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 .
Remarks
This Office Action is responsive to Applicants' Amendment filed on July 8, 2026, in which claims 10, 15, and 16 are currently amended. Claims 10-20 are currently pending.
Response to Arguments
Applicant’s arguments with respect to rejection of claims 10-20 under 35 U.S.C. 101 based on amendment have been considered, however, are not persuasive. The claim as a whole appears to be directed towards a mental process of observation, evaluation, and judgement, which can be seen from a few key claim limitations: “recording data indicative of an observed data set” (observation), “based on the recorded data simulating” (evaluation), “obtaining user feedback”, “based on the user feedback, selecting” (judgement). The additional elements are seen as mere instructions to apply the judicial exception using generic computer components and well-understood, routine, and conventional insignificant extra-solution activity of gathering data which does not integrate the judicial exception into a practical application. For at least these reasons and those further detailed below, Examiner asserts that it is reasonable and appropriate to maintain the rejection under 35 U.S.C. 101.
Applicant’s arguments with respect to rejection of claims 10-20 under 35 U.S.C. 102/103 based on amendment have been considered and are persuasive. The argument is moot in view of a new ground of rejection set forth below.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 10 and 15, "different levels of abstraction" is indefinite. First, "level of abstraction" has no apparent objective boundary. What distinguishes one level from another? Different from what? Second, there is a structural ambiguity about what the phrase modifies. For example, the language could mean: each synthetic set is simulated at a respective level of abstraction, every synthetic set is simulated at multiple levels of abstraction, or the collection of sets collectively involves different levels, without any required one-to-one relationship. Since there are multiple contradictory interpretations the scope of the claim cannot reasonably be determined. In the interest of further examination the claim is interpreted as "the collection of sets collectively involves different levels, without any required one-to-one relationship".
The remaining claims are rejected with respect to their dependence on the rejected claims.
Claim Rejections - 35 USC § 101
101 Rejection
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 10-20 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 10: Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed towards a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 10 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass machine learning processing, including the following:
based on the recorded data, simulating, at different levels of abstraction, multiple different synthetic sets of automated inputs provided via automated engagement with the graphical elements of the GUI, wherein each synthetic set comprises different automated inputs that are varied from the manual inputs (observation, evaluation, and judgement. Examiner notes that simulating abstractions of automated inputs is seen as encompassing a mental process just as writing pseudocode is seen as a mental process of simulating an abstraction of compilation/machine execution logic),
based on the user feedback, selecting one of the multiple different synthetic sets of automated inputs (observation, evaluation, and judgement)
Therefore, claim 10 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 10 recites additional elements “one or more processors” and “causing a machine learning model to be trained to generate output indicative of the selected synthetic set of automated inputs”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Claim 10 also recites additional elements “recording data indicative of an observed set of manual inputs provided via manual engagement by a user with graphical elements of a graphical user interface (GUI) rendered at a computing device”, “obtaining user feedback about each of the multiple different synthetic sets of automated inputs” which amounts to gathering and outputting data which is insignificant extra-solution activity (See MPEP 2106.05(g)). Therefore, claim 10 is directed to a judicial exception.
Step 2B Analysis: Claim 10 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 10 amount to no more than mere instructions to apply the judicial exception using a generic computer component and insignificant extra-solution activity. The gathering and outputting of data is considered well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)).
For the reasons above, claim 10 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claim 15 which recite a system, as well as to dependent claims 10-14 and 16-20.
The additional elements of independent claim 15 “one or more processors and memory storing instructions that, in response to execution by the one or more processors, cause the one or more processors to” amount to mere instructions to apply the judicial exception using generic computer components.
The additional limitations of the dependent claims are addressed briefly below:
Dependent claim 11 recites additional observation, evaluation, and judgement “the simulating is performed based on the machine learning model.”
Dependent claims 12 and 17 recite additional instructions to apply the judicial exception using generic computer components “the machine learning model is trained to facilitate intelligent process automation.”
Dependent claims 13 and 18 recite additional mathematical calculations and relationships “the machine learning model is trained to generate a probability distribution over an action space”.
