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 .
Claims 1-20 have been examined.
P = paragraph e.g. P[0001] = paragraph[0001]
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 6-9 and 15-18 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.
As per Claim 6, it is unclear what the limitation “an epistemic value that represents a potential reduction in uncertainty for the current driving policy” is and how it is calculated. As a result, it is also unclear how the “expected free energy value” is “further based on” the “epistemic value”.
The limitation “that represents a potential reduction in uncertainty for the current driving policy” does not limit the “epistemic value” in any meaningful way, as what may or may not represent a “potential reduction in uncertainty for the current driving policy” is entirely subjective, and this description of what the “epistemic value” “represents” does not clearly indicate what the scope of the “epistemic value”.
Therefore, the claim is unclear.
As per Claim 7, it is unclear if the step of “selecting a new policy” is or is not equivalent to the Claim 1 limitation “selecting a different policy”, and it is unclear when the step of “selecting a new policy” occurs with respect to the step of “selecting a different policy” of Claim 1.
Furthermore, it is unclear if the limitations “continually computing a looming value relative to another road user or object on the road; and selecting a new policy after the looming value satisfies a threshold” are introducing an alternative to Claim 1 or further limit Claim 1. Specifically, Claim 1 already recites a condition of “determining that an accumulated observation deviation value satisfies a threshold” for performing the step of “selecting a different policy”, and it is unclear if this step of Claim 1 still occurs prior to the steps of Claim 7, or if the limitation “selecting a new policy” replaces the step of “selecting a different policy” of Claim 1.
Therefore, the claim is unclear.
As per Claim 8, the limitation “pragmatic values” is unclear. Specifically, it is unclear what the “pragmatic values” are and how or if they are different from the “observation deviation values”.
Therefore, the claim is unclear.
As per Claim 9, the limitation “negative pragmatic values” is unclear. Specifically, it is unclear what the “negative pragmatic values” are, and it is unclear if the word “negative” refers to an undesired value or to a numerical value that is less than zero.
Therefore, the claim is unclear.
As per Claim 15, it is unclear what the limitation “an epistemic value that represents a potential reduction in uncertainty for the current driving policy” is and how it is calculated. As a result, it is also unclear how the “expected free energy value” is “further based on” the “epistemic value”.
The limitation “that represents a potential reduction in uncertainty for the current driving policy” does not limit the “epistemic value” in any meaningful way, as what may or may not represent a “potential reduction in uncertainty for the current driving policy” is entirely subjective, and this description of what the “epistemic value” “represents” does not clearly indicate what the scope of the “epistemic value”.
Therefore, the claim is unclear.
As per Claim 16, it is unclear if the step of “selecting a new policy” is or is not equivalent to the Claim 10 limitation “selecting a different policy”, and it is unclear when the step of “selecting a new policy” occurs with respect to the step of “selecting a different policy” of Claim 10.
Furthermore, it is unclear if the limitations “continually computing a looming value relative to another road user; and selecting a new policy after the looming value satisfies a threshold” are introducing an alternative to Claim 10 or further limit Claim 10. Specifically, Claim 10 already recites a condition of “determining that an accumulated observation deviation value satisfies a threshold” for performing the step of “selecting a different policy”, and it is unclear if this step of Claim 10 still occurs prior to the steps of Claim 16, or if the limitation “selecting a new policy” replaces the step of “selecting a different policy” of Claim 10.
Therefore, the claim is unclear.
As per Claim 17, the limitation “pragmatic values” is unclear. Specifically, it is unclear what the “pragmatic values” are and how or if they are different from the “observation deviation values”.
Therefore, the claim is unclear.
As per Claim 18, the limitation “negative pragmatic values” is unclear. Specifically, it is unclear what the “negative pragmatic values” are, and it is unclear if the word “negative” refers to an undesired value or to a numerical value that is less than zero.
Therefore, the claim is unclear.
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. See below.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method (i.e., a process). Therefore, claim 1 is 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 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer-implemented method comprising:
continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent;
computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps;
determining that an accumulated observation deviation value satisfies a threshold; and
in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, regarding the “continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent” limitation, a user may mentally continually compute, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent. Regarding the “computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps” limitation, a user may mentally compute an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps. Regarding the “determining that an accumulated observation deviation value satisfies a threshold” limitation, a user may mentally determine that an accumulated observation deviation value satisfies a threshold. Regarding the “and in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold” limitation, a user may mentally select a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold in response.
