DETAILED ACTION
This Office Action is in response to the RCE filed on 09/26/2025.
Claims 1, 13, and 76 currently amended.
Claims 1-17 and 76-78 currently pending in this application and have been examined.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/06/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
In reference to Applicant’s arguments on page(s) 7-10 regarding rejections made under 35 U.S.C. 101:
Claims 1-17 and 76-78 stand rejected under 35 U.S.C. § 101 as allegedly being directed to an abstract idea without significantly more.
Applicants respectfully traverse the rejection.
To advance prosecution and without conceding the merits of the interpretations and conclusions used in the Office Action to make the rejection, Applicants have amended independent claim 1 (and similarly independent claims 13 and 76) to recite, inter alia,
"configuring the machine learning model based on the selected machine learning model parameter set to perform a base functionality in the device by switching a current machine learning model parameter set of the machine learning model with the selected machine learning model parameter set; and processing, using the configured machine learning model, sensor data collected by the device to perform the base functionality by detecting one or more road attributes."
The Examiner's rejection is based on the premise that the claims recite a mental process. However, as reminded in the USPTO's August 4, 2025 Memorandum ("Memo") from Deputy Commissioner Charles Kim to Technology Centers 2100, 2600, and 3600, the mental process grouping is limited to what can "practically be performed in the human mind" and Examiners should not expand this grouping to encompass claim limitations that cannot. In particular, Deputy Commissioner Kim reminds examiners that "[c]laim limitations that encompass Al in a way that cannot be practically performed in the human mind do not fall within this grouping." The Memo further states that "Examiners are reminded that if it is a 'close call' as to whether a claim is eligible, they should only make a rejection when it is more likely than not (i.e., more than 50%) that the claim is ineligible." Applicants submit that the claimed subject matter does not rise to even a close call because the claimed process, taken as a whole, cannot be practically performed by a human. While a human can conceptually select a strategy based on context, a human cannot perform the claimed technical steps of dynamically switching the operative parameters (e.g., millions of weights and biases) of a complex computational model and then immediately processing a stream of sensor data through that newly configured model to detect road attributes. Such steps are not analogous to human thinking; they are a specific, technical computer process for which the human mind is not equipped.
The Examiner's primary concerns appear to be that (1) the claimed steps could be performed mentally, and (2) the claims recite configuring a model to perform a function without positively reciting the model actually performing that function on the device. As indicated above, Applicants have amended independent claims 1, 13, and 76 to address these points directly. More specifically, the amended feature step clarifies the claim subject matter provides for a concrete and practical application. This application is a tangible, technical process in which the specifically configured machine learning model is put to use to process real-world sensor data from the device to produce a useful, concrete result of the detection of road attributes.
The combination of dynamically selecting a specific set of model parameters (e.g., weights and biases per claim 4) based on context, technically reconfiguring a model with those parameters, and then applying that configured model to process sensor data constitutes a specific technological improvement. This improves the functioning of the device itself by making it a more accurate, efficient, and context-aware sensor processing tool (see, e.g., Specification at paragraphs [0056] and [0079]). The Memo reiterates that "[i]n computer related-technologies, examiners can conclude that claims are eligible under Step 2A Prong Two by finding that a claim reflects an improvement to the function of a computer or to another technology or technical", and that this improvement can be determined based on "the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome." In the instant case, the claims recite a "particular way to achieve a desired outcome" (e.g., more accurate object detection as stated in, for instance, paragraph [0079] of the Specification) rather than merely the idea of a solution.
Therefore, consistent with the guidance of the August 24, 2025 Memo, the claim as a whole integrates the judicial exception into a practical application that provides "significantly more." The added elements are not merely "apply it" instructions; they are specific, technical steps that result in an improved technological process. For at least these reasons, independent claim 1 is directed to patent-eligible subject matter under 35 U.S.C. § 101. Because independent claims 13 and 76 recite similar features as independent claim 1, they are also patent eligible for the same reasons. Because claims 1-12, 14-17, 77, and 78 depend respectively from independent claims 1, 13, and 76, they are also patent eligible for the same reasons as well as for the additional features that they recite.
