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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 16-23 recite “means for” limitations which are interpreted under 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 and 16-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per Claims 1 and 16:
The original Specification (i.e. the original Specification of Parent Application 19/056,496, hereafter original Specification) does not describe determining one or more characteristics of the combined context and dynamically determining a partition size based on… the one or more characteristics of the combined context.
The original Specification describes “Dynamic context partitioning based on available system resources and document characteristics” but does not describe where the document characteristics are of documents in the combined context. This portion also does not describe where the partition size is determined based on the document characteristics (as opposed to where partition size is determined based on available system resources whereas some other aspect of the partitioning is based on document characteristics).
The original Specification also describes where “partition sizes [are] dynamically adjusted based on system resources and processing requirements” but does not describe where the partition sizes are further determined based on characteristics of the combined context.
The dependent claims include the issues of their respective parent claims.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per Claim 9:
“each context partition” in the 4th to last line of claim 9 is unclear (this phrase can refer to either “each context partition” of the plurality of context partitions OR to “each context partition” of “context partitions” in line 11 of claim 9 [which are not necessarily the same partitions as the “plurality of context partitions” in line 13 of claim 9])
As per Claim 13:
“software” in line 2 seems like it should be –processor—(software typically does not execute itself, see also claim 10 which recites where the software is executed by “the processor”)
As per Claim 15:
“wherein the software is further operable, when executed by the processor, partitioning” in lines 1-2 of claim 15 is grammatically unusual (–wherein partitioning—or –wherein the software is further operable to, when executed by the processor, partition the combined context in a way/manner that preserves document boundaries—perhaps?)
The dependent claims include the issues of their respective parent claims.
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Claim 1 (and similarly claim 16):
The claim(s) recite
A method of adaptive context partitioning… (mental process, a human could take context data [a plurality of documents with information written in them] and divide it into pieces/partitions)
comprising: receiving a query from a user; (mental process, a human could listen to another person’s question/query)
forming a combined context by retrieving a plurality of relevant documents from at least one document database based on the query; (mental process, a human could look for and retrieve documents among a large quantity of documents that are related to the question/query that he/she heard from the another person, and then consider the retrieved documents to be something called a combined context)
determining one or more characteristics of the combined context; (mental process, a human could read the documents in the context to determine characteristics of the documents in the context)
monitoring current system resources including processor availability and memory utilization; (mental process, a human could think about how much brain power he/she has to perform tasks and think about how much memory he/she has available to memorize additional information)
dynamically determining a partition size based on the current system resources and the one or more characteristics of the combined context; (mental process, a human can think about how large the pieces/partitions of the documents/context should be based on the determined characteristics and the determined brain power/memory-space)
partitioning the combined context into a plurality of context partitions according to the partition size while preserving document boundaries to maintain semantic coherence; (mental process, a human could divide the documents into pieces based on how large he/she thinks the pieces/partitions should be while making sure that breaks/divisions do not occur mid-paragraph/sentence/etc.)
generating a plurality of intermediate analysis results by processing each context partition of the plurality of context partitions using a mapper prompt… (mental process, a human could receive an instruction/”mapper prompt” telling him/her what to do with the context partitions and analyze each partition and think of or write down a respective result for each partition based on the “mapper prompt” instruction)
generating a final response by processing the plurality of intermediate analysis results using a reducer prompt… (mental process, a human could think of or write down a natural language answer based on the memorized/written results and based on a “reducer prompt” instruction)
and transmitting the final response to the user (mental process, a human could communicate verbally/in-writing the natural language answer to the another person).
This judicial exception is not integrated into a practical application because
The remaining elements/limitations of the claim are the underlined portions in the next paragraph.
A method of adaptive context partitioning in a retrieval-augmented generation system, comprising: receiving a query from a user; forming a combined context by retrieving a plurality of relevant documents from at least one document database based on the query; determining one or more characteristics of the combined context; monitoring current system resources including processor availability and memory utilization; dynamically determining a partition size based on the current system resources and the one or more characteristics of the combined context; partitioning the combined context into a plurality of context partitions according to the partition size while preserving document boundaries to maintain semantic coherence; generating a plurality of intermediate analysis results by processing each context partition of the plurality of context partitions using a mapper prompt through one or more large language models (LLMs); generating a final response by processing the plurality of intermediate analysis results using a reducer prompt through the one or more LLMs; and transmitting the final response to the user.