Accordingly, the claim recites at least one 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 computer-implemented method comprising:
continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent;
computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps;
determining that an accumulated observation deviation value satisfies a threshold; and
in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold.
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 limitation “computer-implemented”, the “computer” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception.
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 ordered 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 Revised Guidance, representative independent claim 1 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 “computer” of “computer-implemented” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claim(s) 2-9 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims 2-9 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-9 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claim(s) 1-9 are ineligible under 35 USC §101.
See below regarding the dependent claims.
As per Claim 2, said claim is rejected as it fails to correct the deficiency of Claim 1. The claim describes a value, which does not amount to significantly more than the judicial exception.
As per Claim 3, said claim is rejected as it fails to correct the deficiency of Claim 1. The claim describes a value, which does not amount to significantly more than the judicial exception.
As per Claim 4, said claim is rejected as it fails to correct the deficiency of Claim 1. A user may mentally accumulate observation deviation values over a particular time window that includes the plurality of time steps. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 5, said claim is rejected as it fails to correct the deficiency of Claim 1. A user may mentally compute the observation deviation value as a component of the expected free energy value of an active inference modeling framework. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 6, said claim is rejected as it fails to correct the deficiency of Claim 1. A user may mentally base the expected free energy value on an epistemic value that represents a potential reduction in uncertainty for the current driving policy. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 7, said claim is rejected as it fails to correct the deficiency of Claim 1. A user may mentally continually compute a looming value relative to another road user or object on the road, and select a new policy after the looming value satisfies a threshold. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 8, said claim is rejected as it fails to correct the deficiency of Claim 1. A user may mentally accumulate pragmatic values. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 9, said claim is rejected as it fails to correct the deficiency of Claim 1. A user may mentally accumulate negative pragmatic values. Therefore, the claim does not amount to significantly more than the judicial exception.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 10 is directed to a system (i.e., a machine). Therefore, claim 10 is 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 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 10 includes limitations that recite an abstract idea (emphasized below). Claim 10 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:
continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent;
computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps;
determining that an accumulated observation deviation value satisfies a threshold; and
in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, regarding the “continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent” limitation, a user may mentally continually compute, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent. Regarding the “computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps” limitation, a user may mentally compute an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps. Regarding the “determining that an accumulated observation deviation value satisfies a threshold” limitation, a user may mentally determine that an accumulated observation deviation value satisfies a threshold. Regarding the “and in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold” limitation, a user may mentally select a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold in response.
Accordingly, the claim recites at least one 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:
continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent;
computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps;
determining that an accumulated observation deviation value satisfies a threshold; and
in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold.
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 limitation “A system comprising: one or more computers and one or more storage devices storing instructions”, the “one or more computers” are recited at a high level of generality and amount to nothing more than a generic computer used to apply the exception. Furthermore, the “one or more storage devices storing instructions” are recited at a high level of generality and amount to nothing more than a generic computer used to apply the exception and amount to nothing more than a generic computer component used to apply the exception and mere instructions to apply an exception using a generic computer component.
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 ordered 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 Revised Guidance, independent claim 10 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 “one or more computers” are recited at a high level of generality and amount to nothing more than a generic computer used to apply the exception, and the “one or more storage devices storing instructions” are recited at a high level of generality and amount to nothing more than a generic computer used to apply the exception and amount to nothing more than a generic computer component used to apply the exception and mere instructions to apply an exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claim(s) 11-18 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims 11-18 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 11-18 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claim(s) 10-18 are ineligible under 35 USC §101.
See below regarding the dependent claims.
As per Claim 11, said claim is rejected as it fails to correct the deficiency of Claim 10. The claim describes a value, which does not amount to significantly more than the judicial exception.
As per Claim 12, said claim is rejected as it fails to correct the deficiency of Claim 10. The claim describes a value, which does not amount to significantly more than the judicial exception.
