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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 27 May 2026 has been entered.
Information Disclosure Statement
An Information Disclosure Statement (IDS) has not been submitted as of the mailing of the last Office Action dated 9 April 2026. Applicant is reminded of the continuing obligation under 37 CFR 1.56 to timely apprise the Office of any information which is material to patentability of the claims under consideration in this application.
Introductory Remarks
In response to communications filed on 27 May 2026, claims 1, 3-6, 10-11, 13, 15-16, and 19-20 are amended per Applicant's request. Claims 2 and 14 are cancelled. No claims were withdrawn. No new claims were added. Therefore, claims 1, 3-13, and 15-20 are presently pending in the application, of which claims 1, 13, and 20 are presented in independent form.
The previously raised 112 rejections of claims 10-11 and 19 are maintained.
The previously raised 101 rejection of the pending claims is maintained.
The previously raised prior art rejection of the pending claims is withdrawn in view of the amendments to the claims. A new ground(s) of rejection has been issued.
Response to Arguments
Applicant’s arguments filed 27 May 2026 with respect to the rejection of the claims under 35 U.S.C. 112 (see Remarks, p. 7-8) have been fully considered but are not persuasive. The claim language does not reflect the interpretation of the Specification provided by Applicant. See the 112 rejections below for further detail.
Applicant’s arguments filed 27 May 2026with respect to the rejection of the claims under 35 U.S.C. 101 (see Remarks, p. 8-10) have been fully considered but are not persuasive.
Applicant’s arguments filed 27 May 2026with respect to the rejection of the claims under 35 U.S.C. 102/103 (see Remarks, p. 10-14) have been fully considered.
With respect to the independent claims (Remarks, p. 11-12), Applicant’s arguments are moot because the arguments do not apply to the new reference being used in the current rejection to reject the claimed feature concerning the multiple collections of a vector database, which Applicant’s arguments were based on.
With respect to claims 3, 5, and 15 (Remarks, p. 12), and claims 4, 6-8, and 16-17 (see Remarks, p. 13), Applicant’s arguments are not persuasive. Applicant states that “the collection type directly determines how similarity correction is applied and how retrieval is configured, it is functionally involved in the downstream retrieval and correction steps” (see Remarks, p. 12).
However, notably, there are no changes in the manner in which the downstream and correction steps are accomplished. Rather, they all utilize the same exact functions/logic/steps, regardless of the type of information within the collection. The claims, therefore, are written in such a manner that the steps operate regardless of the type of information involved, rather than as a result of the type of information involved. The latter may possibly be accomplished if the steps specifically claimed using a different set of logic, steps, applications, etc., as a result of different information being operated on. However, as seen, the claimed steps do not change from one information type to another; rather, it is the same steps that are being claimed for all information types. Applicant may be improperly importing limitations from the Specification into the claims, as Applicant’s arguments rest on a greater significance to the different types of information than what is actually being claimed.
Thus, there is no functional difference by which the steps are being applied. In other words, there is no change in the underlying function of the computer, as the same logic of performing similarity correction and retrieval utilize exactly the same steps, regardless of the type of information.
Therefore, as Applicant’s claims do not reflect any different logic/steps being performed within the context of different collections, Applicant’s arguments are not persuasive.
Applicant’s argument with respect to claims 9 and 18 (see Remarks, p. 13-14) and claims 10-11 and 19 (see Remarks, p. 14) are moot.
More specifically, Applicant argues that Qin does not have “per-collection retrieval” (see Remarks, p. 13). This is moot, as Qin, in combination with Godbole, was cited to disclose this feature.
Applicant additionally argues that Balasubramanian does not disclose “per-collection similarity scores” are corrected, and a PHOSITA would not have found it obvious to transpose this to Qin’s vector retrieval system as Qin does not disclose a “per-collection structurer onto which per-collection weights could meaningfully be applied”. Again, this is moot, as Qin, in combination with Godbole, was cited to disclose this feature.
Claim Objections
Claims 1, 13, and 20 are objected to because of the following informalities: the claims recite “the” user query, which should be “a” user query. Appropriate correction is required.
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 10-11 and 19 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.
