DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1 recites “Al model”, however, “AI” needs to be spelled out at least the first time it is mentioned. Appropriate correction is required.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims’ subject matter eligibility will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
With respect to claim 1.
Claim 1 is 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 to a process, machine, manufacture, or composition of matter? Yes—claim 1 recites an apparatus .
Step 2A, prong one: the limitations identified below each, under its broadest reasonable interpretation, covers mental processes abstract idea grouping (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)), see MPEP 2106.04(a)(2), subsection III and the 2019 PEG, but for the recitation of generic computer components:
“an acquisition process of acquiring trial information including a parameter used in a trial in a process of constructing an AI model;
an inference process of inferring association between a plurality of trials on the basis of a difference between respective pieces of trial information of the plurality of trials; and
an output process of outputting display data including (i) a plurality of nodes respectively representing the plurality of trials and (ii) a link representing the association”: (Mental processes- concept of collecting information about AI model development, analyzing the information to determine relationship and presenting results, which is known that it can be done in the mind or using pen and paper).
This falls within the mental process grouping of abstract ideas that can be performed in the human mind, or by a human with pencil and paper. Thus, Claim 1 recites an abstract idea.
Step 2A, prong two: the judicial exception is not integrated into a practical application. The claim includes the additional elements:
“apparatus comprising at least one processor, the at least one processor carrying out”: high-level of generality using generic computer components (i.e., using a generic processor with generic memory to do generic computer functions).
“an acquisition process of acquiring trial information including a parameter used in a trial in a process of constructing an AI model;” involves the mere gathering of data or transmitting data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
“an output process of outputting display data including (i) a plurality of nodes respectively representing the plurality of trials and (ii) a link representing the association”: involves the mere gathering of data or transmitting data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
Therefore, the above additional elements do not integrate the judicial exception into a practical application.
Step 2B: The additional elements do not amount to significantly more because:
“an acquisition process of acquiring trial information including a parameter used in a trial in a process of constructing an AI model and an output process of outputting display data including (i) a plurality of nodes respectively representing the plurality of trials and (ii) a link representing the association;” involves the mere gathering of data, which is well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Regarding Claim 2,
Claim 2 is dependent on claim 1, “wherein the plurality of nodes included in the display data are arranged in an order in which the plurality of trials have been carried out.”.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding Claim 3,
Claim 3 is dependent on claim 1, “each piece of trial information includes a first parameter and a second parameter different from the first parameter; in the inference process, the at least one processor extracts, as a first group of trials associated with each other, a plurality of trials between which the first parameter is the same and the second parameter changes; and in the output process, the at least one processor outputs the display data including (i) a plurality of nodes representing the first group of trials and (ii) a link connecting the plurality of nodes representing the first group of trials to each other.”.
includes additional elements directed to additional mental processes of data analysis such as observing and evaluating the data.
Regarding Claim 4,
Claim 4 is dependent on claim 3, “each piece of trial information further includes a third parameter different from the first parameter and from the second parameter; in the inference process, the at least one processor extracts, as a second group of trials associated with each other, a plurality of trials between which each of the second parameter and the third parameter is the same and the first parameter changes, and identifies, as a branch point in the first group of trials, a trial in the first group of trials which trial has the same first parameter and the same second parameter as those of a temporally first trial in the second group of trials; and in the output process, the at least one processor outputs the display data in which nodes and a link representing the second group of trials (i) branch from a node representing the trial at the branch point among the plurality of nodes representing the first group of trials and (ii) are connected to each other.”.
includes additional elements directed to additional mental processes of data analysis such as observing and evaluating the data.
Regarding Claim 5,
Claim 5 is dependent on claim 4, “wherein in the display data, a plurality of nodes corresponding to a plurality of trials included in the first group of trials and the second group of trials are arranged in a predetermined direction in an order in which the plurality of trials have been carried out”.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding Claim 6,
Claim 6 is dependent on claim 1, “wherein the display data includes information indicative of performance of the AI model obtained in each trial.”.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding Claim 7,
Claim 7 is dependent on claim 1, “wherein the display data includes a parameter used in each trial”.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding Claim 8,
Claim 8 is dependent on claim 1, “wherein: in the inference process, the at least one processor identifies, among the plurality of trials, a trial in which performance of the AI model has improved or degraded in comparison to a preceding trial having the association with the trial;
includes additional elements directed to additional mental processes of data analysis such as observing and evaluating the data.
and in the output process, the at least one processor outputs (i) a node representing the trial identified and/or (ii) a link connecting the node and a node representing the preceding trial to each other, in a mode different from that of another node or another link.”