As per Claim 13, said claim is rejected as it fails to correct the deficiency of Claim 10. A user may mentally accumulate observation deviation values over a particular time window that includes the plurality of time steps. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 14, said claim is rejected as it fails to correct the deficiency of Claim 10. A user may mentally compute the observation deviation value as a component of the expected free energy value of an active inference modeling framework. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 15, said claim is rejected as it fails to correct the deficiency of Claim 10. A user may mentally base the expected free energy value on an epistemic value that represents a potential reduction in uncertainty for the current driving policy. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 16, said claim is rejected as it fails to correct the deficiency of Claim 10. A user may mentally continually compute a looming value relative to another road user or object on the road, and select a new policy after the looming value satisfies a threshold. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 17, said claim is rejected as it fails to correct the deficiency of Claim 10. A user may mentally accumulate pragmatic values. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 18, said claim is rejected as it fails to correct the deficiency of Claim 10. A user may mentally accumulate negative pragmatic values. Therefore, the claim does not amount to significantly more than the judicial exception.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 19 is directed to a computer storage medium. Therefore, claim 19 is not 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 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 19 includes limitations that recite an abstract idea (emphasized below). Claim 19 recites:
A computer storage medium encoded with a computer program, the program comprising instructions that are operable, when executed by data processing apparatus, to cause the data processing apparatus to perform operations comprising:
continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent;
computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps;
determining that an accumulated observation deviation value satisfies a threshold; and
in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, regarding the “continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent” limitation, a user may mentally continually compute, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent. Regarding the “computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps” limitation, a user may mentally compute an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps. Regarding the “determining that an accumulated observation deviation value satisfies a threshold” limitation, a user may mentally determine that an accumulated observation deviation value satisfies a threshold. Regarding the “and in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold” limitation, a user may mentally select a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold in response.
Accordingly, the claim recites at least one 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 computer storage medium encoded with a computer program, the program comprising instructions that are operable, when executed by data processing apparatus, to cause the data processing apparatus to perform operations comprising:
continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent;
computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps;
determining that an accumulated observation deviation value satisfies a threshold; and
in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold.
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 limitation “A computer storage medium encoded with a computer program, the program comprising instructions”, the “computer storage medium” is recited at a high level of generality and amounts to nothing more than transient, propagating signals, and mere instructions to apply the exception. The Examiner notes that even if the “computer storage medium” was interpreted as a physical device, it would amount to nothing more than a generic computer component used to apply the exception.
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 ordered 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 Revised Guidance, independent claim 19 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 “computer storage medium encoded with a computer program, the program comprising instructions” is recited at a high level of generality and amounts to nothing more than transient, propagating signals, and mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claim 20 does not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claim 19 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claim 19 is similarly rejected as being directed towards non-statutory subject matter.
Therefore, claim(s) 19-20 are ineligible under 35 USC §101.
See below regarding the dependent claim.
As per Claim 20, said claim is rejected as it fails to correct the deficiency of Claim 19. The claim describes a value, which does not amount to significantly more than the judicial exception.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because Claim 19 is nominally directed to a computer storage medium and is therefore directed to non-statutory subject matter. The claims broadly cover transient, propagating signals. Since a claim to a "computer storage medium" reasonably broadly covers both forms of non-transitory tangible media (e.g. memory, disk, tape) and transient, propagating signals (e.g. signals, carrier waves), it necessarily covers non-statutory subject matter. This is so because transient, propagating signals are not patentable subject matter. See In re Nuijten, 500 F.3d 1346, 1356 (Fed. Cir. 2007). The Examiner also notes that the specification does not provide a definition of the term “computer storage medium”.
Examiner Note: Applicant can amend to narrow the claim to cover only statutory embodiments by adding the limitation “non-transitory” to the claim (i.e. A non-transitory computer storage medium …”), such an amendment would not raise the issue of new matter, even when the specification is silent, unless the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment.
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.
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 1-4, 8-13 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Robinson et al. (2022/0227379).
