DETAILED ACTION
This Non-Final communication is in response to Application No. 18/674,734 filed 5/24/2024 which claims priority from Provisional Application No. 63/566,105 filed 3/15/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 9 and 17 are provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claim 3 and 15 of co-pending Application No. 18/805,401 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 9 and 17 are merely broadening the scope of claims 3 and 15 of the co-pending application.
This is a provisional non-statutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a system for performing simulation operations;”, “a system for performing digital twin operations;”, “a system for performing light transport simulation;”, “a system for performing collaborative content creation for 3D assets;”, “a system for performing deep learning operations;”, “a system for generating or presenting at least one of virtual reality content, mixed reality content, or augmented reality content;”, “a system for performing conversational AI operations;”, and “a system for generating synthetic data” in claim 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 20 is 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.
Regarding claim 20, claim limitations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, however, the corresponding structure for performing the claimed functions for the limitations that are interpreted as means-plus-function, as shown above, does not appear disclosed within the specification. Specifically, the structure of “a system for performing simulation operations;”, “a system for performing digital twin operations;”, “a system for performing light transport simulation;”, “a system for performing collaborative content creation for 3D assets;”, “a system for performing deep learning operations;”, “a system for generating or presenting at least one of virtual reality content, mixed reality content, or augmented reality content;”, “a system for performing conversational AI operations;”, and “a system for generating synthetic data” has not been found in the specification (See specification at [0040], [0064]). Therefore, claim 20 fails the written description requirement of 35 U.S.C. §112(a).
Claim Rejections - 35 USC § 112
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.
Claim 20 is 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.
Regarding claim 20, the claim limitations of “a system for performing simulation operations;”, “a system for performing digital twin operations;”, “a system for performing light transport simulation;”, “a system for performing collaborative content creation for 3D assets;”, “a system for performing deep learning operations;”, “a system for generating or presenting at least one of virtual reality content, mixed reality content, or augmented reality content;”, “a system for performing conversational AI operations;”, and “a system for generating synthetic data” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the function of the referenced systems in the claims (See specification at [0040], [0064]). Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-9, 13-17, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bell et al. (US 2024/0404687 A1, which claims priority to at least Provisional Application No. 63/515,532 filed 7/25/2023, citations are to the provisional).
Regarding claim 1, Bell teaches a method comprising:
providing, using a processing device executing an application programming interface (API), visual representations of a plurality of document processing pipelines (DPPs) for presentation within a user interface; receiving, via the API, from the user interface, a selection of a DPP from the plurality of DPPs. More specifically, Figures 9A-9C show a flow including a path for generating embeddings from documents (e.g., PDF documents), which includes segmenting/chunking documents and generating embedding for the chucks (Bell, [0087]). Agents/models (pipelines) are built/selected via a user interface that include certain configuration and document indexes/tools with parameters and interface with an API (Bell, [0006], [0031], [0059]-[0063], [0065], [0066], [0068] [00107], [00109]). The parameters can include the designation of a chunking strategy/algorithm, embedding model, chunk size, chunk overlap, etc. (Bell, [0066], [0082]-[0083]).
segmenting, according to predetermined settings of the selected DPP, an input document into a plurality of segments. More specifically, Figures 9A and 9B depict process flows that include a splitting/chunking step for segmenting an input document according to the agent/model configuration/document index (Bell, Figures 9A and 9B, [0066], [0082]-[0084], [0087]).
causing an embeddings model to process the plurality of segments to generate a plurality of embeddings; and causing the plurality of embeddings to be stored in a data store. More specifically, Figures 9A and 9B depict process flows that include a generating embeddings/vectorization step for each chunk of the input document and storing the embeddings and chunks/segments in a database according to the agent/model configuration/document index (Bell, Figures 9A and 9B, [0066], [0082]-[0084], [0087]).
