DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Applicant is advised that should claims -2, 4-8, be found allowable, claims -14, 16-20 will be objected to under 37 CFR 1.75 as being respectively a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim objected to under 37 CFR 1.75 as being respectively a substantial duplicate of claim -2, 4-8. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 1-21 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.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “non-transformed data” in claim 1 is used by the claim to mean “non modified data,” while the accepted meaning is “unlabeled data”. The term is indefinite because the specification does not clearly redefine the term.
Independent claims 1 and 13 recite “a vehicle” multiple times, so this renders the claims indefinite as it is unclear if there are multiple different vehicles, or if it is only one vehicle. Clarification is required on this matter.
Dependent claims 8-12 and 21 also recite “a vehicle”, and they should be “the vehicle”. This renders the claims indefinite as it is unclear if there are multiple different vehicles, or if it is only one vehicle. Clarification is required on this matter.
Dependent claims 2-12, 14-21 depend from and contain same deficiencies as claims 1 and 13 and are rejected for the same reason. Clarification is required on this matter.
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.
Claim 1-21 are rejected under 35 U.S.C. 101 because the claimed invention are directed to abstract ideas without significantly more.
Regarding Claim 1:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? – Yes, the claim recites the abstract ideas:
creating a first training set comprising the first collected set of data, the first modified set of data and a first set of non-transformed data: - This limitation is directed to the abstract idea of a mental process, as the process of creating the training set is a thought process that can be performed in a human mind by observing, evaluating and judging (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
creating a second training set for a second stage of training comprising the first training set and the first set of non-transformed data that are incorrectly transformed after the first stage of training: - This limitation is directed to the abstract idea of a mental process, as the process of creating the training set is a thought process that can be performed in a human mind by observing, evaluating and judging (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
collecting a first set of data relevant to automatically collecting, analyzing, and transmitting data to and from a vehicle: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
applying one or more transformations to the collected first set of data to create a first modified set of data: - This limitation is selecting a particular data source or type of data to be manipulated as it is merely adding data to the training set, being insignificant extra-solution activity (see MEPEP n2106.05(g));
training the neural network in a first stage using the first training set: - This limitation does not integrate a judicial exception into a practical application as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
and training the neural network in a second stage using the second training set to automatically collect, analyze, and transmit data to and from a vehicle: - This limitation does not integrate a judicial exception into a practical application as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
collecting a first set of data relevant to automatically collecting, analyzing, and transmitting data to and from a vehicle: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II.);
applying one or more transformations to the collected first set of data to create a first modified set of data: - This limitation is analogous to electronic recordkeeping because it’s adding data to the training set and keeping record of it (see MPEP 2106.05(d) II (iii));
training the neural network in a first stage using the first training set: - This limitation does not amount to significantly more than the judicial exception as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
and training the neural network in a second stage using the second training set to automatically collect, analyze, and transmit data to and from a vehicle: - This limitation does not amount to significantly more than the judicial exception as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Regarding Claim 2:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? -Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? – Yes, the is dependent on claim 1 which recites an abstract idea (see rejection on claim 1).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
the first collected set of data including data that originates from a vehicle’s engine control unit: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
the first collected set of data including data that originates from a vehicle’s engine control unit: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II.).
Regarding Claim 3:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? – Yes, the claim is dependent on claim 2, claim 2 is dependent on claim 1 which recites an abstract idea (see rejection on claim 1).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
the first collected set of data including any of the vehicle’s engine temperature, engine speed, airflow rate, mass airflow rate, throttle position, spark timing, fuel injection timing, oxygen sensor readings, knock sensor readings, exhaust gas temperature, or exhaust gas oxygen content: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
the first collected set of data including any of the vehicle’s engine temperature, engine speed, airflow rate, mass airflow rate, throttle position, spark timing, fuel injection timing, oxygen sensor readings, knock sensor readings, exhaust gas temperature, or exhaust gas oxygen content: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II.).
Regarding Claim 4:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which recites an abstract idea (see rejection on claim 1).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
the one or more transformations including expanding the first collected set of data by making random changes to the first collected set of data by a random number generator to create the first modified set of data, the first modified set of data being an expanded set of data greater in size than the first collected set of data: - This limitation is directed to selection of a particular data source or type of data to be manipulated as it is merely adding data to the first collected set of data, being insignificant extra-solution activity (see MEPEP n2106.05(g)).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
the one or more transformations including expanding the first collected set of data by making random changes to the first collected set of data by a random number generator to create the first modified set of data, the first modified set of data being an expanded set of data greater in size than the first collected set of data: - This limitation is analogous to electronic recordkeeping because it’s adding data to the first collected set of data and keeping record of it (see MPEP 2106.05(d) II (iii)).
