DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the application filed on 2/27/2025.
Claims 1-12 are currently pending and have been examined.
Information Disclosure Statement
All references listed in the IDS documents filed on 2/27/2025 and 7/17/2025 have been considered.
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 control unit configured to…” of Claim 1.
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. Particularly, this term will be interpreted in view of at least Paragraphs 0061 and 0063 as filed.
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.
Claims 6-7 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 6 includes the following limitation: “perform frequency analysis related to the speech included in the know-how information.” The original disclosure fails to provide sufficient detail for this “frequency analysis” such that one of ordinary skill in the art could reasonably conclude that Applicant had possession of this claimed feature at the time of filing, and as such this claimed feature lacks the requisite written description required under 112(a). MPEP 2161.01 states, in relevant part, that “original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed” (emphasis in original). The present invention lacks such an algorithm for this claimed frequency analysis. Specifically, this feature is mentioned in Paragraphs 0100 and 0116-0117 as filed, none of which contain any details as to what this “frequency analysis” might entail or how Applicant contemplates it might be performed. Claim 7 is rejected due to its dependence upon Claim 6.
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 2-7 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.
Claim 2 contains the following limitation: “wherein the know-how information and the inquiry information include at least an image related to the work, a speech related to the work, and a text that has been converted from the speech, all of which have been recorded by the terminal device.” This limitation requires that two subjects (the know-how information and the inquiry information) include three types of data (an image related to the work, a speech related to the work, and a text that has been converted from the speech). It is unclear as drafted whether each of these subjects individually is to include all three of these data types, or whether all three of these data types must be found between these two subjects (e.g., each of these three data types must be found in at least one of the know-how information and the inquiry information). For the purposes of this examination, this limitation will be interpreted as “wherein the know-how information or the inquiry information include at least an image related to the work, a speech related to the work, and a text that has been converted from the speech, all of which have been recorded by the terminal device.” Claims 3-7 are rejected due to their dependence upon Claim 2.
Claim 2 contains the following limitation: “the control unit is configured to generate the question represented by at least one of the image, the speech, and the text according to a modality capable of accepting the large language model.” It is unclear as drafted how the modality of the feature representing the question may be “capable of accepting the large language model.” Examiner makes the following observations regarding the description of this functionality in the original disclosure: (1) at least Paragraphs 0040-0041 and 0113 as filed appear to disclose a question modality capable of being accepted by the LLM rather than one capable of accepting the LLM; and (2) Paragraph 0112 contains language highly similar to this claim language, followed by the language “[a]s a result of this, it is possible to generate an appropriate question in accordance with the large language model that is used by the generative AI,” which appears to agree with Examiner’s observation regarding point (1). For the purposes of this examination, and in view of the original disclosure, this limitation is interpreted as “the control unit is configured to generate the question represented by at least one of the image, the speech, and the text according to a modality capable of being accepted by the large language model.” Claims 3-7 are rejected due to their dependence upon Claim 2.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claims 1, 11, and 12, the limitations of acquire know-how information on work related to at least a plant or infrastructure from the work performed by a person engaged in the work in a work site; accumulate the know-how information; generate, when inquiry information related to the work is accepted, based on the inquiry information, a question that requests generation of a reply produced using the know-how information; generate presentation information that is presented to the terminal device corresponding to an inquiry source based on the reply to the question received from the generative AI; transmit the presentation information to the terminal device; managing a work support operation of work related to at least a plant or infrastructure; transmits the know-how information to the server device in a case where the terminal device is in a first mode; transmits the inquiry information to the server device in a case where the terminal device is in a second mode; receives the presentation information with respect to the inquiry information from the server device; and presents the presentation information to the person engaged in the work, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of acquire know-how information on work related to at least a plant or infrastructure from the work performed by a person engaged in the work in a work site; accumulate the know-how information; generate, when inquiry information related to the work is accepted, based on the inquiry information, a question that requests generation of a reply produced using the know-how information; generate presentation information that is presented to the terminal device corresponding to an inquiry source based on the reply to the question received from the generative AI; transmit the presentation information to the terminal device; managing a work support operation of work related to at least a plant or infrastructure; transmits the know-how information to the server device in a case where the terminal device is in a first mode; transmits the inquiry information to the server device in a case where the terminal device is in a second mode; receives the presentation information with respect to the inquiry information from the server device; and presents the presentation information to the person engaged in the work, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a control unit/server device/computer, a terminal device that is used for the work performed by a person engaged in the work in a work site, and generative AI that uses a large language model. These, in the context of the claims as a whole, amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claims are therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception for the same reasons as discussed above in relation to integration into a practical application. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claims, and thus the claims are not patent eligible.
