DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 16-30 are presented for examination.
Claim Rejections - 35 USC § 101
3. 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.
3.1 Claims 16-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Is the claim directed to a statutory category?
Yes. The claims are to a method (claim 16), a system (claim 23), a non-transitory medium (claim 29).
Step 2A- Prong One
The claim(s) recite(s) a method(claim 16), a system (claim 23), a non-transitory medium (claim 29) for automatically creating a list of computer-implemented engineering tools designed for solving an engineering problem, comprising: The step of: “a) detecting a series of interactions of a user with a main software tool, the interactions aiming to solve an engineering problem by way of the main software tool”; “b) temporally tagging each detected interaction in order to create a time series of the interactions”; “c) automatically creating a semantic construct from the time series of the interactions”; “d) automatically performing a semantic search in a library of computer-implemented engineering tools, the library containing a semantics description of each of the computer-implemented engineering tools, and the semantic search being configured for identifying at least one computer-implemented engineering tool whose semantics description matches the semantic construct”; “e) automatically generating a list with all identified computer-implemented engineering tools”, under the broadest reasonable interpretation fall under a mental process. Therefore, the claims are directed to an abstract idea, by use of generic computer components and thus are clearly directed to an abstract idea, as constructed.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional limitation such as: “a processor”, “a memory”, “non-transitory … medium”, “executable instructions”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0015-0023], and fig.1) which can be of any type, including general-purpose computer previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “f) displaying the list”, under the broadest reasonable interpretation, reasonable fall under data insignificant post-solution activities that is also well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as previously discussed above with reference to the integration of abstract idea into a practical application, the additional elements of: “a processor”, “a memory”, “non-transitory … medium”, “executable instructions”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0015-0023], and fig.1) which can be of any type, including general-purpose computer previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “f) displaying the list”, under the broadest reasonable interpretation, reasonable fall under data insignificant post-solution activities that is also well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101. Therefore, using computer components amount to no more than mere instructions to perform the abstract, and thus are not sufficient to amount to significantly more than the recited abstract, as constructed.
3.2 Dependent claims 17-22, 24-28, and 30 merely include limitations pertaining to further mathematical computations (claims 17, 24, and 30), “using a machine learning algorithm for performing the semantic search” (mental process). (claims 18, 25); “wherein the semantics description comprises, for each computer-implemented engineering tool, a behavior description of the computer-implemented engineering tool on a target object, a description of the target object, a property of the target object, and an identification of an engineering domain” (mental process); (claims 19 and 26); “wherein the semantics description is organized in a knowledge graph” (mental process); (claims 20 and 27); “identifying the series of interactions with at least one process selected from the group consisting of tracking a user interaction with a mouse, tracking a user interaction with a keyboard, tracking a user interaction with a touch screen, analyzing an HMI log file, and mapping an activity of the main software tool” (mental process); (claims 21 and 28); “wherein the step of automatically creating a semantic construct from the time series comprises: automatically identifying each interaction of the time series; and automatically generating a semantic element for each identified interaction or for a set of identified interactions by using a semantics description of the main software tool” (mental process); (claim 22) “automatically loading and opening the at least one computer-implemented engineering tool” (mental process); all of which further amount to further mental process similar to that already recited by the independent claims and already addressed above and thus are further not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 103
4. 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.
4.0 Claim(s) 16-18, 20-25, 27-30 are rejected under 35 U.S.C. 103 as being unpatentable over Jacobs, II (USPG_PUB No. 2018/0039253), in view of Baer et al. (USPG_PUB No. 2019/0278695).
