DETAILED ACTION
This communication is in response to the Application filed on 03/12/2024. Claims 1-20 are pending and have been examined.
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 .
Information Disclosure Statement
The IDS dated 03/12/2024 has been considered and placed in the application file.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 7, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. All of the claims are method claims (1-15), apparatus/machine claims (16-20) or manufacture claim under (Step 1), but under Step 2A all of these claims recite abstract ideas and specifically mental processes. These mental processes are more particularly recited in claims 1, 16, and 20 as:
identifying a domain of the geoscience data…
translating, based on the identified domain, the natural language query…
executing, based on the set of recommendations specifying one or more parameter values, the set of processing actions for processing the geoscience data…
Under Step 2A Prong One, claims 1, 16, and 20 are directed to an abstract idea and specifically a mental process. As detailed above, the steps of identifying, translating, executing, etc. may be practically performed in the human mind with the use of a physical aid such as a pen and paper. For example, a human could receive geoscience data, identify which area of geoscience the data belongs to, infer parameters from a supervisor’s instruction to process the geoscience data, and then perform a set of processing actions based on the inferred parameters in order to process the geoscience data.
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because claims 1-20 do not recite additional elements that integrate the exception into a practical application. In particular, claims 1, 16, and 20 recite the additional elements of a processor (¶ [00161]), memory storing instructions (¶ [0163]) and non-transitory computer readable media (¶ [0163]). These additional elements are recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). Further, claims 1, 16, and 20 recite the additional elements of “receiving…” which amounts to insignificant extra-solution activities which are not indicative of integration into a practical application as per MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under Step 2B, the claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is noted as a general computer {processor (¶ [00161]); memory storing instructions (¶ [0163]); non-transitory computer readable media (¶ [0163])}. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitations in the claims noted above are directed towards insignificant extra-solution activities. The claims are not patent eligible.
With respect to claims 2 and 17, the claim relates to integrating user input into the set of processing actions. This relates to a human presenting the recommended processing steps to their supervisor, receiving supervisor feedback, and revising the recommended processing steps to reflect the supervisor feedback. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 3 and 18, the claim relates to updating a hierarchy of models based on user input. This relates to a human creating three collections of data, the collections each corresponding to data relevant to a user, a group of users, and all users respectively. Each collection could be linked to one another based off of the shared knowledge between an individual user, a group of users, and all users. The human could then update each collection of data by adding new data relevant to an individual user, then propagating that new data to the group model, and then to the global model. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 4 and 19, the claim relates to visualizing geoscience data in a user interface and allowing additional natural language queries selecting a parameter recommendation. This relates to a human manually analyzing geoscience data, drawing a frequency graph based on the geoscience data on paper, and then presenting the frequency graph to their supervisor. The human could also ask the supervisor if the derived parameters were acceptable, and if so, proceed with processing the geoscience using the parameters. The additional limitation of a “user interface” is recited at a high level of generality (¶ [0131]) and merely equates to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 5, the claim relates to processing actions comprising an executable processing script. This relates to a human manually writing out programming code that could process the geoscience data using pen and paper. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 6, the claim relates to specifying the domain to be at least one of a source, a type, or a representative of a type of geoscience data. This relates to a human identifying the source of the geoscience data they are analyzing. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 7, the claim relates to signal features representing one of a set of statistics or a frequency spectrum. This relates to a human identifying the portion of the geoscience data to be a frequency spectrum. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 8, the claim relates to cleaning data of irregularities based on the domain. This relates to a human identifying a feature of the geoscience data to be unusual for the domain, and then subsequently removing the feature from the geoscience data. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 9, the claim relates to integrating actions from at least two separate software modules. This relates to a human writing a script to process the geoscience data, the script comprising functions from two different software applications. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 10, the claim relates to determining a format of the geoscience data based on the identified domain. This relates to a human determining that the domain of the geoscience data is earth science, and recognizing that earth science data is typically represented in NetCDF format. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 11, the claim relates to a determined parameter comprising a filter configuration parameter. This relates to a human translating from a supervisor’s request a parameter that relates to the cutoff frequency of a low-pass filter. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 12, the claim relates to outputting a natural language statement listing a set of processing actions. This relates to a human reporting a high-level overview of the processing script to their supervisor that they derived from the supervisor’s initial instruction. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 13 and 14, the claim relates to receiving a natural language query using a certain types of interfaces. The additional element of “receiving…” amounts to insignificant extra-solution activities which are not indicative of integration into a practical application as per MPEP 2106.05(g). No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 15, the claim relates to receiving a natural language query in response to a prompt to the user. This relates to a human asking their supervisor how the supervisor would want the geoscience data to be processed. The additional limitation of a “natural language model” is recited at a high level of generality (¶ [0012]) and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
For all of the above reasons, taken alone or in combination, claims 1-20 recite a non-statutory mental process.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-6, 9-10, 12-17, and 19-20 are rejected under 35 U.S.C. 103 as obvious over "FlowMind: Automatic Workflow Generation with LLMs" (Zeng et al.) in view of "GEO-WMS: an improved approach to geoscientific workflow management system on HPC" (Guo et al.).
