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 Amendment
This application is hereby reopened, and this Non-Final Rejection is filed in response to Pre-Appeal Brief Conference Decision filed 07/09/2026.
Claims 1, 5-11, 13-16, and 21-30 remain pending.
Response to Arguments
Argument 1, applicant argues in Pre-Appeal Brief Conference Request filed 05/07/2026, pg. 1-2, that Perez does not teach to, “generate encodings based on an input query and candidate responses using parameters trained using hyperparameters optimized using coordinate descent and line searching”.
Response to Argument 1, Applicants arguments have been fully considered and are persuasive in light of the amendments. However, upon further search and consideration a newly found combination of references (U.S. Patent Application Publication NO. 20210174161 “Perez” and further in light of U.S. Patent Application Publication NO. 20210141383 “Silander”, in light of U.S. Patent Application Publication NO. 20220027757 “Phan”, in light of U.S. Patent Application Publication NO. 20200238074 “Song”, and in light of U.S. Patent Application Publication NO. 20210142160 “Mohensi”) is applied to updated rejections.
Argument 2, applicant argues in Pre-Appeal Brief Conference Request filed 05/07/2026, pg. 2-4, that Perez does not teach the Claim 6 limitations,
an encoder module configured to generate encodings based on an input query and candidate responses using parameters trained using hyperparameters optimized using coordinate descent and line searching…a distance module configured to generate a distance matrix including distance values between the candidate responses, respectively, and the input query…a results module configured to select one of the candidate responses as a response to the input query based on the distance values.
Response to Arg 2, Applicants arguments have been fully considered and are persuasive in light of the amendments. However, upon further search and consideration a newly found combination of references (U.S. Patent Application Publication NO. 20210174161 “Perez” and further in light of U.S. Patent Application Publication NO. 20210141383 “Silander”, in light of U.S. Patent Application Publication NO. 20220027757 “Phan”, in light of U.S. Patent Application Publication NO. 20200238074 “Song”, and in light of U.S. Patent Application Publication NO. 20210142160 “Mohensi”) is applied to updated rejections.
Argument 3, applicant argues in Pre-Appeal Brief Conference Request filed 05/07/2026, pg. 2-4, that Lin fails to teach the claim 21 limitation, “a distance module
configured to generate a distance matrix including distance values between the candidate responses, respectively, and the input query”
Response to Argument 3, the examiner respectfully disagrees. The examiner notes that the limitation for a distance matrix to be an “image-to image nearest neighbor matching or selecting a closest image to one or more images captured from a camera” is not found in the claims. Under BRI the claims merely require an input query, a candidate response, and a distance matrix that defines a distance value between the two. The claims as constructed do not contain limitations linking the distance matrix to the function of the encoder module that is configured to identify a closest image or the metric that defines a closest image is a measure of similarity and not distance. Since Lin teaches in para. [0012, 0018] that “recording edge lengths in a distance matrix M.sub.dist, to obtain a topological map G={V, E, M.sub.dist} of the passable region… valuating each candidate point in the candidate point set P.sub.candidate by a Multi-Criteria-Decision-Making approach based on a fuzzy measure function, taking the candidate point with the highest score as the Next-Best-View p.sub.NBV “,which results in building a topological map from camera input and finding a distance matrix in order to execute movement in the shortest physical edge length, Lin teaches the BRI for the claim limitation, “a distance module configured to generate a distance matrix including distance values between the candidate responses, respectively, and the input query”, wherein an optimized distance is found between a current input location and next coordinate path location.
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.
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.
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, 5-6, 8, 11, 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication NO. 20210174161 “Perez” and further in light of U.S. Patent Application Publication NO. 20210141383
“Silander”, in light of U.S. Patent Application Publication NO. 20220027757 “Phan”, in light of U.S. Patent Application Publication NO. 20200238074 “Song”, and in light of U.S. Patent Application Publication NO. 20210142160 “Mohensi”.