Regarding claim 2, Bell teaches the method of claim 1, wherein the predetermined settings comprise one or more of: a size of an individual segment of the plurality of segments, an amount of overlap between adjacent segments of the plurality of segments, or a selection of the embeddings model. More specifically, the parameters can include the designation of a chunking strategy/algorithm, embedding model, chunk size, chunk overlap, etc. (Bell, [0066], [0082]-[0083]).
Regarding claim 3 Bell teaches the method of claim 1, further comprising: causing the plurality of segments to be stored in at least the data store or a second data store. More specifically, Figures 9A and 9B depict process flows that include a generating embeddings/vectorization step for each chunk of the input document and storing the embeddings and chunks/segments in a database according to the agent/model configuration/document index (Bell, Figures 9A and 9B, [0066], [0082]-[0084], [0087], [00109]).
Regarding claim 4, Bell teaches the method of claim 3, further comprising: storing indexation data that maps the plurality of embeddings to the plurality of segments. More specifically, a stored document index for mapping the chunks/segments and embeddings (Bell, [0066], [0087]. [00109])
Regarding claim 6, Bell teaches the method of claim 1, further comprising: receiving, from a remote computing device, a container image comprising the API; and executing the API in a container instantiated using the container image. More specifically, an agent host is construable as a container that includes a task-specific agents, APIs, and a document index which can include an embedding model and chunking/segmenting strategy (Bell, [0006], [0030], [0031], [0059], [0066], [00116]).
Regarding claim 7, Bell teaches the method of claim 6, wherein the container image further comprises at least one of: a segmentation engine that segments the input document into the plurality of segments, or the embeddings model. More specifically, an agent host is construable as a container that includes a task-specific agents, APIs, and a document index which can include an embedding model and chunking/segmenting strategy (Bell, [0006], [0030], [0031], [0059], [0066], [00116]).
Regarding claim 8, Bell teaches the method of claim 1, further comprising: receiving, using the API, a query; causing the embeddings model to process the query to generate one or more query embeddings; computing a plurality of similarity scores characterizing similarity of the one or more query embeddings to the plurality of embeddings; selecting, using the plurality of similarity scores, one or more segments of the plurality of segments; and generating a prompt into a language model (LM), wherein the prompt is based at least on the query and the one or more selected segments. More specifically, Figure 9B depicts the process of receiving a question/query, generated embeddings of the query, determining similarities between the query embedding and chunk vector/embeddings, the query and identified chunks are combined into a prompt sent to an LLM (Bell, Figure 9B, [0082], [0085], [0087], [00121], interaction with the system/agent/models is via an API, [0030]-[0031], [0039], [0059]-[0061], Figure 9C)
Regarding claim 9, Bell teaches a method comprising: receiving, using a processing device executing an application programming interface (API), a query; causing an embeddings model to process the query to generate one or more query embeddings; computing a plurality of similarity scores characterizing similarity of the one or more query embeddings to a plurality of embeddings associated with one or more stored documents; selecting, using the plurality of similarity scores, one or more segments of the one or more stored documents; and processing, using a language model (LM), an LM prompt to obtain a response to the query, wherein the LM prompt is based at least on the query and the one or more selected segments. More specifically, Figure 9B depicts the process of receiving a question/query, generated embeddings of the query, determining similarities between the query embedding and chunk vector/embeddings, the query and identified chunks are combined into a prompt sent to an LLM (Bell, Figure 9B, [0082], [0085], [0087], [00121], interaction with the system/agent/models is via an API, [0030]-[0031], [0039], [0059]-[0061], Figure 9C)
Regarding claim 13, Bell teaches the method of claim 9, wherein the selecting the one or more segments comprises: accessing stored indexation data that maps the plurality of embeddings to the one or more stored documents. More specifically, a stored document index for mapping the chunks/segments and embeddings (Bell, [0066], [0087]. [00109])
Regarding claim 14, Bell teaches the method of claim 9, further comprising: receiving, from a remote computing device, a container image comprising one or more of: the API, or the embeddings model; and executing the one or more of the API or the embeddings model in a container instantiated using the container image. More specifically, an agent host is construable as a container that includes a task-specific agents, APIs, and a document index which can include an embedding model and chunking/segmenting strategy (Bell, [0006], [0030], [0031], [0059], [0066], [00116]).