Claim 1
Regarding claim 1, Zeng et al. disclose a method for guided processing of workflows for geoscience data, the method comprising:
receiving [geoscience] data (Zeng et al. pg. 75, Section 3.1, Paragraph 1, "The first stage of the FlowMind framework involves a lecture on the context, available APIs, and the need to generate workflow code for the LLM.") [describing a subsurface region];
identifying a domain of the [geoscience] data (Zeng et al. pg. 76, Section 4, Paragraph 1, "we introduce the context which covers the domain of the expected tasks/queries from the user. For example, in our experiments, we set up the context as handling information queries from user, as shown in Figure 3.") [, the domain representing a set of signal features for that geoscience data that are relevant to data processing of the geoscience data;
receiving a natural language query specifying a data processing objective to be performed for the [geoscience] data having the domain (Zeng et al. pg. 75, Section 3.1, Paragraph 1, "We adhere to our proposed generic lecture recipe to generate an informative lecture on the context and APIs, ensuring the LLM has a clear understanding of the overall goal, as well as the scope, inputs, and outputs of the functions in the APIs." See Figure 3, which illustrates a natural language query specifying a data processing objective (request to write python code that processes data reports) to be performed for data (provided APIs) having the domain (provided context)), the data processing objective specifying particular output data for generation by a set of processing actions (Zeng et al. pg. 75, Section 3.1, Paragraph 1, "Lastly we ask the LLM to prepare to write the workflow code using the provided APIs upon receiving user query/task.");
translating, based on the identified domain, the natural language query, the translating comprising generation of a set of recommendations for processing the [geoscience] data (Zeng et al. pg. 75, Section 3.2, Paragraph 1, "In the second stage, LLM leverages the API knowledge gained from the first stage to take user queries or tasks and generate corresponding workflow code. ... In code generation, LLM creates a workflow, making use of the introduced APIs to address the user’s query or task effectively." See Figure 4-6. Suggested code such as parameter values for API functions are considered analogous to recommendations), the recommendations specifying one or more parameter values (Zeng et al. pg. 76, Figure 3, "The proposed generic lecture recipe includes: ... 2) enumerating the available APIs with each function declaration, parameters, and high-level descriptions" See Figures 4-6, which illustrates generated workflow code specifying a parameter value (fund_name="COMWELL CENTERSQUARE REAL ESTATE FUND")); and
executing, based on the set of recommendations specifying one or more parameter values, the set of processing actions for processing the [geoscience] data (Zeng et al. pg. 75, Section 3.2, Paragraph 1, "In the second stage, LLM leverages the API knowledge gained from the first stage to take user queries or tasks and generate corresponding workflow code. ... The workflow is then executed to generate the output to user. We show examples of the auto-generated workflows by FlowMind and derived answers to several sample user questions in our Experiments (Figure 4, 5, 6).").
Zeng et al. do not explicitly disclose all of an guiding the process of geoscientific workflows.