Regarding Claim 5:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? – Yes, the claim is dependent on claim 1 which recites an abstract idea (see rejection on claim 1). Additionally, claim 5 recites the abstract ideas:
the first stage training set using stochastic learning with backpropagation that uses a gradient of a mathematical loss function to adjust weights of the neural network: - This claim is directed to the abstract idea of mathematical concepts, as the process of using stochastic learning with backpropagation that uses a gradient of a mathematical loss function to adjust weights of the neural network is a process of organizing information and manipulating information through mathematical correlation (see MPEP 2106.04(a)(2)(I) subsection A).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? – Yes, the claim is dependent on claim 1 which recites an abstract idea (see rejection on claim 1). Additionally, claim 6 recites the abstract ideas:
the second stage training set using stochastic learning with backpropagation that uses a gradient of a mathematical loss function to adjust weights of the neural network: - This claim is directed to the abstract idea of mathematical concepts, as the process of using stochastic learning with backpropagation that uses a gradient of a mathematical loss function to adjust weights of the neural network is a process of organizing information and manipulating information through mathematical correlation (see MPEP 2106.04(a)(2)(I) subsection A).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 6 which included an abstract idea (see rejection for claim 6).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
the second stage training minimizing false positives by performing an iterative training algorithm, in which the neural network is retrained with an updated training set comprising the false positives produced after the first stage training: - This limitation is directed to selection of a particular data source or type of data to be manipulated as it is merely adding data, being insignificant extra-solution activity (see MEPEP n2106.05(g)).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
the second stage training minimizing false positives by performing an iterative training algorithm, in which the neural network is retrained with an updated training set comprising the false positives produced after the first stage training: - This limitation is directed to performing repetitive calculations. The courts (as per Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values)) have recognized performing repetitive calculations as well-understood, routine, and conventional activity in particular fields (see MPEP 2106.05(d)(II)(ii)).
Regarding Claim 8:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). Additionally, claim 8 recites the abstract ideas:
the analyzing including diagnosing a vehicle condition …: - This limitation is directed to the abstract idea of a mental process, as the process of analyzing is a thought process that can be performed in a human mind by observing, evaluating and judging (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
the analyzing including diagnosing a vehicle condition by the neural network: - This limitation does not integrate a judicial exception into a practical application as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
the analyzing including diagnosing a vehicle condition by the neural network: - This limitation does not amount to significantly more than the judicial exception as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Regarding Claim 9:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). Additionally, claim 9 recites the abstract ideas:
the analyzing including tuning a vehicle’s performance …: - This limitation is directed to the abstract idea of a mental process, as the process of analyzing is a thought process that can be performed in a human mind by observing, evaluating and judging (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
the analyzing including tuning a vehicle’s performance by the neural network: - This limitation does not integrate a judicial exception into a practical application as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
the analyzing including tuning a vehicle’s performance by the neural network: - This limitation does not amount to significantly more than the judicial exception as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Regarding Claim 10:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). Additionally, claim 10 recites the abstract ideas:
the analyzing including improving a vehicle’s fuel efficiency …: - This limitation is directed to the abstract idea of a mental process, as the process of analyzing is a thought process that can be performed in a human mind by observing, evaluating and judging (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
the analyzing including improving a vehicle’s fuel efficiency by the neural network: - This limitation does not integrate a judicial exception into a practical application as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
the analyzing including improving a vehicle’s fuel efficiency by the neural network: - This limitation does not amount to significantly more than the judicial exception as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Regarding Claim 11:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). Additionally, claim 11 recites the abstract ideas:
the analyzing including improving a vehicle’s emission control …: - This limitation is directed to the abstract idea of a mental process, as the process of analyzing is a thought process that can be performed in a human mind by observing, evaluating and judging (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
the analyzing including improving a vehicle’s emission control by the neural network: - This limitation does not integrate a judicial exception into a practical application as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
the analyzing including improving a vehicle’s emission control by the neural network: - This limitation does not amount to significantly more than the judicial exception as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Regarding Claim 12:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). Additionally, claim 12 recites the abstract ideas:
the analyzing including improving a vehicle’s safety …: - This limitation is directed to the abstract idea of a mental process, as the process of analyzing is a thought process that can be performed in a human mind by observing, evaluating and judging (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
the analyzing including improving a vehicle’s safety by the neural network: - This limitation does not integrate a judicial exception into a practical application as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
the analyzing including improving a vehicle’s safety by the neural network: - This limitation does not amount to significantly more than the judicial exception as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Regarding independent Claim 13, this claim is directed to a process and is rejected on the same basis as independent claim 1 since they are analogous claims.