4.1 In considering claims 16, 23, and 29, Jacobs, II teaches a method for automatically creating a list of computer-implemented engineering tools designed for solving an engineering problem, the method comprising the following steps:
a) detecting a series of interactions of a user with a main software tool, the interactions aiming to solve an engineering problem by way of the main software tool (see fig.1A-3, para [0057] For example, spectrum GUI 204 may include a structure 304, which may provide a representation of CAD model 224. Spectrum GUI 204 may further include a viewer tools window 308, which may display indicators (e.g., icons or buttons) of viewer tools a user has access to such that the user may select the indicators to activate one or more of the tools. In this example, spectrum GUI 204 provides viewer tools buttons (i.e., CAD-tool selectors) 312 in the form of soft selection buttons that a user may select to activate one or more viewer tools. Spectrum GUI 204 may implement viewer tools buttons 312 in the form of soft-selection buttons or any other appropriate user interface elements; in some embodiments, a user may interact with model display GUI 104 and/or spectrum GUI 204 using voice commands. Further [0088], [0104]); b) temporally tagging each detected interaction in order to create a time series of the interactions (see para [0064], Step 505 includes receiving user natural language and input/output device inputs, in other words the user speaks and may also use keyboard or mouse to highlight. For example, a user may say “I need a bolt to fit this hole” while using the mouse to identify the hole and natural language command program module 110 will appropriately associate the two different types of inputs based on context and proximity in time. For example, the context of the parse can include recognition of keyboard/mouse commands such as “make this <highlighted with mouse>, aluminum.” With each input/command happens at respective timeframe. In a further alternative, distinct commands may be assigned to correspond to the mouse or keyboard commands so that context is not required, for example, predetermining that spoken “this” plus an immediate physical I/O action go together.); c) automatically creating a semantic construct from the time series of the interactions (see para [0064], Step 505 includes receiving user natural language and input/output device inputs, in other words the user speaks and may also use keyboard or mouse to highlight. For example, a user may say “I need a bolt to fit this hole” while using the mouse to identify the hole and natural language command program module 110 will appropriately associate the two different types of inputs based on context and proximity in time. Example, the context of the parse can include recognition of keyboard/mouse commands such as “make this <highlighted with mouse>, aluminum.” With each input or command happens at respective timeframe.); d) automatically performing a search in a library of computer-implemented engineering tools (see para [0030], for example, to communicate with resource provider server module 132. Language database 128 is a larger, more powerful version of database 116, and may include multiple specialized, plurally accessible library-type databases. Query generator 130 may include the artificial intelligence for query generation as described herein below (see, for example, FIGS. 6, 7A and 7B) and thus may include a processor and memory of its own, as well as other associated hardware and software suited for its query generation function. CAD context database 144 contains CAD specific information as shown in FIG. 5 and described in more detail below. [0031] Resource provider server module 132 provides external services and/or information when called for by module 122. For example, when information needed to respond to a query resides outside of the CAD system and natural language program server module 122, automated searching of appropriate databases is initiated, the databases being supplied as resource provider server modules 132 in order to provide information from suppliers, marketplaces, and other external services. Information identifying CAD tools may include information identifying viewing tools. Information identifying CAD tools may include information identifying CAD manipulation tools. Information identifying CAD-tool functionality may include information identifying marketplace tools as further defined below. Further [0086-0087], [0112]), e) automatically generating a list with all identified computer-implemented engineering tools (see para [0031], Resource provider server module 132 and/or natural language program server 122 may further contain or provide information identifying CAD-tool functionality; information identifying CAD-tool functionality may include information identifying CAD tools as further defined below. Information identifying CAD tools may include information identifying viewing tools. Information identifying CAD tools may include information identifying CAD manipulation tools. Information identifying CAD-tool functionality may include information identifying marketplace tools as further defined below. As a non-limiting example, information identifying a CAD-tool functionality may include one or more relationships of particular words or phrases to the CAD-tool functionality; relationships may be identified or implemented according to any method of correlation, natural language processing, or referential storage, including without limitation vector similarity methods, links between tables or data structures such as relational databases, and the like. Information identifying CAD-tool functionality may include relationships between CAD-tool functionality and particular commands, [0044], [0116], Designers may use a CAD program to design virtual computer models and/or to generate data to send to model display GUI 104 and/or a designer database 260); and f) displaying the list ([0031], Although marketplace tools and/or CAD tools are illustrated as being parts of current profile 232, they may be stored outside of the current profile, spectrum GUI 204, and/or model display GUI 104, as appropriate. In one example, the tools, such as spectrum tools 240 may be temporarily or permanently stored on a user's computer. [0048] Model display GUI 104 may further include a CAD tool database 248, which may include a database or group of databases and may store and/or index CAD tools/CAD tool functionality.[0090]). However, he does not expressly show searching the library containing a semantics description of each of the computer-implemented engineering tools, and the semantic search being configured for identifying at least one computer-implemented engineering tool whose semantics description matches the semantic construct.