However, Guo et al. disclose a method for guided processing of workflows for geoscience data, the method comprising:
receiving geoscience data describing a subsurface region (Guo et al. pg. 366, Section 4.4, Paragraph 1, "In the proposed system, the integration of data from different sources is implemented. ... we provide support for remote sensing data (Zheng et al. 2021) and geophysical data (Sun et al. 2017) in the system." By definition, geophysical data includes subsurface data);
identifying a domain of the geoscience data (Guo et al. pg. 363, Section 3.2, Paragraph 1, "When a scientific computing user logs on to the workflow management system, the system gives a welcome message and determines whether the user is a veteran user, and if so, gives a recommendation for use. This recommendation comes from the analysis of the user’s history operation records." pg. 364, Section 4.3, Paragraph 1, "For old users, the method uses the operation frequency ranking to provide operation suggestions to users; for new users, the method will provide operation suggestions to the user based on the team to which the user belongs and the operation habits of other old users in the group." Modifying the current system configuration based on historical operation data implies identifying a domain of the geoscience data based on user identification), the domain representing a set of signal features for that geoscience data that are relevant to data processing of the geoscience data (Guo et al. pg. 364-365, Section 4.3, Paragraph 2, "The user data output from the Earth system model is often in a binary format based on NetCDF (Rew and Davis 1990), which is not directly comprehensible to the user. In previous practice, users often needed to use some third-party tool to convert this binary data into a readable format as well as a picture. Therefore, we have integrated automatic diagnostic analysis of the data into the user agent so that users can automate the visualisation of model runs. In addition, for research areas such as remote sensing and geophysical evolution, there will be visualisation results stored in video format which need to be presented to the user." By definition, NetCDF data includes signal features for scientific data that are relevant to data processing of scientific data. NetCDF data for geophysical data is considered analogous to a set of signal features for geoscience data that are relevant to data processing of that geoscience data);
receiving a natural language query (Guo et al. pg. 363, Section 3.2, Paragraph 1, "The user submits a description of the scientific workflow, and the system calls the parser to parse the workflow description and compare it with the historical workflow records in the workflow library") [specifying a data processing objective to be performed for the geoscience data having the domain, the data processing objective specifying particular output data for generation by a set of processing actions];
translating, based on the identified domain, the natural language query, the translating comprising generation of a set of recommendations for processing the geoscience data (Guo et al. pg. 363-364, Section 4.2, Paragraph 1, "The proposed parser converts the workflow requirements submitted by the user into XML format, determines the actions to be performed at each step of the workflow, and drives the executables required for each step through shell scripts" See Figures 2 and 3. A shell script translated from a user description of a workflow is considered analogous to a set of recommendations for processing the geoscience data), the recommendations specifying one or more parameter values (Guo et al. pg. 364, Section 4.2, Paragraph 1, "As can be seen, the model types, job names, commands to be executed at each step and their parameters are stored in an orderly manner in the description of the workflow, which creates a common workflow interaction interface for the different models, thus greatly enhancing the versatility of the management system and thus better serving researchers in various fields of earth system science."); and
executing, based on the set of recommendations specifying one or more parameter values, the set of processing actions for processing the geoscience data (Guo et al. pg. 364, Section 4.2, Paragraph 2, "In addition to sequential execution, concurrent execution is also a common work scenario in everyday use. ... When a user initiates a concurrent workflow, if the parser reads information about the concurrency from the description, it automatically generates a concurrent execution script file and submits it to the HPC compute cluster in parallel, as can be seen in Fig. 3.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zeng et al.’s guided processing system to incorporate Guo et al.’s application to geosciences.
The suggestion/motivation for doing so would have been that, “With the idea of scientific workflow management at its core, [GEO-WMS] abstracts various experiments in geoscientific research into workflows, enabling efficient management of experiments and data, saving computing resources and simplifying user operations, thus improving the efficiency of scientific research,” as noted by Guo et al. in pg. 361, Section 1, Paragraph 6.
Claim 2
Regarding claim 2, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Zeng et al. further disclose receiving at least one user input validating the recommendations specifying one or more parameter values (Zeng et al. pg. 75, Section 3.2, Paragraph 2, "A distinct feature of FlowMind is the ability to take user feedback during the second stage. The system presents a high-level description of the generated workflow to the user, enabling users to understand the workflow’s functionality and structure without the need to closely examine the underlying code. This allows the users to effectively provide feedback on the generated workflow, which the LLM can then incorporate to refine the workflow if necessary, ensuring that the system accurately addresses the user’s needs."); and
responsive to the user input, updating the set of processing actions for processing the [geoscience] data to change at least one processing step to a different processing step (Zeng et al. pg. 80, Figure 7 illustrates updating the set of processing actions (workflow code) to change at least one processing step to a different processing step (month parameter removed from line 1; month variable removed from line 8 ) responsive to user feedback).
Claim 4
Regarding claim 4, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above.