Regarding dependent Claim 14, this claim is directed to a process and is rejected on the same basis as dependent claim 2 since they are analogous claims.
Regarding dependent Claim 15, this claim is directed to a process and is rejected on the same basis as dependent claim 3 since they are analogous claims.
Regarding dependent Claim 16, this claim is directed to a process and is rejected on the same basis as dependent claim 4 since they are analogous claims.
Regarding dependent Claim 17, this claim is directed to a process and is rejected on the same basis as dependent claim 5 since they are analogous claims.
Regarding dependent Claim 18, this claim is directed to a process and is rejected on the same basis as dependent claim 6 since they are analogous claims.
Regarding dependent Claim 19, this claim is directed to a process and is rejected on the same basis as dependent claim 7 since they are analogous claims.
Regarding Claim 20:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). Additionally, claim 12 recites the abstract ideas:
the analyzing including determining a geographical vehicle course by the neural network and visualizing it on the user device: - This limitation is directed to the abstract idea of a mental process, as the process of determining a geographical vehicle course is a thought process that can be performed in a human mind by observing, evaluating and judging (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
… by the neural network …: - This limitation does not integrate a judicial exception into a practical application as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
… by the neural network …: - This limitation does not amount to significantly more than the judicial exception as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Regarding Claim 21:
Step 1 - Is the claim directed to a process, a process, machine, manufacture or composition of matter? - Yes, the claim is directed to a process.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). Additionally, claim 12 recites the abstract ideas:
the analyzing including determining a geographical vehicle course by the neural network and visualizing it on the user device including a vehicle’s conformity to staying within the geographical vehicle course: - This limitation is directed to the abstract idea of a mental process, as the processes of determining a geographical vehicle course and vehicle’s conformity to staying within the geographical vehicle course are thought processes that can be performed in a human mind by observing, evaluating and judging (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
… by the neural network …: - This limitation does not integrate a judicial exception into a practical application as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
… by the neural network …: - This limitation does not amount to significantly more than the judicial exception as the neural network is recited at a high level of generality, therefore this amounts to mere instructions to implement an abstract idea 2106.05(f).
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Stenson et al (US12097873B2 - hereinafter Stenson) in view of Wang et al (CN 103916446A - hereinafter Wang).
Referring to Claim 1, Stenson teaches:
collecting a first set of data relevant to automatically collecting, analyzing, and transmitting data to and from a vehicle (see Stenson at Column 17: lines 31 to 34: “the generator model 510 may receive input sensor data 502, for example, acquired by a sensor system (e.g., the sensor system 28) at an autonomous vehicle (e.g., the autonomous vehicle 10, 202, 204”) Examiner interprets the received input sensor data to be equivalent as the claimed “collecting a first set of data”);
applying one or more transformations to the collected first set of data to create a first modified set of data (see Stenson at Column 17 Lines 38 to 46: ”The generator model 510 may generate modified sensor data 512 from the captured input sensor data 502. The modified sensor data 512 may include a modified object (e.g., a second object) including a modified element of the first object. In some instances, the modified element may be associated with at least one of a size, a shape, a color, a white balance, a saturation, or a behavior representative of the first object.”. Examiner interprets the modification of a size, a color, a white balance, or a saturation representative of the first object to be equivalent as the claimed “applying one or more transformations to the collected first set”);
creating a first training set comprising the first collected set of data, the first modified set of data and a first set of non-transformed data (see Stenson at Column 17 Lines 13 to 30: ”The process 500 may utilize a generator model 510 to modify real-time captured sensor data prior to providing the sensor data to a discriminator model 520 for object classification. In some embodiments, the generator model 510 may be trained to modify sensor data associated with a first sensing condition into sensor data that the discriminator model 520 (trained for object identification under a second sensing condition) may correctly identify objects from. For instance, the generator model 510 may correspond to the generator model 310 of FIG. 3 after the generator model 310 is trained, based at least in part on a difference between the input sensor data 304 and the modified sensor data 312, to modify sensor data associated with the first sensing condition as discussed above with reference to FIG. 3. The discriminator model 520 may correspond to the discriminator model 320 trained to identify objects from sensor data captured under the second sensing condition as discussed above with reference to FIG. 3. Examiner interprets the real-time captured sensor data and the modified sensor data to be equivalent as the claimed “the first collected set of data” and “the first modified set of data” respectively. The non-transformed data is interpreted to be included in the first collected set);