Dependent claims 14, 19, and 20 recite additional instructions to apply the judicial exception using generic computer components “the machine learning model comprises a private machine learning model” as well as additional insignificant extra-solution activity of gathering and outputting data “providing parameters of the trained private machine learning model for federated learning of a global machine learning model” which is well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)(i))
Dependent claim 16 recites additional instructions to apply the judicial exception using a generic computer component “wherein the machine learning model is used to simulate the multiple different synthetic sets of automated inputs”
Therefore, when considering the elements separately and in combination, they do not add significantly more to the inventive concept. Accordingly, claims 10-20 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 10-12 and 15-17 are rejected under U.S.C. §102(a)(1) as being anticipated by Paynter (“Applying machine learning to programming by demonstration”, 2004).
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FIG. 1 of Paynter
Regarding claim 10, Paynter teaches A method implemented using one or more processors and comprising: ([p. 162] "This paper describes how Familiar uses ML methods to learn from, and adapt to, the user. Familiar works on the Macintosh and is based on Apple’s scripting language" [p. 170] "Figure 9 depicts Familiar’s architecture in terms of major system components")
recording data indicative of an observed set of manual inputs provided via manual engagement by a user with graphical elements of a graphical user interface (GUI) rendered at a computing device;([p. 163] "the user first asks the agent to start observing their actions by selecting Begin Recording from the Familiar menu (figure 1a), which is available in every application, and proceeds to demonstrate the task by performing it")
based on the recorded data, simulating, at different levels of abstraction, multiple different synthetic sets of automated inputs provided via automated engagement with the graphical elements of the GUI([p. 168] "Its inferencing works on two levels. First, iterative patterns are sought in the types of events that have been demonstrated. Two sequence recognition schemes search for candidate patterns and make suggestions. […] five pattern analysis schemes are asked to make candidate predictions, and the ‘best’ prediction is selected. The resulting cycle of parameterized commands is presented in the predictions window" Paynter's first abstraction level is sequence structure, the second is concrete parameterization where once a cycle is selected five pattern analysis schemes generate competing predictions for its parameters. Inferencing interpreted as synonymous with simulating.)
wherein each synthetic set comprises different automated inputs that are varied from the manual inputs; ([p. 165] "the next actions will select file ‘apple’ (event 6) and set its position […] In this example, they are satisfied with the predictions, and press the one time button (figure 3a). Familiar responds by sending the commands to the Finder")
obtaining user feedback about each of the multiple different synthetic sets of automated inputs;([p. 171] "The user can give explicit feedback through the prediction window, or implicit feedback by ignoring it. In the latter case, new commands are recorded by the event recorder and processed as described above. The user can explicitly reject parameter and sequence predictions (represented by dashed grey lines in figure 9)")
based on the user feedback, selecting one of the multiple different synthetic sets of automated inputs;([p. 171] "The user can explicitly reject parameter and sequence predictions (represented by dashed grey lines in figure 9). Incorrect parameter predictions reinvoke the pattern analysis manager, which finds the next best prediction and passes it to the prediction window. Incorrect cycle predictions reinvoke the sequence recognition manager, which finds the next best pattern and sends it to the pattern analysis manager to have its parameters bound")
causing a machine learning model to be trained to generate output indicative of the selected synthetic set of automated inputs([p. 180] "Familiar uses ML algorithms trained on historical data to guide prediction" [p. 183] "After each prediction, the classifier is retrained to incorporate the new examples, thereby modelling a dynamic learner that updates itself after every user action. The experiment was repeated for each user who participated in the evaluation [...] The adaptive version immediately incorporates corrections into its model").
Regarding claim 11, Paynter teaches The method of claim 10, wherein the simulating is performed based on the machine learning model.(Paynter [p. 162] "Familiar employs standard machine learning (ML) techniques to infer the user’s intent" [p. 168] "Its inferencing works on two levels. First, iterative patterns are sought in the types of events that have been demonstrated. Two sequence recognition schemes search for candidate patterns and make suggestions. […] five pattern analysis schemes are asked to make candidate predictions, and the ‘best’ prediction is selected. The resulting cycle of parameterized commands is presented in the predictions window").
Regarding claim 12, Paynter teaches The method of claim 11, wherein the machine learning model is trained to facilitate intelligent process automation.(Paynter [p. 180] "Familiar uses ML algorithms trained on historical data to guide prediction" [p. 183] "After each prediction, the classifier is retrained to incorporate the new examples, thereby modelling a dynamic learner that updates itself after every user action. The experiment was repeated for each user who participated in the evaluation [...] The adaptive version immediately incorporates corrections into its model").