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding Claim 9,
Claim 9 is dependent on claim 8, “wherein in the output process, the at least one processor outputs at least one selected from the group consisting of: a color tone of the node representing the trial identified; a size of the node; a shape of the node; a color tone of the link; and a thickness of the link, in a mode different from that of another node or another link.”.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding Claim 10,
Claim 10 is dependent on claim 8, “wherein the display data includes a node in a mode corresponding to a degree of improvement or degradation in the performance.”.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding Claim 11,
Claim 11 is dependent on claim 8, “wherein in a case where a difference between performance of the AI model obtained in the trial identified and performance of the AI model obtained in the preceding trial is not more than a predetermined threshold, the node representing the trial identified and the node representing the preceding trial are included in the display data in a mode in which the node representing the trial identified and the node representing the preceding trial overlap each other at least partially.”.
includes additional elements directed to additional mental processes of comparing data with a threshold such as observing and evaluating the data and using a computer to display data.
Claim 12 is directed to method performing a process that has limitations similar to the limitations of claim 1. Thus, claim 8 is rejected with the same rationale applied against claim 1, as performing a mental process or abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 8 remains subject matter ineligible.
Claim 13 is directed to non-transitory storage medium performing a process that has limitations similar to the limitations of claim 1. Thus, claim 13 is rejected with the same rationale applied against claim 1, as performing a mental process or abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 13 remains subject matter ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 and 3, 6-10 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lipkin et al. (US 20170178021 A1) in view of Convertino et al. (US 20200097847 A1).
Regarding claim 1.
Lipkin teaches a model generation assistance apparatus comprising at least one processor, the at least one processor (see ¶ 48 and 54, processor and multi-processor) carrying out: an acquisition process of acquiring trial information including a parameter used in a trial in a process of constructing an AI model (see ¶ 22, “Note that the different permutations may be useful for executing experiment graphs with different nodes providing different input. In the illustrated example shown in FIG. 1, this takes one of two different forms. The first form is when two copies of the same node are used to provide input, but where the two copies each have different operating parameters. For example, FIG. 1 illustrates the nodes 104-3 and 104-5 are both principal component analysis nodes. However, in this example, the node 104-3 may use five coefficients while the node 104-5 may use seven coefficients. Thus, different permutations of the graph 108 will be executed where the project columns node 104-1 receives input from a principle component analysis using five coefficients in some permutations and receives input from a principle component analysis using seven coefficients in other permutations. Thus, in this example, the nodes represent the same analysis tool, but with different parameters.”, i.e. same node with different operating parameters, also see ¶ 27, “Each connection to the same input port defines a variation to the base experiment, since input ports can only run with a single connection at a time. When executing a base experiment, a new experiment is created for each variation. In some embodiments, a new experiment is created by removing all but one connection from each input port with more than one connection. A new experiment is created by the system for of the combinations of inputs across all nodes with more than one input port connection.”, i.e. experiment variation);
an inference process of inferring association between a plurality of trials on the basis of a difference between respective pieces of trial information of the plurality of trials (see ¶ 21, “the user interface 100 may include graph execution elements that allows the graph 108 to be executed. When a user selects a graph execution element, the system 200 will automatically identify the various permutations of the graph that may exist. In particular, in some embodiments, a given permutation reduces the graph such that each input port has only a single input coupled to it for the given permutation. Thus, for example, as illustrated in FIG. 1, there are four possible permutations: a first permutation where node 104-3 provides input data to the node 104-1 and the node 104-4 provides input data to the node 104-2; a second permutation where node 104-3 provides input data to the node 104-1 and the node 104-6 provides input data to the node 104-2; a third permutation where node 104-5 provides input data to the node 104-1 and the node 104-4 provides input data to the node 104-2; and a fourth permutation where node 104-5 provides input data to the node 104-1 and the node 104-6 provides input data to the node 104-2.”, also see ¶ 22-23, “FIG. 1 illustrates that both a multiclass neural network node 104-4 and a multiclass logical regression node 104-6 provide inputs to the input port 106-2 for the train model node 104-2. Thus, different permutations of the graph 108 that are executed due to these two different nodes (i.e., nodes 104-4 and 104-6) will be executed with different analysis tools.” and ¶ 27, i.e. permutations differ by parameters and node choices);