The remaining elements/limitations only require a programmed generic computer to perform the human-implementable functions discussed above (“large language models” are functions that a generic computer is programmed to perform in order to simulate a human’s thinking/response to a question/query/instruction/prompt) which is not sufficient to integrate an abstract idea into a practical application or amount to significantly more than an abstract idea (see “even if an element does not integrate a judicial exception into a practical application or amount to significantly more on its own (e.g., because it is merely a generic computer component performing generic computer functions)” in MPEP 2106.07[b], “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible” and “For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer” in MPEP 2106.05[f], “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality… iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential)” and “Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology” in MPEP 2106.05[a], MPEP 2106.04[a][2] III., “In bracket 3, explain why the combination of additional elements fails to integrate the judicial exception into a practical application. For example, if the claim is directed to an abstract idea with additional generic computer elements, explain that the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer” in 2106.07[a][1])
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The remaining elements/limitations only require a programmed generic computer to perform the human-implementable functions discussed above (“large language models” are functions that a generic computer is programmed to perform in order to simulate a human’s thinking/response to a question/query/instruction/prompt)
As per Claim 2 (and similarly claim 17):
Mental process - A human could sense that he/she is not thinking/working at optimal capacity and could break what’s left of the retrieved documents into smaller pieces so that he/she is not overwhelmed by the amount of information in each piece/partition.
As per Claim 3 (and similarly claim 18):
Mental process – A human could assign tasks that would use different parts of his/her brain for different times.
As per Claim 4 (and similarly claim 19):
Mental process without integrating into a practical application or amounting to significantly more – A human could enlist the aid of other humans such that each human analyzes a respective assigned context partition and generates a respective result at the same time, where “using a plurality of LLM instances” involves a programmed generic computer performing the functions that each enlisted human performs.
As per Claim 5 (and similarly claim 20):
Mental process – A human could read the contents of the documents and make sure to break the documents into pieces/partitions in a way that the pieces/partitions contains at least one complete document or complete paragraphs/sentences/segments.
As per Claim 6 (and similarly claim 21):
Mental process – A human could memorize the “intermediate results” in a portion of his/her memory that could be called a “cache”, listen to a subsequent query/question that is related to the another person’s question/query, recall the “intermediate results”, and think of/write-down a new “final response” based on the recalled results.
As per Claim 7 (and similarly claim 22):
Mental process – A human can mentally assign a score to each result, think of a set of weighted results, and think of/write-down a final response based on the weighted results.
As per Claim 8 (and similarly claim 23):
Mental process – A human can mentally think about what he/she and the another person talked about in the past and think of a natural language answer that is based on what he/she and the another person talked about in the past.
As per Claim 9:
The claim(s) recite
…adaptive context partitioning in a retrieval-augmented generation process,… (mental process, a human could take context data [a plurality of documents with information written in them] and divide it into pieces/partitions as part of thinking of an answer based on documents retrieved by the human)
receive a query from a user; (mental process, a human could listen to another person’s question/query)
retrieve a plurality of relevant documents that form a combined context; (mental process, a human could look for and retrieve documents among a large quantity of documents that are related to the question/query that he/she heard from the another person, and then consider the retrieved documents to be something called a combined context)
monitor available system resources; (mental process, a human could think about how much brain power he/she has to perform tasks and think about how much memory he/she has available to memorize additional information)
dynamically adjust a partition size for context partitions based on the available system resources; (mental process, a human can think about how large the pieces/partitions of the documents/context should be based on the determined brain power/memory-space)
partition the combined context into a plurality of context partitions according to the partition size; (mental process, a human could divide the documents into pieces based on how large he/she thinks the pieces/partitions should be)
implement a map phase operable to generate a plurality of intermediate analysis results, wherein each context partition is processed in parallel; (mental process, a human could enlist the aid of other humans such that each human [“in parallel” with the other humans] writes down a result based on analyzing a respective assigned one of the context partitions)
implement a reduce phase operable to synthesize the plurality of intermediate analysis results into a coherent response; (mental process, the human who enlisted the aid of the other humans could gather the written results, read them and think of or write down a natural language response based on the content of the written results)
and transmit the coherent response to the user (mental process, the human who enlisted the aid of the other humans could verbally/in-writing communicate the natural language response to the another person).
This judicial exception is not integrated into a practical application because
The remaining elements/limitations of the claim are the underlined portions in the next paragraph.