training the neural network in a first stage using the first training set (see Stenson at Column 17 Lines 13 to 30: ”The process 500 may utilize a generator model 510 to modify real-time captured sensor data prior to providing the sensor data to a discriminator model 520 for object classification. In some embodiments, the generator model 510 may be trained to modify sensor data associated with a first sensing condition into sensor data that the discriminator model 520 (trained for object identification under a second sensing condition) may correctly identify objects from. For instance, the generator model 510 may correspond to the generator model 310 of FIG. 3 after the generator model 310 is trained, based at least in part on a difference between the input sensor data 304 and the modified sensor data 312, to modify sensor data associated with the first sensing condition as discussed above with reference to FIG. 3. The discriminator model 520 may correspond to the discriminator model 320 trained to identify objects from sensor data captured under the second sensing condition as discussed above with reference to FIG. 3”. Examiner interprets the utilization of the generator model 510 to modify real-time captured sensor data to be equivalent as the claimed “training the neural network in a first stage”);
creating a second training set for a second stage of training comprising the first training set and the first set of non-transformed data that are incorrectly transformed after the first stage of training (see Stenson at Column 17 Lines 47 to 51: ”The modified sensor data 512 may be input into the discriminator model 520. The modified sensor data 512 may be processed by each layer of the discriminator model 520 (in a feedforward direction), and the discriminator model 520 may output a classification 522”. Examiner interprets the modified sensor data 512 to be equivalent as the claimed “second training set for a second stage of training”); and
training the neural network in a second stage using the second training set to automatically collect, analyze, and transmit data to and from a vehicle (see Stenson at Column 17 Lines 47 to 51: ”The modified sensor data 512 may be input into the discriminator model 520. The modified sensor data 512 may be processed by each layer of the discriminator model 520 (in a feedforward direction), and the discriminator model 520 may output a classification 522.”. Examiner interprets the fact that each layer of the discriminator model 520 processed the modified sensor data 512 to be equivalent as the claimed “training the neural network in a second stage using the second training set”).
However, Stenson fails to teach:
collecting a first set of data relevant to automatically collecting, analyzing, and transmitting data to and from a vehicle; and
automatically collect, analyze, and transmit data to and from a vehicle.
Wang teaches, in an analogous system,
collecting a first set of data relevant to automatically collecting, analyzing, and transmitting data to and from a vehicle (see Wang at Paragraph 46: “The remote data center 3 and the wireless communication device 2 achieve two-way communication, receiving and organizing vehicle status data and vehicle location data from the vehicle terminal device 1. The remote data center 3 sends remote commands to the vehicle terminal device 1 based on the received information. The vehicle terminal device 1 can adjust its working mode and data collection and transmission methods according to the remote”. Examiner interprets receiving, analyzing and sending data to be equivalent as the claimed “collecting, analyzing, and transmitting data to and from a vehicle”); and
training the neural network in a second stage using the second training to automatically collect, analyze, and transmit data to and from a vehicle (see Wang at Paragraph 46: “The remote data center 3 and the wireless communication device 2 achieve two-way communication, receiving and organizing vehicle status data and vehicle location data from the vehicle terminal device 1. The remote data center 3 sends remote commands to the vehicle terminal device 1 based on the received information. The vehicle terminal device 1 can adjust its working mode and data collection and transmission methods according to the remote”. Examiner interprets receiving data, organizing it for determining vehicle status and sending back data to be equivalent as the claimed “collecting, analyzing, and transmitting data to and from a vehicle”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, for the purpose of automatically collect, analyze and transmit data to and from the vehicle, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to record a vehicle’s trajectory in real time, analyze such data in real time, and send control signals from the service center to the vehicle in real time to reduce the vehicle failure rate, and improve vehicle driving safety (as suggested by Wang at Paragraph 6: “Therefore, in many situations, such as when it is necessary to monitor the engine operation of a car in real time (fuel consumption, vehicle speed, etc.), record the vehicle's trajectory in real time, transmit OBD data to the service center in real time, and receive control signals from the service center”, and at [0063]: “the remote data center 3 sends an information reminder to the remote terminal device 4 so that the driver, vehicle after-sales service department, or vehicle management service department can be notified of the vehicle abnormality in a timely manner, reduce the vehicle failure rate, and improve vehicle driving safety”).