Claims 10 and 19 recite “generating the prompt by combining text information of the user query, text information of the retrieved information, a retrieval result comprising the corrected similarity for each collection, and a predetermined system prompt”. There is no support for a separate retrieval result comprising the corrected similarity for each collection. See, e.g., Specification, [FIG. 9], where the prompt generation unit 250 receives retrieval results comprising text information and correction distance for each of the three collections. Thus, the text information of the retrieved information is part of the claimed retrieval result, not separate as is claimed.
Dependent claim 11 is rejected for at least by virtue of its dependency on claim 10, and for failing to cure the deficiencies of claim 10.
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 10-11 and 19 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.
Claims 10 and 19 recite “generating the prompt by combining text information of the user query, text information of the retrieved information, a retrieval result comprising the corrected similarity for each collection, and a predetermined system prompt”. Specification, [FIG. 9], shows the prompt generation unit 250 receives retrieval results comprising text information and correction distance for each of the three collections. Thus, the text information of the retrieved information is part of the claimed retrieval result, not separate as is claimed.
Therefore, it appears to be contradictory to that which is described in the Specification, i.e., appearing to imply that the retrieval result is separate from the text information.
For purposes of examination, the interpretation that various information that was retrieved, e.g., that the text information is part of the retrieval result, as is the corrected similarity, has been taken.
Dependent claim 11 is rejected for at least by virtue of its dependency on claim 10, and for failing to cure the deficiencies of claim 10
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, 3-13, and 15-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent Claims 1, 13, and 20 recite performing a vector similarity operation between user query information of the user query and information vectors. This encompasses a mathematical formula or equation, and mathematical calculations, which falls under the “Mathematical Concepts” grouping of abstract ideas. The claims further recite generating a prompt to be input to a large language model (LLM) using certain information. This encompasses an evaluation, observation, and/or judgment, which falls under the “Mental Processes” grouping of abstract ideas. Furthermore, the independent claims recite retrieving information relating to a user query, and using that retrieved information when generating the prompt. These encompass managing personal behavior, which falls under the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.1
Dependent Claims 4, 6, and 16 recite performing the vector similarity operation between user query information of the user query and collection information using a similarity measurement (method) and retrieving information relating to the user query based on the measured similarity. This encompasses an evaluation, observation, and/or judgment which falls under the “Mental Processes” grouping of abstract ideas, “Mathematical Concepts” (e.g., the similarity measurement method), as well as managing personal behavior, e.g., filtering data, which falls under the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Dependent Claims 9 and 18 recite correcting a similarity of the retrieved information based on a weight for each collection. This encompasses an evaluation, observation, and/or judgment, which falls under the “Mental Processes” grouping of abstract ideas, as well as “Mathematical Concepts”, e.g., a similarity is “corrected”, i.e., adjusted, based on a weight for each collection.
Dependent Claims 10 and 19 recite generating the prompt by combining various information. This encompasses an evaluation, observation, and/or judgment, which falls under the “Mental Processes” grouping of abstract ideas.
With the exception of limitations reciting the use of a computing system and various hardware components, nothing in the claims preclude the claimed steps from being practically performed in the mind. If a claim limitation covers performance of the limitation in the mind but for the recitation of generic computer components, then such claims still fall within the “mental processes” grouping of abstract ideas. Additionally, other limitations recite “certain methods of organizing human activity” but for the recitation of such computing elements as described. Accordingly, the claims recite an abstract idea.
The claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed computing components, e.g., database, algorithm, server, processor, memory, non-transitory computer readable medium, etc., are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Thus, even with the claims’ attempts to narrow the claimed computing components to, e.g., a large language model (LLM) (independent claims 1, 13, and 20), multiple collections of a “vector” database (independent claims 1, 13, and 20, and dependent claims 3, 5, and 15), a “similarity measurement” algorithm (dependent claims 4, 6-7, and 16-17) that is one of a Euclidean distance algorithm, a cosine similarity algorithm, and a dot product algorithm (dependent claims 7 and 17), and a “response” server (independent claim 13), do nothing more than provide a context rather than a particular manner of achieving the result, i.e., insignificant field-of-use limitations. As a matter of law, narrowing or reformulating an abstract idea does not add “significantly” more to it.2
In a similar vein, the claimed limitations attempting to limit the claims to retrieving information from each of the collections in the vector database (independent claims 1, 13, and 20), narrowing the type of information stored within and retrieved from the vector database, e.g., that the vector database comprises multiple collections storing different types of information vectors (independent claims 1, 13, and 20), that the vector database and retrieved information relates to one or more of a general information vector, a second collection storing an FAQ/Q&A information vector, and a third collection storing a refined information vector (dependent claims 3-6, 8, and 15-16), and that the weight for each collection is pre-configured (dependent claims 9 and 18), that the prompt is generated by combining various types of information (dependent claims 10 and 19), and that the predetermined system prompt comprises instructions configured to cause the LLM to generate a response in consideration of the corrected similarity (dependent claim 11), are all insignificant field-of-use limitations, describing the context rather than a particular manner of achieving the result.
