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 .
DETAILED ACTION
The Action is responsive to the Application filed on 6/27/2025. Claims 1-20 are pending claims. Claims 1, 16, and 20 are written in independent form.
Priority
Applicant’s claim for benefit of prior-filed provisional application 63/665,596 (filed 6/28/2024) under 35. U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Claim Objections
Claims 1, 13, 19, and 20 are objected to because of the following informalities:
Claim 1 appears to recite a typographical error of “one or more application module” which is understood as intended to recite “one or more application [[module]] modules”.
Claim 13 appears to recite a typographical error of “wherein the non-volatile memory, volatile memory, and the at least one processor are included in the packaging” which is understood as intended to recite “wherein [[the]] non-volatile memory, volatile memory, and the at least one processor are included in the packaging” because Claim 13 depends on Claim 1 and neither of “non-volatile memory” and “volatile memory” were previously recited in Claim 13 or Claim 1.
Claim 19 appears to recite a typographical error of “and generating a response. sending, by the second processor,…” which is understood as intended to recite “and generating a response; and[[.]] sending, by the second processor,…”.
Claim 20 appears to recite a typographical error of “the computer readable medium when placed…” which is understood as intended to recite “the non-transitory computer readable medium when placed…”.
Claim 20 appears to recite a typographical error of “the edge computing system…” which is understood as intended to recite “the edge computing [[system]] device…” to be consistent with “an edge computing device”.
Claim 20 appears to recite a typographical error of “a processor of the computing system…” which is understood as intended to recite “a processor of the edge computing device…” to be consistent with “an edge computing device”.
Appropriate correction is required.
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:
“an input module configured to…” and “the input module being further configured to…” in claim 1.
The corresponding structure is described in paragraphs [0023] and [] of the specification respectively by reciting “The processor 320 may be implemented in hardware, software, or a combination of hardware and software. For example, the processor 320 may include a common processor (e.g., a CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field- programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed and/or execute software instructions to perform the operations disclosed herein” and “the system can be a combination of two or more systems, hardware, and/or modules or may be implemented within a single system, hardware, and/or module. A
single system, hardware, and/or module may be implemented as multiple, distributed systems, hardware, and/or modules. Additionally, or alternatively, a set of systems, a set of hardware, and/or a set of modules (e.g., one or more systems, one or more hardware devices, one or more modules) may perform one or more functions described as being performed by another set of systems, another set of hardware, or another set of modules.”. This structure is considered to merely be a general purpose processor.
“an embedding module configured to…” and “the embedding module being further configured to…” in claim 1.
The corresponding structure is described in paragraphs [0023] and [] of the specification respectively by reciting “The processor 320 may be implemented in hardware, software, or a combination of hardware and software. For example, the processor 320 may include a common processor (e.g., a CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field- programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed and/or execute software instructions to perform the operations disclosed herein” and “the system can be a combination of two or more systems, hardware, and/or modules or may be implemented within a single system, hardware, and/or module. A
single system, hardware, and/or module may be implemented as multiple, distributed systems, hardware, and/or modules. Additionally, or alternatively, a set of systems, a set of hardware, and/or a set of modules (e.g., one or more systems, one or more hardware devices, one or more modules) may perform one or more functions described as being performed by another set of systems, another set of hardware, or another set of modules.”. This structure is considered to merely be a general purpose processor.
“an extraction module configured to…” in claim 1 and “the extraction module is configured to…” in claim 7.
The corresponding structure is described in paragraphs [0023] and [] of the specification respectively by reciting “The processor 320 may be implemented in hardware, software, or a combination of hardware and software. For example, the processor 320 may include a common processor (e.g., a CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field- programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed and/or execute software instructions to perform the operations disclosed herein” and “the system can be a combination of two or more systems, hardware, and/or modules or may be implemented within a single system, hardware, and/or module. A
single system, hardware, and/or module may be implemented as multiple, distributed systems, hardware, and/or modules. Additionally, or alternatively, a set of systems, a set of hardware, and/or a set of modules (e.g., one or more systems, one or more hardware devices, one or more modules) may perform one or more functions described as being performed by another set of systems, another set of hardware, or another set of modules.”. This structure is considered to merely be a general purpose processor.
“a similarity search module configured to…” in claim 1 and “the similarity search module is configured to…” in claim 8.
The corresponding structure is described in paragraphs [0023] and [] of the specification respectively by reciting “The processor 320 may be implemented in hardware, software, or a combination of hardware and software. For example, the processor 320 may include a common processor (e.g., a CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field- programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed and/or execute software instructions to perform the operations disclosed herein” and “the system can be a combination of two or more systems, hardware, and/or modules or may be implemented within a single system, hardware, and/or module. A
single system, hardware, and/or module may be implemented as multiple, distributed systems, hardware, and/or modules. Additionally, or alternatively, a set of systems, a set of hardware, and/or a set of modules (e.g., one or more systems, one or more hardware devices, one or more modules) may perform one or more functions described as being performed by another set of systems, another set of hardware, or another set of modules.”. This structure is considered to merely be a general purpose processor.
“an output module configured to…” in claim 1.