Claim 6:
Perez teaches a search system of a navigating robot, comprising: an encoder module configured to:
Determine a location of the navigating robot for propulsion control; and
generate encodings based on an input query and candidate responses using parameters (i.e. para. [0017], “The neural network model, trained using distant supervision and distance based ranking loss, combines an interaction matrix, Weaver blocks and adaptive subsampling to map to a fixed size representation vector, which is used to emit a score. The method presented here generally comprises these aspects: (i) learning parameters of the neural network model (i.e., a scoring model); and (ii) using the scoring model to determine the most relevant portion of text to answer a given question”, wherein the BRI for encodings encompasses the representation vectors based on a users input to a runtime question and the BRI for candidate responses encompasses generating potentially relevant runtime answers based on sentences associated with trained parameters such as scores associated with a distanced based ranking loss) trained [and] using hyperparameter(i.e. para. [0050], “The n-uplet loss allows the occurrence of some positives being unrelated to the query. It only requires that the best positive sentences get a better score than all negatives sentences, in other words, the n-uplet loss penalizes only the case when there exists a negative
sentence that is closer to the query than all positives… the n-uplet loss is calculated as follows
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Wherein the BRI for a hyperparameter encompasses how Perez uses a predefined parameter ‘α’ used in the n-uplet loss equation to find a balance between positive and negative sentences candidates. The examiner notes that while the parameters are not trained using the hyperparameter, Perez at least teaches using hyperparameters in conjunction with optimized parameters when generating encodings relevant to a user’s input) optimized (i.e. para. [0053], “retrieving documents 202 concerning the runtime question using search engine 24; identifying sentences 102, or more generally portions of text, in the retrieved documents; computing a runtime score 402 for the identified sentences using the neural network model”, wherein the BRI for line searching encompasses searching and identifying sentences containing portions of text);
wherein the encoder module is trained to determine a target balance between positive and negative samples using the (i.e. para. [0035, 0047-0050], “The scoring model in step 104 of FIG. 2 is adapted from the Weaver model … Then, every sentence of the corpus is labelled either as positive if it contains the exact text of the answer, or negative if it does not. …the use of distant supervision allows existing datasets to be combined in order to train a model for performing a task for which no dataset exists already… It only requires
that the best positive sentences get a better score than all negatives sentences, in other words, the n-uplet loss penalizes only the case when there exists a negative sentence that is closer to the query than all positives”, Wherein the encoding module is a weaver deep encoding module. Wherein the BRI for a target balance encompasses a forcing a target score gap between positive and negative samples sets );
a distance module configured to, based on the encodings (i.e. para. [0034], “the input question and input sentence are tokenized using word embeddings (i.e., a set of language modeling and feature learning techniques in natural language processing where words from the vocabulary, and possibly phrases thereof, are mapped to vectors of real numbers in a low dimensional space”, wherein the BRI for encodings encompasses the numerical representations the positive and negative samples which are used in the distance based ranking loss), generate a distance matrix including distance values between the candidate responses, respectively, and the input query (i.e. para. [0036-0041], “The input of the neural network model (i.e., scoring model) 205 is an interaction tensor M between input question matrix Q and input sentence matrix S, it may be calculated as m.sub.ij=[q.sub.i; s.sub.j; q.sub.i⊙s.sub.j], where: [0037] 0<i<n, [0038] 0<i<m, [0039] u.sub.k=u[k, :] for u being q or s, [0040] the operator ⊙ represents the element-wise product, and [0041] ; represents the concatenation over the last dimension, giving a tensor having shape n×m×e, where e is the dimension of the concatenated vector m.sub.ij”, wherein the BRI for a distance module encompasses a scoring module that generates a matrix calculating a score for
candidate sentence embeddings likely or not to contain an answer to an input query); and a results module configured to select one of the candidate responses as a response to the input query based on the distance values (i.e. para. [0053], computing a runtime score 402 for the identified sentences using the neural network model 205 trained using distant supervision and distance based ranking loss; selecting the sentence corresponding to the highest score to provide an answer 404; and sending the answer to the client device 11).
While Perez teaches generating encodings for input data and using optimized hyperparameter and contrastive losses to calculate a distance between positive and negative examples to find an optimal balance of similarity metrics to use, Perez may not explicitly teach to
Determine a location of the navigating robot for propulsion control.