Accordingly, Applicants respectfully request reconsideration and withdrawal of the rejection.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive in light of the amendments made on the claims,
Applicant argues that the amended claims do not recite any abstract ideas as they cannot be performed in the human mind. Examiner disagrees. Applicant argues that the human mind cannot dynamically switch the operative parameters of a neural network. Examiner agrees, that is why that limitation is seen as an additional element directed to applying the additional element using a generic computer component, in this case a neural network. Applicant argues that the action of processing sensor data to identify road attributes cannot be performed in the human mind. Examiner disagrees. The act of processing data is something that the human mind does every second. It is very possible for the human mind to look at raw sensor data and notice trends or patterns within the data. The sensor data could represent various speeds from cars on a roadway, from which it could be identified that a sudden slowdown in speed could indicate some form of traffic control like a stop sign, traffic light, speed bump, etc. The act of processing data is not something that requires the use of a neural network, and as discussed, can be reasonably performed in the human mind. The claims as amended further recite actions of determining the context of a device and choosing a set of parameters from a plurality of parameter sets both of which are functions that can be performed in the human mind and are therefore mental processes.
Applicant argues that the instant invention recites a practical application that provides significantly more than the abstract idea. Examiner disagrees. The inventive concept in the instant application is a machine learning model that dynamically changes parameters to process data to detect road attributes. As mentioned above, the act of processing this sensor data is itself an abstract idea. What is not clear to the Examiner is what exactly this practical application accomplishes. If the results of this method are identified road attributes, what are they used for? As far as the claims are concerned, there is no application of the identified road attributes. Without any application of the identified objects, in this case road attributes, there cannot be arguments made that there is a practical application of the disclosed invention.
Applicant argues that the instant application provides an improvement on existing technology. Examiner disagrees. The actions of training a model with different parameters does not constitute a technological improvement because it does not change the way the model is trained, just what the model is trained on. There is no mention of how the training done with different parameters is performed in a such a way that it would provide an improvement on the efficiency of the model, account for training costs, lower overhead, etc. As it is written, the claim simply performs a base function with different parameters.
In light of the amendments made on the independent claims, the rejections made under 35 U.S.C. 101 are withdrawn and new grounds for rejection is presented below.
In response to Applicant’s arguments on page(s) 10-12 regarding rejections made under 35 U.S.C. 102:
Claims 1-17 and 76-78 stand rejected under 35 U.S.C. § 102 as being anticipated by Schafer.
Applicants respectfully traverse the rejection.
As noted above, Applicants have amended independent claim 1 (and similarly independent claims 13 and 76) to recite, inter alia, "configuring the machine learning model to perform a base functionality in the device by switching a current machine learning model parameter set of the machine learning model with the selected machine learning model parameter set."
Applicants respectfully submit that the claims, particularly as amended, are patentably distinct from Schafer. In particular, while Schafer's disclosure at paragraph [0047] that Schafer's "plurality of particularized convolutional neural networks" may share the same underlying structure, there is a clear and patentable distinction in the mechanism of adaption of the network disclosed in Schafer and the mechanism of adaption of the machine learning model recited in the claimed subject matter.
To clarify this distinction, independent claims 1, 13, and 76 have been amended to recite that the model is configured by "switching a current machine learning model parameter set with the selected machine learning model parameter set." This "switching" recites a specific technical action that is distinct from the method taught by Schafer. Schafer describes a select-and- load paradigm that operates on a library of discrete, separately trained network entities (see, e.g., Schafer at paragraphs [0035] and [0036]). Schafer's system selects "the right one of the plurality of particularized convolutional neural networks 40" and then it is "activated." Alternatively, a server can "send the most appropriate one" to the vehicle (see, e.g., Schafer at paragraph [0038]). This describes choosing a complete, self-contained network entity and loading it for use, replacing the previous one.
