CTNF 19/091,039 CTNF 100219 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 abstract ideas without significantly more. Below is an evaluation using the 2019 Revised Patent Subject Matter Eligibility Guidance. Claim 1 Step 1 : Claim 1 is to a process. Step 2A Prong 1 : Abstract Idea Bolded portions indicate the abstract idea. Claim 1 recites obtaining, by a device, natural language descriptions of issues detected in a computing system prompting , by a device, one or more language models to generate sets of possible causal dependencies between the issues based on their natural language descriptions ; forming, by the device and using the one or more language models , an issue dependency graph that reaches consensus among the sets of possible causal dependencies between the issues; and using , by the device, the issue dependency graph to determine a particular one of the issues as a root cause of an indicated problem in the computing system. which are abstract ideas of a mental processes that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Obtaining a natural language description of issues can be performed by reading text, observing a system and writing text, hearing a description from another individual, or other mental processes. Prompting a language model can be performed by simply writing down text. Forming an issue dependency graph can be performed in the human mind. A graph is an abstract representation of the data. The example graph in Fig. 5, for example, is clearly tractable in the human mind. Using the graph to determine a root cause can be performed in the human mind. Fig. 5, for example, demonstrates that a human being could follow the graph edges to determine which node is the root cause. The Specification does not explicitly define or disavow a particular interpretation of the term “language model”. In view of this fact, the broadest reasonable interpretation of the claims includes an abstract language model (e.g. a statistical language model, or a human being’s internal model of language). Therefore the language model may be part of the mental process. In the interest of compact prosecution, the case where the language model is interpreted as specifically a large language computer model is also addressed below. Step 2A Prong 2 : Additional elements Claim 1 recites by a device which is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1). Additionally, the phrase “a device” is a generic claim term with a wide broadest reasonable interpretation, and does not indicate a particular machine. Claim 1 further recites in a computing system in the computing system which is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h). Claim 1 further recites one or more language models using the one or more language models. As stated above, the language model may be an abstract model that is part of the identified abstract ideas. In the case where the language model is instead interpreted as specifically a large language computer model, this claim limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). The ordinary capacity as a tool of a large language model is to provide appropriate text output to a prompt. The claims do not include limitations that would preclude an individual providing a prompt to a publicly available large language model, or provide any indication that the language model is a particular model that would integrate the abstract ideas into a practical application. Step 2B : Significantly more Claim 1 recites by a device which is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1). Additionally, the phrase “a device” is a generic claim term with a wide broadest reasonable interpretation, and does not indicate a particular machine. Claim 1 further recites in a computing system in the computing system which is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Claim 1 further recites one or more language models using the one or more language models. As stated above, the language model may be an abstract model that is part of the identified abstract ideas. In the case where the language model is instead interpreted as specifically a large language computer model, this claim limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). The ordinary capacity as a tool of a large language model is to provide appropriate text output to a prompt. The claims do not include limitations that would preclude an individual providing a prompt to a publicly available large language model, or provide any indication that the language model is a particular model that would amount to significantly more. Claim 2 Step 1 : Claim 2 is to a process. Step 2A Prong 1 : Abstract Idea Claim 2 recites the abstract ideas of Claim 1 by dependency. Claim 2 recites wherein the natural language descriptions of the issues in the computing system is obtained by causing a language model to translate a sequence of logs into the natural language descriptions which is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Step 2A Prong 2 : Additional elements. Claim 2 does not recite additional elements. Step 2B : Significantly more Claim 2 does not recite additional elements. Claim 3 Step 1 : Claim 3 is to a method. Step 2A Prong 1 : Abstract Idea Claim 3 recites the abstract ideas of Claim 1 by dependency. Step 2A Prong 2 : Additional elements Claim 3 recites obtaining metadata including one or more of network topology information or configuration files of a service in the computing system which is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h). Furthermore it is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). This is necessary data gathering to computing a model of the computing system. Step 2B : Significantly more Claim 3 