The corresponding structure is described in paragraphs [0023] and [] of the specification respectively by reciting “The processor 320 may be implemented in hardware, software, or a combination of hardware and software. For example, the processor 320 may include a common processor (e.g., a CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field- programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed and/or execute software instructions to perform the operations disclosed herein” and “the system can be a combination of two or more systems, hardware, and/or modules or may be implemented within a single system, hardware, and/or module. A
single system, hardware, and/or module may be implemented as multiple, distributed systems, hardware, and/or modules. Additionally, or alternatively, a set of systems, a set of hardware, and/or a set of modules (e.g., one or more systems, one or more hardware devices, one or more modules) may perform one or more functions described as being performed by another set of systems, another set of hardware, or another set of modules.”. This structure is considered to merely be a general purpose processor.
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 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 1-19 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.
Regarding Claims 1, 3, 9-10, and 16-17, the multiple recitations of “a trained neural network” and “the trained neural network” renders the claims indefinite because it is unclear if the recited trained neural networks are the same or different trained neural networksFor the purpose of compact prosecution, the recitations of “a trained neural network” and “the trained neural network” are being understood as referring to trained neural networks that can be different from each other.
Regarding Claims 3 and 6, the recitation of “the container” renders the claims indefinite because “one or more containers” is previously recited in the parent claim(s) and Claim 3 depends upon Claim 2 which recites “the one or more containers includes plural containers. Therefore, it is unclear which container is being referred to by “the container” in the “one or more containers” and/or the “plural containers”.For the purpose of compact prosecution, “the container” is understood as referring to any of the “one or more containers”.
Regarding Claims 1, 7, and 8, claim limitations “input module configured to…”, “embedding module configured to…”, and “output module configured to…” recited in Claim 1, claim limitation “extraction module configured to …” recited in Claims 1 and 7, and claim limitation “similarity search module configured to…” recited in Claims 1 and 8 all 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 functions and to clearly link the structure, material, or acts to the functions.As stated in the 35 U.S.C. 112(f) analysis above, the disclosed structures in paragraphs [0019] and [0023] of the Specification appear to be a general purpose computer/processor without disclosing an algorithm for performing the claimed specific computer functions or transforming the general purpose computer/processor into a special purpose computer programmed to perform he disclosed algorithm. It is noted that MPEP 2181(II)(B) recites that “an algorithm is defined, for example, as ‘a finite sequence of steps for solving a logical or mathematical problem or performing a task.’ Microsoft Computer Dictionary, Microsoft Press, 5th edition, 2002. Applicant may express the algorithm in any understandable terms including as a mathematical formula, in prose, in a flow chart, or ‘in any other manner that provides sufficient structure.’” The present specification does not recite an algorithm for performing “a finite sequence of steps” for the corresponding “input module configured to receive…input” (Claim 1), “embedding module configured to generate a query embedding vector” (Claim 1), “input module configured to receive…containers” (Claim 1), “extraction module configured to extract text” (Claims 1 and 7), “embedding module…configured to generate text embeddings” (Claim 1), “similarity search module configured to compare” (Claims 1 and 8), and “output module configured to format and output” (Claim 1). Therefore, the Claims 1, 7 and 8 are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph because it is unclear what structure, if any, is related to the claimed parts.
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.
Dependent Claims 2-15 and 17-19 inherit the deficiencies of their parent claims and are therefore being rejected based upon the same reason(s) stated for their 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claimed invention is directed to one or more abstract ideas without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below.
As per Claims 1, 16, and 20,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed system (claims 1-15), method (claims 16-19), and non-transitory computer readable medium (claim 20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The independent claims 1, 16, and 20 recite the following limitations directed to an abstract idea:
generate a query embedding vector from the textual input;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating textual input, and based on the observation and evaluation, making a judgement and/or opinion of the a query embedding vector from the textual input.
extract text from the one or more containers and generating text chunks of specified length from the extracted data;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating one or more containers, and based on the observation and evaluation, making a judgement and/or opinion of extracted text from the one or more containers and text chunks of specified length from the extracted data.
generate text embeddings from the text chunks and
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the text chunks, and based on the observation and evaluation, making a judgement and/or opinion of text embeddings from the text chunks.
compare the query embeddings with the text embeddings to determine relevant context information;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the query embeddings and the text embeddings, and based on the observation and evaluation, making a determination of relevant context information.
a trained neural network configured to receive the relevant context information and the query and generate a response; and
The limitation recites a mathematical concept of executing a mathematical formula in the form of a trained neural network that takes as input “the relevant context information and the query” and outputs “a response”.
format the response generated by the trained neural network.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the response generated by the trained neural network, and based on the observation and evaluation, making a judgement and/or opinion to format the response.
STEP 2A Prong Two:Claim 1 recites that the steps are performed using “memory”, “a trained neural network”, “at least one processor”, and “a user interface”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Claim 16 recites that the steps are performed using “an edge computing system”, “at least one processor”, and “a user interface”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Claim 20 recites that the steps are performed using “a non-transitory computer readable medium”, “a neural network”, “an edge computing device”, “a processor”, and “a user interface”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The claim recites the following additional elements:
receive a natural language textual input as a query from a user interface;
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
receive one or more containers of documentation over a communication channel;
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
store the text embeddings in the memory for a specified period;
The limitation also recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
output the response generated by the trained neural network to the user interface.