Even in the case of Schafer's "incremental updates" which "delta information" of "weights and biases" as described in Schafer at paragraph [0047], this "delta information" is used to generate "a new one of the plurality of particularized convolutional neural networks 40 or CNN by applying this delta to the general purpose CNN" or to "generate the appropriate CNNs on the fly if storage on the vehicle memory unit 38 is limited." In other words, Schafer always generates a "new" network even when receiving just "delta information" and does not teach or suggest "switching" as claimed.
More specifically, in stark contrast, the amended claims recite an in-place-reconfiguration paradigm. This claimed concept is strongly supported by the Specification. The very title of FIG. 2C is "Dynamic model parameter switching." The Specification at paragraph [0064] explicitly describes the application "continues applying the Object Detection Model A yet switching to a parameter set B." This language describes modifying the operative parameters of a single, persistent model instance that is already loaded on the device, without needing to load or generate an entirely new network entity as required in Schafer.
This technical distinction is significant for an edge device, as in-place parameter switching is a more lightweight and efficient operation than de-instantiating one network and instantiating another as performed in Schafer. Schafer fails to teach or suggest this specific mechanism of configuring a model by dynamically switching its current parameter set with a selected parameter set in-place. Because Schafer does not teach this limitation, it cannot anticipate the amended claims.
For at least these reasons, claim 1 is patentable over Schafer. Because claims 13 and 76 recite similar features, they are also patentable over Schafer. Because claims 2-12, 14-17, 77, and 78 depend respectively from claims 1, 13, and 76, they are also patentable over Schafer for the same reasons as well as for the additional features that they recite.
Accordingly, Applicants respectfully request reconsideration and withdrawal of the rejection.
Examiner’s response:
Applicant’s arguments have been fully considered but are moot in light of the amendments made on the independent claims.
Applicant claims that the provided prior art reference does not teach the claims as amended. Examiner agrees. Since the amended limitations were not previously examined, they require further search and consideration.
The rejections made under 35 U.S.C. 102 are withdrawn and new grounds for rejection is presented below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-17 and 76-78 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 1 recites, in part, a method, therefore falling into the statutory category of process. Independent Claim 13 recites, in part, an apparatus, therefore falling into the statutory category of machine. Independent Claim 76 recites, in part, a non-transitory computer readable medium, therefore falling into the statutory category of manufacture.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“determining a context of a device”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses determining the location of a device.
“selecting a machine learning model parameter set from the plurality of machine learning model parameter sets based on the context”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses selecting a set of parameters from a plurality of parameter sets.
“processing sensor data collected by the device to perform the base functionality”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses processing data in order to determine road attributes.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“providing a plurality of machine learning model parameter sets for a machine learning model”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g)): i.e., pre-solution activity of gathering data for use in the claimed process.