Referring to Claim 2, Stenson - Wang teaches a method of claim 1.
However, Stenson fails to teach:
the first collected set of data including data that originates from a vehicle’s engine control unit.
Wang teaches, in analogous system,
the first collected set of data including data that originates from a vehicle’s engine control unit (see Wang at Paragraph 20: “The data acquisition steps involve acquiring engine operating data via the OBDII bus of the acquisition unit, acquiring vehicle status data via the internal auxiliary sensor unit, acquiring vehicle location data via the positioning unit, and then sending the vehicle status data and vehicle location data to the wireless communication device via the on-board terminal device”. Examiner interprets the acquisition of engine operating data via the OBDII bus of the acquisition unit to be equivalent as the claimed “data that originates from a vehicle’s engine control unit”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, and to collect a first set of data that originates from vehicle’s engine control unit, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to organize the engine operation data (as suggested by Wang at Paragraph 22: “In the data processing step, the remote data center receives and organizes the engine operation data, vehicle status data, and vehicle location data”).
Referring to Claim 3, Stenson - Wang teaches a method of claim 2.
However, Stenson fails to teach:
the first collected set of data including any of the vehicle’s engine temperature, engine speed, airflow rate, mass airflow rate, throttle position, spark timing, fuel injection timing, oxygen sensor readings, knock sensor readings, exhaust gas temperature, or exhaust gas oxygen content.
Wang teaches, in analogous system,
the first collected set of data including any of the vehicle’s engine temperature, engine speed, airflow rate, mass airflow rate, throttle position, spark timing, fuel injection timing, oxygen sensor readings, knock sensor readings, exhaust gas temperature, or exhaust gas oxygen content (see Wang at Paragraph 6: “Therefore, in many situations, such as when it is necessary to monitor the engine operation of a car in real time (fuel consumption, vehicle speed, etc.), record the vehicle's trajectory in real time, transmit OBD data to the service center in real time, and receive control signals from the service center, traditional solutions have limitations”. Examiner interprets the fuel consumption, vehicle speed, etc. to be equivalent as the claimed “any of the vehicle’s engine temperature, engine speed, airflow rate, mass airflow rate, throttle position, spark timing, fuel injection timing, oxygen sensor readings, knock sensor readings, exhaust gas temperature, or exhaust gas oxygen content”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, and to collect a first set of data that originates from vehicle’s engine control unit, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to monitor the engine operation of a car in real time (as suggested by Wang at Paragraph 6: “Therefore, in many situations, such as when it is necessary to monitor the engine operation of a car in real time (fuel consumption, vehicle speed, etc.), record the vehicle's trajectory in real time, transmit OBD data to the service center in real time, and receive control signals from the service center, traditional solutions have limitations”).
Referring to Claim 4, Stenson - Wang teaches a method of claim 1, Stenson further teaches:
the one or more transformations including expanding the first collected set of data by making random changes to the first collected set of data by a random number generator to create the first modified set of data, the first modified set of data being an expanded set of data greater in size than the first collected set of data (see Stenson at Column 3 Lines 26 to 35: ” In some embodiments, the first and second sensor data may correspond to first and second images, respectively, and the generator model may generate the second sensor data by modifying one or more pixels of the first image. In some instances, the modified element may be associated with at least one of a size, a shape, a color, a white balance, or a saturation representative of the first object. In some instances, the generator model may generate the second sensor data by adding random elements to the first sensor data (e.g., based on random input data)”. Examiner interprets the addition of random elements to the first sensor data to generate the second sensor data to be equivalent as the claimed “expanding the first collected set of data by making random changes to the first collected set of data by a random number generator to create the first modified set of data, the first modified set of data being an expanded set of data greater in size than the first collected set of data”).