Claims 2-10, describing various additional limitations to the device of Claim 1, amount to substantially the same unintegrated abstract idea as Claim 1 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 2 additionally discloses wherein the know-how information and the inquiry information include at least an image related to the work, a speech related to the work, and a text that has been converted from the speech, all of which have been recorded by the terminal device (further defining the abstract idea already set forth in Claim 1); and the control unit is configured to generate the question represented by at least one of the image, the speech, and the text according to a modality capable of accepting the large language model (further defining the abstract idea already set forth in Claim 1), which do not integrate the claim into a practical application.
Claim 3 additionally discloses wherein the large language model is a multimodal large language model (generally linking the use of a judicial exception to a particular technological environment or field of use), which does not integrate the claim into a practical application.
Claim 4 additionally discloses perform image analysis related to the image that is included in the know-how information (insignificant extra-solution activity); and add a result of the image analysis to the know-how information (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application. The limitation found to recite insignificant extra-solution activity is further found to be well-understood, routine, and conventional as per the standards of 112(a) as one of ordinary skill in the art at the time of filing would recognize it as such in view of the high-level description of this functionality in the original disclosure (see, e.g., Paragraphs 0099, 0103, and 0114 as filed).
Claim 5 additionally discloses wherein the result of the image analysis includes information related to a position of a tool that is used by the person engaged in the work at the time of the work, a posture of the tool, and a point of application of the tool (further defining the abstract idea already set forth in Claim 4), which does not integrate the claim into a practical application.
Claim 6 additionally discloses perform frequency analysis related to the speech included in the know-how information (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and add a result of the frequency analysis to the know-how information (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application.
Claim 7 additionally discloses wherein the terminal device is an augmented reality (AR) terminal (generally linking the use of a judicial exception to a particular technological environment or field of use), which does not integrate the claim into a practical application.
Claim 8 additionally discloses wherein the terminal device is an augmented reality (AR) terminal (generally linking the use of a judicial exception to a particular technological environment or field of use), which does not integrate the claim into a practical application.
Claim 9 additionally discloses wherein the control unit is configured to generate the presentation information such that the presentation information is superimposed onto a real space in an AR display space of the AR terminal (insignificant extra-solution activity), which does not integrate the claim into a practical application. The limitation found to recite insignificant extra-solution activity is further found to be well-understood, routine, and conventional as per the standards of 112(a) as one of ordinary skill in the art at the time of filing would recognize it as such in view of the high-level description of this functionality in the original disclosure (see, e.g., Paragraphs 0021-0022, 0080, and 0118-0119 as filed).
Claim 10 additionally discloses wherein the know-how information includes technical information that is related to the work and that is provided by the person engaged in the work who is a skilled technical person (further defining the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
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)(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.
Claims 1-2, 4, and 8-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Izquierdo-Domenech et al, "Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality," Int'l Journal of Interactive Multimedia and Artificial Intelligence, Vol. 9, No. 3 (hereafter, “Izquierdo-Domenech”).