“wherein the plurality of machine learning model parameter sets is based on a plurality of respective regional differences”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (regional differences) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“configuring the machine learning model to perform a base functionality in the device by switching a current machine learning model parameter set of the machine learning model with the selected machine learning model parameter set”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
“processing, using the configured machine learning model, sensor data”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
“by detecting one or more road attributes”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (road attributes) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “providing a plurality of machine learning model parameter sets for a machine learning model” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The additional element(s) of “wherein the plurality of machine learning model parameter sets is based on a plurality of respective regional differences” and “by detecting one or more road attributes” is/are directed to particular field(s) of use (regional data and road attributes) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As discussed above, the additional element(s) of “configuring the machine learning model to perform a base functionality in the device by switching a current machine learning model parameter set of the machine learning model with the selected machine learning model parameter set” and “processing, using the configured machine learning model, sensor data” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the context includes a geographic location of the device”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (location data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the context includes a geographic location of the device” is/are directed to particular field(s) of use (location data) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“initiating a training of the machine learning”. This additional elements is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (train a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“model using a plurality of respective regional datasets to provide for the plurality of respective regional differences in the plurality of machine learning model parameter sets”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (location data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “initiating a training of the machine learning model” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome (train a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
The additional element(s) of “using a plurality of respective regional datasets to provide for the plurality of respective regional differences in the plurality of machine learning model parameter sets” is/are directed to particular field(s) of use (location data) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein one or more parameters in the plurality of parameter sets include a connection weight parameter, a bias value, an activation function, or a combination thereof”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (machine learning parameters) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein one or more parameters in the plurality of parameter sets include a connection weight parameter, a bias value, an activation function, or a combination thereof” is/are directed to particular field(s) of use (machine learning parameters) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 5:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the device is a mobile edge device capable of moving between a plurality of geographic regions”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (mobile devices) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the device is a mobile edge device capable of moving between a plurality of geographic regions” is/are directed to particular field(s) of use (mobile devices) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the context is determined dynamically and the machine learning model parameter set is selected dynamically as the device moves”. This additional elements is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (initiate a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein the context is determined dynamically and the machine learning model parameter set is selected dynamically as the device moves” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome (initiate a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the base functionality includes feature detection, feature classification, or a combination thereof based on sensor data collected using one or more sensors of the device”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (feature engineering) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the base functionality includes feature detection, feature classification, or a combination thereof based on sensor data collected using one or more sensors of the device” is/are directed to particular field(s) of use (feature engineering) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 8:
Step 2A: Prong 1 analysis:
Claim 8 recites in part:
“wherein the base functionality includes road hazard detection, road furniture detection, road sign detection, or a combination thereof”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses identifying hazards/signs on a road.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 9:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the machine learning model is a feature detection model for detecting one or more features from image data collected by the device”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (machine learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the machine learning model is a feature detection model for detecting one or more features from image data collected by the device” is/are directed to particular field(s) of use (machine learning models) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 10:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the device is a vehicle or is associated with the vehicle, and wherein the base functionality includes an autonomous operation of the vehicle”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (autonomous vehicles) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the device is a vehicle or is associated with the vehicle, and wherein the base functionality includes an autonomous operation of the vehicle” is/are directed to particular field(s) of use (autonomous vehicles) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 11:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the machine learning model is included in a software development kit used for compiling an application package for execution by the device”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (software development) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the machine learning model is included in a software development kit used for compiling an application package for execution by the device” is/are directed to particular field(s) of use (software development) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 12:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the selected machine learning model parameter set is downloaded to or enabled at the device on demand”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g)): i.e., pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein the selected machine learning model parameter set is downloaded to or enabled at the device on demand” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 13:
Due to similar claim language to Claim 1, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of the limitation(s) covered below.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“at least one processor”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (processor) (See MPEP 2106.05(f)).
“at least one memory including computer program code for one or more programs”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (memory) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “at least one processor” and “at least one memory including computer program code for one or more programs” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 14:
Due to similar claim language to Claim 2, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 15:
Due to similar claim language to Claim 3, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 16:
Due to similar claim language to Claim 4, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 17:
Due to similar claim language to Claim 5, Claim 17 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 76:
Due to similar claim language to Claims 1 and 13, Claim 76 is rejected for the same reasons as presented above in the rejection of Claims 1 and 13.
Regarding Claim 77:
Due to similar claim language to Claims 2 and 14, Claim 77 is rejected for the same reasons as presented above in the rejection of Claims 2 and 14.
Regarding Claim 78:
Due to similar claim language to Claims 5 and 17, Claim 78 is rejected for the same reasons as presented above in the rejection of Claims 5 and 17.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-17 and 76-78 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schafer et al (US 20190213451 A1, hereinafter Schafer), in view of Blanc-Paques et al (US 11073401 B1, hereinafter Blanc), and in further view of Jiang et al (US 10816973 B2, hereinafter Jiang).
