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 .
Response to Arguments
Applicant's arguments filed 4/7/2026 have been fully considered but they are not persuasive. On page 7, applicant argues that the claims are similar, having slightly different scopes, such that there should be no burden since classes and subclasses searched should substantially overlap. Examiner respectfully quotes MPEP § 808.02 regarding how burden is demonstrated:
“In order to demonstrate a serious search burden, the examiner must show by appropriate explanation one of the following:
“(A) Separate classification thereof: This shows that each invention has attained recognition in the art as a separate subject for inventive effort, and also a separate field of search. Patents need not be cited to show separate classification.
“(B) A separate status in the art when they are classifiable together: Even though they are classified together, each invention can be shown to have formed a separate subject for inventive effort when the examiner can show a recognition of separate inventive effort by inventors. Separate status in the art may be shown by citing patents which are evidence of such separate status, and also of a separate field of search.
“(C) A different field of search: Where it is necessary to search for one of the inventions in a manner that is not likely to result in finding art pertinent to the other invention(s) (e.g., searching different classes/subclasses or electronic resources, or employing different search queries), a different field of search is shown, even though the two are classified together. The indicated different field of search must in fact be pertinent to the type of subject matter covered by the claims. Patents need not be cited to show different fields of search.”
Examiner respectfully asserts that the differences in prediction and navigation/control require different subclasses to search as detailed in the Office Action dated 1/27/2026. Even if there is overlap in all subclasses that need to be searched as the applicant alleges, as detailed in the Office Action dated 1/27/2026, burden still exists as at least a different field of search (C above) would be required to search for the different elements of scope. Examiner respectfully reminds applicant that, since the claims contain some overlapping subject matter, the claims may be eligible for rejoinder at time of allowance provided all claims contain subject matter considered allowable. Until allowable subject matter is found however, the restriction is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 line 13 reads “until all layers are transformed”, but there is no antecedent basis for transformation of the layers themselves. Instead, there is only antecedent basis for the transformation of a navigation state estimate with parameters and functions from a layer. It is unclear if this transformation of the navigation state estimate is also supposed to include a transformation of the layers (such as adjusting weights/biases during calculation to perform relearning/retraining) or if the transformation of layers is not meant to be interpreted prima facie and instead be interpreted as having antecedent basis to the transformation of the navigation state estimate. For the purpose of examination, “until all layers are transformed” will be interpreted as meaning that the transformation process involving a layer has been performed for every layer.
Claim(s) 2-10 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being dependent on rejected claim 1 and failing to cure the deficiencies listed above.
Claim 8 recites the limitation “parameters… comprise… network parameters” which appears to be a circular definition. It is unclear as to what this definition is meant to entail regarding the “network paragraph” and how a network parameter is different than a parameter from a layer of the neural network.
Claim 8 further recites the limitation “the parameters and functions comprise… other information associated with the neural network model”. Even with the limitation that the other information is associated with the network model, it is unclear the metes and bounds of the other information. This could feasibly encompass any and all elements related to neural networks disclosed or not disclosed, rendering the scope of the claim unascertainable.
For the purpose of examination, claim 8 will be interpreted as reading as follows:
“The method of claim 1, wherein the parameters comprise one or more of weights or biases, and the functions comprise one or more of network parameters, weights, or biases.”
Notice here that the functions can comprise network parameters. Examiner understands that the network parameters have antecedent basis towards the parameters. Therefore, the function may comprise the parameters (which may comprise weights or biases) or may comprise different weights or biases than those belonging to the parameters.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the following limitations:
providing a neural network model to a disk storage of a computer onboard the vehicle, wherein the computer is configured to execute the neural network model onboard the vehicle;
reading parameters and functions from a layer of the neural network model into a memory of the computer;
transforming a navigation state estimate with the parameters and functions via the computer and
saving a transformed navigation state estimate to the memory;
removing the parameters and functions from the memory;
incrementing to a next layer of the neural network model;
repeating the steps of reading, transforming, and removing for the next layer of the neural network model until all layers are transformed; and
outputting a navigation command.