The claims variously recite insignificant extra-solution activities, including collecting/retrieving information (independent claims 1, 13, and 20, and dependent claims 4, 6, 9, 16, and 18), and acquiring a response to the user query (from the LLM) (independent claims 1, 13, and 20 and dependent claim 12). Dependent claim 12 further recites insignificant field-of-use limitations, e.g., that it is a user terminal originating the user query that is provided with the acquired response.
Accordingly, the claims are not integrated into a practical application of the idea.
The claims do not recite additional elements that amount to significantly more than the judicial exception.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of various computing hardware components, which 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.
Furthermore, the claimed limitations regarding retrieving information, receiving it for generating a prompt, and acquiring a response, are all well-understood, routine, and conventional activities of computers. See, e.g., MPEP § 2106.05(d)(II) (“Receiving or transmitting data over a network”; “Storing and retrieving data from memory”; and “Electronic recordkeeping”).
Lastly, the concept of utilizing vector similarity operations/comparisons for retrieving information is also well-understood, routine, and conventional. See, e.g., Qin et al. (US 2024/0346256 A1) at [0051]; Godbole et al. (US 12,450,217 B1) at [7:51-67]-[8:1-54]); Kar (US 2025/0190802 A1) at [0053], [0058-0066], and [0080-0081]; McCartney, Jr. (US 12,585,658 B1) at [6:60-67]; and Pfadler et al. (WO 2025/019000 A1) at [0046] and [0063-0064].
Even when considered as an ordered combination, the claimed elements do not add anything that is not already present when the steps are considered separately. In other words, even when considering the claimed invention as a whole, i.e., as an ordered combination, the additional elements add nothing that would move the claims outside the realm of abstract ideas. The claims, as a whole, do not state how—by what particular process or structure—the claimed steps are performed (other than contextualizing the claims to the type of information or algorithm/calculation that is involved). Instead, the claimed steps are recited in a purely functional manner, rather than representing a concrete embodiment of the claimed steps.
See, e.g., Affinity Labs of Texas LLC v. DirecTV., 838 F.3d 1266 (Fed. Cir. 2016) at p. 7-8 (“At that level of generality, the claims do no more than describe a desired function or outcome, without providing any limiting detail that confines the claim to a particular solution to an identified problem. The purely functional nature of the claim confirms that it is directed to an abstract idea, not to a concrete embodiment of that idea”); and Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016), slip op. 12 (“[The] essentially result-focused, functional character of claim language has been a frequent feature of claims held ineligible under § 101”).
Thus, despite the claims’ attempt to narrow the claims to particular types of information or algorithms, such limitations do not move the claims outside the realm of abstract ideas, as the claims are recited at such a high level of generality that they fall within certain methods of organizing human activity and mental processes.
At this level of generality, the claims do no more than describe a desired function or outcome, and without providing any limiting detail that confines the claims to a particular solution to an identified problem. The purely functional nature of the claims confirm that they are directed to an abstract idea, not to a concrete embodiment of the idea. A desired goal (i.e., result or effect), absent of structural or procedural means for achieving that goal, is an abstract idea. In this case, the claims are directed to an abstract idea for failing to describe how—by what particular process or structure—the goal is accomplished. Even with the additional elements, the claimed limitations fail to restrict how the goal is accomplished.
Thus, for at least the aforementioned reasons, the claims are rejected under 35 U.S.C. 101 for being directed to a judicial exception (i.e., an abstract idea) without significantly more.
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.