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to “receive a natural language textual input as a query from a user interface;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to “receive one or more containers of documentation over a communication channel;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to “output the response generated by the trained neural network to the user interface.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As per Dependent Claims 2-15 and 17-19,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed system (claims 1-15), method (claims 16-19), and non-transitory computer readable medium (claim 20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The dependent claims 2-15 and 17-19 recite the following limitations directed to an abstract idea:
The limitation(s) of Dependent Claims 3 and 17 include the step(s) of:
Wherein each of the at least one processor is configured to execute a trained neural network according to the specified data domain of the container.
The limitation recites a mathematical concept of executing a mathematical formula in the form of a trained neural network that is executed “according to the specified data domain of the container”.
The limitation of Dependent Claim 7 includes the step(s) of:
Wherein the extraction module is configured to extract text from the pdf documents using a pdf reader.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating pdf documents, and based on the observation and evaluation, making a judgement and/or opinion of extracted text from the one or more containers.
The limitation of Dependent Claim 8 includes the step(s) of:
Wherein the similarity search module is configured to compare query embeddings with the text embeddings using a cosine similarity computation.
The limitation recites a mathematical concept of executing a mathematical formula in the form of a “cosine similarity computation”.
STEP 2A Prong Two:The claim(s) recite the following additional elements:
The limitation of Dependent Claim 2 includes the step(s) of:
Wherein the one or more containers includes plural containers, and each container containing documentation relevant to a specified data domain.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the one or more containers as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claims 4 and 18 include the step(s) of:
Wherein the at least one processor includes plural processors are connected in a mesh network, and
The limitation also recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
each such connected processor is configured to communicate with at least one other processor in the mesh network to generate at least a portion of the response to the query.
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 5 includes the step(s) of:
Wherein a first processor in the mesh network is configured to send at least part of a received query to a second processor in the mesh network to generate the response to the query.
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 6 includes the step(s) of:
Wherein the documentation in the container includes pdf documents.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the documentation in the container as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 9 includes the step(s) of:
Wherein the trained neural network is a large language model.
The limitation recites an insignificant extra-solution activity as selecting a particular type of trained neural network being used as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 10 includes the step(s) of:
Wherein the trained neural network is configured for Retrieval Augmented Generation.
The limitation recites an insignificant extra-solution activity as selecting a particular type of configuration of the trained neural network being used as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 11 includes the step(s) of:
Wherein the input module includes a user interface and a non-internet network interface.
The limitation also recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation of Dependent Claim 12 includes the step(s) of:
Wherein the memory includes volatile memory for storing text embeddings.
The limitation also recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation of Dependent Claim 13 includes the step(s) of:
Packaging configured for deployment in a resource-constrained environment,
The limitation also recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Wherein the non-volatile memory, volatile memory, and the at least one processor are included in the packaging
The limitation also recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation of Dependent Claim 14 includes the step(s) of:
Wherein the packaging is configured as a wearable device.
The limitation also recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation of Dependent Claim 15 includes the step(s) of:
the system being arranged as a wearable device.
The limitation also recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Regarding Claim 19:
Zhou and Rennie further teach:
wherein the plural processors include a first processor and a second processor,
The limitation also recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
the method comprising:
sending, by the first processor, at least part of a received query to the second processor for processing the query and generating a response; and
The limitation recites an insignificant extra solution activity as sending/receiving of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
sending, by the second processor, the generated response to the first processor.
The limitation recites an insignificant extra solution activity as sending/receiving of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to Claim 2 reciting “Wherein the one or more containers includes plural containers, and each container containing documentation relevant to a specified data domain.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claims 4 and 18 reciting “each such connected processor is configured to communicate with at least one other processor in the mesh network to generate at least a portion of the response to the query.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 5 reciting “Wherein a first processor in the mesh network is configured to send at least part of a received query to a second processor in the mesh network to generate the response to the query.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 6 reciting “Wherein the documentation in the container includes pdf documents.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 9 reciting “Wherein the trained neural network is a large language model.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 10 reciting “Wherein the trained neural network is configured for Retrieval Augmented Generation.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 19 reciting “sending, by the first processor, at least part of a received query to the second processor for processing the query and generating a response;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 19 reciting “sending, by the second processor, the generated response to the first processor.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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) 1-3, 6-7, 9-13, 16-17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rennie et al. (Foreign Publication WO 2024/015323, hereinafter referred to as Rennie).