and an output process of outputting display data including (i) a plurality of nodes respectively representing the plurality of trials and (ii) a link representing the association (see ¶ 17, “FIG. 1 illustrates a user interface 100. As will be illustrated in more detail below, and with reference to FIG. 2, the user interface 100 may be displayed to a user by a machine learning system 200. The user interface 100 includes an experiment canvas 102. A user can place various nodes (sometimes referred to as ‘nodes’ or ‘graph nodes’) on the experiment canvas and connect the nodes by edges. In particular, nodes may have one or more input ports and one or more output elements. In a typical graph, each input port is only able to be connected to a single edge. However, embodiments described herein implement input ports that can be connected to multiple inputs (see e.g., node 104-1 and input port 106-1 and node 104-2 and input port 106-2).”, also see ¶ 44, “FIG. 4, a method 400 is illustrated. The method 400 may be practiced in a computing environment. The method 400 includes acts for providing interaction with a graph. The method includes displaying a graph with one or more nodes coupled to alternative inputs through a single port (act 402). As illustrated above, the graph 108 is displayed with nodes, such as node 104-1 connected to multiple alternative inputs.”).
Lipkin do not specifically teach parameter used in a trial in a process of constructing an AI model and an inference process of inferring association between a plurality of trials.
Convertino teaches a parameter used in a trial in a process of constructing an AI model and an inference process of inferring association between a plurality of trials (see ¶ 59, “Developing an ML model often involves iteratively experimenting with datasets, features, model algorithms, and parameters such as hyperparameters. To support this iterative process, the DS platform 320 can be configured to support running and repeating versioned experiments in parallel and on demand, as users analyze results and modify certain aspects of the ML model (e.g., through hyperparameter tuning). In the context of this disclosure, an “experiment” generally refers to any data processing workload that enables users to compare versioned reproduceable ML models.”, also see ¶ 88, “process 900 for facilitating hyperparameter tuning using visual analytics, according to some embodiments of the introduced technique. The example process 900 begins at step 902 with receiving results of a batch of experiments. The received results may include a plurality of performance metric values generated by the execution of multiple experiments using a dataset and a plurality of hyperparameter values. Hyperparameter values and performance metric values may include continuous, discrete, and/or categorical values.”, also see ¶ 90, “o support versioning of such data, each experiment is executed in an individual (i.e., isolated) resource container in the distributed computing cluster. Without versioned experiments, a user would need to consistently track the differences in the training artifacts (e.g., data, hyperparameters, code, performance metrics) across different experiments on their own. Even if the user somehow manages to track these varying values on their own, the lack of versioning would make it difficult to reproduce results and to explain results to other users.”).
Both Lipkin and Convertino pertain to the problem of development of machine learning (ML) models, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Lipkin and Convertino to teach the above limitations. The motivation for doing so would be “facilitating the tuning of hyperparameter values during the development of machine learning (ML) models using visual analytics in a data science platform. In an example embodiment, a computer-implemented data science platform is configured to generate, and display to a user, interactive visualizations that dynamically change in response to user interaction. Using the introduced technique, a user can, for example, 1) tune hyperparameters through an iterative process using visual analytics to gain and use insights into how certain hyperparameters affect model performance and convergence, 2) leverage automation and recommendations along this process to optimize the tuning given available resources, 3) collaborate with peers, and 4) view costs associated with executing experiments during the tuning process.” (see Convertino Abstract).
Regarding claim 3.