However, Silander teaches to
Determine a location of the navigating robot for propulsion control (i.e. para. [0024, 0046], a navigating robot is described and includes: a camera configured to capture images within a field of view in front of the navigating robot… the control module 112 may actuate the propulsion devices 108 to turn the navigating robot 100 to the right by the predetermined angle in response to the output of the trained model 116 being in the second state. The control module 112 may actuate the propulsion devices 108 to turn the navigating robot 100 to the left by the predetermined angle in response to the output of the trained model 116 being in the third state. The control module 112 may not actuate the propulsion devices
108 to not move the navigating robot 100 in response to the output of the trained model 116 being in the fourth state”, wherein the propulsion is in a certain direction is based on the identified state of the robot at a presently updated and analyzed camera location).
It would have been obvious to one of ordinary skill in the art at the time of filing to add to determine a location of the navigating robot for propulsion control, to Perez’s hyperparameter optimization and candidate response selection, with the base chassis for a robot and propulsion methods, as taught by Silander. One would have been motivated candidate decision making formulas of Perez and image machine learning model for recognition of Silander in order create a better autonomous decision making robot a with less supervision, thus saving a user time and effort.
While Perez and Silander teach using a hyperparameter to find a balance of training samples in order save computation power when searching for candidate responses, Perez and Silander may not explicitly teach
parameters trained using hyperparameters
However, Phan teaches
parameters trained using hyperparameters (i.e. para. [0053], “For a given set of hyper-parameters, some embodiments of the present invention train the model on the training data, then evaluate the model performance on the validation data. Some commonly used metrics include accuracy, precision, recall, F1-score, and AUC (Area Under the Curve). The goal is to tune hyper-parameters for the model to maximize the performance on the validation data”, wherein it is noted that
Phan teaches that parameters may specifically be trained using tuned hyper-parameters as part of evaluating a model’s performance)
It would have been obvious to one of ordinary skill in the art at the time of filing to add parameters trained using hyperparameters, to Perez-Silander’s hyperparameters that are used to find a balance between negative and positive training samples, with using hyperparameters to train model parameters, as taught by Phan. One would have been motivated to combine the hyperparameter application to parameters of Phan and parameter and hyperparameter use of Perez-Silander in order save on computational costs such as using coordinate descent and line searching to maximize the performance on the validation data.
While Perez, Silander, and Phan teach optimizing and tuning hyperparameters, Perez and Silander may not explicitly teach
using hyperparameters optimized using coordinate descent and line searching.
However, Song teaches,
using (i.e. para. [0053], Group lasso estimation is implemented with a local coordinate descent (LCD) method, in which the model coefficients are updated one by one along fixed descent directions with line search to minimize the target function as illustrated in FIG. 3C. Since the computational cost increases only linearly with the number of coefficients (i.e., model scale), LCD can be reliably and efficiently applied to solve very large-scale model estimation problem).
It would have been obvious to one of ordinary skill in the art at the time of filing to add using hyperparameters optimized using coordinate descent and line searching, to Perez-Silander-Phan’s hyperparameters that are used to find a balance between negative and positive training samples, with the parameter optimization methods of using both coordinate descent and line searching on a model parameters, as taught by Song. One would have been motivated to combine the parameter optimization of Song and hyperparameters of Perez-Silander-Phan in order save on computational costs as using coordinate descent and line searching to optimize a parameter can be reliably and efficiently applied to solve very large-scale model estimation problems.
While Perez-Silander-Phan-Song teach using optimizing hyperparameters with specific techniques and a model may be trained to find a balance of some sort that may be found between positive and negative samples, Perez-Silander-Phan-Song may not explicitly teach wherein the encoder module is trained to determine a target balance between positive and negative samples
using the hyperparameters
However, Mohensi teaches
wherein the encoder module is trained to determine a target balance between positive and negative samples using the hyperparameters (i.e. para. [0060], “a predetermined ratio of IND samples to OOD samples is used during training OOD detectors, such as a ratio of one IND sample to five OOD samples, or a ratio of one IND sample to four OOD samples. In at least one embodiment, a ratio of IND samples to OOD samples is progressively altered during training OOD detectors from a predetermined initial ratio to a predetermined final ratio”, wherein the BRI
for positive and negative samples encompasses In-Distribution (IND) samples and Out-of-Distribution (OOD) samples that are dynamically altered during training according to specific hyperparameters that represent predetermined ratios).