Baer et al. teaches searching the library containing a semantics description (see abstract, [0018] User instrumentation data may be used to alleviate some or all of the above challenges in various examples discussed herein. For example, user instrumentation data may provide a standard semantic model for how user actions and responses to those actions are represented., [0030], In various examples, the name “leaf” portion of UserAction describes the semantic action being taken (e.g., it may contain a verb+noun). By using a semantic vocabulary, product managers and data scientists may define a user-centric hierarchy of features that is independent of the “physical” implementation of those features defined by the engineers., [0054] In various examples, a data scientist may log in to instrumentation system 102 and access data such as presented in FIG. 4. Instrumentation system 102 may present a user interface to the data scientist that permits request for user action histories, queries for specific user actions, etc. A search across multiple users may also be made (e.g., how often was action ‘A’ taken in the past day). In some instances, actions leading up to a crash (as reported by an application running on user device 104) may be displayed. , 0066), and the semantic search being configured for identifying at least one computer-implemented engineering tool whose semantics description matches the semantic construct (see para [0018], For example, user instrumentation data may provide a standard semantic model for how user actions and responses to those actions are represented. In some examples, user actions represent intent on the part of the user. Code instrumentation data may be used to represent any code execution and therefore may be associated with background or indirect tasks as well (e.g., file sync, spell check, storage optimization, etc.). Using user instrumentation data, it may be able to determine what object a user acted upon, in what order, etc. [0030], In various examples, the name “leaf” portion of UserAction describes the semantic action being taken (e.g., it may contain a verb+noun). By using a semantic vocabulary, product managers and data scientists may define a user-centric hierarchy of features that is independent of the “physical” implementation of those features defined by the engineers. [0054] In various examples, a data scientist may log in to instrumentation system 102 and access data such as presented in FIG. 4. Instrumentation system 102 may present a user interface to the data scientist that permits request for user action histories, queries for specific user actions, etc. A search across multiple users may also be made (e.g., how often was action ‘A’ taken in the past day)).
Jacobs, II and Baer et al. are analogous art because they are from the same field of endeavor and that the model analyzes by Baer et al. is similar to that of Jacobs, II. Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Baer et al. with that of Jacobs, II because Baer et al. teaches the benefit is the cost of data retention as the code instrumentation data may include more data than user instrumentation data which allows flexibility to apply different sampling policies (see para [0017]).
4.2 As per claims 17, 24, and 30, the combined teachings of Jacobs, II and Baer et al. teaches the step of using a machine learning algorithm for performing the semantic search (see Baer et al. “machine learning model 118 of fig.1”, para [0048] In various examples, data in user action database 112 may be used with one or more machine learning models 118. The data may be used as training data for the models as well as inputs to determine an expected output. For example, a machine learning model may be trained on the order of actions taken by a user. Thus, the machine learning model may output that for the sequence of actions “A, B, C” in an application, 95% of users change a layout preference to Z [0055] The data scientist may also be able to gather data for training a machine learning model. Based on the machine learning model, the data scientist may also define actions to take based on the classification of user actions according to a machine learning model. Further see [0070]). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Baer et al. with that of Jacobs, II because Baer et al. teaches the benefit is the cost of data retention as the code instrumentation data may include more data than user instrumentation data which allows flexibility to apply different sampling policies (see para [0017]).
4.3 Regarding claims 18 and 25, the combined teachings of Jacobs, II and Baer et al. teaches that wherein the semantics description comprises, for each computer-implemented engineering tool, a behavior description of the computer-implemented engineering tool on a target object (see Jacobs, II fig.3, para [0031] Resource provider server module 132 and/or natural language program server 122 may further contain or provide information identifying CAD-tool functionality; information identifying CAD-tool functionality may include information identifying CAD tools as further defined below. Information identifying CAD tools may include information identifying viewing tools. Information identifying CAD tools may include information identifying CAD manipulation tools. Information identifying CAD-tool functionality may include information identifying marketplace tools as further defined below. As a non-limiting example, information identifying a CAD-tool functionality may include one or more relationships of particular words or phrases to the CAD-tool functionality; relationships may be identified or implemented according to any method of correlation, natural language processing, or referential storage, including without limitation vector similarity methods, links between tables or data structures such as relational databases, and the like., [0044]), a description of the target object (see Jacobs, II para [0023], For example, the functionalities may correspond to various manipulations of a CAD model of a structure that is the subject of a request for pricing for fabrication, and a user may add to and/or delete from this set the functionalities contained therein as desired. Herein, a structure may be an object or part having a particular geometry, while a computer model may be a virtual representation of a structure and may be created using one or more appropriate CAD programs. [0085-0087]), a property of the target object (see Jacobs, II para [0023] As used herein and in the appended claims, the term “spectrum” is used to denote that a corresponding viewer or other interface has a set of functionalities that a user can modify as desired.For example, the functionalities may correspond to various manipulations of a CAD model of a structure that is the subject of a request for pricing for fabrication, and a user may add to and/or delete