Zeng et al. further disclose receiving, for at least one processing step of the set of data processing actions, an additional natural language query selecting a recommendation of a parameter value for performing the at least one processing step (Zeng et al. pg. 75, Section 3.2, Paragraph 2, "A distinct feature of FlowMind is the ability to take user feedback during the second stage. The system presents a high-level description of the generated workflow to the user, enabling users to understand the workflow’s functionality and structure without the need to closely examine the underlying code. This allows the users to effectively provide feedback on the generated workflow, which the LLM can then incorporate to refine the workflow if necessary, ensuring that the system accurately addresses the user’s needs." See Figure 7, which illustrates an additional natural language query ("The month is not a part of the question, it's just part of the fund name") selecting a recommendation of a parameter value ("month" recommendation was selected for removal from the workflow code)).
Guo et al. further disclose presenting, in a user interface after at least one processing step, a visualization of the geoscience data being processed at the at least one processing step (Guo et al. pg. 365, Section 4.3, Paragraph 2, "In previous practice, users often needed to use some third-party tool to convert this binary data into a readable format as well as a picture. Therefore, we have integrated automatic diagnostic analysis of the data into the user agent so that users can automate the visualisation of model runs. In addition, for research areas such as remote sensing and geophysical evolution, there will be visualisation results stored in video format which need to be presented to the user.").
Claim 5
Regarding claim 5, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Zeng et al. further disclose wherein the one or more processing actions comprise an executable processing script (Zeng et al. pg. 75, Section 3.2, Paragraph 1, "In the second stage, LLM leverages the API knowledge gained from the first stage to take user queries or tasks and generate corresponding workflow code. This stage involves two key components: code generation and code execution. In code generation, LLM creates a workflow, making use of the introduced APIs to address the user’s query or task effectively.").
Guo et al. further disclose wherein the one or more processing actions comprise an executable processing script (Guo et al. pg. 363-364, Section 4.2, Paragraph 1, "The proposed parser converts the workflow requirements submitted by the user into XML format, determines the actions to be performed at each step of the workflow, and drives the executables required for each step through shell scripts").
Claim 6
Regarding claim 6, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Guo et al. further disclose wherein the domain specifies at least one of a source of the geoscience data, a type of the geoscience data, and a type of geoscience represented in the geoscience data (Guo et al. pg. 364-365, Section 4.3, Paragraph 2, "The user data output from the Earth system model is often in a binary format based on NetCDF (Rew and Davis 1990), which is not directly comprehensible to the user. ... we have integrated automatic diagnostic analysis of the data into the user agent so that users can automate the visualisation of model runs." NetCDF data is considered a type of geoscience data).
Claim 9
Regarding claim 9, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Guo et al. further disclose integrating the set of data processing actions to be performed by a set of at least two software modules from separate data processing applications (Guo et al. pg. 362, Section 3.1.2, Paragraph 2, "For all users in the system to operate several types of scientific software, the framework maintains a database that tracks the frequency of software use by each user. Based on the identity of the user, when the user logs in, the framework can recommend the most likely software to the user based on the results of the history records. By analyzing the user’s utilization history, the framework can identify the most frequently used software category for each user.").
Claim 10
Regarding claim 10, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Guo et al. further disclose determining a format of the geoscience data based on the identified domain (Guo et al. pg. 364-365, Section 4.3, Paragraph 2, "The user data output from the Earth system model is often in a binary format based on NetCDF (Rew and Davis 1990), which is not directly comprehensible to the user. ... we have integrated automatic diagnostic analysis of the data into the user agent so that users can automate the visualisation of model runs." NetCDF binary is considered a format of geoscience data).
Claim 12
Regarding claim 12, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Zeng et al. further disclose wherein the one or more recommendations comprise a natural language output (Zeng et al. pg. 75, Section 3.2, Paragraph 2, "The system presents a high-level description of the generated workflow to the user, enabling users to understand the workflow’s functionality and structure without the need to closely examine the underlying code." A high-level workflow description is considered analogous to a natural language output. See Figure 7 for an example of a high-level workflow description) listing a set of processing actions for subsequent performance on the [geoscience] data (Zeng et al. pg. 75, Figure 2 description, "During stage 2, we enable a feedback loop between FlowMind and the user, where FlowMind provides high-level description of the generated workflow in plain-language, and the user inputs feedback to FlowMind to approve or refine the workflow if needed." See Figure 7, which illustrates a high-level workflow description listing a set of processing actions for performance on data) based on a current processing status of the [geoscience] data (Zeng et al. pg. 80, Figure 7 illustrates the high-level workflow description being based on a current processing status of the data (depicted initial workflow code)). Guo et al. disclose an application to geoscience data.