It would have been obvious to one of ordinary skill in the art at the time of filing to add using wherein the encoder module is trained to determine a target balance between positive and negative samples using the hyperparameters, to Perez-Silander-Phan-Song’s parameters that are used to find a balance between negative and positive training samples, with using specifically hyperparameters during training to dynamically adjust a ratio of positive and negative samples, as taught by Mohensi. One would have been motivated to combine Perez-Silander-Phan-Song and use of hypermeters of Mohensi in order to maintain classification of inputs above a predetermined classification performance metric.
Claim 1:
Perez, Silander, Phan, Song, and Mohensi teach a training system comprising: the search system of claim 6.
Perez further teaches
a training module configured to: train the parameters using the hyperparameters (i.e. para. [0017], “The method presented here generally comprises these aspects: (i) learning parameters of the neural network model (i.e., a scoring model); and (ii) using the scoring model to determine the most relevant portion of text to answer a given question”, Wherein the BRI for a hyperparameter encompasses how Perez uses a predefined parameter ‘α’ used in the n-uplet loss equation to find a balance between positive and negative sentences candidates);
wherein the hyperparameters include: a first (i.e. para. [0054], “More specifically at training time, a distance model is applied using one selected positive sentence 206a while at runtime the distance model 205 is applied to all candidate sentences 203 to obtain estimated distances that are used as scores 402”, wherein the BRI for positive interactions encompasses sentence entries of potential responses that rank as close on a distance matrix for likelihood of containing an answer response to the question);
a second (i.e. para. [0054], “and one selected negative sentence 206b to obtain estimated distances”, wherein the BRI for negative interactions encompasses sentence entries of potential responses that rank as far on a distance matrix for likelihood of containing an answer response to the question);
and a third (i.e. para. [0034, 0054], “e embeddings for each word of the input question are gathered in a matrix Q=[q.sub.0, . . . , q.sub.n] where n is the number of words in the input question and q.sub.i is the i-th word of the input question, and the token embeddings for each word of the input sentence are gathered in a matrix S=[s.sub.0, . . . , s.sub.m] where m is the number of words in the input sentence and q.sub.i is the i-th word of the input sentence”, wherein the BRI for a dimension of a distance matrix encompasses how distance scores for each of the positive and negative sentences are calculated using a size dependent on the input query words).
Phan further teaches the concept of
parameters trained using hyperparameters (i.e. para. [0053], “For a given set of hyper-parameters, some embodiments of the present invention train the model on the training data, then evaluate the model performance on the validation data. Some commonly used metrics include accuracy, precision, recall, F1-score, and AUC (Area Under the Curve). The goal is to tune hyper-parameters for the model to maximize the performance on the validation data”, wherein it is noted that Phan teaches that parameters may specifically be trained using tuned hyper-parameters as part of evaluating a model’s performance).
Claim 5:
Perez, Silander, Phan, Song, and Mohensi teach the search system of claim 6.
Perez further teaches wherein the parameter are trained: based on minimizing a total contrastive loss determined based on a positive loss and an entropy loss (i.e. para. [0028], “a distance based ranking loss (i.e., training loss) is computed in using the scores of the positive sentences 204a and the negative sentences 204b. This distance based ranking loss aims at giving a higher score to sentences containing an answer to the given question than sentences not containing an answer to the given question, in order to use it to optimize the scoring model 205”, wherein the BRI for a positive loss is a ranking loss that is a higher score for sentences containing a correct answer and entropy loss encompasses a ranking loss that is a lower score for sentences not containing an answer to the given question. It is noted the model training is optimized based on a comparison of the two losses as both ranking losses are used and backpropagated into as part of optimizing the model); and balance the positive loss and the entropy loss based on one of the hyperparameters corresponding to a dimension of the distance matrix (i.e. para. [0017], “The neural network model, trained using distant supervision and distance based ranking loss, combines an interaction matrix, Weaver blocks and adaptive subsampling to map to a fixed size representation vector, which is used to emit a score”, wherein the BRI for balance encompasses how a distance based ranking loss is computed using both positive and negative ranking loss scores).
Claim 8:
Perez, Silander, Phan, Song, and Mohensi teach the search system of claim 6.