Regarding claim 15, Bell teaches the method of claim 9, wherein an individual document of the one or more stored documents is stored using operations comprising: receiving, for the individual document, a selection of a document processing pipeline (DPP) from a plurality of DPPs provided using the API; segmenting, according to predetermined settings of the selected DPP, the individual document into a plurality of segments; causing an embeddings model to process the plurality of segments to generate a set of embeddings for the individual document; and causing the set of embeddings to be stored in a data store. More specifically, Figures 9A-9C show a flow including a path for generating embeddings from documents (e.g., PDF documents), which includes segmenting/chunking documents and generating embedding for the chucks (Bell, [0087]). Agents/models (pipelines) are built/selected via a user interface that include certain configuration and document indexes/tools with parameters and interface with an API (Bell, [0006], [0031], [0059]-[0063], [0065], [0066], [0068] [00107], [00109]). The parameters can include the designation of a chunking strategy/algorithm, embedding model, chunk size, chunk overlap, etc. (Bell, [0066], [0082]-[0083]). Figures 9A and 9B depict process flows that include a splitting/chunking step for segmenting an input document according to the agent/model configuration/document index (Bell, Figures 9A and 9B, [0066], [0082]-[0084], [0087]). Figures 9A and 9B depict process flows that include a generating embeddings/vectorization step for each chunk of the input document and storing the embeddings and chunks/segments in a database according to the agent/model configuration/document index (Bell, Figures 9A and 9B, [0066], [0082]-[0084], [0087]).
Regarding claim 16, Bell teaches the method of claim 15, wherein the predetermined settings comprise one or more of: a size of an individual segment of the plurality of segments, an amount of overlap between adjacent segments of the plurality of segments, or a selection of the embeddings model. More specifically, the parameters can include the designation of a chunking strategy/algorithm, embedding model, chunk size, chunk overlap, etc. (Bell, [0066], [0082]-[0083]).
Regarding claim 17, this claim recites the system that performs the method of claim 9, therefore, the same rationale of rejection is applicable.
Regarding claim 20, Bell teaches the system of claim 17, wherein the system is comprised in at least one of: an in-vehicle infotainment system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of virtual reality content, mixed reality content, or augmented reality content; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. More specifically, the system can be a cloud computing system, performs conversational AI operations, and includes: word processing, calendaring, mapping, weather, stocks, time keeping, virtual digital assistant, presenting, number crunching (spreadsheets), drawing, instant messaging, e-mail, telephony, video conferencing, photo management, video management, a digital music player, a digital video player, 2D gaming, 3D (e.g., virtual reality) gaming, electronic book reader, and/or workout support (Bell, [0009], [0033], [0044], [0048]).
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) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bell, and further in view of Murthy et al. (US 12,417,352 B1, filed 6/1/2023, hereinafter “Murthy”).
Regarding claim 5, Bell teaches the method of claim 1, however, may not explicitly teach every aspect of wherein the input document is received from a client device remotely communicating with the processing device over a network.
Murthy discloses a using a machine learning model for receiving a user query, generating an embedding/vector for the query, matching the query vector to segment vectors generated from segments of documents, and creating a prompt to the machine learning model combining the matched segments and the user query (Murthy, abstract, col 6, lines 42-44). A user interface is used by a client to interact with the server which includes receiving user queries and also designate documents for the segmenting/embedding components of the server (Murthy, col 4, lines 19-28, col 5, lines 52-58, col 6, lines 7-15).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention given the teachings of Bell and Murthy that a method for segmenting documents and generating embeddings for said segments to be used in combination with a user query to create a prompt to an LLM would include wherein the input document is received from a client device remotely communicating with the processing device over a network. With Bell and Murthy disclosing methods for segmenting documents and generating embeddings for said segments to be used in combination with a user query to create a prompt to an LLM, and with Murthy additionally disclosing that a user can designate one or more documents to be segmented with embeddings at a device remote from the server that performs the segmenting, embedding, and prompt generating, one of ordinary skill in the art of implementing a method of segmenting documents and generating embeddings for said segments to be used in combination with a user query to create a prompt to an LLM would include wherein the input document is received from a client device remotely communicating with the processing device over a network in order to allow a user to have control over the sources used in creating the prompt to the LLM. One would therefore be motivated to combine these teachings as in doing so would create this method of segmenting documents and generating embeddings for said segments to be used in combination with a user query to create a prompt to an LLM.