Regarding Claim 1:
Schafer teaches
A method comprising: providing a plurality of machine learning model parameter sets for a machine learning model (Schafer [0035]: “a set of region and environment specific neural networks (i.e., the plurality of particularized convolutional neural networks 40) are created”)
wherein the plurality of machine learning model parameter sets is based on a plurality of respective regional differences (Schafer [0035]: “Similarly, one of the plurality of particularized convolutional neural networks 40 can be trained only with rain images and another of the plurality of particularized convolutional neural networks 40 is only trained with cloud images and yet another one which was only trained with sun images”; (EN): With reference to the networks, SCHAFER states “particularized convolutional neural networks 40 to process the image data” ([0033], SCHAFER), outlining the base functionality of processing image data. SCHAFER’s weather conditions (e.g., cloudy and sunny) correspond to the respective regional differences);
determining a context of a device (Schafer [0033-0034]: “The vehicle processor 36 is configured to collect vehicle 26 location data with the location determining module 30… the vehicle processor 36 is further configured to determine whether it is raining using the vehicle 26 environmental data from the plurality of environmental sensors 32, 34. The vehicle processor 36 can also be configured to determine a time of day using the vehicle 26 environmental data from the plurality of environmental sensors 32, 34”);
selecting a machine learning model parameter set from the plurality of machine learning model parameter sets based on the context (Schafer [0034-0035]: “Additionally, the vehicle processor 36 may be configured to select one of the plurality of particularized convolutional neural networks 40 to use for the processing of the image data based on the determination of whether it is raining and the time of day and the vehicle location data… Therefore… the plurality of particularized convolutional neural networks 40 are activated based on the information provided… Depending on the readings from the daylight detection sensor 32 , the right one of the plurality of particularized convolutional neural networks 40 is activated”);
configuring the machine learning model to perform a base functionality in the device (Schafer [0035]: “Depending on the readings from the daylight detection sensor 32, the right one of the plurality of particularized convolutional neural networks 40 is activated”; (EN): As outlined above, SCHAFER is directed to a base functionality of processing image data)
Schafer does not distinctly disclose
by switching a current machine learning model parameter set of the machine learning model with the selected machine learning model parameter set
However, Jiang teaches
by switching a current machine learning model parameter set of the machine learning model with the selected machine learning model parameter set (Jiang [Col 7 lines 22-28]: “Based on the one or more types of data received, the switching module 313 may perform an analysis to determine which subsystem (deliberation or intuitive) to invoke. The analysis may determine various factors that may be weighted. The switching module 313 may perform the analysis to determine a set of driving or environment conditions”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Schafer and Jiang before him or her, to modify the system and method for highly automated driving of a vehicle to detect and classify pedestrians and traffic signs and other vehicles of Schafer to include the system and method for providing a flexible decision system for autonomous driving as shown in Jiang. The motivation for doing so would have been to use the method for automated driving of Schafer and combine the use of different decision systems that are evaluated for use by the autonomous driving control system (Jiang [Col 2 lines 14-21]: " Described is a system (and method) for providing a flexible decision system for autonomous driving. The system may include a framework that allows a decision system to switch between a deliberate rule-based decision framework and an intuitive machine-learning model-based decision framework. Accordingly, the system may invoke the appropriate framework (or subsystem) based on a particular set of driving conditions or environment").