The limitations (b-g) recited above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and using mathematical concepts but for the recitation of generic computer components. That is, other than reciting a disk memory and a computer, nothing in the claim element precludes the steps from practically being performed in the mind and using mathematical concepts. For example, regarding performance in the mind, a person, using pen and paper, can observe a row of a written table containing two functions, wherein each function contains at least one parameter, and write on a different piece of paper (hereinafter “memory paper”) the functions (b). The person can then, using another piece of paper (hereinafter “calculation paper”) plug in input variables into each function observed on the memory paper to obtain output variables of the functions (c) that can be written on the memory paper (d). The functions comprising parameters written on the memory paper can then be scratched out or erased from the paper (e). The person can then observe the next row and repeat the above steps, using the most recent outputs as the inputs for the next functions, until calculation has been performed with each row of the table (f-g). Regarding performance using mathematical concepts, the claim limitations are naught but a recitation to iteratively perform generic transformation functions (containing parameters) on stored data (b-g). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in using mathematical calculations but for the recitation of generic computer components, then it falls within the "Mathematical Concepts" grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the disk memory is/are 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(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f). The computer and neural network is/are recited at a high level of generality such that it amounts to no more than mere instructions to use the computer and neural network as tools to perform the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept. See MPEP § 2106.05(f). Further, the recitation of an abstract idea applied to a computer does not prohibit the idea from being performed mentally as detailed in MPEP 2106.04(a)(2)(III)(C) and the court cases cited therein. If the reading, saving, and removing steps (b and d-e) are not interpreted as observational/mental steps or additional parts of the mathematical equations, these steps may still be interpreted as insignificant extra pre/post-solution activities of mere data gathering. Mere data gathering cannot form an inventive concept. See MPEP § 2106.05(g). The limitations of outputting a navigation command (h) are insignificant extra post-solution activities of mere data transmission. Mere data transmission cannot form an inventive concept. See MPEP § 2106.05(g).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the disk storage, computer, and neural network are all generically claimed as detailed above. A conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, and conventional (WURC) activity in the field. The limitations of reading, saving, and storing data (b and d-e) is a WURC activity because Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 indicated that the mere storage and retrieval of data from memory is a WURC function. See MPEP § 2106.05(d)(II). The limitation of outputting a navigation command (h) is a WURC activity because buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) indicated that the mere reception or transmission of data over a network is a WURC function. See MPEP § 2106.05(d)(II). Hence, the claims are not patent eligible. To overcome the 101, examiner recommends amending the independent claim to include details of performing vehicular control (beyond mere display, data transmission, or other WURC functions) in reaction to the output command (see claim 2 below for example).
Dependent claim(s) 3-10 do(es) not recite any further limitations that cause the claim(s) to be patent eligible.
Claims 3-6 and 8-10 detail further aspects of the abstract idea.
Claim 7 recites generic training of the neural network model. The neural network training is recited at a high level of generality such that it amounts to no more than mere general linking of the judicial exception to the field of training neural networks. There is no concrete explanation of the training algorithm(s) provided to be significantly more than mere general linking. As written, the training is a mere black box that takes, as input, possible trajectories and outputs a trained neural network. General linking cannot provide an inventive concept. See MPEP § 2106.05(d).
Dependent claim(s) 2 recite(s) further limitations that cause the claim(s) to be patent eligible. Claim 2 executing the navigation command. Since navigation is understood, in light of the specification, as not merely including WURC functions (e.g. mere display), claim 2 applies the judicial exception in a practical application.
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.
Claim(s) 1-2 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Agam US 20230205533 A1 (hereinafter Agam).
Regarding claim 1,
Agam teaches
An incremental neural network model inference method for updating a navigation state estimate of a vehicle, the method comprising:
providing a neural network model to a disk storage (¶ 0076-0077 disclose a processing unit comprising a processor and disk drive memory wherein the memory stores software to perform the method including trained neural networks) of a computer onboard the vehicle (Figure 2A shows the processing unit 110 is aboard a vehicle), wherein the computer is configured to execute the neural network model onboard the vehicle (¶ 0076 discloses software is executed by the processor);
reading parameters and functions from a layer of the neural network model into a memory of the computer (¶ 0006 discloses caching neural network coefficients related to a particular layer of the neural network used by neural network processors in iterative processing of neural network operations);
transforming a navigation state estimate with the parameters and functions via the computer (¶ 0006 discloses neural network processors perform neural network operations wherein cached coefficients are used by the processors; ¶ 0393 discloses network operations are related to functions of modules including velocity, acceleration, and navigation response modules indicating results include navigational states; see, for example, ¶ 0157 regarding an example of performing processing to construct a vehicle path and ¶ 0256 which performs processing to obtain a distance to a landmark in the environment and ¶ 0397 which details final results comprise various navigational states) and saving a transformed navigation state estimate to the memory (¶ 0006 discloses storing intermediate results in memory);
incrementing to a next layer of the neural network model (¶ 0006 discloses the method is performed iteratively; see also claim 7 “repeating executing, storing, and retrieving for each of the plurality of layers” and ¶ 0380 wherein an example of incrementing from a first layer to a second layer is given);
repeating the steps of reading, transforming, and removing for the next layer of the neural network model until all layers are transformed (¶ 0006 discloses the method is performed iteratively; see also claim 7 “repeating executing, storing, and retrieving for each of the plurality of layers” and ¶ 0380 which discloses repeating operations related to the entire neural network); and
outputting a navigation command (¶ 0006 discloses performing a navigational action based on the final results).