Regarding Claim 1:
Rennie teaches an edge computing system, comprising:
Memory configured for storing programming code for executing one or more application modules and a trained neural network for processing natural language queries for a specified data domain, and storing data associated with the specified data domain;
Rennie teaches “The system 100 is configured to ingest source documents (e.g., a customer’s voluminous library of documents, or other repositories of data such as e-mail data, collaborative platform data, third-party data repositories, etc.) to transform the documents to document objects (referred to as document object model, or DOM, documents) that represent a mapping from the source documents to searchable resultant objects (resultant transformed) documents. Those document objects may be stored in a DOM repository (also referred to as knowledge distillation, knowledge domain, or KD, repository)” (Para. [0095]).Rennie further teaches “ The transform modules (BERT-based, or based on any other language transform model) may be implemented through neural networks (or through other machine learning architectures) that have been pre-trained to produce transformed content associated with question-answer pairs. Other transform implementations may be realized using filters and algorithmic transforms. Training of neural network implementations may be achieved with a large training samples of question-answer ground truths that may be publicly available, or may have been internally / privately developed by the customer using the system 100 to manage its document library.” (Para. [0116]).
At least one processor configured to execute the programming code stored in memory and generate:
Rennie teaches “ one or more memory storage devices to store one or more documents, and a processor-based controller communicatively coupled to the one or more memory storage devices” (Para. [0021]).
An input module configured to receive a natural language textual input as a query from a user interface;
Rennie teaches “A user associated with the customer that provided that document library (e.g., an employee of the customer) can subsequently submit a query (e.g., a natural language query, such as “how many vacation days does an employee with 2 years seniority get a year?”)” (Para. [0095]).
An embedding module configured to generate a query embedding vector from the textual input;
Rennie teaches “Such an ML engine may be a separate / independent system from the machine learning engine performing the query on the Q-A system 100 (through, for example, a machine learning system implementing a vector transformation mapping a natural language query into an embedding / vector space)” (Para. [0179]).
The input module being further configured to receive one or more containers of documentation over a communication channel;
Rennie teaches “Those documents may be arranged in a document library 160 (which may be part of the computing platform of the customer network 150a), and are accessible by various authorized users at user stations 154a-c within the network 150a, and by an administrator (via an administrator station 152). Any number of stations may be deployed in any particular customer network I system. The administrator station 152 can control access to the documents in the library 160 by controlling privileges, and otherwise managing the documents (e.g., access to specific documents within the library 160, management of content to conceal portions that do not comply with privacy requirements, etc.) As will be discussed in greater detail below, in addition to the library 160 (containing documents relating to operation of the entity operating on the network), other sources of data or information may be available from various applications employed by the customer (e.g., an e-mail application, a chat application such as Slack, customer relationship applications such as Salesforce, etc.) to process through the document processing implementations” (Para. [0101]).
An extraction module configured to extract text from the one or more containers and generating text chunks of specified length from the extracted data;
Rennie teaches “document pre-processing can be performed as two separate tasks. In one processing task, the source document is properly segmented and organized into small chunks, e.g., paragraphs, with additional augmentations (e.g., the vector sequence that represents the heading of a section can be appended to the vectors of every paragraph in that section)” (Para. [0123]).
The embedding module being further configured to generate text embeddings from the text chunks and store the text embeddings in the memory for a specified period;
Rennie teaches “One type of transformation that can be applied to the segment is based on transforming the fixed sized (or substantially fixed-sized) segments, typically comprising multiple words / tokens, into numerical vectors (also referred to as embeddings) in order to implement a fast-search process.” (Para. [0105]).
A similarity search module configured to compare the query embeddings with the text embeddings to determine relevant context information;
Rennie teaches “the matching operation may be based on some closeness or similarity criterion corresponding to some computed distance metric between a computed vector transformed query data and various vector transformed content records in the repository 140” (Para. [0132]).
A trained neural network configured to receive the relevant context information and the query and generate a response; and
Rennie teaches “The transform modules may be implemented through neural networks (or other machine learning architectures) that have been pre-trained to produce transformed content associated with question-answer pairs” (Para. [0125]).
An output module configured to format and output the response generated by the trained neural network to the user interface.
Rennie teaches “the curation and normalization operations performed on the source content result in a searchable document on which searching is performed, but the source document is still preserved so that the user may be provided with original content when presented with the search results. Thus, in such examples, a document object (e.g., a DOM object) may include a normalized, canonicalized retrieval view (and may be provided in the text-based form, as well as in the vectorized representation of that retrieval form), and may also include an appropriately visually pleasant display view” (Para. [0241]).
Regarding Claim 2:
Rennie further teaches:
Wherein the one or more containers includes plural containers, and each container containing documentation relevant to a specified data domain.
Rennie teaches “Those documents may be arranged in a document library 160 (which may be part of the computing platform of the customer network 150a), and are accessible by various authorized users at user stations 154a-c within the network 150a, and by an administrator (via an administrator station 152). Any number of stations may be deployed in any particular customer network I system. The administrator station 152 can control access to the documents in the library 160 by controlling privileges, and otherwise managing the documents (e.g., access to specific documents within the library 160, management of content to conceal portions that do not comply with privacy requirements, etc.)” (Para. [0101]) and “pre-determined ontologies for different subject matters (arranged as a list of concepts, or as a hierarchical data arrangement) are available, and upon determining a relevant ontology for the particular content portion (or to the document that includes that content portion), the relevant ontology is retrieved” (Para. [0178]).
Regarding Claim 3:
Rennie further teaches:
Wherein each of the at least one processor is configured to execute a trained neural network according to the specified data domain of the container.