Regarding Claims 1, 11, and 12, Izquierdo-Domenech discloses:
a control unit/server device/computer (Abstract; pg. 4; Appendix; support system for shop floor operations in industrial settings; this appendix illustrates different requests the application can make to the server; AR systems can reduce the cost of having SMEs on-site);
a terminal device that is used for the work performed by a person engaged in the work in a work site (pgs. 4, 6; work-related information on an industrial shop floor is acquired from an AR device or mobile device used by the subject matter expert (SME); mobile application developed for use with both Android and iOS devices; AR devices such as depicted in Figs. 2-3);
acquire know-how information on work related to at least a plant or infrastructure from a terminal device that is used for the work performed by a person engaged in the work in a work site (pgs. 4, 6; Fig. 1; Section A. SME: Context Enrichment With Information; work-related information on an industrial shop floor is acquired from an AR device or mobile device used by the subject matter expert (SME); mobile application developed for use with both Android and iOS devices; AR devices such as depicted in Figs. 1-3);
accumulate the know-how information (pgs. 4, 6; work-related information on an industrial shop floor is acquired from an AR device or mobile device used by the subject matter expert (SME); persisting of the information on the server-side using a Python FastAPI framework; information is added to repository, and checked against previously recorded information for consistency and/or redundancy);
generate, when inquiry information related to the work is accepted, based on the inquiry information, a question that requests generative AI that uses a large language model to generate a reply produced using the know-how information (Abstract; pgs. 2, 4; Figs. 2-3, 5; Appendix; our method employs Large Language Models (LLMs) to allow experts to describe elements from the real environment in NL and select corresponding AR elements in a dynamic and iterative process; an ideal solution would offer multiple interaction options, including operators' ability to ask questions and receive answers in Natural Language (NL), as discussed in [12]; this information "pills" will be used by the system with two purposes: 1. To retrieve a specific "pill" linked to a specific person in the environment as-is, and 2. For obtaining answers to specific questions; paraphrasing the input questions; using architectures that have already been trained with vast amounts of data allows the pre-trained transformers to have already learned most of the semantics of NL, so they can process and answer most of the questions or suggestions that the user asks in NL; this appendix illustrates different requests the application can make to the server; uses of a GPT-JT model to generate a reply based on the know-how information of the SME);
generate presentation information that is presented to the terminal device corresponding to an inquiry source based on the reply to the question received from the generative AI (pg. 4; Figs. 2-3, 5; in response to region selection/speech recognition & question in natural language, the system generates a response, e.g., incorporating a notes list or answers to received questions, said answers anchored to physical locations; presentation of answer to the question on the terminal device of the operator, potentially in the form of AR feedback);
transmit the presentation information to the terminal device (pg. 4; Figs. 2-3, 5; in response to region selection/speech recognition & question in natural language, the system generates a response, e.g., incorporating a notes list or answers to received questions, said answers anchored to physical locations; presentation of answer to the question on the terminal device of the operator, potentially in the form of AR feedback);
the terminal device transmits the know-how information to the server device in a case where the terminal device is in a first mode (pgs. 4, 6; Fig. 1; Section A. SME: Context Enrichment With Information; work-related information on an industrial shop floor is acquired from an AR device or mobile device used by the subject matter expert (SME); mobile application developed for use with both Android and iOS devices; AR devices such as depicted in Figs. 1-3);
transmits the inquiry information to the server device in a case where the terminal device is in a second mode (Abstract; pgs. 2, 4; Figs. 2-3, 5; Appendix; an ideal solution would offer multiple interaction options, including operators' ability to ask questions and receive answers in Natural Language (NL), as discussed in [12]; speech recognition & questions in natural language; shop floor operator can ask any query in NL via their device which is submitted to the system as in Figs. 2-3; this information "pills" will be used by the system with two purposes: 1. To retrieve a specific "pill" linked to a specific person in the environment as-is, and 2. For obtaining answers to specific questions; paraphrasing the input questions; using architectures that have already been trained with vast amounts of data allows the pre-trained transformers to have already learned most of the semantics of NL, so they can process and answer most of the questions or suggestions that the user asks in NL; this appendix illustrates different requests the application can make to the server);
receives the presentation information with respect to the inquiry information from the server device (pg. 4; Figs. 2-3, 5; in response to region selection/speech recognition & question in natural language, the system generates a response, e.g., incorporating a notes list or answers to received questions, said answers anchored to physical locations; presentation of answer to the question on the terminal device of the operator, potentially in the form of AR feedback); and
presents the presentation information to the person engaged in the work (pg. 4; Figs. 2-3, 5; in response to region selection/speech recognition & question in natural language, the system generates a response, e.g., incorporating a notes list or answers to received questions, said answers anchored to physical locations; presentation of answer to the question on the terminal device of the operator, potentially in the form of AR feedback).