Perez further teaches wherein the encoder module includes a neural network configured to generate the encodings (i.e. para. [0030], “distance based ranking loss (i.e., training loss) is computed …This distance based ranking loss aims at giving a higher score to sentences containing an answer to the given question than sentences not containing an answer to the given question, in order to use it to optimize the scoring model 205”, wherein a neural network model generates first positive encodings and second negative encodings for candidate responses to a user query using training loss parameters) using the parameters trained using hyperparameters (i.e. para. [0017], “The method presented here generally comprises these aspects: (i) learning parameters of the neural network model (i.e., a scoring model); and (ii) using the scoring model to determine the most relevant portion of text to answer a given question”, wherein the BRI for hyperparameters encompasses parameters that control how the model learns) optimized using coordinate descent (i.e. para. [0030], “This distance based ranking loss aims at giving a higher score to sentences containing an answer to the given question than sentences not containing an answer to the given question, in order to use it to optimize the scoring model 205”, wherein the BRI for coordinate descent encompasses how model parameters are optimize by minimizing loss) and line searching (i.e. para. [0054], “More specifically at training time, a distance model is applied using one selected positive sentence 206a while at runtime the distance model 205 is applied to all candidate sentences 203 to obtain estimated distances that are used as scores 402”, wherein the BRI for positive interactions encompasses sentence entries of potential responses that rank as close on a distance matrix for likelihood of containing an answer response to the question).
Claim 11:
Perez, Silander, Phan, Song, and Mohensi teach the search system of claim 6.
Perez further teaches wherein the
a first (i.e. para. [0054], “More specifically at training time, a distance model is applied using one selected positive sentence 206a while at runtime the distance model 205 is applied to all candidate sentences 203 to obtain estimated distances that are used as scores 402”, wherein the BRI for positive interactions encompasses sentence entries of potential responses that rank as close on a distance matrix for likelihood of containing an answer response to the question);
a second (i.e. para. [0054], “and one selected negative sentence 206b to obtain estimated distances”, wherein the BRI
for negative interactions encompasses sentence entries of potential responses that rank as far on a distance matrix for likelihood of containing an answer response to the question); and
a third (i.e. para. [0034, 0054], “e embeddings for each word of the input question are gathered in a matrix Q=[q.sub.0, . . . , q.sub.n] where n is the number of words in the input question and q.sub.i is the i-th word of the input question, and the token embeddings for each word of the input sentence are gathered in a matrix S=[s.sub.0, . . . , s.sub.m] where m is the number of words in the input sentence and q.sub.i is the i-th word of the input sentence”, wherein the BRI for a dimension of a distance matrix encompasses how distance scores for each of the positive and negative sentences are calculated using a size dependent on the input query words).
Phan further teaches the concept of
parameters trained using hyperparameters (i.e. para. [0053], “For a given set of hyper-parameters, some embodiments of the present invention train the model on the training data, then evaluate the model performance on the validation data. Some commonly used metrics include accuracy, precision, recall, F1-score, and AUC (Area Under the Curve). The goal is to tune hyper-parameters for the model to maximize the performance on the validation data”, wherein it is noted that Phan teaches that parameters may specifically be trained using tuned hyper-parameters as part of evaluating a model’s performance).
Claim 13:
Perez, Silander, Phan, Song, and Mohensi teach the search system of claim 6.
Silander further teaches
wherein the candidate responses include images (i.e. para. [0024, 0046], a navigating robot is described and includes: a camera configured to capture images within a field of view in front of the navigating robot… the control module 112 may actuate the propulsion devices 108 to turn the navigating robot 100 to the right by the predetermined angle in response to the output of the trained model 116 being in the second state. The control module 112 may actuate the propulsion devices 108 to turn the navigating robot 100 to the left by the predetermined angle in response to the output of the trained model 116 being in the third state. The control module 112 may not actuate the propulsion devices 108 to not move the navigating robot 100 in response to the output of the trained model 116 being in the fourth state”, wherein the propulsion is in a certain direction is based on the identified state of the robot at a presently updated and analyzed camera location).
Claim 14:
Perez, Silander, Phan, Song, and Mohensi teach the teaches the search system of claim 6.
Perez further teaches wherein the candidate responses include text (i.e. para. [0054], After computing a runtime answer in step 404 based on the sentences associated with the highest of the runtime scores computed in step 402, the predicted answer, which includes identified sentences 203 associated with the documents 202 from the corpus 20, is returned in response to the runtime question).
Claim 15:
Perez, Silander, Phan, Song, and Mohensi teach the search system of claim 6.