recites obtaining metadata including one or more of network topology information or configuration files of a service in the computing system which is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Furthermore it is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). This is necessary data gathering to computing a model of the computing system. Claim 4 Step 1 : Claim 4 is to a process. Step 2A Prong 1 : Abstract Idea Claim 4 recites the abstract ideas of Claim 1 and 3 by dependency. Claim 4 wherein the root cause of the indicated problem encountered by a user of the computing system is identified further based on the metadata which is a continuation of the root cause determination abstract idea of Claim 1. Step 2A Prong 2 : Additional elements Claim 4 does not recite additional elements. Step 2B : Significantly more Claim 4 does not recite additional elements. Claim 5 Step 1 : Claim 5 is to a process. Step 2A Prong 1 : Abstract Idea Claim 5 recites the abstract ideas of Claim 1 by dependency. Claim 5 recites determining a certainty indication indicating a strength of a causal dependency associated with the root cause which is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). A human being can make an evaluation as to how strong a causal dependency may be. Step 2A Prong 2 : Additional elements Claim 5 does not recite additional elements. Step 2B : Significantly more Claim 5 does not recite additional elements. Claim 6 Step 1 : Claim 6 is to a process. Step 2A Prong 1 : Abstract Idea Claim 6 recites the abstract ideas of Claim 1 and 5 by dependency. Step 2A Prong 2 : Additional elements Claim 6 recites causing the issue dependency graph, a description of the root cause, and the certainty indication associated with the root cause to be displayed to a user via a user interface which is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Step 2B : Significantly more Claim 6 recites causing the issue dependency graph, a description of the root cause, and the certainty indication associated with the root cause to be displayed to a user via a user interface which is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Displaying information via an interface is well-known, routine and conventional. See MPEP § 2106.05(d). Claim 7 Step 1 : Claim 7 is to a process. Step 2A Prong 1 : Abstract Idea Claim 7 recites the abstract ideas of Claim 1 by dependency. Claim 7 recites reusing archived sets of possible causal dependencies between issues identified in previous dependency detection request runs as inputs to influence future dependency detection request runs which is a continuation of the dependency graph forming abstract idea of claim 1. Using prior knowledge is well-known element of similar analysis methods that can be performed by the human mind. Step 2A Prong 2 : Additional elements Claim 7 does not recite additional elements. Step 2B : Significantly more Claim 7 does not recite additional elements. Claim 8 Step 1 : Claim 8 is to a process. Step 2A Prong 1 : Abstract Idea Claim 8 recites the abstract ideas of Claim 1 by dependency. Claim 8 recites wherein the issue dependency graph is based on natural language descriptions of rationales behind a list of edges produced by the one or more language models for the consensus among the sets of possible causal dependencies between issues which is a continuation of the dependency graph forming abstract idea of claim 1. This limitation merely describes how the graph is formed from a natural language description. Given a natural language description of edges of a graph, a human being would be able to construct the corresponding graph. Step 2A Prong 2 : Additional elements Claim 8 does not recite additional elements. Step 2B : Significantly more Claim 8 does not recite additional elements. Claim 9 Step 1 : Claim 9 is to a process. Step 2A Prong 1 : Abstract Idea Claim 9 recites the abstract ideas of Claim 1 by dependency. Claim 9 recites wherein forming the issue dependency graph that reaches consensus among the sets of possible causal dependencies between the issues includes generating a direct acyclic graph by removing edges forming a cycle in an initial graph of possible causal dependencies between the issues. which is a continuation of the dependency graph forming abstract idea of claim 1. This limitation merely describes the abstract process taken to construct the graph. A direct acyclic graph is still an abstract graph, and cycle detection is an algorithm performed on such an abstract graph. Step 2A Prong 2 : Additional elements Claim 9 does not recite additional elements. Step 2B : Significantly more Claim 9 does not recite additional elements. Claim 10 Step 1 : Claim 10 is to a process. Step 2A Prong 1 : Abstract Idea Claim 10 recites the abstract ideas of Claim 1 by dependency. Step 2A Prong 2 : Additional elements Claim 10 recites providing an indication for display that the particular one of the issues is the root cause which is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Step 2B : Significantly more Claim 10 recites providing an indication for display that the particular one of the issues is the root cause which is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Displaying information via an interface is well-known, routine and conventional. See MPEP § 2106.05(d). Claims 11 – 19 Step 1 : Claims 11 – 19 are to a machine. Step 2A Prong 1 : Abstract Idea Claims 11 – 19 recite similar language to Claims 1 – 10, and recite similar abstract ideas. Step 2A Prong 2 : Additional elements Claim 11 recites one or more network interfaces which is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Claim 11 further recites a processor coupled to the one or more network