Regarding Claim 2, Izquierdo-Domenech discloses the limitations of Claim 1. Izquierdo-Domenech additionally discloses:
wherein the know-how information and the inquiry information include at least an image related to the work, a speech related to the work, and a text that has been converted from the speech, all of which have been recorded by the terminal device (pgs. 4, 6; Figs. 2-3; Sections A. SME: Context Enrichment With Information, and B. Shop Floor Operator: Information Retrieval; know-how input and querying using AR images, speech and text converted from the speech); and
the control unit is configured to generate the question represented by at least one of the image, the speech, and the text according to a modality capable of accepting the large language model (pgs. 4, 6; Figs. 2-3; Sections A. SME: Context Enrichment With Information, and B. Shop Floor Operator: Information Retrieval; know-how input and querying using AR images, speech and text converted from the speech; paraphrasing the input questions; using architectures that have already been trained with vast amounts of data allows the pre-trained transformers to have already learned most of the semantics of NL, so they can process and answer most of the questions or suggestions that the user asks in NL).
Regarding Claim 4, Izquierdo-Domenech discloses the limitations of Claim 2. Izquierdo-Domenech additionally discloses:
perform image analysis related to the image that is included in the know-how information (pg. 4; Section A. SME: Context Enrichment With Information; mapping of the pills of information to the environment using ray-casting techniques); and
add a result of the image analysis to the know-how information (pg. 4; Section A. SME: Context Enrichment With Information; mapping of the pills of information to the environment using ray-casting techniques).
Regarding Claim 8, Izquierdo-Domenech discloses the limitations of Claim 1. Izquierdo-Domenech additionally discloses wherein the terminal device is an augmented reality (AR) terminal (Title; Abstract; pg. 4; Figs. 2-3; Augmented reality (AR) has become a powerful tool for assisting operators in complex environments, such as shop floors, laboratories, and industrial settings; the presented architecture implementation relies on the fact that the environment needs to be previously scanned, a common feature in current SLAM-based AR solutions; using ray-casting techniques, alongside touch interaction in AR, enables SMEs to pinpoint and enrich specific features of the 3D scanned mesh from the virtual environment; system presents AR feedback to received questions).
Regarding Claim 9, Izquierdo-Domenech discloses the limitations of Claim 8. Izquierdo-Domenech additionally discloses wherein the control unit is configured to generate the presentation information such that the presentation information is superimposed onto a real space in an AR display space of the AR terminal (Title; Abstract; pg. 4; Figs. 2-3; system presents AR feedback to received questions, e.g., "touchable" anchor points for selection/linked to answer).
Regarding Claim 10, Izquierdo-Domenech discloses the limitations of Claim 1. Izquierdo-Domenech additionally discloses wherein the know-how information includes technical information that is related to the work and that is provided by the person engaged in the work who is a skilled technical person (pg. 4; Fig. 1; Section A. SME: Context Enrichment With Information; work-related information on an industrial shop floor is acquired from an AR device or mobile device used by the subject matter expert (SME); using ray-casting techniques, alongside touch interaction in AR, enables SMEs to pinpoint and enrich specific features of the 3D scanned mesh from the virtual environment).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Izquierdo-Domenech in view of Yongquiang et al, "Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model," arXiv:2310.20357, Cornell Univ. Library (hereafter, “Yongquiang”).
Regarding Claim 3, Izquierdo-Domenech discloses the limitations of Claim 2. Izquierdo-Domenech does not explicitly disclose but Yongquiang does disclose wherein the large language model is a multimodal large language model (Abstract; Introduction; Multi-Modal Large Language Model (MLLM);).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the multi-modal LLM-based structure and question answering techniques of Yongquiang with the AI- and LLM-based expertise data collection and question answering system of Izquierdo-Domenech because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Yongquiang are applicable to the base device (Izquierdo-Domenech), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Izquierdo-Domenech in view of Jo et al (KR 102104326) (hereafter, “Jo”).