Claims 1, 3-8, 12-13, 15-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Qin et al. (“Qin”) (US 2024/0346256 A1), in view of Godbole et al. (“Godbole”) (US 12,450,217 B1).
Regarding claim 1: Qin teaches A processor-implemented method, the method comprising:
performing, for [datasets] storing different types of information vectors, a vector similarity operation between user query information of the user query and information vectors stored in the corresponding collection; retrieving, from [the datasets], information related to the user query based on the vector similarity operation for the corresponding collection (Qin, [0041-0045], where a first feature vector 226 representing the meaning of query text string 220 is generated. A second feature vector that was generated for each piece of augmentation information in dataset(s) 112 are stored as second feature vectors. Comparator 208 receives the first and second feature vectors and compares them to determine the similarity between the first feature vector 226 and second feature vectors 228. Retriever 210 determines and retrieves pieces of augmentation information from dataset(s) 112 that correspond to the second feature vectors having a cosine similarity to the first feature vector that satisfies a first predetermined relationship with a first predetermined threshold.
See also Qin, [0036], where dataset(s) 112 may include one or more databases storing augmentation information, where augmentation information stored in dataset(s) 112 may include various information, e.g., domain-specific information, entity-specific information, product-specific information, etc. See Qin, [0041], where a second feature vector 224 is generated for each piece of augmentation information in dataset(s) 112 and stored as second feature vectors 206 for future use, where “future use” includes retrieval operations corresponding to a query 216, as disclosed in Qin, [0045], described in further detail below);
generating a prompt to be input into a large language model (LLM) based on the information retrieved from the [datasets] (Qin, [0045-0046], where retriever 210 may provide augmentation information 232 to prompt generator 212, where prompt generator 212 generates a prompt for LLM 214 based on one or more of query 216, first feature vector 226, one or more of second feature vectors 228, indications 230, and/or augmentation information 232); and
acquiring a response to the user query from the LLM using the generated prompt (Qin, [0053-0054], where an augmented prompt is provided to a large language model. The LLM 214 processes augmented prompt 236 to generate a response 238, and a response generated by the large language model is received).
Qin does not appear to explicitly teach that a vector similarity operation is performed and information related to the user query is retrieved for each of multiple collections of a vector database.
Godbole teaches that a vector similarity operation is performed and information related to the user query is retrieved for each of multiple collections of a vector database (Godbole, [6:38-64], where content within a first set of electronic files may be converted into a first set of vector embeddings and stored in a first vector database. This is repeated for a second/third set of electronic files, which are converted into a second/third set of vector embeddings and stored in a second/third vector database. Each of these vector databases makes up the claimed “collection”. See Godbole, [7:51-67]-[8:1-54], where queries are converted into vector embeddings and compared to vector embeddings stored in vector database(s) 135, including each of the three disclosed vector databases (see also, e.g., Godbole, [14:24-50]), using cosine similarity. These comparisons are used to select sets or subsets of vector embeddings, which are then provided, e.g., as context for a particular prompt, to a particular large language model 133).
Although Godbole discloses multiple vector databases, one of ordinary skill in the art would have found it obvious to have modified Godbole such that all of these constitute, e.g., logical separations of a single vector database (thus corresponding to multiple collections of “a” vector database as claimed), with the motivation of minimizing operational complexity, e.g., shared query syntax, etc.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Qin and Godbole (hereinafter “Qin as modified”) by utilizing multiple collections of a vector database with the motivation of facilitating efficient similarity searches and increasing the number of databases queried, which increases the likelihood that the system generates an augmented output sequence that is factually accurate as a result of performing the disclosed process making use of each of the plurality of databases3.
Regarding claim 3: Qin teaches The method of claim 1, wherein the multiple collections comprises a first collection storing a general information vector, a second collection storing an FAQ/Q&A information vector, and a third collection storing a refined information vector (Godbole, [6:38-64], where content within a first set of electronic files may be converted into a first set of vector embeddings and stored in a first vector database. This is repeated for a second/third set of electronic files, which are converted into a second/third set of vector embeddings and stored in a second/third vector database. Each of these vector databases makes up the claimed “collection”).