Claim 13
Regarding claim 13, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Guo et al. further disclose wherein receiving the natural language query is performed using a natural language input-output interface (Guo et al. pg. 363, Section 4.1, Paragraph 1, "In GEO-WMS, a visual, easy-to-operate human-machine interface is provided to the user via web services. Users can access the system via web pages. We provide clean and simple pages on which workflows can be created quickly with a single mouse click" Figure 1 description, “Users can initiate requests for geoscience workflows and view visualised final computation results via the web front-end.” See Figure 1).
Claim 14
Regarding claim 14, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Guo et al. further disclose wherein receiving the natural language query is performed using a chatbot or artificial intelligence agent (Guo et al. pg. 362, Section 3.1.2, Paragraph 1, "User agent: The function of this component is twofold: on the one hand, it analyzes the past operation history of scientific computing users, and on the other hand, it recommends software choices for new users." See Figure 1, which illustrates a user's interaction with the user agent component of GEO-WMS).
Claim 15
Regarding claim 15, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Zeng et al further disclose wherein receiving the natural language query is responsive to generating a prompt to a user (Zeng et al. pg. 75, Section 3.2, Paragraph 1, “In the second stage, LLM leverages the API knowledge gained from the first stage to take user queries or tasks and generate corresponding workflow code.”), the prompt being tuned using a natural language model (Zeng et al. pg. 75, Section 3.1, Paragraph 1, “The first stage of the FlowMind framework involves a lecture on the context, available APIs, and the need to generate workflow code for the LLM” See Figure 3, which illustrates tuning a natural language model (LLM) with context and API documentation in order to prepare for user queries.).
Claim 16
Regarding claim 16, Guo et al. disclose a system for a guided processing of workflows for geoscience data, the system comprising:
at least one processor (Guo et al. pg. 361, Section 1, Paragraph 6, "Based on the above ideas, we have designed a Geoscientific Workflow Management System (GEO-WMS) for the earth science field. ... After 24 weeks of testing, the system has proven to be an excellent performer on Sunway TaihuLight supercomputer." The use of a supercomputer implies the use of at least one processor); and
a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations (Guo et al. pg. 361, Section 1, Paragraph 6, "Based on the above ideas, we have designed a Geoscientific Workflow Management System (GEO-WMS) for the earth science field. ... After 24 weeks of testing, the system has proven to be an excellent performer on Sunway TaihuLight supercomputer." The use of a supercomputer implies the use of memory storing instructions).
The remaining limitations of claim 16 are similar in scope to that of claim 1 and therefore are rejected for similar reasons as described above.
Claim 17
Regarding claim 17, the rejection of claim 16 is incorporated. The limitations of claim 17 are similar in scope to that of claim 2 and therefore are rejected for similar reasons as described above.
Claim 19
Regarding claim 19, the rejection of claim 16 is incorporated. The limitations of claim 19 are similar in scope to that of claim 4 and therefore are rejected for similar reasons as described above.
Claim 20
Regarding claim 20, Guo et al. disclose one or more non-transitory computer readable media storing instructions for a guided processing of workflows for geoscience data (Guo et al. pg. 361, Section 1, Paragraph 6, "Based on the above ideas, we have designed a Geoscientific Workflow Management System (GEO-WMS) for the earth science field. ... After 24 weeks of testing, the system has proven to be an excellent performer on Sunway TaihuLight supercomputer." The use of a supercomputer implies the use of one or more non-transitory computer readable media).
The remaining limitations of claim 20 are similar in scope to that of claim 1 and therefore are rejected for similar reasons as described above.
Claims 3 and 18 are rejected under 35 U.S.C. 103 as obvious over Zeng et al. in view of Guo et al. as applied to claims 1 and 16 above, and further in view of US Patent Publication 20250124223 A1 (Kirk) in view of US Patent Publication 20250131321 A1 (Yu et al.).