Schafer + Jiang does not distinctly disclose
processing, using the configured machine learning model, sensor data collected by the device to perform the base functionality by detecting one or more road attributes
However, Blanc teaches
processing, using the configured machine learning model, sensor data collected by the device to perform the base functionality by detecting one or more road attributes (Blanc [Col 10 line 62-Col 11 line 5]: “Optionally, the system is in communication with a classifier information database (e.g., one or more databases, or a storage subsystem) that can store information describing one or more visual classifiers (e.g., information utilized by one or more machine learning models, such as support vector machines, k-means clustering, neural networks, and so on). A reviewing user can review the road condition images, and correctly classify the types of road conditions identified in the sensor data. This correctly classified information can be used to update the one or more visual classifiers”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Schafer + Jiang and Blanc before him or her, to modify the system and method for highly automated driving of a vehicle to detect and classify pedestrians and traffic signs and other vehicles of Schafer + Jiang to include the system and methods for road network optimization based on vehicle sensor data as shown in Blanc. The motivation for doing so would have been to use the method for automated driving of Schafer + Jiang and combine the use of sensor data to identify road attributes and potential road hazards (Blanc [Col 10 line 62-Col 11 line 5]: “Optionally, the system is in communication with a classifier information database (e.g., one or more databases, or a storage subsystem) that can store information describing one or more visual classifiers (e.g., information utilized by one or more machine learning models, such as support vector machines, k-means clustering, neural networks, and so on). A reviewing user can review the road condition images, and correctly classify the types of road conditions identified in the sensor data. This correctly classified information can be used to update the one or more visual classifiers”).
Regarding Claim 2:
Schafer teaches
The method of claim 1, wherein the context includes a geographic location of the device (Schafer [0036]: “The country which the vehicle 26 is currently located in can be determined based on the vehicle location data available in the vehicle 26 (e.g., based on the combination of Global Navigation Satellite System (GNSS) information and navigation maps). Therefore, the right one of the plurality of particularized convolutional neural net-works 40 for the specific country can be activated”).
Regarding Claim 3:
Schafer teaches
The method of claim 1, further comprising: initiating a training of the machine learning model using a plurality of respective regional datasets to provide for the plurality of respective regional differences in the plurality of machine learning model parameter sets (Schafer [0035-0036]: “So, instead of training one of the plurality of particularized convolutional neural networks 40, a set of the plurality of particularized convolutional neural networks 40 is trained and the right one is activated in the vehicle 26 based on sensor (i.e., from the plurality of environmental sensors 32, 34) and digital map information (i.e., vehicle location data). For instance, one of the plurality of particularized convolutional neural networks 40 is trained only with night images and another of the plurality of particularized convolutional neural networks 40 only with day images… Similarly, one of the plurality of particularized convolutional neural networks 40 can be trained only with rain images and another of the plurality of particularized convolutional neural networks 40 is only trained with cloud images and yet another one which was only trained with sun images… for example, in the United States, the plurality of particularized convolutional neural networks 40 can be created with some being state specific. However, it should be appreciated that other categories or divisions of the plurality of particularized convolutional neural networks 40 may be beneficial besides those that are administratively divided. The plurality of particularized convolutional neural networks 40 could also be created only for urban areas (e.g., cities, and other ones for rural areas or for highways)”).
Regarding Claim 4:
Schafer teaches
The method of claim 1, wherein one or more parameters in the plurality of parameter sets include a connection weight parameter, a bias value, an activation function, or a combination thereof (Schafer [0005]: “parameterization parameters (reflecting the structure, the weights and biases of the network)”).
Regarding Claim 5:
Schafer teaches
The method of claim 1, wherein the device is a mobile edge device capable of moving between a plurality of geographic regions (Schafer [0005]: “The system includes a camera disposed on the vehicle for receiving image data near the vehicle. A location determining module is also disposed on the vehicle to determine a location of the vehicle. The system also includes a vehicle memory unit disposed on the vehicle storing at least one particularized convolutional neural networks to process the image data. A vehicle processor is disposed on the vehicle and communicatively coupled to the vehicle memory unit and the camera and the location determining module and is configured to collect vehicle location data with the location determining module”).