Agam does not explicitly teach
removing the parameters and functions from the memory.
However, Agam implicitly teaches
removing the parameters and functions from the memory (¶ 0006 discloses that coefficients used for operations are cached up to a first duration implying removal of cached coefficients after the first duration and ¶ 0360 details that coefficients are only stored for a single iteration; this implies a deletion, removal, or discarding of the cached coefficients occurs when the duration/iteration expires).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have used this implicit recitation to incorporate a deletion of cached coefficients into the teachings of Agam. This modification would be made with a reasonable expectation of success to save storage space, reduce overall bandwidth associated with retrieval of coefficients, and reduce processing time as disclosed in Agam (¶ 0359-0360).
Regarding claim 2, Agam teaches all of claim 1 as detailed above.
Agam further teaches
executing the navigation command for a current target epoch (¶ 0398-0399 discloses the navigational command is a command implemented to adjust or maintain a current movement such as a current heading).
Regarding claim 10, Agam teaches all of claim 1 as detailed above.
Agam further teaches that
the step of transforming the navigation state estimate with the parameters and functions is performed for one layer of the neural network model at a time (¶ 0006 discloses the method is performed iteratively; see also claim 7 “repeating executing, storing, and retrieving for each of the plurality of layers” and ¶ 0380 which discloses repeating operations related to the entire neural network one selected layer at a time), wherein the neural network model comprises a plurality of layers (see at least claim 7 “the neural network comprises a plurality of layers”).
Claim(s) 3-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Agam as applied to claim 1 above, and further in view of Shuai et al. CN 114154231 A (hereinafter Shuai; a translated copy has been provided which the examiner relies upon).
Regarding claim 3, Agam teaches all of claim 1 as detailed above.
Agam does not teach that
the step of transforming the navigation state estimate comprises performing a linear transformation on the navigation state estimate.
Shuai teaches that
the step of transforming the navigation state estimate comprises performing a linear transformation on the navigation state estimate (¶ 0022 discloses performing a linear transformation of input data in select layers of a neural network).
There is only a finite list of options for the transformation functions: non-linear functions, linear functions, or a combination thereof. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to try the teachings of Shuai and incorporate it into the teachings of Agam since there is a finite number of identified, predictable potential solutions (i.e. function types) to the recognized need (transforming input data) and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
Regarding claim 4, the modified Agam reference teaches all of claim 3 as detailed above.
Agam further teaches
determining whether the layer of the neural network model currently in memory is the last layer following the step of performing the functions (¶ 0380 discloses determining that there is no other layer to be selected after performing operations).
Agam does not teach
the step of performing the linear transformation.
Shuai further teaches
the step of performing the linear transformation (¶ 0022 discloses performing a linear transformation of input data in select layers of a neural network).
There is only a finite list of options for the transformation functions: non-linear functions, linear functions, or a combination thereof. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to try the teachings of Shuai and incorporate it into the teachings of Agam since there is a finite number of identified, predictable potential solutions (i.e. function types) to the recognized need (transforming input data) and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
Regarding claim 5, the modified Agam reference teaches all of claim 4 as detailed above.
Agam further teaches that
upon determining that the layer of the neural network model layer is the last layer, proceeding to the step of outputting the navigation command based on the transformed navigation state estimate (¶ 0380 discloses that when there is no other layer to select, the processing ends; ¶ 0006 discloses final result of processing operations can be obtained and a navigational action can be performed in reaction to the final result).
