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 .
Status of Claims
This Office Action is in response to the application and the election filed on 19 December 2023 and 03 November 2025, respectively. Claims 7-20 are presently pending and are presented for examination. Claims 1-6 have not been elected.
Information Disclosure Statement
The Information Disclosure Statement(s) was/were submitted on 07 August 2024, 29 January 2025, and 03 November 2025. The submission(s) is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the Information Disclosure Statement(s) is/are being considered by the Examiner.
Priority
Request for priority to Provisional App. No. 63/433,717 is acknowledged. Examiner notes that the current claims do not appear to be fully supported by the provisional application and further notes that the Applicant may be requested to perfect one or more of the claims in the situation where applied prior art has priority falling between the filing date of the non-provisional application, dated 19 December 2023, and the provisional application, dated 19 December 2022. No action on the part of the Applicant is requested at this time.
Claim Objections
Claim(s) 7, 11, 14, and 18 is/are objected to because of the following informalities:
Claim 7 and 14: “do not belong to any of the expectation ranges” should be “do not belong to any of the one or more associated expectation ranges”; and
Claim 11 and 18: “when the posterior distribution does not exceed the prior distribution” should be “when the posterior probability distribution does not exceed the prior probability distribution”.
Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities:
Pg. 5, lines 26-30: reference characters "155" and "134" have both been used to designate “input sensor data”; and
Pg. 5, line 26 – pg. 6, line 20: reference character "134” has been used to designate both “generative model subsystem” and “input sensor data”.
Appropriate correction is required.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: “111”, “121”, “123”, “130”, “170” in FIG. 1. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "121" and "123" have both been used to designate “Training Data” in FIG. 1. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claim(s) 20 is/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.
Claim 20 recites the limitation "the method," twice. There is insufficient antecedent basis for this limitation in the claim.
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 7-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The independent claims 7 and 14, recite subject matter (a method and a system for computing probability distributions and a surprise metric) that falls within the following group of abstract ideas: mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
101 Analysis – Step 1
Claim 7 is directed to a method (i.e., a process) and claim 14 is directed to a system (i.e., a machine). Therefore, claims 7 and 14 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG), the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 14 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 35 U.S.C. 101 rejection. Claim 14 recites:
A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
receiving a prior probability distribution representing a predicted state of an agent at a particular time, wherein the prior probability distribution is based on a previous state of the agent at a previous time, and wherein the prior probability distribution has one or more associated expectation ranges that each represent a respective range of expected states of the agent at the particular time;
receiving an updated state of the agent for the particular time;
computing a posterior probability distribution based on the updated state of the agent; and
computing an antithesis surprise metric using the posterior probability distribution, wherein the antithesis surprise metric represents how much of the posterior probability distribution exceeds the prior probability distribution in regions that do not belong to any of the expectation ranges associated with the prior probability distribution.
The examiner submits that the foregoing bolded limitation(s) constitute “mathematical concepts.” Specifically, the “computing a posterior probability distribution” limitation is a mathematical relationship, from the mathematical discipline of statistics. Furthermore, the “computing an antithesis surprise metric” limitation, is a mathematical calculation, specifically, an act of calculating using mathematical methods to determine a variable or number. Accordingly, claim 14 recites an abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
receiving a prior probability distribution representing a predicted state of an agent at a particular time, wherein the prior probability distribution is based on a previous state of the agent at a previous time, and wherein the prior probability distribution has one or more associated expectation ranges that each represent a respective range of expected states of the agent at the particular time;
receiving an updated state of the agent for the particular time;
computing a posterior probability distribution based on the updated state of the agent; and
computing an antithesis surprise metric using the posterior probability distribution, wherein the antithesis surprise metric represents how much of the posterior probability distribution exceeds the prior probability distribution in regions that do not belong to any of the expectation ranges associated with the prior probability distribution.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of a “system comprising one or more computers and one or more storage devices storing instructions…”, the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the “system comprising one or more computers and one or more storage devices storing instructions…” is recited at a high level of generality and merely automates the “receiving” and “computing” limitations, therefore acting as a generic computer to perform the abstract idea. The system is claimed generically and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitations are no more than mere instructions to apply the exception using a computer (the “system”).
Furthermore, regarding the additional limitations of “receiving a prior probability distribution” and “receiving an updated state”, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (the “system”) to perform the process. In particular, the “receiving…” limitations are recited at a high level of generality (i.e., as a general means of gathering data on an agent for use in the “computing…” limitations), and amount to mere data gathering, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an order combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the PEG, representative independent claim 14 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a “system comprising one or more computers and one or more storage devices storing instructions…” amount to nothing more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the additional elements of “receiving…” amount to nothing more than the insignificant extra-solution activities of data gathering, which cannot provide an inventive concept.
Further, a conclusion that an additional element is an insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “receiving a prior probability distribution” and “receiving an updated state,” are well-understood, routine, and conventional activities because the background recites that the sensors are all conventional sensors mounted on a vehicle (see specification, pg. 3, lines 29-31; pg. 5, lines 14-18), and the specification does not provide any indication that the vehicle “on-board system” is anything other than a conventional computer within a vehicle (see specification, pg. 5, lines 3-5). MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, independent claim 14, and analogous independent claim 7, are not subject matter eligible.