Although Qin as modified does not appear to explicitly state that the type of information relates to general information, FAQ/Q&A information, and refined information as claimed, the claimed invention does not distinguish over the prior art because the differences in the claim limitations and the prior art’s disclosure are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The retrieval of the data from each of the datasets would have been performed the same regardless of the specific data involved (i.e., general information and refined information as claimed, or some other data). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 1385, 217 USPQ2d 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art to have referred to Qin as modified’s teachings in making the claimed invention, because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention over the prior art.
Regarding claim 4: Qin teaches The method of claim 3, wherein the retrieving comprises:
performing the vector similarity operation between user query information of the user query and information vectors stored in the first to third collections using a predetermined similarity measurement algorithm; and retrieving general information, FAQ/Q&A information, and refined information related to the user query based on the vector similarity operation (Godbole, [7:51-67]-[8:1-62] and Godbole, [14:24-50], where queries are converted into vector embeddings and compared to vector embeddings stored in vector database(s) 135, including each of the three disclosed vector databases, using cosine similarity (i.e., “predetermined similarity measurement algorithm”). These comparisons are used to select sets or subsets of vector embeddings, which are then provided, e.g., as context for a particular prompt, to a particular large language model 133, which is then used to retrieve information from each of the vector databases as, e.g., first, second, and third replies (i.e., “retrieving [information from each of the three collections]”). See Godbole in claim 3 above with respect to the information pertaining to “general information”, “FAQ/Q&A information”, and “refined information”).
Regarding claim 5: Qin teaches The method of claim 1, wherein the multiple collections comprises a first collection storing a general information vector and a second collection storing a refined information vector (Godbole, [6:38-64], where content within a first set of electronic files may be converted into a first set of vector embeddings and stored in a first vector database. This is repeated for a second/third set of electronic files, which are converted into a second set of vector embeddings and stored in a second vector database. Each of these vector databases makes up the claimed “collection”).
Although Qin as modified does not appear to explicitly state that the type of information relates to general information and refined information as claimed, the claimed invention does not distinguish over the prior art because the differences in the claim limitations and the prior art’s disclosure are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The retrieval of the data from each of the datasets would have been performed the same regardless of the specific data involved (i.e., general information and refined information as claimed, or some other data). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 1385, 217 USPQ2d 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art to have referred to Qin as modified’s teachings in making the claimed invention, because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention over the prior art.
Regarding claim 6: Qin teaches The method of claim 5, wherein the retrieving comprises:
performing the vector similarity operation between user information of the user query and information vectors stored in the first and second collections using a predetermined similarity measurement algorithm; and retrieving general information and refined information related to the user query based on the vector similarity operation (Godbole, [7:51-67]-[8:1-62] and Godbole, [14:24-50], where queries are converted into vector embeddings and compared to vector embeddings stored in vector database(s) 135, including each of the disclosed vector databases, using cosine similarity (i.e., “predetermined similarity measurement algorithm”). These comparisons are used to select sets or subsets of vector embeddings, which are then provided, e.g., as context for a particular prompt, to a particular large language model 133, which is then used to retrieve information from each of the vector databases as, e.g., first and second replies (i.e., “retrieving [information from each of the two collections]”). See, e.g., Godbole, [3:7-28], where there is a set of one or more electronic documents 123 (i.e., resulting in one or more corresponding vector databases; thus, Godbole discloses that there may be two sets of electronic documents, thus two vector databases, or “collections”, as claimed). See Godbole in claim 3 above with respect to the information pertaining to “general information” and “refined information”).
Regarding claim 7: Qin teaches The method of claim 4, wherein the similarity measurement algorithm is one of a Euclidean distance algorithm, a cosine similarity algorithm, and a dot product algorithm (Godbole, [7:51-67]-[8:1-62] and Godbole, [14:24-50], where queries are converted into vector embeddings and compared to vector embeddings stored in vector database(s) 135, including each of the three disclosed vector databases, using cosine similarity. See also Qin, [0045], where cosine similarity is used to compare similarities between the first feature vector representing the query and the second feature vectors).