interfaces and configured to execute one or more processes; and a memory configured to store a process that is executable by the processor, the process, when executed, configured to which are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1). Claims 11 – 19 otherwise recite similar language to Claims 1 – 10, and similarly do not recite additional elements that integrate the abstract ideas into a practical application. Step 2B : Significantly more Claim 11 recites one or more network interfaces which is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). Claim 11 further recites a processor coupled to the one or more network interfaces and configured to execute one or more processes; and a memory configured to store a process that is executable by the processor, the process, when executed, configured to which are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1). Claims 11 – 19 otherwise recite similar language to Claims 1 – 10, and similarly do not recite additional elements that amount to significantly more. Claim 20 Step 1 : Claim 20 is to a non-transitory computer-readable medium. Step 2A Prong 1 : Abstract Idea Claim 20 recites similar language to claim 1, and recites similar abstract ideas. Step 2A Prong 2 : Additional elements Claim 20 recites A tangible, non-transitory, computer-readable medium having computer-executable instructions stored thereon that, when executed by the processor on a computer, cause the computer to perform a method which is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1). Claim 20 otherwise recites similar language to Claim 1, and similarly does not recite additional elements that integrate the abstract ideas into a practical application. Step 2B : Significantly more Claim 20 recites A tangible, non-transitory, computer-readable medium having computer-executable instructions stored thereon that, when executed by the processor on a computer, cause the computer to perform a method which is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1). Claim 20 otherwise recites similar language to Claim 1, and similarly does not recite additional elements that amounts to significantly more. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1 – 5, and 7 – 9 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (NPL, MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems ) , hereinafter Zheng, in view of Ban et al. (NPL, Causal Structure Learning Supervised by Large Language Model ) , hereinafter Ban . Regarding claim 1, Zheng teaches a method, comprising: obtaining, by a device, natural language descriptions of issues detected in a computing system (Section 3.1 Representation Extraction via Log-tailored Language Model , the MULAN method starts by transforming raw system logs into structured log representations using a large language model; Table 1 shows that the system logs contain errors describing failures in a computer system) ; prompting, by a device, one or more machine learning models (Examiner notes that the models of Zheng are not language models as claimed) to generate sets of possible causal dependencies between the issues based on their natural language descriptions (Section 3.2 Contrastive Multi-model Causal Structure Learning , two sets of encoders and decoders take the representations produced by the large language model to produce two causal graphs learned from metrics and logs) ; forming, by the device and using the one or more machine learning models (Examiner notes that the models of Zheng are not language models as claimed) , an issue dependency graph that reaches consensus among the sets of possible causal dependencies between the issues (Section 3.3 Causal Graph Fusion with KPI-Aware Attention , the two separate graphs are combined using an attention module while being aware of key performance indicators) ; using, by the device, the issue dependency graph to determine a particular one of the issues as a root cause of an indicated problem in the computing system (Section 3.4 Network Propagation based Root Cause Localization , the system traverses the final fused causal graph to determine which nodes are the root cause of the failure) . Zheng does not teach that the machine learning model used to form the issue dependency graph is a language model (Zheng teaches a specialized graph learning model instead) . Ban teaches a large language model that generates sets of possible causal dependencies between issues based on their natural language descriptions (Section IV Iterative LLM Supervised Causal Structure Learning , based on each possible pair of variables in a graph, the LLM generates which dependency is correct) , and forms, by a device and using the one or more large language models, an issue dependency graph that reaches consensus among the sets of possible causal dependencies between the issues (Section IV, the LLM combined with a scoring function iteratively adjusts the graph until it reaches a final DAG). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that the graph model of Zheng could be replaced with a large language model given an appropriate prompt. It would be obvious because implementing this task with a general-purpose large language model is effective (Ban Introduction , LLMs outperform human analysis in determining causality between variable pairs) . Furthermore, given the ubiquity of large language models in the art, and the market forces encouraging use of large language models (Ban Introduction , studies in LLMs have positioned them as “a valuable and readily available resource for knowledge-based causal interference“) , one of ordinary skill in the art would obviously consider using a large language model to perform a given machine learning task. The authors of Zheng were already familiar with large language models, given that they use them to extract information from logs. Regarding claim 2, Zheng in view of Ban teaches the method of claim 1, wherein the natural language descriptions of the issues in the computing system is obtained by causing a language model to translate a sequence of logs into the natural language descriptions (Zheng section 3.1, MULAN generates high-quality representations from raw system logs via a large language model) . Regarding claim 3, Zheng in view of Ban teaches the method of claim 1, further comprising: obtaining metadata including one or more of network topology information (Zheng Section 2 Preliminaries , the KPIs representing the efficiency of a microservice architecture, and entity metrics on the attributes of network components such as containers and pods. This information describes the entities in a microservice network which are connected to one another, and therefore comprises network topology information) or configuration files of a service in the computing system. Regarding claim 4, Zheng in view of Ban teaches the method of claim 3, wherein the root cause of the indicated problem encountered by a user of the computing system (Zheng Section 1, a fault in the microservice impacts the user’s experience) is identified further based on the metadata (Zheng section 3.3, the final causal graph is produced based on the KPI and entity metrics including the network topology information; This final graph is used in Zheng section 3.4 to find the root cause of the encountered fault) . Regarding claim 5, Zheng in view of Ban teaches the method of claim 1, further comprising: determining a certainty indication indicating a strength of a causal dependency associated with the root cause (Zheng section 3.3, the strength of s v quantifying the causal relationship between an entity and KPI; Zheng section 3.4, the probability scores of the nodes used to rank the top most likely root causes; Ban section IV.A, the LLM may answer that the causal dependency exists, does not exist, or that it is uncertain) . Regarding claim 7, Zheng in view of Ban teaches the method of claim 1, further comprising: reusing archived sets of possible causal dependencies between issues identified in previous dependency detection request runs as inputs to influence future dependency detection request runs (The method of Ban is iterative. See Ban Algorithm 1: in each cycle in the repeat loop, new constraints are added to the set of structural constraints based on the result of the LLM’s inference, influencing each future iteration of the loop) . Regarding claim 8, Zheng in view of Ban teaches the method of claim 1, wherein the issue dependency graph is based on natural language descriptions of rationales behind a list of edges produced by the one or more language models for the consensus among the sets of possible causal dependencies between the issues (Ban section IV.A, the LLM either describes the direction of the causality, a lack of correlation, or uncertainty. This information is used to form the graph) . Regarding claim 9, Zheng in view of Ban teaches the method of claim 1, wherein forming the issue dependency graph that reaches consensus among the sets of possible causal dependencies between the issues includes generating a direct acyclic graph by removing edges forming a cycle in an initial graph of possible causal dependencies between the issues (Zheng section 3.3, rather than simple addition which can lead to cyclical graphs, Zheng combines the two causal graphs into a final graph by optimizing an objective function. This objective function includes a trace exponential function that is minimized when the graph is acyclic. That is, the objective function removes edges which would form a cycle; Ban Section III.A Causal Bayesian Network notes that the causal graph is acyclic) . 07-21-aia AIA Claim s 6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng in view of Ban, further in view of the commonly known technique of displaying information via an interface . Regarding claim 6, Zheng in view of Ban teaches the method of claim 5, further comprising: causing the issue dependency graph, a description of the root cause, and the certainty indication associated with the root cause to be output (Examiner notes that Zheng in view of Ban does not “display” this information as claimed; The issue dependency graph is an output of the method of Ban; Zheng section 3.4 teaches that the method provides a list of potential root causes and their scores; Zheng section 4.2 Performance Evaluation describes evaluating the results of the methods) . Zheng in view of Ban does not explicitly teach that these are to be displayed to a user via a user interface. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that the information provided by Zheng in view of Ban would be provided by a user interface. The examiner takes official notice that displaying information via an interface is a commonly known and well-understood technique in the art. One of ordinary skill in the art would understand that the information would be useful if viewed by a human, which would require some form of user interface. Regarding claim 10, Zheng in view of Ban teaches the method of claim 1, further comprising: providing an indication that the particular one of the issues is the root cause (Zheng section 3.4 teaches that the method provides a list of potential root causes and their scores) . Zheng in view of Ban does not explicitly teach that this indication is for display. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that the information provided by Zheng in view of Ban would be provided by a display The examiner takes official notice that displaying information via an interface is a commonly known and well-understood technique in the art. One of ordinary skill in the art would understand that the information would be useful if viewed by a human, which would require some form of display . 07-21-aia AIA Claim s 11 – 15 and 17 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng in view of Ban, and commonly known techniques for performing computer methods . Regarding claim 11, Zheng and Ban do not teach: one or more network interfaces to communicate with a network; a processor coupled to the one or more network interfaces and configured to execute one or more processes; and a memory configured to store a process that is executable by the processor (Zheng and Ban are academic publications which do not need to disclose information more typical of patents) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that the computer methods of Zheng and Ban are performed by a computer which includes one or more network interfaces to communicate with a network, a processor coupled to the one or more network interfaces and configured to execute one or more processes, and a memory configured to store a process that is executable by the processor. The examiner takes official notice that these components are commonly known and well-understood components of a modern computer. It would be clear to one of ordinary skill in the art that computer methods such as those of Zheng and Ban are commonly performed by standard computers which contain a network interface, processor, and memory. Claim 11 otherwise recites similar language to claim 1, and is similarly obvious. Claim 12 recite similar language to claim 2, and is similarly rejected. Claim 13 recite similar language to claim 3, and is similarly rejected. Claim 14 recite similar language to claim 4, and is similarly rejected. Claim 15 recite similar language to claim 5, and is similarly rejected. Claim 17 recite similar language to claim 7, and is similarly rejected. Claim 18 recite similar language to claim 8, and is similarly rejected. Claim 19 recite similar language to claim 9, and is similarly rejected. Regarding claim 20, Zheng and Ban do not teach: A tangible, non-transitory, computer-readable medium having computer-executable instructions stored thereon that, when executed by a processor on a computer, cause the computer to perform a method (Zheng and Ban are academic publications which do not need to disclose information more typical of patents) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that the computer methods of Zheng and Ban are embodied as a tangible, non-transitory, computer-readable medium having computer-executable instructions stored thereon that, when executed by a processor on a computer, cause the computer to perform a method. The examiner takes official notice that this is a common embodiment for a computer method, especially for patents. It would be clear to one of ordinary skill in the art that computer methods such as those of Zheng and Ban are commonly performed by through such a method. Claim 20 otherwise recites similar language to claim 1, and is similarly obvious . 07-22-aia AIA Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Zheng, Ban and commonly known techniques for performing computing methods as applied to claim 11 above, and further in view of the commonly known technique of displaying information via an interface . Claim 16 recites similar language to claim 6, and is similarly rejected . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wan et al. (NPL, Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions ) teaches causal discovery using LLMs and further demonstrates the market incentives to use LLMs . Application 19/532,206 is noted for sharing inventors and some subject matter with the current application, but does not appear to be grounds for a double patenting rejection. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN PAI SONG HUANG whose telephone number is (571)272-0510. The examiner can normally be reached Monday - Friday 11:30 AM - 8:30 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, ASHISH THOMAS can be reached at (571) 272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /B.P.H./Examiner, Art Unit 2114 /ASHISH THOMAS/Supervisory Patent Examiner, Art Unit 2114 Application/Control Number: 19/091,039 Page 2 Art Unit: 2114 Application/Control Number: 19/091,039 Page 3 Art Unit: 2114 Application/Control Number: 19/091,039 Page 4 Art Unit: 2114 Application/Control Number: 19/091,039 Page 5 Art Unit: 2114 Application/Control Number: 19/091,039 Page 6 Art Unit: 2114 Application/Control Number: 19/091,039 Page 7 Art Unit: 2114 Application/Control Number: 19/091,039 Page 9 Art Unit: 2114 Application/Control Number: 19/091,039 Page 10 Art Unit: 2114 Application/Control Number: 19/091,039 Page 11 Art Unit: 2114 Application/Control Number: 19/091,039 Page 12 Art Unit: 2114 Application/Control Number: 19/091,039 Page 13 Art Unit: 2114 Application/Control Number: 19/091,039 Page 14 Art Unit: 2114 Application/Control Number: 19/091,039 Page 15 Art Unit: 2114 Application/Control Number: 19/091,039 Page 16 Art Unit: 2114 Application/Control Number: 19/091,039 Page 17 Art Unit: 2114 Application/Control Number: 19/091,039 Page 18 Art Unit: 2114 Application/Control Number: 19/091,039 Page 19 Art Unit: 2114 Application/Control Number: 19/091,039 Page 20 Art Unit: 2114 Application/Control Number: 19/091,039 Page 21 Art Unit: 2114 Application/Control Number: 19/091,039 Page 22 Art Unit: 2114 Application/Control Number: 19/091,039 Page 23 Art Unit: 2114 Application/Control Number: 19/091,039 Page 24 Art Unit: 2114
Prosecution Insights