Regarding Claim 1, Robinson et al. teaches the claimed computer-implemented method comprising:
continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent (“In either case, the method may iteratively repeat. For instance, the method may then return to step 404 for determine a prediction for the next time step. In this case, the “second time” would be the next time step (the third time) and the method would continue, iteratively obtaining sensor data, making predictions for a later time step, and then determining a prediction error based on observed sensor data at that later time step”, see P[0215]);
computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194] and “In either case, the method may iteratively repeat. For instance, the method may then return to step 404 for determine a prediction for the next time step. In this case, the “second time” would be the next time step (the third time) and the method would continue, iteratively obtaining sensor data, making predictions for a later time step, and then determining a prediction error based on observed sensor data at that later time step”, see P[0215]);
determining that an accumulated observation deviation value satisfies a threshold (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]); and
in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold (“As mentioned above, the edge case module 326 may not only reproduce the inputs (including any actions taken by the autonomous vehicle control system 324) but may also output its own action(s). These may include remedial actions in an attempt to mitigate any damage or cost from the edge case occurring”, see P[0196]).
Regarding Claim 2, Robinson et al. teaches the claimed method of claim 1, wherein the observation deviation value represents a difference between a goal of the current driving policy and a state of the agent based on one or more observations of an environment of the agent (“The node predicts a future state (e.g. the sensor data for a future time-step such as the next time-step) and, upon measuring the actual future state (receiving the sensor data for the future time-step) determines the prediction error (the difference between the predicted state and the actual measured state)”, see P[0134]).
Regarding Claim 3, Robinson et al. teaches the claimed method of claim 1, wherein the observation deviation value of a policy represents a degree to which the agent will advance toward a goal (“The node predicts a future state (e.g. the sensor data for a future time-step such as the next time-step) and, upon measuring the actual future state (receiving the sensor data for the future time-step) determines the prediction error (the difference between the predicted state and the actual measured state)”, see P[0134]).
Regarding Claim 4, Robinson et al. teaches the claimed method of claim 1, wherein computing the accumulated observation deviation value comprises accumulating observation deviation values over a particular time window that includes the plurality of time steps (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]).
Regarding Claim 8, Robinson et al. teaches the claimed method of claim 1, wherein accumulating the observation deviation values comprises accumulating pragmatic values (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]).
Regarding Claim 9, Robinson et al. teaches the claimed method of claim 8, wherein accumulating the pragmatic values comprises accumulating negative pragmatic values (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]).
Regarding Claim 10, Robinson et al. teaches the claimed 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 (see P[0238]) comprising:
continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent (“In either case, the method may iteratively repeat. For instance, the method may then return to step 404 for determine a prediction for the next time step. In this case, the “second time” would be the next time step (the third time) and the method would continue, iteratively obtaining sensor data, making predictions for a later time step, and then determining a prediction error based on observed sensor data at that later time step”, see P[0215]);
computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194] and “In either case, the method may iteratively repeat. For instance, the method may then return to step 404 for determine a prediction for the next time step. In this case, the “second time” would be the next time step (the third time) and the method would continue, iteratively obtaining sensor data, making predictions for a later time step, and then determining a prediction error based on observed sensor data at that later time step”, see P[0215]);
determining that an accumulated observation deviation value satisfies a threshold (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]); and
in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold (“As mentioned above, the edge case module 326 may not only reproduce the inputs (including any actions taken by the autonomous vehicle control system 324) but may also output its own action(s). These may include remedial actions in an attempt to mitigate any damage or cost from the edge case occurring”, see P[0196]).
Regarding Claim 11, Robinson et al. teaches the claimed system of claim 10, wherein the observation deviation value represents a difference between a goal of the current driving policy and a state of the agent based on one or more observations of an environment of the agent (“The node predicts a future state (e.g. the sensor data for a future time-step such as the next time-step) and, upon measuring the actual future state (receiving the sensor data for the future time-step) determines the prediction error (the difference between the predicted state and the actual measured state)”, see P[0134]).
Regarding Claim 12, Robinson et al. teaches the claimed system of claim 10, wherein the observation deviation value of a policy represents a degree to which the agent will advance toward a goal (“The node predicts a future state (e.g. the sensor data for a future time-step such as the next time-step) and, upon measuring the actual future state (receiving the sensor data for the future time-step) determines the prediction error (the difference between the predicted state and the actual measured state)”, see P[0134]).
Regarding Claim 13, Robinson et al. teaches the claimed system of claim 10, wherein computing the accumulated observation deviation value comprises accumulating observation deviation values over a particular time window that includes the plurality of time steps (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]).