Regarding claim 8: Qin teaches The method of claim 1, wherein the retrieved information comprises one or more of general information and one or more of refined information (Godbole, [7:51-67]-[8:1-62] and Godbole, [14:24-50], where queries are converted into vector embeddings and compared to vector embeddings stored in vector database(s) 135, including each of the disclosed vector databases, using cosine similarity (i.e., “predetermined similarity measurement algorithm”). These comparisons are used to select sets or subsets of vector embeddings, which are then provided, e.g., as context for a particular prompt, to a particular large language model 133, which is then used to retrieve information from each of the vector databases as, e.g., first and second replies (i.e., “retrieving [information from each of the two collections]”). See, e.g., Godbole, [3:7-28], where there is a set of one or more electronic documents 123 (i.e., resulting in one or more corresponding vector databases; thus, Godbole discloses that there may be two sets of electronic documents, thus two vector databases, or “collections”, as claimed)).
Although Qin as modified does not appear to explicitly state that the type of information relates to general information and refined information as claimed, the claimed invention does not distinguish over the prior art because the differences in the claim limitations and the prior art’s disclosure are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The retrieval of the data from each of the datasets would have been performed the same regardless of the specific data involved (i.e., general information and refined information as claimed, or some other data). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 1385, 217 USPQ2d 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art to have referred to Qin as modified’s teachings in making the claimed invention, because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention over the prior art.
Regarding claim 12: Qin teaches The method of claim 1, further comprising providing the acquired response to a user terminal originating the user query (Qin, [0094], where the system provides the response generated by the large language model (based on domain-specific information contained in the retrieved pieces of augmentation information) to the user through the user interface, the user interface being displayed on a display 854 of computing device 802 (Qin, [0078]). See also Qin, [0030], where each of clients 104 may include a GUI 114, and are connected to servers 102 that include a response generator 110).
Regarding claim 13: Claim 13 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Qin teaches A response server, the server comprising: processors configured to execute instructions; and a memory storing the instructions, wherein execution of the instructions configures the processors to [implement the claimed steps] (Qin, [0030], where the disclosed system generates a response using a retrieval augmented LLM, the system including one or more servers 102 that includes a response generator 110. See Qin, [0070-0073] and [0076], where computing device 802 for implementing the disclosed steps may include a processor 810 that executes program code (instructions) stored in a computer readable medium, e.g., storage 820).
Regarding claim 15: Claim 15 recites substantially the same claim limitations as claim 5, and is rejected for the same reasons.
Regarding claim 16: Claim 16 recites substantially the same claim limitations as claim 6, and is rejected for the same reasons.
Regarding claim 17: Claim 17 recites substantially the same claim limitations as claim 7, and is rejected for the same reasons.
Regarding claim 18: Claim 18 recites substantially the same claim limitations as claim 9, and is rejected for the same reasons.
Regarding claim 20: Claim 20 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Qin teaches A computer-readable storage medium storing one or more programs for execution by one or more processors of a computing device, the one or more programs comprising instructions for [implementing the claimed steps] (Qin, [0030], where the disclosed system generates a response using a retrieval augmented LLM, the system including one or more servers 102 that includes a response generator 110. See Qin, [0070-0073] and [0076], where computing device 802 for implementing the disclosed steps may include a processor 810 that executes program code (instructions) stored in a computer readable medium, e.g., storage 820).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Qin et al. (“Qin”) (US 2024/0346256 A1), in view of Godbole et al. (“Godbole”) (US 12,450,217 B1), in further view of Balasubramanian et al. (“Balasubramanian”) (US 2022/0269735 A1).
Regarding claim 9: Qin teaches The method of claim 8, but does not appear to explicitly teach further comprising: correcting a similarity of the retrieved information, based on a pre-configured weight for each collection.
Balasubramanian teaches correcting a similarity of the retrieved information, based on a pre-configured weight for each collection (Balasubramanian, [0070], where a semantic/keyword search score by multiplying various factors together, including the weight of the source system (i.e., “each collection”), and the similarity score of the match against each record in a data source stored in the file storage 114 (i.e., “similarity of the retrieved information”), i.e., the multiplying being a form of “correcting a similarity of the retrieved information”, as both pertain to adjusting another initial score (i.e., Balasubramanian’s “similarity score of the match”). See also, e.g., Balasubramanian, [0067], where weights were assigned to source systems at step 602 ranked in a previous step 601 (i.e., “pre-configured weight for each collection”). See Godbole in claim 1 above with respect to collections being of a vector database).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Qin as modified and Balasubramanian (hereinafter “Qin as modified”) with the motivation of improving search relevancy and accuracy.