Lipkin and Convertino teaches the model generation assistance apparatus according to claim 1,
Lipkin further teaches wherein: each piece of trial information includes a first parameter and a second parameter different from the first parameter (see ¶ 22, “Note that the different permutations may be useful for executing experiment graphs with different nodes providing different input. In the illustrated example shown in FIG. 1, this takes one of two different forms. The first form is when two copies of the same node are used to provide input, but where the two copies each have different operating parameters. For example, FIG. 1 illustrates the nodes 104-3 and 104-5 are both principal component analysis nodes. However, in this example, the node 104-3 may use five coefficients while the node 104-5 may use seven coefficients. Thus, different permutations of the graph 108 will be executed where the project columns node 104-1 receives input from a principle component analysis using five coefficients in some permutations and receives input from a principle component analysis using seven coefficients in other permutations. Thus, in this example, the nodes represent the same analysis tool, but with different parameters.”, i.e. same node with different operating parameters, 5 coefficient vs 7 coefficient); in the inference process, the at least one processor extracts, as a first group of trials associated with each other, a plurality of trials between which the first parameter is the same and the second parameter changes (see ¶ 27, “Each connection to the same input port defines a variation to the base experiment, since input ports can only run with a single connection at a time. When executing a base experiment, a new experiment is created for each variation. In some embodiments, a new experiment is created by removing all but one connection from each input port with more than one connection. A new experiment is created by the system for of the combinations of inputs across all nodes with more than one input port connection.”, i.e. experiment variation); and in the output process, the at least one processor outputs the display data including (i) a plurality of nodes representing the first group of trials and (ii) a link connecting the plurality of nodes representing the first group of trials to each other (see ¶ 17, “FIG. 1 illustrates a user interface 100. As will be illustrated in more detail below, and with reference to FIG. 2, the user interface 100 may be displayed to a user by a machine learning system 200. The user interface 100 includes an experiment canvas 102. A user can place various nodes (sometimes referred to as ‘nodes’ or ‘graph nodes’) on the experiment canvas and connect the nodes by edges. In particular, nodes may have one or more input ports and one or more output elements. In a typical graph, each input port is only able to be connected to a single edge. However, embodiments described herein implement input ports that can be connected to multiple inputs (see e.g., node 104-1 and input port 106-1 and node 104-2 and input port 106-2).”, also see ¶ 44, “FIG. 4, a method 400 is illustrated. The method 400 may be practiced in a computing environment. The method 400 includes acts for providing interaction with a graph. The method includes displaying a graph with one or more nodes coupled to alternative inputs through a single port (act 402). As illustrated above, the graph 108 is displayed with nodes, such as node 104-1 connected to multiple alternative inputs.”).
Regarding claim 6.
Lipkin and Convertino teaches the model generation assistance apparatus according to claim 1,
Lipkin further teaches wherein the display data includes information indicative of performance of the AI model obtained in each trial (see ¶ 24-¶ 29 teaches performance of the AI model obtained in each trial).
Convertino teaches display data includes information indicative of performance (see ¶ 88-¶ 91, information displayed)
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 6.
Regarding claim 7.
Lipkin and Convertino teaches the model generation assistance apparatus according to claim 1,
Lipkin further teaches wherein the display data includes a parameter used in each trial (see ¶ 24-¶ 29 teaches performance of the AI model obtained in each trial).
Convertino teaches display data (see ¶ 88-¶ 91, information displayed)
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 7.
Regarding claim 8.
Lipkin and Convertino teaches the model generation assistance apparatus according to claim 1,
Lipkin further teaches wherein: in the inference process, the at least one processor identifies, among the plurality of trials, a trial in which performance of the AI model has improved or degraded in comparison to a preceding trial having the association with the trial (see ¶ 24-¶ 29 comparing metrics among variations); and in the output process, the at least one processor outputs (i) a node representing the trial identified and/or (ii) a link connecting the node and a node representing the preceding trial to each other, in a mode different from that of another node or another link (see ¶ 20, teaches visual differentiation by color, shape etc.).
Regarding claim 9.
Lipkin and Convertino teaches the model generation assistance apparatus according to claim 8,
Lipkin further teaches wherein in the output process, the at least one processor outputs at least one selected from the group consisting of: a color tone of the node representing the trial identified; a size of the node; a shape of the node; a color tone of the link; and a thickness of the link, in a mode different from that of another node or another link (see ¶ 20, teaches visual differentiation by color, shape etc.).