Perez further teaches wherein: the encoder module is configured to receive the input query from a computing device via a network (i.e. para. [0023], The client equipment 11 has one or more question for querying the large-scale text or corpus stored in the first server 10a to obtain answers thereto in an identified collection of documents);
and the search system further includes a transceiver module configured to transmit the response including the one of the candidate responses to the computing device via the network (i.e. para. [0054], is returned in response to the runtime question (for example, from server 10a to client equipment 11)).
Claim 16:
Perez, Silander, Phan, Song, and Mohensi teach the search system of claim 6.
Perez further teaches wherein the results module is configured to select one of the candidate responses as a response to the input query based on the distance values (i.e. para. [0030], this distance based ranking loss aims at giving a higher score to sentences containing an answer to the given question than sentences not containing an answer to the given question, in order to use it to optimize the scoring model 205).
Claim(s) 21, 23, 26-30, is/are rejected under 35 U.S.C. 103 as being unpatentable over in light of U.S. Patent Application Publication NO. 20210141383 “Silander”, in light of U.S. Patent Application Publication NO. 20210174161 “Perez”, in light of U.S. Patent Application Publication NO. 20220027757 “Phan”, in light of U.S. Patent Application Publication NO. 20200238074 “Song”, and in light of U.S. Patent Application Publication NO. 20210142160 “Mohensi”, and further in light of U.S. Patent Application Publication NO. 20210109537 “Lin”.
Claim 21:
Silander teaches a navigating robot (i.e. para. [0030], FIG. 1 is a functional block diagram of an example implementation of a navigating robot), comprising:
a camera configured to capture one or more images within a predetermined field of view in front of the navigating robot (i.e. para. [0024], a navigating robot is described and includes: a camera configured to capture images within a field of view in front of the navigating robot);
an encoder module configured to generate encodings based on an input query and candidate responses (i.e. para. [0050]. “The Visual navigation may be modeled as a Partially Observed Markov Decision Process (POMDP) as a tuple P:=custom-characterS,A,Ω,R,T,O,P.sub.Ocustom-character, where S is the set of states, A is the set of actions, Ω, is the set of observations, all of which may be finite sets”, wherein the BRI for an input query encompasses the input image encoded as observations and wherein the BRI for candidate responses encompasses a set of actions that may be candidate movement responses for the robot) using parameters trained using hyperparameters optimized using coordinate descent and line searching, wherein the encoder module is configured to identify a closest image to the one or more images captured from the camera, wherein the closet image is used to determine a present location of the navigating robot (i.e. para. [0045], “The trained model 116 may generate an output each time the input from the camera 104 is updated. The trained model 116 may be configured to set the output at a given time to one of a group consisting of: a first state (corresponding to moving forward by a predetermined distance, such as 1 foot or ⅓ of a meter), a second state (corresponding to turning right by a predetermined angle, such as 45 or 90
degrees), a third state (corresponding to turning left by a predetermined angle, such as 45 or 90 degrees), and a fourth state (corresponding to not moving)”, wherein an updated camera image is input to the model which encodes observations to determine a present location on the route from a starting location to a goal location, and calculate movement state for the propulsion motor);
a distance module configured to generate a distance matrix including distance values between the candidate responses, respectively, and the input query; and a
results module configured to select one of the candidate responses as a response to the input query based on the distance values (i.e. para. [0045], “The trained model 116 may generate an output indicative of an action to be taken by the navigating robot 100 based on the input from the camera 104”, wherein a propulsion direction may be determined based on the identified state of the robot based on a current camera location);
a control module configured to control propulsion of the navigating robot based on the present location of the navigating robot (i.e. para. [0046], “the control module 112 may actuate the propulsion devices 108 to turn the navigating robot 100 to the right by the predetermined angle in response to the output of the trained model 116 being in the second state. The control module 112 may actuate the propulsion devices 108 to turn the navigating robot 100 to the left by the predetermined angle in response to the output of the trained model 116 being in the third state. The control module 112 may not actuate the propulsion devices 108 to not move the navigating robot 100 in response to the output of the trained
model 116 being in the fourth state”, wherein the propulsion in a certain direction is based on the identified state of the robot at a present location).