Claim 3
Regarding claim 3, the rejection of claim 2 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above. Guo et al. further disclose a user model associated with the user (Guo et al. pg. 362, Section 3.1.2, Paragraph 2, "Based on the identity of the user, when the user logs in, the framework can recommend the most likely software to the user based on the results of the history records. By analyzing the user’s utilization history, the framework can identify the most frequently used software category for each user."), the user model linked to a group model (Guo et al. pg. 362, Section 3.1.2, Paragraph 2, "By analyzing the user’s utilization history, the framework can identify the most frequently used software category for each user. On this basis, we can profile users in different groups, such as climate model group, weather model group, operation and maintenance group, etc.").
Zeng et al. in view of Geo et al. do not explicitly disclose all of a hierarchy of models.
However, Kirk discloses updating, based on the user input (Kirk ¶ [0067], "At 820, the server 815 may receive user input that may indicate one or more user preferences associated with the user."), a user model associated with the user (Kirk ¶ [0077], "At 865, the server 815 may update the metadata associated with the user-level LLM based on the updated user-level LLM."), the user model linked to a group model and a global model in a model hierarchy (Kirk ¶ [0068]-[0071], "At 825, the server 815 may derive a first intermediate LLM based on a broad spectrum LLM and the first intermediate LLM is associated with a first level of a hierarchy ... At 835, the server 815 may derive a user-level LLM based on the first intermediate LLM" A first intermediate LLM is considered analogous to a group model. A broad spectrum LLM is considered analogous to a global model), wherein the group model is configured to receive first processing parameters data (Kirk ¶ [0057], "Additionally, or alternatively, the system may continuously or regularly track the LLM's performance and user feedback, make updates to existing LLMs, and create new derivatives if required.") [from a set of user models including the user model based on execution of the set of processing actions for processing the geoscience data for the user model], and wherein the global model is configured to receive second processing parameters data (Kirk ¶ [0057], "Additionally, or alternatively, the system may continuously or regularly track the LLM's performance and user feedback, make updates to existing LLMs, and create new derivatives if required.") [from a set of group models including the group model based on the first processing parameters received by the group model].
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zeng et al. in view of Guo et al. to incorporate Kirk’s hierarchy of models.
The suggestion/motivation for doing so would have been that, “a multi-layered customization framework may be employed to create derivative LLMs tailored to the needs, preferences, or use cases for particular users of an organization. Unlike existing solutions, this framework enables customization at multiple hierarchical levels, including software companies, customers, organizations, and individual users,” as noted by the Kirk disclosure in paragraph [0018].
Zeng et al. in view of Geo et al. in view of Kirk do not explicitly disclose all of passing parameter data between multiple models.
However, Yu et al. disclose wherein the group model is configured to receive first processing parameters data from a set of user models including the user model (Yu et al. ¶ [0080], "the training system(s) 114 can, for example, train the proxy model 112 based, at least in part, on reference data 118 from the reference model 102." A reference model is considered analogous to a user model. A proxy model is considered analogous to a group model) based on execution of the set of processing actions for processing the [geoscience] data for the user model (Yu et al. ¶ [0071], "reference data 118 can be, for example, one or more values generated by the reference model 102 based on training inputs (e.g., training sampled 120) passed to the reference model 102 from the training systems 114 during subsequence (b)."), and wherein the global model is configured to receive second processing parameters data from a set of group models including the group model (Yu et al. ¶ [0082], "Block (b) of FIG. 1 also depicts one or more distribution updates 128, in which the training system(s) 114 can update the learned distribution parameters 126. The distribution updates 128 can be performed periodically throughout the training of the proxy model 112, such as one distribution update 128 after every training iteration 116." ¶ [0101]-[0102], "The calibrated training distribution 138 can be based on the learned distribution parameters 126. ... the training system(s) 134 can, for example, train the primary model 132 based, at least in part, on a calibrated training distribution 138." A primary model (global model) is trained on component 138 which is based on learned parameters 126. Training the proxy model (group model) updates learned parameters 126. Therefore, the primary model (global model) is configured to receive parameter data from proxy models (group models)) based on the first processing parameters received by the group model (Yu et al. ¶ [0080], "the training system(s) 114 can, for example, train the proxy model 112 based, at least in part, on reference data 118 from the reference model 102.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zeng et al. in view of Guo et al. in view of Kirk to incorporate Yu et al.’s update propagation.