Rennie teaches “The network is further trained (e.g., fine-tuned, transfer learning) on supervised training data for the target domain (e.g., using Stanford Question Answering Dataset, or SQuAD). Having trained the network for question answering for the target domain, further training may be used to adapt the network to a new domain (e.g., fine-tuning of the trained model to improve its capacity to answer question related to the desired domain to which the Q-A system is to be adapted)” (Para. [0109]).
Regarding Claim 6:
Rennie further teaches:
Wherein the documentation in the container includes pdf documents.
Rennie teaches “Documents may arrive in a variety of original formats, including PDF, HTML, PPT, and DOCX. Extracting the text from each of these requires format- specific processing to generate a plain-text representation of the content (this may be followed by transforming the resultant document into another representation using, for example, a transform-based language model)” (Para. [0225]).
Regarding Claim 7:
Rennie further teaches:
Wherein the extraction module is configured to extract text from the pdf documents using a pdf reader.
Rennie teaches “Documents may arrive in a variety of original formats, including PDF, HTML, PPT, and DOCX. Extracting the text from each of these requires format-specific processing” (Para. [0225]) thereby teaching a pdf reader for extracting text from documents when the document format is PDF.
Regarding Claim 9:
Rennie further teaches:
Wherein the trained neural network is a large language model.
Rennie teaches “examples of such additional language model transforms include:… RAG language model - the Retrieval- Augmented Generation (RAG) language model combines pre-trained parametric and non-parametric memory for language generation” (Para. [0113])
Regarding Claim 10:
Rennie further teaches:
Wherein the trained neural network is configured for Retrieval Augmented Generation.
Rennie teaches “examples of such additional language model transforms include:… RAG language model - the Retrieval- Augmented Generation (RAG) language model combines pre-trained parametric and non-parametric memory for language generation” (Para. [0113])
Regarding Claim 11:
Rennie further teaches:
Wherein the input module includes a user interface and a non-internet network interface.
Rennie teaches “The document processing agent 110 can be implemented as an independent remote server that serves multiple customers like the customer systems 150a and 150n, and can communicate with such customers via network communications (be it a private network, or a public network such as the Internet)” (Para. [0099]) and “Content-embedded contextual information may include one or more of, for example, tags embedded in HTML objects within the document, user-inserted non- renderable information included in the document (e.g., information that would generally not be presented on a user interface when the document content is presented, even though the user who created the content added that information to, for example, capture some important observation), or content headings and markers.” (Para. [0197]).
Regarding Claim 12:
Rennie further teaches:
Wherein the memory includes volatile memory for storing text embeddings.
Rennie teaches “a computer accessible storage medium may include storage media such as magnetic or optical disks and semiconductor (solid-state) memories, DRAM, SRAM, etc.” (Para. [0278]).
Regarding Claim 13:
Rennie further teaches:
Packaging configured for deployment in a resource-constrained environment,
Rennie teaches “ it can be implemented as a process running on one of the customer’s one or more processor-based devices, or may be a logical remote node implemented on the same computing device as a logical local node (it is to be noted that the term “remote device” can refer to the customer station, while “local device” can refer to the document processing agent 110, or vice versa). An arrangement where the agent 1 10 executes within the customer’s network (such as any of the customer networks 150a-n) may improve data security, but may be more expensive to privately run.” (Para. [0099])
Wherein the non-volatile memory, volatile memory, and the at least one processor are included in the packaging
Rennie teaches “The computing platform can include one or more CPU’s, one or more graphics processing units (GPU’s, such as NVIDIA GPU’s, which can be programmed according to, for example, a CUDA C platform), and may also include special purpose logic circuitry, e.g., an FPGA (field programmable gate array), an ASIC (application- specific integrated circuit), a DSP processor, an accelerated processing unit (APU), an application processor, customized dedicated circuity, etc., to implement, at least in part, the processes and functionality for the neural network, processes, and methods described herein.” and “a computer accessible storage medium may include storage media such as magnetic or optical disks and semiconductor (solid-state) memories, DRAM, SRAM, etc.” (Para. [0278]).
Regarding Claim 16:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 1.
Regarding Claim 17:
Some of the limitations herein are similar to some or all of the limitations as recited in Claim 3.
Rennie further teaches:
wherein the at least one processor includes plural processors,
Rennie teaches “ The neural networks (and other network configurations and implementations for realizing the various procedures and operations described herein) can be implemented on any computing platform, including computing platforms that include one or more microprocessors, microcontrollers, and/or digital signal processors that provide processing functionality, as well as other computation and control functionality.” (Para. [0278]).
Regarding Claim 20:
Some of the limitations herein are similar to some or all of the limitations as recited in Claim 1.
Rennie further teaches:
a non-transitory computer readable medium encoded with program code for generating, the non-transitory computer readable medium when placed in communicable contact with an edge computer device, configured the edge computing device to perform steps.
Rennie teaches “The computing platforms used to implement the neural networks typically also include memory for storing data and software instructions for executing programmed functionality within the device. Generally speaking, a computer accessible storage medium may include any non-transitory storage media accessible by a computer during use to provide instructions and/or data to the computer.” (Para. [0278]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4-5 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rennie and further in view of Zhou et al. (U.S. Pre-Grant Publication No. 2024/0378208, hereinafter referred to as Zhou).