Referring to Claim 5, Stenson - Wang teaches a method of claim 1, Stenson further teaches:
the first stage training set using stochastic learning with backpropagation that uses a gradient of a mathematical loss function to adjust weights of the neural network (see Stenson at Column 14 Lines 25 to 34: ”The training of the generator model 310 may be based on a backpropagation process 314. The backpropagation process 314 may perform a backward pass through the layers in the generator model 310 while adjusting the weights and/or biases at each layer. The backpropagation process 314 can be repeated to minimize an error (e.g., a gradient) between the output of the generator model 310 and a desired output (e.g., a desired modified characteristic such as hue, white balance, saturation, size, shape, behaviors, dynamics, etc.) of the generator model 310”. Examiner interprets the backpropagation process 314 performing a backward pass through the layers in the generator model 310 while adjusting the weights and/or biases at each layer to be equivalent as the claimed “using stochastic learning with backpropagation that uses a gradient of a mathematical loss function to adjust weights of the neural network”).
Referring to Claim 6, Stenson - Wang teaches a method of claim 1, Stenson further teaches:
the second stage training set using stochastic learning with backpropagation that uses a gradient of a mathematical loss function to adjust weights of the neural network (see Stenson at Column 16 Lines 42 to 50: ”To train the discriminator model 420, a backpropagation process 424 may be performed. The backpropagation process 424 performs a backward pass through the layers in the discriminator model 420 while adjusting the weights and/or biases at each layer. The backpropagation process 424 can be repeated to minimize an error between the output (e.g., the classification 422) of the discriminator model 420 and a desired output (e.g., with at least a 90% confidence level of the object is a tree)”. Examiner interprets the backpropagation process 424 performing a backward pass through the layers in the discriminator model 420 while adjusting the weights and/or biases at each layer to be equivalent as the claimed “using stochastic learning with backpropagation that uses a gradient of a mathematical loss function to adjust weights of the neural network”).
Referring to Claim 7, Stenson - Wang teaches a method of claim 6, Stenson further teaches:
the second stage training minimizing false positives by performing an iterative training algorithm, in which the neural network is retrained with an updated training set comprising the false positives produced after the first stage training (see Stenson at Column 16 Lines 42 to 50: ”To train the discriminator model 420, a backpropagation process 424 may be performed. The backpropagation process 424 performs a backward pass through the layers in the discriminator model 420 while adjusting the weights and/or biases at each layer. The backpropagation process 424 can be repeated to minimize an error between the output (e.g., the classification 422) of the discriminator model 420 and a desired output (e.g., with at least a 90% confidence level of the object is a tree)”. Examiner interprets the backpropagation process 424 being repeated to minimize an error between the output of the discriminator model 420 and a desired output).
Referring to Claim 8, Stenson - Wang teaches a method of claim 1.
However, Stenson fails to teach:
the analyzing including diagnosing a vehicle condition by the neural network.
Wang teaches, in analogous system,
the analyzing including diagnosing a vehicle condition by the neural network (see Wang at Paragraph 62: “The remote data center 3 displays engine operating data, vehicle status data, and vehicle location data to the vehicle after-sales service department or vehicle management service department. Based on the data, such as the general operating status of the vehicle engine, lubrication system status, cooling system status, emissions status, and transmission system status, the vehicle after-sales service department or management service department can determine whether there are any problems with the vehicle”. Examiner interprets the fact to determine whether there are any problems with the vehicle to be equivalent as the claimed “analyzing including diagnosing a vehicle condition”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, and to diagnose vehicle condition, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine whether there are any problems with the vehicle (as suggested by Wang at Paragraph 62: “The remote data center 3 displays engine operating data, vehicle status data, and vehicle location data to the vehicle after-sales service department or vehicle management service department. Based on the data, such as the general operating status of the vehicle engine, lubrication system status, cooling system status, emissions status, and transmission system status, the vehicle after-sales service department or management service department can determine whether there are any problems with the vehicle”).
Referring to Claim 9, Stenson - Wang teaches a method of claim 1.
However, Stenson fails to teach:
the analyzing including tuning a vehicle’s performance by the neural network.