A system for adaptive context partitioning in a retrieval-augmented generation process, comprising: a processor; a communication device positioned in communication with the processor and configured to send and receive transmissions across a digital network; and a non-transitory computer-readable storage medium having stored thereon software that, when executed by the processor, is operable to: receive a query from a user; retrieve a plurality of relevant documents that form a combined context; monitor available system resources; dynamically adjust a partition size for context partitions based on the available system resources; partition the combined context into a plurality of context partitions according to the partition size; implement a map phase operable to generate a plurality of intermediate analysis results, wherein each context partition is processed in parallel; implement a reduce phase operable to synthesize the plurality of intermediate analysis results into a coherent response; and transmit the coherent response to the user.
The remaining elements/limitations only require a programmed generic computer to perform the human-implementable functions discussed above (for relevant MPEP citations, see 101 rejection of claims 1 and 16, above)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The remaining elements/limitations only require a programmed generic computer to perform the human-implementable functions discussed above.
As per Claim 10:
Mental process without integrating into a practical application or amounting to significantly more – A human can perform a mental analysis used to divide the documents/context a second time using results of the mental analysis he/she performed to divide the documents/context a first time, can think of whether he/she remembers similar queries before dividing the documents/context, and could think of a coherent response while bearing in mind constraints/”guardrails” that define characteristics that he/she thinks that the coherent response should have. Lines 1-2 of claim 10 are directed to generic computer implementation.
As per Claim 11:
Mental process without integrating into a practical application or amounting to significantly more – A human could enlist the aid of other humans and distribute the context partitions among the other humans so that each enlisted human has a respective assigned partition and analyzes the respective assigned partition, and the human who enlisted the other humans can rearrange and sort the intermediate analysis results before performing the thinking involved in “the reduce phase”. “Mapper instances” involve generic computer implementation of each enlisted person’s thinking/analysis of the respective assigned partition.
As per Claim 12:
Mental process – A human could decide to divide the documents into bigger pieces when he/she thinks that he/she has more than enough brain power, and can decide to divide the documents into smaller pieces when he/she thinks that he/she does not have enough brain power.
As per Claim 13:
Mental process without integrating into a practical application or amounting to significantly more - A human can mentally assign a score to each result, think of a set of weighted results, and think of/write-down a coherent response based on the weighted results. Lines 1-2 of claim 13 are directed to generic computer implementation.
As per Claim 14:
Mental process - A human can mentally think about what he/she and the another person talked about in the past and think of a natural language answer that is based on what he/she and the another person talked about in the past.
As per Claim 15:
Mental process without integrating into a practical application or amounting to significantly more - A human could read the contents of the documents and make sure to break the documents into pieces/partitions in a way that the pieces/partitions contains at least one complete document or complete paragraphs/sentences/segments. “wherein the software is further operable, when executed by the processor” in lines 1-2 of claim 15 is directed to generic computer implementation.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
As per Claim(s) 1 (and similarly claim[s] 16, and consequently claim[s] 2-8 and 17-23 which depend on claim[s] 1 and 16), the prior art of record does not teach or suggest the combination of all limitations in claim(s) 1, including (i.e. in combination with the remaining limitations in claim[s] 1) A method of adaptive context partitioning in a retrieval-augmented generation system, comprising: receiving a query from a user; forming a combined context by retrieving a plurality of relevant documents from at least one document database based on the query; determining one or more characteristics of the combined context; monitoring current system resources including processor availability and memory utilization; dynamically determining a partition size based on the current system resources and the one or more characteristics of the combined context; partitioning the combined context into a plurality of context partitions according to the partition size while preserving document boundaries to maintain semantic coherence; generating a plurality of intermediate analysis results by processing each context partition of the plurality of context partitions using a mapper prompt through one or more large language models (LLMs); generating a final response by processing the plurality of intermediate analysis results using a reducer prompt through the one or more LLMs; and transmitting the final response to the user.
As per Claim(s) 9 (and consequently claim[s] 10-15 which depend on claim[s] 9), the prior art of record does not teach or suggest the combination of all limitations in claim(s) 9, including (i.e. in combination with the remaining limitations in claim[s] 9) A system for adaptive context partitioning in a retrieval-augmented generation process, comprising: a processor; a communication device positioned in communication with the processor and configured to send and receive transmissions across a digital network; and a non-transitory computer-readable storage medium having stored thereon software that, when executed by the processor, is operable to: receive a query from a user; retrieve a plurality of relevant documents that form a combined context; monitor available system resources; dynamically adjust a partition size for context partitions based on the available system resources; partition the combined context into a plurality of context partitions according to the partition size; implement a map phase operable to generate a plurality of intermediate analysis results, wherein each context partition is processed in parallel; implement a reduce phase operable to synthesize the plurality of intermediate analysis results into a coherent response; and transmit the coherent response to the user
CN118839021 teaches “Firstly, each text document in a mass of text documents is preprocessed to be divided into a plurality of text blocks, so that a Large Language Model (LLMs) can be processed more effectively, performance bottlenecks or memory limitations possibly encountered by LLMs when a large amount of text is processed are avoided, wherein the size of the text blocks is usually set according to specific conditions, and hundreds to thousands of tokens are generally contained in each text block, so that the capacity of LLMs can be fully utilized, and processing efficiency is not lowered due to overlong text” and “A text recognition unit for extracting relevant text blocks from text documents relevant to the recognized entities to generate a final response to the user query” and “an information fusion and generation unit for fusing the generated final response using LLMs to generate an answer”. This reference seems to suggest where the division of documents into text blocks determines the size of text blocks based on processing limitations (performance bottlenecks and memory limitations “are avoided”), but does not specifically describe where the text block size is set based on “available system resources” that are “monitored” (as opposed to where the block size is set in a way that the text blocks definitely do not cause processing issues regardless of how many system resources are available).