Regarding claim 6, the modified Agam reference teaches all of claim 4 as detailed above.
Agam does not teach that
upon determining that the layer of the neural network model layer is not the last layer, performing a nonlinear transformation on the transformed navigation state estimate.
Shuai further teaches that
upon determining that the layer of the neural network model layer is not the last layer, performing a nonlinear transformation on the transformed navigation state estimate (¶ 0022 discloses that specific layers, which are not the final layer, perform a non-linear calculation on the already transformed input).
There is only a finite list of options for the transformation functions: non-linear functions, linear functions, or a combination thereof. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to try the teachings of Shuai and incorporate it into the teachings of Agam since there is a finite number of identified, predictable potential solutions (i.e. function types) to the recognized need (transforming input data) and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success. Furthermore, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have modified Agam to incorporate the teachings of Shuai such that non-linear calculation does not occur on the final layer as taught by Shuai. This modification would be made with a reasonable expectation of success to reduce processing time.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Agam as applied to claim 1 above, and further in view of Xia et al. CN 114383617 A (hereinafter Xia; a translated copy has been provided which the examiner relies upon).
Regarding claim 7, Agam teaches all of claim 1 as detailed above.
Agam teaches that
the neural network model is previously trained (at least ¶ 0077 discloses the neural network is a trained system)
Agam does not explicitly teach that
the parameters and functions are previously obtained by training the neural network model on a set of possible trajectories for a maneuver of the vehicle.
Xia teaches that
the parameters and functions are previously obtained by training the neural network model on a set of possible trajectories for a maneuver of the vehicle (¶ 0009-0012 discloses a neural network is trained using historic vehicle trajectory data to adjust weights of nodes; examiner understands historic trajectories as equivalent to “possible trajectories” as they are ground truth data that was previously performed).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have modified Agam to incorporate the teachings of Xia such that the neural network comprising coefficients related to layers utilized for operations of Agam can be node weights, trained on historic trajectory data as taught by Xia. This modification would be made with a reasonable expectation of success to improve accuracy and robustness of the neural network model by training on real, historic data.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Agam as applied to claim 1 above, and further in view of Rohani et al. US 10796204 B2 (hereinafter Rohani).
Regarding claim 8, Agam teaches all of claim 1 as detailed above.
Agam further teaches that
the functions comprise one or more of network parameters (¶ 0006 discloses neural network processors may use neural network coefficients during operations), weights, or biases.
Agam does not teach that
the parameters comprise one or more of weights or biases.
Rohani teaches that
the parameters and functions comprise one or more of weights (column 21 line 31-67 discloses layer outputs can be calculated with functions using weight factors) or biases (column 21 line 31-67 discloses layer outputs can be calculated with functions using bias factors).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have modified Agam to incorporate the teachings of Rohani such that the coefficients used for operations of Agam can include weight and bias factors as taught by Rohani. This modification would be made with a reasonable expectation of success to improve accuracy of state estimate.
Regarding claim 9, Agam teaches all of claim 1 as detailed above.
Agam does not teach that
the navigation state estimate is progressively transformed upon repeating steps for the next layer of the neural network model.
Rohani teaches that
the navigation state estimate is progressively transformed upon repeating steps for the next layer of the neural network model (Figure 10 shows that nodes are connected such that the output of one node is fed into every other node in the next layer; see also column 21 lines 29-67).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have modified Agam to incorporate the teachings of Rohani such that the neural network layers are intricately connected wherein the results of iterations of one layer of a neural network of Agam can be fed into a following layer as taught by Rohani. This modification would be made with a reasonable expectation of success to improve accuracy of results by iteratively adjusting results utilizing differing coefficients present in each layer.
Documents Considered but not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Unnikrishnan et al. US 20220237402 A1 discloses inputting data into a neural network iteratively through each layer to output a state of another vehicle in an environment.
Yao et al. US 20190101917 A1 discloses feeding inputs through each layer of a neural network iteratively, wherein each layer has its own biases and weights in order to detect a state of a host vehicle.
Chen et al. CN 116448104 A discloses that layers perform various transformations on input data and the output layer only performs a linear transformation.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Tiffany Schoech whose telephone number is (571)272-2937. The examiner can normally be reached 4:45 am - 3:15 pm PT Monday - Thursday.
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/A.T.S./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
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