Wang teaches, in analogous system,
the analyzing including tuning a vehicle’s performance by the neural network (see Wang at Paragraph 62: “Preferably, when there are errors in the data collected by the vehicle terminal device 1, the remote data center 3 sends a remote command to the vehicle terminal device 1, and the vehicle terminal device 1 adjusts its working mode and data collection and transmission method according to the remote command to correct the errors in the collection process”. Examiner interprets the adjustment by vehicle terminal device 1 of its working mode, data collection, and transmission method to correct errors to be equivalent as the claimed “analyzing including tuning a vehicle’s performance”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, and to tune a vehicle performance, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to correct the errors in the collection process (as suggested by Wang at Paragraph 62: “Preferably, when there are errors in the data collected by the vehicle terminal device 1, the remote data center 3 sends a remote command to the vehicle terminal device 1, and the vehicle terminal device 1 adjusts its working mode and data collection and transmission method according to the remote command to correct the errors in the collection process”).
Referring to Claim 10, Stenson - Wang teaches a method of claim 1.
However, Stenson fails to teach:
the analyzing including improving a vehicle’s fuel efficiency by the neural network
Wang teaches, in analogous system,
the analyzing including improving a vehicle’s fuel efficiency by the neural network (see Wang at Paragraph 62: “According to settings or user requirements, the remote data center 3 sends the processed engine operation data, vehicle status data, and vehicle location data to the remote terminal device 4, such as the processed vehicle driving status, fuel consumption, driving route, and other data”. Examiner interprets the processed fuel consumption data to be equivalent as the claimed “analyzing including improving a vehicle’s fuel efficiency”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, and to improve the vehicle’s fuel efficiency, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to transmit the processed data (as suggested by Wang at Paragraph 62: “According to settings or user requirements, the remote data center 3 sends the processed engine operation data, vehicle status data, and vehicle location data to the remote terminal device 4, such as the processed vehicle driving status, fuel consumption, driving route, and other data”).
Referring to Claim 11, Stenson - Wang teaches a method of claim 1.
However, Stenson fails to teach:
the analyzing including improving a vehicle’s emission control by the neural network.
Wang teaches, in analogous system,
the analyzing including improving a vehicle’s emission control by the neural network (see Wang at Paragraph 62: “Remote Data Center 3 receives and organizes engine operation data, vehicle status data, and vehicle location data. The remote data center 3 displays engine operating data, vehicle status data, and vehicle location data to the vehicle after-sales service department or vehicle management service department. Based on the data, such as the general operating status of the vehicle engine, lubrication system status, cooling system status, emissions status, and transmission system status, the vehicle after-sales service department or management service department can determine whether there are any problems with the vehicle. According to settings or user requirements, the remote data center 3 sends the processed engine operation data, vehicle status data, and vehicle location data to the remote terminal device 4, such as the processed vehicle driving status, fuel consumption, driving route, and other data”. Examiner interprets the emission status data being organized and processed to be equivalent as the claimed “analyzing including improving a vehicle’s emission control”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, and to improve the vehicle’s emission control, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine whether there are any problems with the vehicle (as suggested by Wang at Paragraph 62: “Based on the data, such as the general operating status of the vehicle engine, lubrication system status, cooling system status, emissions status, and transmission system status, the vehicle after-sales service department or management service department can determine whether there are any problems with the vehicle”).
Referring to Claim 12, Stenson - Wang teaches a method of claim 1, Wang further teaches:
However, Stenson fails to teach:
the analyzing including improving a vehicle’s safety by the neural network.
Wang teaches, in analogous system,
the analyzing including improving a vehicle’s safety by the neural network (see Wang at Paragraph 63: “For example, when the engine operation data, vehicle status data, and vehicle location data received by the remote data center 3 from the vehicle terminal device 1 are abnormal (e.g., low coolant), the remote data center 3 sends an information reminder to the remote terminal device 4 so that the driver, vehicle after-sales service department, or vehicle management service department can be notified of the vehicle abnormality in a timely manner, reduce the vehicle failure rate, and improve vehicle driving safety”. Examiner interprets the improvement of vehicle driving safety to be equivalent as the claimed “analyzing including improving a vehicle’s safety”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, and to improve the vehicle’s safety, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to notify the vehicle abnormality in a timely manner, reduce the vehicle failure rate, and improve vehicle driving safety (as suggested by Wang at Paragraph 63: “For example, when the engine operation data, vehicle status data, and vehicle location data received by the remote data center 3 from the vehicle terminal device 1 are abnormal (e.g., low coolant), the remote data center 3 sends an information reminder to the remote terminal device 4 so that the driver, vehicle after-sales service department, or vehicle management service department can be notified of the vehicle abnormality in a timely manner, reduce the vehicle failure rate, and improve vehicle driving safety”).