Regarding Claim 17, Robinson et al. teaches the claimed system of claim 10, wherein accumulating the observation deviation values comprises accumulating pragmatic values (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]).
Regarding Claim 18, Robinson et al. teaches the claimed system of claim 17, wherein accumulating the pragmatic values comprises accumulating negative pragmatic values (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]).
Regarding Claim 19, Robinson et al. teaches the claimed computer storage medium encoded with a computer program, the program comprising instructions that are operable, when executed by data processing apparatus, to cause the data processing apparatus to perform operations (see P[0238]) comprising:
continually computing, at each time step of a plurality of time steps, an observation deviation value for a current driving policy of an agent (“In either case, the method may iteratively repeat. For instance, the method may then return to step 404 for determine a prediction for the next time step. In this case, the “second time” would be the next time step (the third time) and the method would continue, iteratively obtaining sensor data, making predictions for a later time step, and then determining a prediction error based on observed sensor data at that later time step”, see P[0215]);
computing an accumulated observation deviation value including accumulating observation deviation values computed for each of a plurality of time steps (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194] and “In either case, the method may iteratively repeat. For instance, the method may then return to step 404 for determine a prediction for the next time step. In this case, the “second time” would be the next time step (the third time) and the method would continue, iteratively obtaining sensor data, making predictions for a later time step, and then determining a prediction error based on observed sensor data at that later time step”, see P[0215]);
determining that an accumulated observation deviation value satisfies a threshold (“By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]); and
in response, selecting a different policy for the agent to execute after the accumulated observation deviation value satisfies the threshold (“As mentioned above, the edge case module 326 may not only reproduce the inputs (including any actions taken by the autonomous vehicle control system 324) but may also output its own action(s). These may include remedial actions in an attempt to mitigate any damage or cost from the edge case occurring”, see P[0196]).
Regarding Claim 20, Robinson et al. teaches the claimed computer storage medium of claim 19, wherein the observation deviation value represents a difference between a goal of the current driving policy and a state of the agent based on one or more observations of an environment of the agent (“The node predicts a future state (e.g. the sensor data for a future time-step such as the next time-step) and, upon measuring the actual future state (receiving the sensor data for the future time-step) determines the prediction error (the difference between the predicted state and the actual measured state)”, see P[0134]).
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 5-7 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Robinson et al. (2022/0227379) in view of Wei et al. ("Modeling Driver Responses to Automation Failures With Active Inference," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18064-18075, Oct. 2022).
Regarding Claim 5, Robinson et al. does not expressly recite the claimed method of claim 2, wherein the observation deviation value is computed as a component of the expected free energy value of an active inference modeling framework.
However, Wei et al. ("Modeling Driver Responses to Automation Failures With Active Inference," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18064-18075, Oct. 2022) teaches wherein the observation deviation value is computed as a component of the expected free energy value of an active inference modeling framework (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”, particularly the discussion of “expected free energy (EFE)” in subsection “A. Active Inference: The Observable States Case”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Robinson et al. with the teachings of Wei et al., and wherein the observation deviation value is computed as a component of the expected free energy value of an active inference modeling framework, as rendered obvious by Wei et al., in order to “model human decision-making under uncertainty in partially observable environments” (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”).
Regarding Claim 6, Robinson et al. does not expressly recite the claimed method of claim 5, wherein the expected free energy value is further based on an epistemic value that represents a potential reduction in uncertainty for the current driving policy.
However, Wei et al. ("Modeling Driver Responses to Automation Failures With Active Inference," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18064-18075, Oct. 2022) teaches wherein the expected free energy value is further based on an epistemic value that represents a potential reduction in uncertainty for the current driving policy (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”, particularly the discussion of “expected free energy (EFE)” in subsection “A. Active Inference: The Observable States Case”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Robinson et al. with the teachings of Wei et al., and wherein the expected free energy value is further based on an epistemic value that represents a potential reduction in uncertainty for the current driving policy, as rendered obvious by Wei et al., in order to “model human decision-making under uncertainty in partially observable environments” (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”).
Regarding Claim 7, Robinson et al. does not expressly recite the claimed method of claim 1, further comprising:
continually computing a looming value relative to another road user or object on the road; and
selecting a new policy after the looming value satisfies a threshold.