Regarding Claim 6:
Schafer teaches
The method of claim 1, wherein the context is determined dynamically and the machine learning model parameter set is selected dynamically as the device moves (Schafer [0042]: “The server 42 can also track the vehicle 26, and send a new one of the plurality of particularized convolutional neural networks 40 whenever it detects that a new one would be beneficial due to environmental changes (e.g., the vehicle 26 moving from a highway into a city)”; [0045]: “the vehicle processor 36 can also be configured to communicate the image data to the server 42. Accordingly, the server processor 44 is configured to determine whether it is raining using the image data from the camera 28. The server processor 44 can also be configured to determine a time of day using the image data from the camera 28 and select one of the plurality of particularized convolutional neural networks 40 to use for the processing of the image data based on the determination of whether it is raining and the time of day and the vehicle 26 location data”).
Regarding Claim 7:
Schafer teaches
The method of claim 1, wherein the base functionality includes feature detection, feature classification, or a combination thereof based on sensor data collected using one or more sensors of the device (Schafer [0028]: “CNNs are used for classifying/detecting vehicles, pedestrians, bicycles, traffic signs and other objects”; (EN): Paragraph [0047] of the instant specification states “features (e.g., map feature 105)” and paragraph [0048] of the instant specification states “map features 105 like traffic lights, signs, etc.”, demonstrating that SCHAFER’s traffic signs are encompassed by the BRI of features).
Regarding Claim 8:
Schafer teaches
The method of claim 1, wherein the base functionality includes road hazard detection, road furniture detection, road sign detection, or a combination thereof (Schafer [0028]: “CNNs are used for classifying/detecting vehicles, pedestrians, bicycles, traffic signs and other objects”).
Regarding Claim 9:
Schafer teaches
The method of claim 1, wherein the machine learning model is a feature detection model for detecting one or more features from image data collected by the device (Schafer [0033]: “The vehicle processor 36 is also configured to process the image data using the at least on particularized convolutional neural network 40… to detect and classify at least one of pedestrians 22 and traffic signs 24 and other vehicles 26”).
Regarding Claim 10:
Schafer teaches
The method of claim 1, wherein the device is a vehicle or is associated with the vehicle, and wherein the base functionality includes an autonomous operation of the vehicle (Schafer [Abstract]: “A system and method for highly automated driving of a vehicle to detect and classify pedestrians and traffic signs and other vehicles are provided.”).
Regarding Claim 11:
Schafer teaches
The method of claim 1, wherein the machine learning model is included in a software development kit used for compiling an application package for execution by the device (Schafer [0064]: “implemented as one or more computer programs”; [0066]: “A computer program (also known as a program, module, engine, software, software application, application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and the program can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment… A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network”).
Regarding Claim 12:
Schafer teaches
The method of claim 1, wherein the selected machine learning model parameter set is downloaded to or enabled at the device on demand (Schafer [0038]: “send the most appropriate one of the plurality of particularized convolutional neural networks 40 to the vehicle 26. Note that the activation of an appropriate one of the plurality of particularized convolutional neural networks 40 could also be based on cloud information (i.e., information on the server 42)”).
Regarding Claim 13:
Due to similar claim language to Claim 1, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of the limitation(s) covered below.
Schafer teaches
An apparatus comprising: at least one processor (Schafer [0064]: “Some embodiments can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on a tangible computer storage medium for execution by one or more processors (e.g., voice processor or classification processor)”)
at least one memory including computer program code for one or more programs (Schafer [0064]: “Some embodiments can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on a tangible computer storage medium for execution by one or more processors (e.g., voice processor or classification processor)”)
Regarding Claim 14:
Due to similar claim language to Claim 2, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 15:
Due to similar claim language to Claim 3, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 16:
Due to similar claim language to Claim 4, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 17:
Due to similar claim language to Claim 5, Claim 17 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 76:
Due to similar claim language to Claims 1 and 13, Claim 76 is rejected for the same reasons as presented above in the rejection of Claims 1 and 13.
Regarding Claim 77:
Due to similar claim language to Claims 2 and 14, Claim 77 is rejected for the same reasons as presented above in the rejection of Claims 2 and 14.
Regarding Claim 78:
Due to similar claim language to Claims 5 and 17, Claim 78 is rejected for the same reasons as presented above in the rejection of Claims 5 and 17.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128