Regarding claim 10.
Lipkin and Convertino teaches the model generation assistance apparatus according to claim 8,
Lipkin further teaches wherein the display data includes a node in a mode corresponding to a degree of improvement or degradation in the performance (see ¶ 20, teaches visual differentiation by color, shape etc., also see ¶ 24-25, “Metrics may be obtained for each permutation that allow a user to compare how efficiently one permutation operates in comparison to other permutations. Metrics may be obtained for each permutation that allow a user to compare how precise (e.g., how many significant figures a permutation has) one permutation is in comparison to other permutations. Metrics may be obtained for each permutation that allow a user to compare how quickly one permutation operates (assuming the same or similar computing hardware or compensating for differences in computing hardware) comparison to other permutations.”).
Claim 12 recites a method to perform the apparatus recited in claim 1. Therefore the rejection of claim 1 above applies equally here.
Claim 13 recites a non-transitory storage medium to perform the apparatus recited in claim 1. Therefore the rejection of claim 1 above applies equally here.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lipkin et al. (US 20170178021 A1) in view of Convertino et al. (US 20200097847 A1) in further in view Vathauer et al. (US 20190310756 A1).
Regarding claim 2.
Lipkin and Convertino teaches the model generation assistance apparatus according to claim 1,
Lipkin and Convertino do not teach claim 2.
Vathauer teaches wherein the plurality of nodes included in the display data are arranged in an order in which the plurality of trials have been carried out (see ¶ 46, “The method 200 may include, at block 204, sorting the objects list 222 by depth in the depth direction (Z) to develop a sorted objects list 224. In this example, the order in the objects list 222 is the same as the sorted objects list 224, but these lists may have a different order of objects relative to each other.”, also see ¶ 55, “The prioritized object is highlighted and allowed to be interacted with by clicking the cursor to select a permissible interaction. In some embodiments, clicking a currently prioritized object in the virtual environment may provide a default interaction with the object, such as to cause the virtual environment to automatically change some object parameter. An object parameter may include size, shading, location, etc. With respect to FIG. 1B, depending on the perspective of the objects relative to point Pt4, Pt4′, or point Pt5, the arrangement of the objects may appear differently on the screen or relative to the viewer.”).
Lipkin and Convertino and Vathauer pertain to the problem of parameters and model building , thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Lipkin and Convertino and Vathauer to teach the above limitations. The motivation for doing so would be “a set of objects of the plurality of objects having the shared coordinates (X.sub.S, Y.sub.S) and at a location along the depth direction (Z.sub.S); and prioritizing, by the processor, an object from the set of objects based on at least two of metadata of the set of objects, screen areas of the set of objects, transparency of the set of objects, and opaqueness of at least one object of the set of objects currently displayed to improve the selection of at least one of mutually occluded objects and mutually partially occluded objects in the virtual environment. The method includes associating the prioritized object with the viewer input device for detecting interactions with the prioritized object displayed on the display device by the viewer input device. The prioritized object is updated on the screen of the display device based on the interactions.” (see Vathauer Abstract).
Allowable Subject Matter
Claims 4-5 and 11 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Related prior arts:
Golovidov et al. (US 11151480 B1) teaches tuning process is repeatedly training and scoring a model type with different sets of values of hyperparameters defined based on the model type. An objective function value is computed for each set of values of the hyperparameters. Data stored in the history table is accessed and used to identify the hyperparameters. (A) A page template is selected from page templates that describe graphical objects presented in the display. (B) The page template is updated with the accessed data. (C) The display is updated using the updated page template. (D) At the end of a refresh time period, new data stored in the history table by the model tuning process is accessed. (E) (B) through (D) are repeated with the accessed data replaced with the accessed new data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMAD M KASSIM whose telephone number is (571)272-2958. The examiner can normally be reached 10:30AM-5:30PM, M-F (E.S.T.).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J. Huntley can be reached at (303) 297 - 4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IMAD KASSIM/Primary Examiner, Art Unit 2129
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