Regarding claim 15, claim 15 is directed towards a system for implementing the method of claim 10. Therefore, the rejection applied to claim 10 also applies to claim 15.
Regarding claim 16, Paynter teaches The system of claim 15, wherein the machine learning model is used to simulate the multiple different synthetic sets of automated inputs(Paynter [p. 162] "Familiar employs standard machine learning (ML) techniques to infer the user’s intent" [p. 168] "Its inferencing works on two levels. First, iterative patterns are sought in the types of events that have been demonstrated. Two sequence recognition schemes search for candidate patterns and make suggestions. […] five pattern analysis schemes are asked to make candidate predictions, and the ‘best’ prediction is selected. The resulting cycle of parameterized commands is presented in the predictions window").
Regarding claim 17, claim 17 applies to a system for implementing the method of claim 12. Therefore, the rejection applied to claim 12 also applies to claim 17.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 13 and 18 are rejected under U.S.C. §103 as being unpatentable over the combination of Paynter and Bıyık (“Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences”, 2021).
Regarding claim 13, Paynter teaches The method of claim 11.
However, Paynter doesn't explicitly teach wherein the machine learning model is trained to generate a probability distribution over an action space.
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FIG. 1 of Bıyık
Bıyık, in the same field of endeavor, teaches The method of claim 11, wherein the machine learning model is trained to generate a probability distribution over an action space. ([p. 4] "Let the belief b be a probability distribution over ω. We initialize b using the trajectory demonstrations, so that b0(ω) = P(ω | D) […] In order to evaluate Equation (3), we need a model of P(ξD | ω)—in other words, how likely is the demonstrated trajectory ξD given that the human’s reward weights are ω [...] A trajectory ξ ∈ Ξ is a finite sequence of state action pairs").
Paynter as well as Bıyık are directed towards machine learning automation. Therefore, Paynter as well as Bıyık are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Paynter with the teachings of Bıyık by using inverse reinforcement learning as the machine learning model in Paynter’s Familiar. Bıyık provides as additional motivation for combination that inverse reinforcement learning is effective ([p. 2] “to synthesize human data sources while maximizing information gain”).
Regarding claim 18, claim 18 is directed towards a system for performing the method of claim 13. Therefore, the rejection applied to claim 13 also applies to claim 18.
Claims 14, 19, and 20 are rejected under U.S.C. §103 as being unpatentable over the combination of Paynter and Liu (“Federated imitation learning: A novel framework for cloud robotic systems with heterogeneous sensor data”, 2020).
Regarding claim 14, Paynter teaches The method of claim 11, wherein the machine learning model comprises a private machine learning model, (Paynter [p. 180] "Familiar uses ML algorithms trained on historical data to guide prediction" [p. 183] "After each prediction, the classifier is retrained to incorporate the new examples, thereby modelling a dynamic learner that updates itself after every user action. The experiment was repeated for each user who participated in the evaluation [...] The adaptive version immediately incorporates corrections into its model").
However, Paynter doesn't explicitly teach and the method further comprises providing parameters of the trained private machine learning model for federated learning of a global machine learning model.
Liu, in the same field of endeavor, teaches and the method further comprises providing parameters of the trained private machine learning model for federated learning of a global machine learning model.([Abstract] "we present a novel framework named FIL. It provides a heterogeneous knowledge fusion mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL is proposed. It enables the cloud to fuse heterogeneous knowledge from local robots and generate guide models for robots with service requests. After that, we introduce a knowledge transfer scheme to facilitate local robots acquiring knowledge from the cloud).
Paynter as well as Liu are directed towards autonomous imitation systems. Therefore, Paynter as well as Liu are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Paynter with the teachings of Liu by applying federated learning to the inverse reinforcement learning system in Paynter. Liu provides as additional motivation for combination ([p. 6] “The results are summarized in Table I. It can be seen from the experimental results that the cloud knowledge improves the local controller that is trained using general imitation learning. The controller based on cloud policies performs better”).
Regarding claims 19 and 20, claims 19 and 20 are directed towards systems for implementing the method of claim 14. Therefore, the rejection applied to claim 14 also applies to claims 19 and 20.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIDNEY VINCENT BOSTWICK whose telephone number is (571)272-4720. The examiner can normally be reached M-F 7:30am-5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang can be reached on (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
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