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 .
1. This action is responsive to the application filed on 06/30/2023.
2. Claims 1-20 are pending.
3. Claims 1-20 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/30/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-18 are directed to one of the four statutory classes of invention (e.g. process, machine, manufacture, or composition of matter). The claims include a device, method, or product and is a method of assigning as task using a computer which is a process (Step 1: YES).
The Examiner has identified independent method Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent method Claim 10. Claim 1 recites the limitations of (abstract ideas highlighted in italics):
intercept a request for a reference to the region of memory from the requesting process; receive the reference;
determine if the requesting process is authorized to access the region of memory;
provide the received reference to the requesting process if the requesting process is authorized;
and develop a modified reference and provide the modified reference to the requesting process instead of the received reference if the requesting process is not authorized, wherein the modified reference prevents the requesting process from accessing the region.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of mental process. Intercepting (i.e., receiving) a request to perform an action, determining if the request is authorized (i.e., comparing the request to an authorized/unauthorized category), and determining to grant access/deny access to a memory (i.e., resource) recites a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a mental process that can be performed by the human mind, including, for example, observations, evaluations, judgements, and opinions, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The system in Claim 1 is just applying generic computer components (i.e., computer-based device) to the recited abstract limitations. Claim 10 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
Additionally, the limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. Observing (i.e., receiving) traffic behavior, classifying a traffic sign and determining whether a conflict exists (i.e., comparing the traffic behavior with a traffic sign), and adjusting the training data, recites a concept performed in the human mind. The claim encompasses a user simply using judgement and observation to compare traffic patterns/behaviors to determine if adjustment is needed using his/her mind. The mere nominal recitation of a generic device does not take the claim out of the mental processes grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The system in Claim 15 is just applying generic computer components to the recited abstract limitations. Claims 8 and 15 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite a method (Claim 1), a computer program product (Claim 8), and a system (claim 15). The computer hardware is recited at a high-level of generality (i.e., as a generic device performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 8, and 15 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Insert comment if appropriate, MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1, 8, and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-7, 9-14, and 16-20 further define the abstract idea that is present in their respective independent claims 1, 8, and 15 and thus correspond to Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claims 2-7, 9-14, and 16-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“computer-usable program code configured to perform…observe…classify…determine…adjust…”
in claim 8,
“computer-usable program code is further configured to…adjust…”
in claim 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof (Specification of instant application, Paragraphs 0021, 0025).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hassnaa Moustafa et al (US 20260159129 A1), hereinafter “Moustafa”.
Regarding Claim 1 and Claim 15, Moustafa discloses a method and a system for countering adversarial attacks on deep neural networks, the method comprising:
at least one processor (Moustafa, Paragraphs 1005-1007, processor);
at least one memory device operably coupled to the at least one processor and storing instructions for execution on the at least one processor (Moustafa, Paragraphs 1005-1007, memory coupled to the processor), the instructions causing the at least one processor to:
observing, by a first system, actual traffic behavior within a transportation network (Moustafa, Fig 1, Paragraph 0167, supporting drones (e.g., ground-based and/or aerial) are used. Paragraph 0168, vehicles obtain sensor data collected by external sensor devices, like supporting drones based on sensor data from these sensor devices (drones));
classifying, by a deep neural network of a second system, a traffic sign for regulating traffic within the transportation network (Moustafa, Paragraph 0174, vehicles include machine learning models, including deep learning models. The machine learning models include one or more model trainer engines to participate in training of one or more of the machine learning models. One or more inference engines 254 may also be provided to utilize the trained machine learning models 256 to derive various inferences, predictions, classifications, and other results. Paragraph 0177, perception engine of vehicle takes as inputs various sensor data to perform object recognition and/or tracking of detected objects, among other example functions corresponding to autonomous perception of the environment encountered (or to be encountered) by the vehicle. Perception engine performs object recognition from sensor data inputs using deep learning, such as through one or more convolutional neural networks and other machine learning models);
determining whether a conflict exists between the actual traffic behavior and expected traffic behavior based on the traffic sign (Moustafa, Paragraph 0184, sensor data is also generated by sensors on ground-based or aerial drones. Paragraph 0185, an autonomous vehicle system 105 may interface with and leverage information and services provided by other computing systems to enhance, enable, or otherwise support the autonomous driving functionality of the device. Paragraph 0220, based on detecting that the sensors of the vehicle have been compromised, the vehicle accesses sensor data, object recognition results, traffic recognition results, road condition recognition results, and other data generated by other devices (e.g., drone) that is relevant to the present locale of the vehicle or locales corresponding to a planned path or route of the vehicle);
and in the event the conflict is deemed to exist, adjusting training data of the deep neural network with respect to classifying the traffic sign (Moustafa, Paragraph 0186, machine learning model is executed by a computing system to progressively improve performance of a specific task. In some embodiments, parameters of a machine learning model may be adjusted during a training phase based on training data. A trained machine learning model may then be used during an inference phase to make predictions or decisions based on input data. Paragraph 0220, using the data provided by the drone for driving recommendations. Paragraph 0275, behavioral models can be provided that are capable of continuous development and improvement through adaptions based on observations from the environment serving as the basis for modifying learned constraints defined in the mode. when a vehicle shares its behavioral model with other vehicles, the version of the behavioral model may be one that has been refined and further tuned based on observations and further learning by the vehicle during on-road operation).