Referring to independent Claim 13, this claim is rejected on the same basis as independent claim 1 since they are analogous claims.
Referring to dependent Claim 14, this claim is rejected on the same basis as dependent claim 2 since they are analogous claims.
Referring to dependent Claim 15, this claim is rejected on the same basis as dependent claim 3 since they are analogous claims.
Referring to dependent Claim 16, this claim is rejected on the same basis as dependent claim 4 since they are analogous claims.
Referring to dependent Claim 17, this claim is rejected on the same basis as dependent claim 5 since they are analogous claims.
Referring to dependent Claim 18, this claim is rejected on the same basis as dependent claim 6 since they are analogous claims.
Referring to dependent Claim 19, this claim is rejected on the same basis as dependent claim 7 since they are analogous claims.
Referring to Claim 20, Stenson - Wang teaches a method of claim 15.
However, Stenson fails to teach:
the analyzing including determining a geographical vehicle course by the neural network and visualizing it on the user device.
Wang teaches, in analogous system,
the analyzing including determining a geographical vehicle course by the neural network and visualizing it on the user device (see Wang at Paragraph 62: “Remote Data Center 3 receives and organizes engine operation data, vehicle status data, and vehicle location data. The remote data center 3 displays engine operating data, vehicle status data, and vehicle location data to the vehicle after-sales service department or vehicle management service department. Based on the data, such as the general operating status of the vehicle engine, lubrication system status, cooling system status, emissions status, and transmission system status, the vehicle after-sales service department or management service department can determine whether there are any problems with the vehicle. According to settings or user requirements, the remote data center 3 sends the processed engine operation data, vehicle status data, and vehicle location data to the remote terminal device 4, such as the processed vehicle driving status, fuel consumption, driving route, and other data”. Examiner interprets the vehicle location data being organized, displayed and processed to be equivalent as the claimed “analyzing including determining a geographical vehicle course” and “visualizing it on the user device”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, and to determine a geographical vehicle course, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine whether there are any problems with the vehicle (as suggested by Wang at Paragraph 62: “The remote data center 3 displays engine operating data, vehicle status data, and vehicle location data to the vehicle after-sales service department or vehicle management service department. Based on the data, such as the general operating status of the vehicle engine, lubrication system status, cooling system status, emissions status, and transmission system status, the vehicle after-sales service department or management service department can determine whether there are any problems with the vehicle”).
Referring to Claim 21, Stenson - Wang teaches a method of claim 20.
However, Stenson fails to teach:
the analyzing including determining a geographical vehicle course by the neural network and visualizing it on the user device including a vehicle’s conformity to staying within the geographical vehicle course.
Wang teaches, in analogous system,
the analyzing including determining a geographical vehicle course by the neural network and visualizing it on the user device including a vehicle’s conformity to staying within the geographical vehicle course (see Wang at Paragraph 62: “Remote Data Center 3 receives and organizes engine operation data, vehicle status data, and vehicle location data. The remote data center 3 displays engine operating data, vehicle status data, and vehicle location data to the vehicle after-sales service department or vehicle management service department. Based on the data, such as the general operating status of the vehicle engine, lubrication system status, cooling system status, emissions status, and transmission system status, the vehicle after-sales service department or management service department can determine whether there are any problems with the vehicle. According to settings or user requirements, the remote data center 3 sends the processed engine operation data, vehicle status data, and vehicle location data to the remote terminal device 4, such as the processed vehicle driving status, fuel consumption, driving route, and other data”. Examiner interprets the vehicle location data being organized, displayed and processed to be equivalent as the claimed “analyzing including determining a geographical vehicle course” and “visualizing it on the user device”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Stenson with the above teachings of Wang by training a neural network to analyze vehicle’s data by creating a first and second training set, as taught by Stenson, and to determine and visualize a geographical vehicle course, as taught by Wang. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine whether there are any problems with the vehicle (as suggested by Wang at Paragraph 62: “The remote data center 3 displays engine operating data, vehicle status data, and vehicle location data to the vehicle after-sales service department or vehicle management service department. Based on the data, such as the general operating status of the vehicle engine, lubrication system status, cooling system status, emissions status, and transmission system status, the vehicle after-sales service department or management service department can determine whether there are any problems with the vehicle”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AWADAGBE G HOUNTON whose telephone number is (571)270-0670. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AWADAGBE G HOUNTON/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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