Claim(s) 10, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bell, and further in view of Buniatyan (US 12,182,125 B1, filed 2/15/2024).
Regarding claim 10, Bell teaches the method of claim 9, further comprising: receiving a selection of a document processing pipeline (DPP) from a plurality of DPPs provided using the API. More specifically, Agents/models (pipelines) are built/selected via a user interface that include certain configuration and document indexes/tools with parameters and interface with an API (Bell, [0006], [0031], [0059]-[0063], [0065], [0066], [0068] [00107], [00109]). The parameters can include the designation of a chunking strategy/algorithm, embedding model, chunk size, chunk overlap, and a number of documents to retrieve (Bell, [0066], [0082]-[0083]).
However, Bell does not explicitly teach every aspect of
the selected DPP comprising a maximum number of segments to be identified.
Buniatyan discloses methods for implementing trained embedding mappings for improved retrieval augmented generation (Buniatyan, abstract). In an example where the corpus of data is text information, each set of embeddings in the corpus embeddings may correspond to a processed chunk, or portion, of the text information (e.g., a sentence, paragraph, predetermined number of words or characters, etc.). The similarity calculation can be performed for each set of embeddings data in the corpus embeddings and transformed embeddings in the query. The sets of embeddings data in the corpus embeddings can be ranked according to the similarity score, and the query executor can identify, in some implementations, a predetermined (or specified) number of top-scoring sets of embeddings data in the corpus embeddings. The query executor can identify the data represented by the top-scoring corresponding sets of embeddings data in the corpus embeddings from the data lake, which can be used to generate query results for the vector search operation. (Buniatyan, col 21, lines 2-21).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention given the teachings of Bell and Buniatyan that a method for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings would include the selected DPP comprising a maximum number of segments to be identified. With Bell and Buniatyan disclosing methods for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings, with Bell additionally providing a user interface for configuring the embedding search with many parameters such as a maximum number of documents to retrieve, and with Buniatyan additionally disclosing the that results are based on a predetermined or specified number of identified embeddings/segments, one of ordinary skill in the art of implementing a method for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings would include the selected DPP comprising a maximum number of segments to be identified in order to control the complication of the results to be sure to utilize the best matches for the LLM prompt. One would therefore be motivated to combine these teachings as in doing so would create this method for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings.
Regarding claim 11, Bell teaches the method of claim 9, wherein the selecting the one or more segments comprises: identifying, using the plurality of similarity scores, one or more embeddings of the plurality of embeddings, the one or more identified embeddings corresponding to the one or more segments associated with the query. More specifically, the query embedding is compared to embeddings in a vector database to identify one or more embeddings (e.g., vector similarity score, Bell, [0082], [0085], [0087], [00121]),
However, because Bell does not appear to explicitly define the parameter “top_p” in the provisional application (Bell, [0083]), Bell may not explicitly teach every aspect of
ranking, using the plurality of similarity scores, the one or more segments by a degree of association with the query.