Regarding Claim 4:
Rennie explicitly teaches all of the elements of the claimed invention as recited above except:
Wherein the at least one processor includes plural processors are connected in a mesh network, and each such connected processor is configured to communicate with at least one other processor in the mesh network to generate at least a portion of the response to the query.
However, in the related field of endeavor of analysis of data from multiple data sources, Zhou teaches:
Wherein the at least one processor includes plural processors are connected in a mesh network, and each such connected processor is configured to communicate with at least one other processor in the mesh network to generate at least a portion of the response to the query.
Zhou teaches “The query base system may use a mesh network structure for allowing a new data source to plug into the query base system quickly and easily as well as flexible and elastic. The elastic mesh concept may address various complex enterprise data source issues which may include hybrid data store including on premise and in cloud, and/or systems across geographic regions and different firewalls, etc. The mesh structure or layout may help to solve complicated enterprise scenarios where data may be scattered in different places such as cloud, on premise, or other locations.” (Para. [0112]). It is noted that a mesh network is understood as a system of interconnected nodes that necessarily communicate with each other.
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhou and Rennie at the time that the claimed invention was effectively filed, to have modified the systems, devices, apparatus, media, and methods for document processing, as taught by Rennie, with the Mesh communication mesh concept, as taught by Zhou.
One would have been motivated to make such combination because Zhou teaches “use of a query communication mesh concept to provide a way to access and process integrated data from multiple data sources in a network without creating redundant data stores. This may provide a way to distribute and push down the query to the data stored at its native location and avoid the need of transferring large amounts of sensitive data across the network.” (Para. [0034]).
Regarding Claim 5:
Bandeira and Rennie further teach:
Wherein a first processor in the mesh network is configured to send at least part of a received query to a second processor in the mesh network to generate the response to the query.
Zhou teaches “use of a query communication mesh concept to provide a way to access and process integrated data from multiple data sources in a network without creating redundant data stores. This may provide a way to distribute and push down the query to the data stored at its native location and avoid the need of transferring large amounts of sensitive data across the network. The enterprise query base process and/or other processes of the disclosure may implement an elastic mesh technique to tackle various complex enterprise data source issues which may include hybrid data sources including on premise and in cloud, and/or systems across geographic regions, different firewalls, and different networks separated by firewalls, etc.” (Para. [0034]).
Regarding Claim 18:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 4.
Regarding Claim 19:
Zhou and Rennie further teach:
wherein the plural processors include a first processor and a second processor,
the method comprising:
sending, by the first processor, at least part of a received query to the second processor for processing the query and generating a response; and
Zhou teaches “The query base system may use a mesh network structure for allowing a new data source to plug into the query base system quickly and easily as well as flexible and elastic. The elastic mesh concept may address various complex enterprise data source issues which may include hybrid data store including on premise and in cloud, and/or systems across geographic regions and different firewalls, etc. The mesh structure or layout may help to solve complicated enterprise scenarios where data may be scattered in different places such as cloud, on premise, or other locations.” (Para. [0112]). It is noted that a mesh network is understood as a system of interconnected nodes that necessarily communicate with each other.Zhou further teaches “use of a query communication mesh concept to provide a way to access and process integrated data from multiple data sources in a network without creating redundant data stores. This may provide a way to distribute and push down the query to the data stored at its native location and avoid the need of transferring large amounts of sensitive data across the network. The enterprise query base process and/or other processes of the disclosure may implement an elastic mesh technique to tackle various complex enterprise data source issues which may include hybrid data sources including on premise and in cloud, and/or systems across geographic regions, different firewalls, and different networks separated by firewalls, etc.” (Para. [0034]).
sending, by the second processor, the generated response to the first processor.
Zhou teaches “The query base system may use a mesh network structure for allowing a new data source to plug into the query base system quickly and easily as well as flexible and elastic. The elastic mesh concept may address various complex enterprise data source issues which may include hybrid data store including on premise and in cloud, and/or systems across geographic regions and different firewalls, etc. The mesh structure or layout may help to solve complicated enterprise scenarios where data may be scattered in different places such as cloud, on premise, or other locations.” (Para. [0112]). It is noted that a mesh network is understood as a system of interconnected nodes that necessarily communicate with each other.Zhou further teaches “use of a query communication mesh concept to provide a way to access and process integrated data from multiple data sources in a network without creating redundant data stores. This may provide a way to distribute and push down the query to the data stored at its native location and avoid the need of transferring large amounts of sensitive data across the network. The enterprise query base process and/or other processes of the disclosure may implement an elastic mesh technique to tackle various complex enterprise data source issues which may include hybrid data sources including on premise and in cloud, and/or systems across geographic regions, different firewalls, and different networks separated by firewalls, etc.” (Para. [0034]).
Claim(s) 8 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Rennie and further in view of Crabtree et al. (U.S. Pre-Grant Publication No. 2025/0259047, hereinafter referred to as Crabtree).
Regarding Claim 8:
Rennie explicitly teaches all of the elements of the claimed invention as recited above except:
Wherein the similarity search module is configured to compare query embeddings with the text embeddings using a cosine similarity computation.