from this set the functionalities contained therein as desired. Herein, a structure may be an object or part having a particular geometry, while a computer model may be a virtual representation of a structure and may be created using one or more appropriate CAD programs. [0085-0087], The corresponding specific examples are, respectively, the voice input of natural language command program module 110 is “I need a bolt to fit this” 1014 and mousing over the hole as I/O device input 1016 plus CAD model data the hole diameter is half an inch and length is two inches 1018, is the basis for a setting of purchased parts catalog 1020, an action of search 1022, and then the direct object or the search terms is “bolt of half an inch diameter and at least two inches length” 1024. These generic algorithm steps correspond, in this specific example, to, respectively, an actual query of purchased parts catalog 1046 plus search 1048 plus Bolt of 0.5 in diameter and at least 2 in length 1050, provides a specific address of a resource catalog 1052, a specific action of the search 1054 and three search terms 1056, 1058 and 1060 that come out of the direct object..), and an identification of an engineering domain (Jacobs. II para 0041], Designer may create or modify CAD model to create manufacturing control instructions for one or more manufacturing devices, including without limitation machine control constructions to machine one or more components or features of structure; designer may create or modify CAD model to provide instructions to one or more suppliers to provide components or perform manufacturing processes on structure. User may be a supplier, who may be an entity or person providing one or more components to be included in or on structure; supplier may be an entity or person providing one or more manufacture services. [0059], For example, first spectrum feature 328 may provide a user with a menu that the user can manipulate to change, add, or delete one or more purchased components that are part of or associated with CAD model 224 or to access a third party purchased parts catalogs such as the PEM (Penn Engineering & Manufacturing Corp.) catalog, which may be accessed through the Internet and/or represented electronically. [0087]; further see Baer et al. abstract, para [0018], [0030], [0030] In various examples, the name “leaf” portion of UserAction describes the semantic action being taken (e.g., it may contain a verb+noun). By using a semantic vocabulary, product managers and data scientists may define a user-centric hierarchy of features that is independent of the “physical” implementation of those features defined by the engineers.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Baer et al. with that of Jacobs, II because Baer et al. teaches the benefit is the cost of data retention as the code instrumentation data may include more data than user instrumentation data which allows flexibility to apply different sampling policies (see para [0017]).
4.4 With regards to claims 20 and 27, the combined teachings of Jacobs, II, Baer et al. teaches the step of identifying the series of interactions with at least one process selected from the group consisting of tracking a user interaction with a mouse, tracking a user interaction with a keyboard, tracking a user interaction with a touch screen, analyzing an HMI log file, and mapping an activity of the main software tool (see Jacobs, II para [0033], Model display GUI 104 communicates between Model display GUI 104 and natural language command program module 110 to facilitate interaction with the user. Optional API 140 inside natural language command program module 110 translates and facilitates communications between the various sub-modules. [0044], [0057-0058], Spectrum GUI 204 may implement viewer tools buttons 312 in the form of soft-selection buttons or any other appropriate user interface elements; in some embodiments, a user may interact with model display GUI 104 and/or spectrum GUI 204 using voice commands.[0058] Spectrum GUI 204 may also include a marketplace tools window 316, which may display indicators of marketplace tools a user has access to such that the user may select the indicators to activate one or more of the tools in order to interact with model display GUI 104.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Baer et al. with that of Jacobs, II because Baer et al. teaches the benefit is the cost of data retention as the code instrumentation data may include more data than user instrumentation data which allows flexibility to apply different sampling policies (see para [0017]).
4.5 As per claims 21 and 28, the combined teachings of Jacobs, II and Baer et al. teaches that wherein the step of automatically creating a semantic construct from the time series comprises: automatically identifying each interaction of the time series and automatically generating a semantic element for each identified interaction or for a set of identified interactions by using a semantics description of the main software tool (see Jacobs, II para [0057], Spectrum GUI 204 may implement viewer tools buttons 312 in the form of soft-selection buttons or any other appropriate user interface elements; in some embodiments, a user may interact with model display GUI 104 and/or spectrum GUI 204 using voice commands. [0058] Spectrum GUI 204 may also include a marketplace tools window 316, which may display indicators of marketplace tools a user has access to such that the user may select the indicators to activate one or more of the tools in order to interact with model display GUI 104. [0090]; further see Baer et al. para [0024], the semantics of the specific UI control being clicked on and the feature that it is associated with may be represented in user event schema as shown above. [0025] An example filled-in event schema may be: [0026] Event.Name: Office.Xaml.Gallery.Click (This may be defined by the engineer and refers to the instrumented code location) further para [0027] UserAction.Name: OneNote.Navigation. NavigateToSection (This may be defined according to a product manager or data scientist) [0028] UserAction.InputType: Mouse [0029] +custom data fields (Data. *) specific to the event . . [0030] In various examples, the name “leaf” portion of UserAction describes the semantic action being taken (e.g., it may contain a verb+noun). By using a semantic vocabulary, product managers and data scientists may define a user-centric hierarchy of features that is independent of the “physical” implementation of those features defined by the engineers.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Baer et al. with that of Jacobs, II because Baer et al. teaches the benefit is the cost of data retention as the code instrumentation data may include more data than user instrumentation data which allows flexibility to apply different sampling policies (see para [0017]).