Dependent claims 8-13 and 15-20 do not recite any further limitations that cause the claim(s) to be subject matter eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or are well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Specifically, claims 15-17 and 19 (and analogous claims 8-10 and 12) further detail the type of data gathered in the “receiving” limitations of claim 14 (and analogous claim 7), claim 18 (an analogous claim 11) further detail the abstract idea, and claim 20 (and analogous claim 13) further detail post-solution activities of data outputting. Therefore, dependent claims 8-13 and 15-20 are not subject matter eligible under the same rationale as provided for in the rejection of independent claims 7 and 14.
Therefore, claims 7-20 are not subject matter eligible 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claim(s) 7-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “An active inference approach to on-line agent monitoring in safety-critical systems”, hereinafter “Avila”.
Regarding claim 14, and analogous claim 7, Avila discloses A system comprising one or more computers and one or more storage devices storing instructions that are operable (Avila, Section 6: “The proposed active inference approach to on-line monitoring of an agent behavior allows its incorporation to a number of applications in diverse domains. Typical examples include the detection of unauthorized access to computer systems [37], irregularities in vital signs and other variables in intensive care patients [38], fraud in financial services [39], detection of saccadic objects for visual applications [40] and detection of path deviation in autonomous vehicles [i.e., A system comprising one or more computers and one or more storage devices storing instructions that are operable] [41].”), when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
receiving a prior probability distribution representing a predicted state of an agent at a particular time, wherein the prior probability distribution is based on a previous state of the agent at a previous time (Avila, end of Section 2.1: “…for on-line monitoring of an agent behavior, an event is surprising not because its probability is small in an absolute sense, but rather because its probability is relatively small given the prior belief distribution [i.e., prior probability distribution] of the observer (monitor) [21].”; Section 2.2: “Prior beliefs about the optimal policy are instrumental in order to predict expected responses given the history of observed state transitions over a rolling horizon. In this work, the monitor beliefs are transformed into a generative (probabilistic) model which makes possible to infer future state transitions resulting from an optimally behaving agent…For an input
x
k
[i.e., previous state], the multivariate predictive distribution
p
x
k
+
1
x
k
is Gaussian distributed.”), and
wherein the prior probability distribution has one or more associated expectation ranges that each represent a respective range of expected states of the agent at the particular time (Avila, Section 2.2: “In the generative model, state transition probabilities are modeled using Gaussian processes (GPs) [22] that provide information about confidence intervals for the predicted next state [i.e., one or more associated expectation ranges that each represent a respective range of expected states of the agent at the particular time].”);
receiving an updated state of the agent for the particular time; computing a posterior probability distribution based on the updated state of the agent (Avila, Section 4.1: “The monitor’s beliefs are updated on-line as new sensory information arrives, transforming prior belief distributions into posterior ones. According to this, the fundamental effect of data
D
[i.e., receiving an updated state of the agent for the particular time] on the monitor is to change its prior distribution
P
M
into a posterior distribution
P
M
D
via the Bayes theorem [i.e., computing a posterior probability distribution based on the updated state of the agent].”); and
computing an antithesis surprise metric using the posterior probability distribution (Avila, Section 4.1: “Conveniently, Bayesian surprise is measured using the distance between the posterior and prior distributions based on the Kullback–Leibler divergence
T
D
,
M
=
K
L
(
P
M
D
P
M
)
[i.e., computing an antithesis surprise metric using the posterior probability distribution]. The
K
L
divergence, or relative entropy, should be understood as a measure of the difficulty of discriminating between two distributions.”),
wherein the antithesis surprise metric represents how much of the posterior probability distribution exceeds the prior probability distribution in regions that do not belong to any of the expectation ranges associated with the prior probability distribution (Avila, Section 4.1: “Conveniently, Bayesian surprise is measured using the distance between the posterior and prior distributions based on the Kullback–Leibler divergence
T
D
,
M
=
K
L
(
P
M
D
P
M
)
. The
K
L
divergence, or relative entropy, should be understood as a measure of the difficulty of discriminating between two distributions [i.e., wherein the antithesis surprise metric represents how much of the posterior probability distribution exceeds the prior probability distribution in regions that do not belong to any of the expectation ranges associated with the prior probability distribution].”).
Regarding claim 15, and analogous claim 8, Avila discloses The system of claim 14,
wherein the prior probability distribution is a mixture model comprising probabilities associated with a plurality of distinct predictions (Avila, Fig. 5: mixture model; Section 1, pg. 1085: “To achieve this, the monitor continuously revises its hypotheses in order to update prior beliefs
μ
k
that include predictive distributions
Ψ
μ
k
over future state transitions [i.e., probabilities associated with a plurality of distinct predictions]. This strategy is characterized in this work in terms of an active inference approach [17], in which the monitor perceives the nearby environment of an agent in order to contrast its prior beliefs about expected state transitions. These prior beliefs are built around a generative model of an optimally controlled stochastic process for state transitions, which involves predictions of what should be sampled in order to validate prior beliefs.”).