However, in the related field of endeavor of distributed computer platforms, Crabtree teaches:
Wherein the similarity search module is configured to compare query embeddings with the text embeddings using a cosine similarity computation.
Crabtree teaches “the most popular vector similarity functions are Euclidean distance, cosine similarity, and the inner product.” (Para. [0010]).
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Crabtree and Rennie at the time that the claimed invention was effectively filed, to have modified the systems, devices, apparatus, media, and methods for document processing, as taught by Rennie, with the independent scaling of services, as taught by Crabtree.
One would have been motivated to make such combination because Crabtree teaches “Services may be scaled independently based on demand, which allows for better resource utilization and improved performance” (Para. [0058]).
Regarding Claim 14:
Crabtree and Rennie further teach:
Wherein the packaging is configured as a wearable device.
Crabtree teaches “large-scale cloud computing, and more particularly to distributed, graph-based computing platforms for artificial intelligence based decision-making and automation systems including those employing large language models (LLMs) and associated services across heterogeneous cloud, managed data center, edge, and wearable/mobile devices.” (Para. [0003]) and “The platform facilitates flexible and scalable integration of statistical, machine learning and artificial intelligence and simulation models into software applications, supported by a dynamic and adaptive DCG architecture that supports execution of data flows and orchestration of resources across cloud (e.g. hyperscale), self-managed (e.g. traditional data center) compute clusters, CDNs (e.g. forward content distribution networks that may expand from historical distribution of web content into forward hosting of data sets, models, AI tools, etc. . . . ), edge devices, wearables and mobile devices, and individual computers.” (Para. [0013]).
Regarding Claim 15:
Crabtree and Rennie further teach:
the system being arranged as a wearable device.
Crabtree teaches “large-scale cloud computing, and more particularly to distributed, graph-based computing platforms for artificial intelligence based decision-making and automation systems including those employing large language models (LLMs) and associated services across heterogeneous cloud, managed data center, edge, and wearable/mobile devices.” (Para. [0003]) and “The platform facilitates flexible and scalable integration of statistical, machine learning and artificial intelligence and simulation models into software applications, supported by a dynamic and adaptive DCG architecture that supports execution of data flows and orchestration of resources across cloud (e.g. hyperscale), self-managed (e.g. traditional data center) compute clusters, CDNs (e.g. forward content distribution networks that may expand from historical distribution of web content into forward hosting of data sets, models, AI tools, etc. . . . ), edge devices, wearables and mobile devices, and individual computers.” (Para. [0013]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Abhigjan et al. (U.S. Pre-Grant Publication No. 2020/0396301) teaches an edge exchange point including at least one processor may receive, from a cloud service provider a request to allocate a first tag to communications between the cloud service provider and a first telecommunication network, transmit an acceptance of the request to allocate the first tag to the communications between the cloud service provider and the first telecommunication network, obtaining a first packet containing the first tag from the cloud service provider, and transmit the first packet to the first telecommunication network in accordance with the first tag.The reference further teaches “ devices 111 and 112 may comprise AR devices such as heads-up displays, wearable or non-wearable optical see-through or video see-through devices, handheld computing devices with at least a camera and a display, and so forth. For instance, as illustrated in FIG. 1, device 111 may comprise a wearable computing device (e.g., smart glasses, augmented reality glasses, a headset, or the like). Similarly, device 112 may comprise a tablet computer, a cellular smartphone, a non-cellular wireless device, or the like.” (Para. [0021]).The reference further teaches “system 100 may include communication links between edge clouds 120 and/or 130 which bypass the core network 102. As just one example, APs 121 and 131 may provide a wireless mesh connectivity for devices to access core network 102, where the mesh connectivity may also be used to provide access to mobile edge infrastructure in edge cloud 130 via edge cloud 120, without transiting via core network 102. Alternatively, or in addition, the edge clouds 120 and/or 130 may be interconnected via physical links, e.g., fiber optic cable(s), or the like, which may similarly provide access to mobile edge infrastructure for devices that do not necessarily connect via an access point of the respective edge cloud.” (Para. [0039]).
Kim et al. (U.S. Pre-Grant Publication No. 2025/0335454) teaches configuring retrieval-augmented generation (RAG). A RAG configuration method may include providing a user with a user interface that allows the user to enter a file path for configuration of RAG or to select elements predefined for configuration of the RAG; configuring the RAG for the user using a file acquired through the file path entered through the user interface or elements selected by the user from among the predefined elements through the user interface; generating a response to a query of the user entered through the user interface using the configured RAG and an artificial intelligence (AI) model; and providing the generated response to the user through the user interface.The reference further teaches “ the network 170 may include at least one of network topologies that include a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree or hierarchical network, and the like. However, these are provided as examples only.” (Para. [0038]).