12405985 teaches “the context identifying one or more documents to be searched using the search text, and to generate a plurality of document chunks by parsing the one or more documents” and “In Stage 1, questions are generated from each chunk. This includes the following steps: 1. Collect a corpus of documents (e.g., technical documents, such as product guides, manuals, etc.). 2. Break the corpus of documents into N chunks of size s. While breaking documents into chunks, it is ensured that a sentence is not left incomplete. Thus, each chunk is not necessarily exactly equal in size. 3. Utilize an LLM (e.g., an open source LLM) to generate questions from each chunk as a context. The LLM is prompted in a way that the questions generated for each chunk are distinct and exhaustive in nature. 4. Across all chunks of total count N, there is a questions_list generated for each one, denoted as questions_list (1), questions_list (2), . . . , questions_list (N). It should be noted that the number of questions in each list need not be the same; different numbers of questions may be generated for different ones of the chunks”. This reference appears to describe breaking documents into chunks of a particular size and ensuring that incomplete sentences/”segments” are not in the chunks, but does not appear to describe where the chunk sizes are based on monitored available system resources.
12505148 teaches “extracting grounding data from the subset of identified relevant documents; generate a answer synthesis prompt including the grounding data and the input query; provide the answer synthesis prompt as input to the generative AI model; receive” and “the system extracts grounding data from the identified relevant documents to generate an answer synthesis prompt for the generative AI model” and “At operation 502, a cutoff prompt is generated to determine the subset of relevant documents to use to respond to an input query” and “Grounding generator 204 generates grounding data from relevant documents (e.g., search results 207) by either extracting sections from relevant documents to form grounding or by synthesizing the summary generated by summary generator 214 to form grounding. Grounding generator 204 may extract all the content that is considered related by the search engine 202. This extraction may include extracting content from documents identified by the search query 205 with a high relevancy score for input query 201”. This reference does not appear to describe where the sections from relevant documents have a size determined by monitored available system resources.
12361038 teaches “wherein the generating the final response further comprises generating the final response including graded results of the first reduced number of documents and the second reduced number of documents” and “One or more top ranked documents included in the third answer, i.e., the second number of documents among the third answer, are provided as citations or document responses in a final response (Step 212)” and “generating a final response including the second answer and one or more documents among the third answer.” This reference does not appear to describe where portions from documents have a size determined by monitored available system resources.
12033618 teaches “determining first memory size representing an amount of memory needed to store the second context embedding data; based at least in part on the first memory size and memory available in the context storage” (claim 2). Context data, in this reference, does not appear to refer to documents, and determining a memory size does not appear to determine a size to be used to partition context documents.
7809697 teaches “Transcoding may also include breaking the document into chunks that are of a reasonable size to transmit to the mobile device 110 over a network (described in greater detail below) or that are of an appropriate size relative to available memory that is included in the mobile device 110”.
Double Patenting
For clarity of the record, NO Double Patenting rejections are required between the claims of this application and the claims of Parent Patent 1 (US 12,405,979) because the claims of Parent Patent 1 do not teach where partition size is determined based on determined characteristic(s) of the combined context (new matter in claims 1 and 16) and partitioning the combined context into a plurality of context partitions according to the dynamically adjusted partition size which is adjusted based on the available system resources (in claim 9; claim 9 of Parent Patent 1 teaches dynamically adjusting a size of the context partitions based on available system resources but this does not describe where the context partitions were partitioned based on the adjusted size [as opposed to where the context partitions are re-sized after being generated by the partitioning])
Conclusion
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EY 1/14/2026
/ERIC YEN/Primary Examiner, Art Unit 2658