The suggestion/motivation for doing so would have been that, “example implementations can obtain an improved training data distribution with less computational expense and can leverage the learned training data distribution to better train a large primary model,” as noted by the Yu et al. disclosure in paragraph [0043].
Claim 18
Regarding claim 18, the rejection of claim 17 is incorporated. The limitations of claim 18 are similar in scope to that of claim 3 and therefore are rejected for similar reasons as described above.
Claims 7, 8, and 11 are rejected under 35 U.S.C. 103 as obvious over Zeng et al. in view of Guo et al. as applied to claim 1 above, and further in view of US Patent Publication 20210019300 A1 (Marathe).
Claim 7
Regarding claim 7, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above.
Zeng et al. in view of Guo et al. do not explicitly disclose all of signal features representing one of a set of statistics or a frequency spectrum.
However, Marathe discloses wherein the signal features represent one or more of a set of statistics on the data such as minimum, maximum, and average values (Marathe ¶ [0054], "The following four statistical metrics on the L and R windows are defined. The mean value of the portion of time-series data 205 that is located within the L-window 220 is denoted by
μ
L
.... The mean value of the portion of time-series data 205 that is located within the R-window 225 is denoted by
μ
R
") or a frequency spectrum (Marathe ¶ [0098]-[0100], "the unsmoothed N statistics are directly processed by the anomaly detection system 100. ... This embodiment may be preferred in application domains in which statistics contain short-duration or “spiky” (i.e. with high-frequency signal components) anomalies. Smoothing might have eliminated such anomalies incorrectly. ").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zeng et al. in view of Guo et al. to include Marathe’s signal features because such a modification is the result of simple substitution of one known element for another producing a predictable result. More specifically, Guo et al.’s NetCDF data and Marathe’s signal features perform the same general and predictable function, the predictable function being providing the system with earth science data. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself - that is in the substitution of Guo et al.’s NetCDF data by replacing it with Marathe’s signal features. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim 8
Regarding claim 8, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above.
Zeng et al. in view of Guo et al. do not explicitly disclose all of a data cleansing method.
However, Marathe discloses performing data cleansing based on the domain of the of the geoscience data (Marathe ¶ [0040], "the anomaly detection method and system is applied to one or more different application domains, such as but not necessarily limited to processing of ... weather data, sensor data, ... , scientific data, geological data, ... , etc. "), the data cleansing removing data irregularities the geoscience data that are identified based on the domain (Marathe ¶ [0039], "Embodiments of the present invention provide for a method and system for automatically detecting anomalies in data, such as time-series data. ... the method and system also provides a mechanism to filter out some of the automatically detected anomalies, for example by removing less important anomalies. The filtering may be based on a statistical score.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zeng et al. in view of Guo et al. to include Marathe’s data cleaning because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Zeng et al. in view of Guo et al. as modified by Marathe’s data cleaning can yield a predictable result of improving geoscientific workflow analysis since cleaner data is easier to analyze than noisy data. Thus, a person of ordinary skill would have appreciated including in Zeng et al. in view of Guo et al. the ability to do Marathe’s data cleaning since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 11
Regarding claim 11, the rejection of claim 1 is incorporated. Zeng et al. in view of Guo et al. disclose all the elements of the claimed invention as stated above.
Zeng et al. in view of Guo et al. do not explicitly disclose all of parameter values comprising filter configuration parameters.
However, Marathe discloses wherein the one or more parameter values comprise filter configuration parameters (Marathe ¶ [0112]-[0113], "For the anomaly filter, variable parameters include the window width used to determine anomaly closeness. ... Some or all of the parameter values may be set based on some or all of the following information: the number of reported anomalies that expert users can handle, per day for example; the number of false-positive anomalies being reported; the number of false-negative anomalies (anomalies being missed); and, if data is streaming in, a capacity of the computing platform to perform anomaly detection tasks in real time.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zeng et al. in view of Guo et al. to include Marathe’s filter parameter because such a modification is the result of simple substitution of one known element for another producing a predictable result. More specifically, Zeng et al.’s function parameters and Marathe’s filter parameters perform the same general and predictable function, the predictable function being providing functions with an appropriate configuration for correct operation. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself - that is in the substitution of Zeng et al.’s function parameters by replacing it with Marathe’s filter parameters. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Conclusion
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/JACOB B VOGT/ Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
11/14/2025
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