4.6 Regarding 22, the combined teachings of Jacobs, II and Baer et al. teaches the step of automatically loading and opening the at least one computer-implemented engineering tool (see Baer et al. para [0021], The program code may be stored on a storage device and loaded into a memory of the processing unit for execution. Portions of the program code may be executed in a parallel across multiple processing units. Execution of the code may be performed on a single device or distributed across multiple devices. In some examples, the program code is executed on a cloud platform (e.g., MICROSOFT AZURE® or AMAZON EC2®) using a shared computing infrastructure. In an example, one or more computing devices such as illustrated in fig. 7, may be used to carry out the functionality described with respect to instrumentation system 102. [0057] At 502, in an example, an indication from a user to perform an action (e.g., add a picture, type, quit an application) is received with respect to an application. The indication may come from an operating system based on an input device being used by a user. At 504, in an example, program code to execute the action for the application may be invoked. For example, code may be loaded into memory and executed. The location of the code may be based in part on the name of the action. Further see Jacobs, II para [0055], [0094] At step 1230, spectrum GUI 204 may load one or more tools the user is permitted or authorized to use from marketplace tools database 256 and/or CAD tool database 248 into current profile 232. In addition, the user may optionally access additional tools or features via access box 228. At step 1235, spectrum GUI 204 may display to the user. spectrum GUI 204 may include icons and/or softselection buttons representing each of marketplace tools and CAD tools to which a user profile has access, and the spectrum GUI 204 may display each tool or feature according to the information contained in spectrum GUI layout information 212. [0095] At step 1240, spectrum GUI 204 may load a CAD model, though in some embodiments spectrum GUI 204 may already contain one or more CAD models; spectrum GUI 204 may load a view of the CAD model. In one example, spectrum GUI 204 may prompt a user to select a CAD model to load from CAD model database 252, another memory location, a networked location, or other appropriate data storage.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Baer et al. with that of Jacobs, II because Baer et al. teaches the benefit is the cost of data retention as the code instrumentation data may include more data than user instrumentation data which allows flexibility to apply different sampling policies (see para [0017]).
5. Claim(s) 19, 26 are rejected under 35 U.S.C. 103 as being unpatentable over Jacobs, II (USPG_PUB No. 2018/0039253), in view of Baer et al. (USPG_PUB No. 2019/0278695), , further in view of Martinez Canedo et al. (USPG_PUB No. 2019/0370671).
5.1 As per claims 19 and 26, Jacobs, II, as modified by Baer et al.; however, he does not expressly that wherein the semantics description is organized in a knowledge graph. Martinez Canedo et al. teaches that wherein the semantics description is organized in a knowledge graph (see para [0010] According to aspects of other embodiments, the method of Claim 1, further comprises arranging the DTG in a layered architecture comprising a core containing the DTG, a first layer defining a digital twin interface language providing a common syntactic and semantic abstraction of domain-specific data, a second layer comprising components of a cognitive CPS, and a third layer comprising advanced CPS applications. [0032], Representing these twins in the form of a Digital Twin Graph 101 (realized by Knowledge-Causal Graphs) will enable semantic and causal connections that will automatically capture cross-cutting information/knowledge between different sub-systems, or in SoS. The knowledge-causal graphs may be viewed not as a snapshot of one point in time, but rather as a series of knowledge causal graphs spanning a portion of timeline 102. Seen as a layered architecture 100, the DTG 101 is at the core. [0059]-[0060]). Jacobs, II, Baer et al., and Martinez Canedo et al. are analogous art because they are from the same field of endeavor and that the model analyzes by Martinez Canedo et al. is similar to that of Jacobs, II and Baer et al. Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Martinez Canedo et al. with that of Jacobs, II and Baer et al. because Martinez Canedo et al. teaches engineering improvements to the DTGs 607 and provide engineers with solutions that are not achievable through other means and optimized operations actions to a controller of a CPS control system 617 which provides the optimized control actions to the physical actuators and controls in the CPS. (see para [0060]).
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
6.1 Eck et al. (USPG_PUB No. 2019/0065992) teaches techniques that facilitate semantic and time series analysis using machine learning.
6.2 El Kaed et al. (USPG_PUB No. 2020/0334253) teaches methods and systems are provided for searching time series information in a distributed data processing system.
7. Claims 16-30 are rejected and this action is non-final. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMERSON C PUENTE can be reached at 571-272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 September 15, 2026
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