Regarding claim 16, and analogous claim 9, Avila discloses The system of claim 15,
wherein the plurality of distinct predictions are generated by a generative model that outputs, for a particular state, a plurality of predictions of a state of the agent at a future point in time (Avila, Section 2.1, pg. 1086: “Predictions about state transitions are the result of a generative model, which has been trained to infer a sequence of fictive state transitions and is described in the next section. Predicted transitions are optimal in the sense they are the result of an optimally acting agent in the face of uncertainty. However, predicted transitions are nothing more than an internal representation used by the monitor that may or may not match the future evolution of the environmental dynamics. The active inference approach to agent monitoring implies that based on prior beliefs, new predictions about environmental state transitions can be made [i.e., the plurality of distinct predictions are generated by a generative model that outputs, for a particular state, a plurality of predictions of a state of the agent at a future point in time].”).
Regarding claim 17, and analogous claim 10, Avila discloses The system of claim 16,
wherein each prediction of the state of the agent is associated with a respective expectation range (Avila, Section 2.2: “In the generative model, state transition probabilities are modeled using Gaussian processes (GPs) [22] that provide information about confidence intervals for the predicted next state [i.e., each prediction of the state of the agent is associated with a respective expectation range].”; Section 5.4: “An advantage of using GPs is that they also provide information about confidence intervals for each prediction.”).
Regarding claim 18, and analogous claim 11, Avila discloses The system of claim 17,
wherein the antithesis surprise metric is zero when the posterior distribution does not exceed the prior distribution outside of any expectation ranges (Avila, Section 4.1: “Surprise quantifies how observing new data affects the internal beliefs a monitor may have about an agent behavior and its control policy. Observations that leave the prior beliefs unaffected are not surprising [i.e., antithesis surprise metric is zero when the posterior distribution does not exceed the prior distribution outside of any expectation ranges] and -revealing that the monitor hypotheses are confirmed by data-, whereas data observations that cause the monitor to significantly revise their prior beliefs give rise to a surprising condition.”).
Regarding claim 19, and analogous claim 12, Avila discloses The system of claim 18,
wherein the previous state of the agent at the previous time and the updated state of the agent for the particular time are determined based on sensor data from one or more sensor subsystems of a vehicle (Avila, Section 2.1, pg. 1086: “After observing the agent environment, the monitor contrasts incoming sensory information with prior beliefs [i.e., the previous state of the agent at the previous time and the updated state of the agent for the particular time are determined based on sensor data from one or more sensor subsystems of a vehicle] that describe the expected behavior of a proficient driver, such that the car efficiently avoids static and dynamic obstacles while obeying traffic rules.”).
Regarding claim 20, and analogous claim 13, Avila discloses The system of claim 19,
wherein the method comprises a planning system of the vehicle determining a control strategy for the vehicle based on the antithesis surprise metric or wherein the method comprises training an autonomous vehicle planning model based on the antithesis surprise metric (Avila, Section 6: “To favor on-line monitoring, a robust metric based on surprise and twin Gaussian processes is introduced to characterize the progressive degradation in the agent behavior by quantifying the distance between the implementation and prior beliefs. A distinctive advantage of computing surprise using Gaussian processes is that the divergence from prior beliefs can be estimated not only using the expected value of state transitions but also the corresponding prediction uncertainty for optimal action selection [i.e., a planning system of the vehicle determining a control strategy for the vehicle based on the antithesis surprise metric].”).
Additional Relevant Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US-20190310634-A1 (2019-10-10) |Para. 0017: “Various embodiments herein look at the amount of new information that each data element provides to the overall set of data elements in order to determine whether to include (or keep) that data element. In some ways, looking at the information contributed may be considered looking at whether the new data is “useful” to the new set of data elements, or whether the new data is “surprising” or informative based on the set of data elements. Various embodiments herein use a measure of information entropy to determine the additional surprisal (or surprise) that a data point provides to a set of data. Information entropy is the expected value of surprisal.” Para. 0028: “In some embodiments, as described elsewhere herein, relative surprisal is calculated using log2(P/Q), where P is the posterior probability of an event occurring after it has occurred divided by the prior probability, Q, of that same event occurring before it has occurred.” Relevant to claims 7 and 14.
WO-2015006517-A2 (2015-01-15)| “Once outcome data become known as new information to be learned over time, a reasonable sequential Bayesian learning approach would use the old posterior outcome probabilities from a previous training sample in time as new prior memory outcome probabilities for the learning of new outcomes in a new training sample in time. So, with new sequential outcome observations, previous posterior probabilities become the new prior memory probabilities and the most probable probabilities are those which are closest to these prior probabilities.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Leah N Miller whose telephone number is (703)756-1933. The examiner can normally be reached M-Th 8:30am - 5:30pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Helal Algahaim can be reached at (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L.N.M./Examiner, Art Unit 3666
/HELAL A ALGAHAIM/SPE , Art Unit 3645