Regarding claim 19: Claim 19 recites substantially the same claim limitations as claim 10, and is rejected for the same reasons.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Qin et al. (“Qin”) (US 2024/0346256 A1), in view of Godbole et al. (“Godbole”) (US 12,450,217 B1), in further view of Balasubramanian et al. (“Balasubramanian”) (US 2022/0269735 A1), in further view of Nagaraju et al. (“Nagaraju”) (US 2024/0185001 A1).
Regarding claim 10: Qin teaches The method of claim 9, wherein the generating of the prompt comprises:
generating the prompt by combining text information of the user query, text information of the retrieved information, [and] a retrieval result comprising the corrected similarity for each collection … (Qin, [0045-0046], where retriever 210 may provide augmentation information 232 to prompt generator 212, where prompt generator 212 generates a prompt for LLM 214 based on one or more of query 216 (i.e., “text information of the user query”), first feature vector 226, one or more of second feature vectors 228, indications 230 (i.e., “a retrieval result comprising the … similarity”), and/or augmentation information 232 (i.e., “text information of the retrieved information”). See Qin, [0044], where “indicators” 230 include identifiers of second feature vectors that are most similar to first feature vector 226 along with a corresponding cosine similarity score indicating the similarity to the first feature vector 226 (i.e., “a retrieval result comprising the … similarity”). See Balasubramanian, [0070] above with respect to the similarity being “corrected” for each collection, i.e., adjusted based on a weight corresponding to each collection. See Godbole in claim 1 above with respect to the collections pertaining to a vector database).
Qin as modified does not appear to explicitly teach [generating the prompt by combining information including] a predetermined system prompt.
Nagaraju teaches [generating the prompt by combining information including] a predetermined system prompt (Nagaraju, [0018], where a template query is combined with a sampling of the structured data, the template including one or more examples of input/output pairings that a LLM may be trained to replicate, e.g., the template query including a domain-specific example query and a natural language response to the example query, the template query also including placeholders that may be replaced by the sampled structured data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Qin as modified and Nagaraju (hereinafter “Qin as modified”) with the motivation of improving the speed of generating a prompt and responding to a request, as well as improving responses through the use of templates.4
Regarding claim 11: Qin teaches The method of claim 10, wherein the predetermined system prompt comprises contents requesting to generate a response in consideration of the corrected similarity for each collection (Nagaraju, [0018], where a template query is combined with a sampling of the structured data, the template including one or more examples of input/output pairings that a LLM may be trained to replicate, e.g., the template query including a domain-specific example query and a natural language response to the example query, the template query also including placeholders that may be replaced by the sampled structured data. The resulting generated query prompt generates a target result 174 (i.e., “to generate a response”. See Qin, [0044-0046] above with respect to the response being generated in consideration of various factors, including indications 230 which comprise the similarity score. See Balasubramanian, [0070] above with respect to the similarity being “corrected” for each collection, i.e., adjusted based on a weight corresponding to each collection (i.e., “in consideration of the corrected similarity for each collection”)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See the enclosed 892 form.
McCartney, Jr. (“McCartney”) (US 12,585,658 B1) is cited to show why one of ordinary skill in the art would have found it obvious to have utilized multiple collections for retrieving information to augment a prompt (see, e.g., McCartney, Jr. US 12,585,658 B1 at [14:35-43]).
The prior art should be considered to define the claims over the art of record.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IRENE BAKER whose telephone number is (408)918-7601. The examiner can normally be reached M-F 8-5PM PT.
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/IRENE BAKER/Primary Examiner, Art Unit 2154
27 June 2026
1 These steps are similar to those found to be previously abstract by the courts, e.g., “filtering content” and “considering historical usage information while inputting data”. See MPEP § 2106.04(a)(2)(II)(C).
2 BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281 (Fed. Cir. 2018) at p. 17-18, citing SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018) at slip op. 14 (“What is needed is an inventive concept in the non-abstract application realm. . . . [L]imitation of the claims to a particular field of information . . . does not move the claims out of the realm of abstract ideas”).
3 See, e.g., McCartney, Jr. US 12,585,658 B1 at [14:35-43].
4 See, e.g., Mishra et al. (“Mishra”) (US 2025/0265243 A1) at [0013], [0015], and [0048].
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