention steering mechanism on a respective layer of the LLM [e.g. DeFoor: FIG. 1-2 and 16; Phukan: FIG. 4-5] and by modifying attention weights [e.g. DeFoor: a weighted average of the subset of the plurality of chunk relevance scores; Venkateshwaran: weight tuning; Phukan: FIG 1-3 and 5; weight of the layers; a sequence of tokens with weighted sums of all their representations] associated with the relevant tokens based on a predetermined multi-head attention function with a predetermined scaling vector [e.g. Venkateshwaran and Phukan: text vectors]; and highlighting the relevant tokens by the LLM based on the modified attention weights [e.g. DeFoor: FIG. 1-2 and 16; Phukan: FIG. 3-5 [0098]].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the retrieving text from a document system disclosed by DeFoor to exploit the well-known tagging sentences in a document technique taught by Venkateshwaran and the well-known concept of performing multimodal attribution within a digital document for a selection of an artificial intelligence generated answer techniques taught by Phukan as above, in order to provide improved machine learning models [See Venkateshwaran; [0137]] and the computational accuracy and efficiency improvements of the multimodal attribution system [See Phukan; [0033]].
Regarding claim 7, DeFoor, Venkateshwaran and Phukan further disclose the output includes an intact version of the plurality of sentence tags [DeFoor: FIG. 1-2 and 14-15; Venkateshwaran: e.g. FIG. 21; tags for sentences].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the retrieving text from a document system disclosed by DeFoor to exploit the well-known tagging sentences in a document technique taught by Venkateshwaran and the well-known concept of performing multimodal attribution within a digital document for a selection of an artificial intelligence generated answer techniques taught by Phukan as above, in order to provide improved machine learning models [See Venkateshwaran; [0137]] and the computational accuracy and efficiency improvements of the multimodal attribution system [See Phukan; [0033]].
Regarding claim 8-14, this is an apparatus that includes same limitation as in claim 1-7 above, the rejection of which are incorporated herein. Furthermore, DeFoor discloses a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display [e.g. FIG. 7; column 16 lines 15-43].
Regarding claim 15-17 and 19-20, this is a non-transitory computer-readable storage medium that includes same limitation as in claim 1-2 and 5-6 above, the rejection of which are incorporated herein.
Regarding claim 18, this is a non-transitory computer-readable storage medium that includes same limitation as in claim 4 and 7 together above, the rejection of which are incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kanuga et al (US 20250094717 A1).
Platanios et al (US 20250259000 A1).
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/ZHUBING REN/ Primary Examiner, Art Unit 2658
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