However, Robinson et al. does teach observing “another road user or object on the road” and “selecting a new policy” when an error based on the observation “satisfies a threshold” (see P[0178] and “By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]).
Furthermore, Wei et al. ("Modeling Driver Responses to Automation Failures With Active Inference," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18064-18075, Oct. 2022) teaches “accumulated errors in expected and observed visual looming” (Wei et al.; see “I. INTRODUCTION”) and modeling driver behavior and an environment using “visual looming observations” and “visual looming observation distribution” (Wei et al.; see “III. ACTIVE INFERENCE BRAKING MODEL”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Robinson et al. with the teachings of Wei et al., and the method of claim 1, further comprising: continually computing a looming value relative to another road user or object on the road; and selecting a new policy after the looming value satisfies a threshold, as rendered obvious by Wei et al., in order to “model human decision-making under uncertainty in partially observable environments” (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”).
Regarding Claim 14, Robinson et al. does not expressly recite the claimed system of claim 11, wherein the observation deviation value is computed as a component of the expected free energy value of an active inference modeling framework.
However, Wei et al. ("Modeling Driver Responses to Automation Failures With Active Inference," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18064-18075, Oct. 2022) teaches wherein the observation deviation value is computed as a component of the expected free energy value of an active inference modeling framework (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”, particularly the discussion of “expected free energy (EFE)” in subsection “A. Active Inference: The Observable States Case”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Robinson et al. with the teachings of Wei et al., and wherein the observation deviation value is computed as a component of the expected free energy value of an active inference modeling framework, as rendered obvious by Wei et al., in order to “model human decision-making under uncertainty in partially observable environments” (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”).
Regarding Claim 15, Robinson et al. does not expressly recite the claimed system of claim 14, wherein the expected free energy value is further based on an epistemic value that represents a potential reduction in uncertainty for the current driving policy.
However, Wei et al. ("Modeling Driver Responses to Automation Failures With Active Inference," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18064-18075, Oct. 2022) teaches wherein the expected free energy value is further based on an epistemic value that represents a potential reduction in uncertainty for the current driving policy (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”, particularly the discussion of “expected free energy (EFE)” in subsection “A. Active Inference: The Observable States Case”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Robinson et al. with the teachings of Wei et al., and wherein the expected free energy value is further based on an epistemic value that represents a potential reduction in uncertainty for the current driving policy, as rendered obvious by Wei et al., in order to “model human decision-making under uncertainty in partially observable environments” (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”).
Regarding Claim 16, Robinson et al. does not expressly recite the claimed system of claim 10, wherein the operations further comprise:
continually computing a looming value relative to another road user; and
selecting a new policy after the looming value satisfies a threshold.
However, Robinson et al. does teach observing “another road user or object on the road” and “selecting a new policy” when an error based on the observation “satisfies a threshold” (see P[0178] and “By outputting predicted inputs for a future time, the edge case module 326 is able to detect when inputs differ significantly from those predicted. Where there is a significant difference (a prediction error that exceeds a threshold), then the edge case module 326 determines that an edge case has been detected, and outputs information regarding the edge case scenario to the training level. This determination may be made based on a combination (e.g. a weighted sum) of prediction errors, each prediction error relating to a different prediction made by the vehicle control system 322, 332”, see P[0194]).
Furthermore, Wei et al. ("Modeling Driver Responses to Automation Failures With Active Inference," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18064-18075, Oct. 2022) teaches “accumulated errors in expected and observed visual looming” (Wei et al.; see “I. INTRODUCTION”) and modeling driver behavior and an environment using “visual looming observations” and “visual looming observation distribution” (Wei et al.; see “III. ACTIVE INFERENCE BRAKING MODEL”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Robinson et al. with the teachings of Wei et al., and wherein the operations further comprise: continually computing a looming value relative to another road user; and selecting a new policy after the looming value satisfies a threshold, as rendered obvious by Wei et al., in order to “model human decision-making under uncertainty in partially observable environments” (Wei et al.; see section “II. ACTIVE INFERENCE IN A PARTIALLY OBSERVABLE MARKOVIAN ENVIRONMENT”).
Conclusion
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/ISAAC G SMITH/ Primary Examiner, Art Unit 3662
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