Leonelli et al. (U.S. Patent No. 10,732,969) teaches creating and managing a controller based remote solution is provided. The method comprises receiving, at a code virtualization server (CVS) from a graphical user interface (GUI), an input of a solution comprising a controller communicably coupled with and operable to control a sensor and/or an actuator, a condition at the at the sensor and/or the actuator, an action for execution by the controller upon the condition being met, and at least one parameter for the solution. A program (AGCP) for the controller is automatically generated on the CVS based on the input solution. The program comprises code to check if the condition is met, and to execute the action when the condition is met. The AGCP is downloaded to a storage on the user device, or sent to the at least one controller, for installation on the at least one controller.The reference further teaches “The edge devices 102 and the gateway 108 form a group of devices (or device group 116) connected to the network 110. In the device group 116, the gateway 108 communicates internally with edge devices 102 along communications paths 106.sub.1, 106.sub.2, . . . 106.sub.n, and externally with the network 110. Although the communications paths are generally wireless paths, in some embodiments, the paths may be wired. Also, in some embodiments, the edge devices 102 communicate amongst themselves along dashed paths 104.sub.1, 104.sub.2, . . . 104.sub.n, or along direct paths (not shown) to all or some other edge devices 102, for example, forming a mesh network. The edge devices 103 communicate directly with the network 110, and amongst themselves along dashed path 109, and in some embodiments, each edge device 103 can communicate with another edge device 103, for example, forming a mesh network. Although a single gateway 108 is depicted, multiple gateways may be used within the group 116, or spanning multiple groups similar to the device group 116. Each edge device 102, each edge device 103, and the gateway 108 is a controller device (or incorporates one), that can be programmed either remotely or through direct connection from the user computer 112. In some embodiments, controller of each of the edge devices 102, the edge devices 103 and the gateway 108 can be programmed by the code virtualization server 114.” (Col. 8 Lines 1-26).
Meunier et al. (U.S. Pre-Grant Publication No. 2020/0394249) teaches propagating data in a technical network by considering runtime requirements. A component tree data structure is generated for a probabilistic graph representing the technical network and its technical constraints. On the component tree a propagation algorithm is applied, which iteratively determines an optimal edge in the generated component tree, which maximizes an expected information flow to a query node to and/or from each network node by considering the technical network constraints and by executing a Monte-Carlo sampling for estimation of the expected information flow for the cyclic components and by computing the expected information flow of the non-cyclic components analytically and which updates the component tree iteratively with each determined optimal edge and re-estimates the expected information flow in the updated component tree for providing a result with nodes in the technical network for data propagation, so that information flow is maximized by considering technical network constraints.The reference further teaches “The network is a technical network. The network may be a telecommunication network, an electric network and/or a WSN network (WSN: wireless sensor technology), which comprise spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, pressure, etc. and to cooperatively pass their data through the network to a certain network location or query node. The topology of these networks can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network is controlled by the optimization method according to the embodiment of the invention.” (Para. [0028])
Foreign Publication WO2025/212685A1 teaches “The plurality of devices 115 that may be in communication with the edge computing system/access point 130 on the network communications system 110 can take the form various types of specialized sensors 118, nodes/stations 120, relays/relay nodes 125, edge computing systems/access points 130, vehicles/machinery 135, drones 140, tracking devices, individuals, devices, smart phones, laptop computers, tablet computers, desktop computers, or other computing systems or the like all interconnected through a local communications network. The first and second networks can be any suitable communication network or combination of communication networks, such as the Internet or a mesh network, suitable to send communications to, and receive communications from various telecommunications type devices.” (Paras. [0062]-[0063])
Walters et al. (U.S. Pre-Grant Publication No. 2023/0070256) teaches a plurality of content items posted to a mesh network and each content item may be associated with a signature stored in a blockchain. A search engine may query each content item based on the corresponding signature in the blockchain. The search engine may parse each content item to obtain a label and store the label, the signature and content associated with each content item in a database. The search engine may query the blockchain to obtain a frequency that each content item has been queried in a predetermined period of time. The search engine may rank the content items based on the frequencies, and determine a subset of the content items as frequently searched content. Accordingly, the search engine may distribute the frequently searched content to a plurality of cached nodes in the mesh network.The reference further teaches “The search engine may receive a request for a content item from a computing device (e.g. a requesting device). The search engine may determine that the content item is frequently searched content. The search engine may retrieve the content from one of the plurality of cached nodes based on the label. One of the plurality of cached nodes may be selected on the basis that the cached node is proximately located to the computing device. The search engine may send the retrieved content item to the computing device. Alternatively, the search engine may determine that the content item is not frequently searched content. The search engine may retrieve the content item from an original hosting node in the mesh network and send the retrieved content item to the computing device.” (Para. [0014]).The reference further teaches “Mesh network 100 can include one or more nodes 110 that may host content. The content can be any type of file such as, for example, a file related to a website, a video file, an audio file, an image file, a multimedia file, or any type of file that may be provided to any other node 110. In some examples, any node 110 may be permitted to host content. In other examples, only certain nodes 110, for example, authorized nodes or registered nodes 110a and 110e may host content. In some examples, any node 110 may be permitted to search for and request hosted content. In other examples, only certain nodes 110, for example, authorized nodes or registered nodes 110a and 110e may search for and request hosted content.” (Para. [0019]).
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/ROBERT F MAY/Examiner, Art Unit 2154 6/11/2026
/SYED H HASAN/Primary Examiner, Art Unit 2154
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