Regarding Claim 2, Moustafa discloses the method of claim 1 above, wherein the first system comprises at least one forerunner vehicle (Moustafa, Fig 1, Paragraph 0167, supporting drones within the driving environment).
Regarding Claim 3, Moustafa discloses the method of claim 2 above, wherein the at least one forerunner vehicle is a swarm of unmanned aerial vehicles (Moustafa, Fig 1, Paragraph 0167, supporting drones).
Regarding Claim 4, Moustafa discloses the method of claim 2 above, wherein the second system is an autonomous vehicle (Moustafa, Fig 1, Paragraph 0166, autonomous vehicles are provided with varying levels of autonomous driving capabilities facilitated through in-vehicle computing systems).
Regarding Claim 5, Moustafa discloses the method of claim 4 above, wherein the at least one forerunner vehicle is configured to travel ahead of the autonomous vehicle when navigating the transportation network (Moustafa, Fig 1, Paragraph 0220, vehicle accesses data from other devices (e.g., drones) that is relevant to the present locale of the vehicle or locales corresponding to the planned path or route of the vehicle).
Regarding Claim 6, Moustafa discloses the method of claim 1 above, wherein determining whether the conflict exists further comprises generating a confidence score indicating an extent to which classification of the traffic sign is incorrect (Moustafa, Paragraph 0281, trustworthiness is used for behaviors of the vehicles. Paragraph 0295, data from a number of autonomous vehicles may be crowdsourced and used to update the HD map. However, in some cases, trust or confidence in the data received may be questionable. One challenge may include understanding and codifying the trustworthiness of the data received from each of the cars. Paragraph 0297, as each autonomous vehicle collects data from its one or more sensors, the autonomous vehicle may determine an amount of confidence placed in datum collected. Paragraph 0298, autonomous vehicle calculates a confidence score, which is maintained in metadata associated with the data).
Regarding Claim 7, Moustafa discloses the method of claim 6 above, further comprising, in the event the confidence score exceeds a threshold, adjusting training data of the deep neural network with respect to classifying the traffic sign (Moustafa, Paragraph 0296, an update is applied based on received information. Paragraph 0302, autonomous vehicles use data from sources that are more distant and can be used for ranking any crowdsourced data required for any other purpose (e.g., training of machine learning models)).
Claim 8 carries similar limitations as discussed with regards to Claim 1 above and therefore is rejected for the same reason.
Regarding Claim 9, this claimed limitation is the same as the limitation addressed to Claim 2 above. Therefore, it is rejected under the same rationale.
Regarding Claim 10, this claimed limitation is the same as the limitation addressed to Claim 3 above. Therefore, it is rejected under the same rationale.
Regarding Claim 11, this claimed limitation is the same as the limitation addressed to Claim 4 above. Therefore, it is rejected under the same rationale.
Regarding Claim 12, this claimed limitation is the same as the limitation addressed to Claim 5 above. Therefore, it is rejected under the same rationale.
Regarding Claim 13, this claimed limitation is the same as the limitation addressed to Claim 6 above. Therefore, it is rejected under the same rationale.
Regarding Claim 14, this claimed limitation is the same as the limitation addressed to Claim 7 above. Therefore, it is rejected under the same rationale.
Regarding Claim 16, this claimed limitation is the same as the limitation addressed to Claim 2 and Claim 9 above. Therefore, it is rejected under the same rationale.
Regarding Claim 17, this claimed limitation is the same as the limitation addressed to Claim 3 and Claim 10 above. Therefore, it is rejected under the same rationale.
Regarding Claim 18, this claimed limitation is the same as the limitation addressed to Claim 4 and Claim 11 above. Therefore, it is rejected under the same rationale.
Regarding Claim 19, this claimed limitation is the same as the limitation addressed to Claim 5 and Claim 12 above. Therefore, it is rejected under the same rationale.
Regarding Claim 20, this claimed limitation is the same as the limitation addressed to Claim 6 and Claim 13 above. Therefore, it is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. All the references listed on 892 are related to the subject matter of determining traffic behavior of autonomous vehicles.
Some of the prior art include:
US 20200394512 A1, which discloses a method of robustness against manipulations in machine learning.
US 11113964 B1, which discloses a method of unmanned aerial vehicle for traffic management and surveillance.
US 20180196427 A1, which discloses a method of managing vehicle driven control entity transitions of an autonomous vehicle based on an evaluation of performance criteria.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAVIER O GUZMAN whose telephone number is (571)270-0588. The examiner can normally be reached Monday - Friday 8 am to 4 pm EST.
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, Jorge L. Ortiz-Criado can be reached at (571)272-7624. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAVIER O GUZMAN/Primary Examiner, Art Unit 2496
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