Buniatyan discloses methods for implementing trained embedding mappings for improved retrieval augmented generation (Buniatyan, abstract). In an example where the corpus of data is text information, each set of embeddings in the corpus embeddings may correspond to a processed chunk, or portion, of the text information (e.g., a sentence, paragraph, predetermined number of words or characters, etc.). The similarity calculation can be performed for each set of embeddings data in the corpus embeddings and transformed embeddings in the query. The sets of embeddings data in the corpus embeddings can be ranked according to the similarity score, and the query executor can identify, in some implementations, a predetermined (or specified) number of top-scoring sets of embeddings data in the corpus embeddings. The query executor can identify the data represented by the top-scoring corresponding sets of embeddings data in the corpus embeddings from the data lake, which can be used to generate query results for the vector search operation. (Buniatyan, col 21, lines 2-21).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention given the teachings of Bell and Buniatyan that a method for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings would include ranking, using the plurality of similarity scores, the one or more segments by a degree of association with the query. With Bell and Buniatyan disclosing methods for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings, and with Buniatyan additionally disclosing ranking the embeddings/segments according to a similarity score, one of ordinary skill in the art of implementing a method for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings would include ranking, using the plurality of similarity scores, the one or more segments by a degree of association with the query in order to be sure to utilize the best matches for the LLM prompt if the matching process returns a certain number of embeddings/segments. One would therefore be motivated to combine these teachings as in doing so would create this method for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings.
Regarding claim 18, this claim recites the system that performs the method of claim 11, therefore, the same rationale of rejection is applicable.
Claim(s) 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bell, and further in view of Felbah et al. (US 2025/0284720 A1, filed 3/11/2024, hereinafter “Felbah”).
Regarding claim 12, Bell teaches the method of claim 9, including that the embedding similarity search and scoring can supplemented with other types of searching involving document snippets (Bell, [0085], [00121], Figure 8B), however, Bell may not explicitly teach every aspect of wherein the selecting the one or more segments comprises: performing a document search to identify one or more additional segments of the one or more stored documents, the one or more additional segments having text associations with the query; and ranking, using a ranking model, a set of segments by relevance to the query, wherein the set of segments comprises: the one or more segments, and the one or more additional segments; and wherein the LM prompt is generated using the ranked set of segments.
Felbah discloses a computer-implemented method for performing searches in a document database. The method includes generating a query embedding for a text query and returning segments based on an embeddings similarity score and returning additional segments based on a query keyword/text match score. The method also comprises ranking scores for sentences in the document database, and displaying a sentence associated with a top score to the user (Felbah, abstract).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention given the teachings of Bell and Felbah that a method for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings would include identifying additional segments having text associations with the query and ranking the segments and additional segments together. With Bell and Felbah disclosing methods for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings, with Bell suggesting the embedding search can be supplemented with an additional document search, and with Felbah additionally disclosing combining an embedding search and a textual search to identify segments for each and ranking the segments, one of ordinary skill in the art of implementing a method for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings would include identifying additional segments having text associations with the query and ranking the segments and additional segments together in order to combine legacy search methodologies with newer, deep-learning-based information retrieval techniques to create a highly accurate and scalable information retrieval system (Felbah, [0024]). One would therefore be motivated to combine these teachings as in doing so would create this method for retrieving segments of documents associated with generated embeddings based on the similarity score of a query with associated embeddings.
Regarding claim 19, this claim recites the system that performs the method of claim 12, therefore, the same rationale of rejection is applicable.
Pertinent Prior Art
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Levinson (US 2025/0061140 A1) – chunking/segmenting/embedding/vectorizing documents used with an embedding/vectorized query to generate an LLM prompt.
Mukhedrjee (US 2024/0354436 A1) –chunking/segmenting/embedding/vectorizing documents used with an embedding/vectorized query to generate an LLM prompt.
Penta (US 12,373,506 B1) –chunking/segmenting/embedding/vectorizing documents used with an embedding/vectorized query to generate an LLM prompt.
Watson (US 11,971,914 B1) –chunking/segmenting/embedding/vectorizing documents used with an embedding/vectorized query to generate an LLM prompt.
Conclusion
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/PATRICK F RIEGLER/ Primary Examiner, Art Unit 2171
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