Regarding Claim 5, Izquierdo-Domenech discloses the limitations of Claim 4. Izquierdo-Domenech additionally discloses wherein the result of the image analysis includes information related to a position of a tool that is used by the person engaged in the work at the time of the work (pg. 4; Fig. 1; this information "pills" will be used by the system with two purposes: 1. To retrieve a specific "pill" linked to a specific position in the environment as-is; SMEs annotate scanned environments, including identifiers, descriptions, and positions of objects therein). Izquierdo-Domenech does not explicitly disclose but Jo does disclose wherein the result of the image analysis also includes a posture of the tool and a point of application of the tool (Abstract; ¶ 0010-0014, 0041-0046; an augmented reality based maintenance training system and method thereof; a camera unit for shooting a live-action image; a control unit for checking whether the tool model is used according to the maintenance procedure based on the identification information received from the tool model, and confirming whether an accurate contact is made to the maintenance position; a control unit that transmits its own identification information to the augmented reality device when the use is detected by the user through the sensor unit, and transmits the attitude and orientation detection values of the tool model detected through the sensor unit to the augmented reality device in real time).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the maintenance training-related image analysis techniques of Jo with the AI- and LLM-based expertise data collection and question answering system of Izquierdo-Domenech because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Jo are applicable to the base device (Izquierdo-Domenech), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Claims 6-7 is rejected under 35 U.S.C. 103 as being unpatentable over Izquierdo-Domenech in view of Koga (JP 2023035970) (hereafter, “Koga”).
Regarding Claim 6, Izquierdo-Domenech discloses the limitations of Claim 2. Izquierdo-Domenech does not explicitly disclose but Koga does disclose perform frequency analysis related to the speech included in the know-how information (pg. 15; Fig. 15; sound pressures and spectrograms of the same plurality of hammering sounds in the hammering test; indicates the possibility of discovering different types of abnormalities by comparing the sound pressure for each frequency).
Izquierdo-Domenech additionally discloses add a result of the data analysis to the know-how information (pgs. 4, 6; Fig. 1; Section A. SME: Context Enrichment With Information; work-related information on an industrial shop floor is acquired from an AR device or mobile device used by the subject matter expert (SME)). Izquierdo-Domenech does not explicitly disclose but Koga does disclose wherein the data analysis includes the frequency analysis (pg. 7, 12, 15; Fig. 15; sound pressures and spectrograms of the same plurality of hammering sounds in the hammering test; indicates the possibility of discovering different types of abnormalities by comparing the sound pressure for each frequency; the video data and sound data of the hammering inspection using the hammering instrument are moved to storage in memory or HDD).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the frequency analysis techniques of Koga with the AI- and LLM-based expertise data collection and question answering system of Izquierdo-Domenech because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results) (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Koga are applicable to the base device (Izquierdo-Domenech), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claim 7, Izquierdo-Domenech discloses the limitations of Claim 6. Izquierdo-Domenech does not explicitly disclose but Koga does disclose wherein the result of the frequency analysis includes a spectrogram image (pg. 15; Fig. 15; taking the spectrogram of an actual hammering sound).
The rationale to combine remains the same as for Claim 6.
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20250021919, claiming the benefit of Provisional 63526947 – “Enterprise Knowledge Retention and Access System,” Belkin et al, disclosing a system for gathering work information of industry experts, using said information to create generative AI-based digital twins of such experts, and using said digital twins to answer questions in place of such experts/simulate the expertise of such experts
PGPub 20240419950 – “Systems, Devices, and Methods for Enterprise System Integration Using Machine Learning,” Tong et al, disclosing a system for building a plurality of AI-based subject experts based on data sources, and filter a request to a corresponding AI expert to answer said request
PGPub 20230274095 – “Autonomous Conversational AI System Without Any Configuration by a Human,” Kelkar et al, disclosing a system for building generative AI-based topic-specific conversation models, and using such models to answer user questions
Conclusion
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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628