While Silander teaches, an encoder module configured to generate encodings based on an input query images and candidate propulsion responses and selecting a control propulsion based on an identified location en route to a goal location, Silander may not explicitly teach
using parameters trained using hyperparameters optimized using coordinate descent and line searching, wherein the encoder module is configured to identify a closest image to the one or more images captured from the camera, wherein the closet image is used to determine a present location of the navigating robot;
a distance module configured to generate a distance matrix including distance values between the candidate responses, respectively, and the input query; and a
results module configured to select one of the candidate responses as a response to the input query based on the distance values.
However, Perez teaches to
generate encodings based on an input query and candidate responses using parameters trained using (i.e. para. [0030], “distance based ranking loss (i.e., training loss) is computed …This distance based ranking loss aims at giving a higher score to sentences containing an answer to the given question than sentences not containing an answer to the given question, in order to use it to optimize the scoring model 205”, wherein a distance ranking module generates first positive encodings and second negative encodings for candidate responses to a user query using training loss parameters. Wherein the BRI for
generating encoding encompasses how raw input data is converted into embeddings that mapped to vectors of real numbers in a low dimensional space ) optimized using (i.e. para. [0053], “retrieving documents 202 concerning the runtime question using search engine 24; identifying sentences 102, or more generally portions of text, in the retrieved documents; computing a runtime score 402 for the identified sentences using the neural network model”, wherein the BRI for line searching encompasses searching and identifying sentences containing portions of text).
It would have been obvious to one of ordinary skill in the art at the time of filing to add using parameters trained using parameters optimized using and line searching, to Silander’s image encoding and propulsion determination, with the specific hyper parameter optimization formulas, as taught by Perez. One would have been motivated to combine the coordinate decent and line searching of text of Perez and the image machine learning model for recognition of Silander in order to further cover different types of visual found in images, such as text, and thus have faster object detection in the field of image recognition.
While Silander and Perez teach using parameters to find a balance of training samples in order save computation power when searching for candidate responses, Perez and Silander may not explicitly teach
parameters trained using hyperparameters
However, Phan teaches
parameters trained using hyperparameters (i.e. para. [0053], “For a given set of hyper-parameters, some embodiments of the present invention train the model
on the training data, then evaluate the model performance on the validation data. Some commonly used metrics include accuracy, precision, recall, F1-score, and AUC (Area Under the Curve). The goal is to tune hyper-parameters for the model to maximize the performance on the validation data”, wherein it is noted that Phan teaches that parameters may specifically be trained using tuned hyper-parameters as part of evaluating a model’s performance)
It would have been obvious to one of ordinary skill in the art at the time of filing to add parameters trained using hyperparameters, to Silander-Perez’s hyperparameters that are used to find a balance between negative and positive training samples, with using hyperparameters to train model parameters, as taught by Phan. One would have been motivated to combine the hyperparameter application to parameters of Phan and parameter and hyperparameter use of Silander-Perez in order save on computational costscosts suchsing coordinate descent and line searching to maximize the performance on the validation data.
While Silander-Perez-Phan teach an encoder module configured to generate encodings based on an input query and candidate responses using parameters trained using parameters optimized using line searching, Silander-Perez may not explicitly teach
Hyperparameters optimized using coordinate descent and line searching
However, Song teaches,
(i.e. para. [0053], Group lasso estimation is implemented with a local coordinate descent (LCD) method, in which the model coefficients are
updated one by one along fixed descent directions with line search to minimize the target function as illustrated in FIG. 3C. Since the computational cost increases only linearly with the number of coefficients (i.e., model scale), LCD can be reliably and efficiently applied to solve very large-scale model estimation problem).
It would have been obvious to one of ordinary skill in the art at the time of filing to add using hyperparameters optimized using coordinate descent and line searching, to Silander-Perez-Phan’s hyperparameters that are used to find a balance between negative and positive training samples, with the parameter optimization methods of using both coordinate descent and line searching on a model parameters, as taught by Song. One would have been motivated to combine the parameter optimization of Song and hyperparameters of Silander-Perez-Phan in order save on computational costs as using coordinate descent and line searching to optimize a parameter can be reliably and efficiently applied to solve very large-scale model estimation problems.
While Perez, Silander, Phan, and Song teach optimizing and tuning hyperparameters, Perez, Silander, Phan, and Song may not explicitly teach
wherein the encoder module is configured to identify a closest image to the one or more images captured from the camera, wherein the closet image is used to determine a present location of the navigating robot;
a distance module configured to generate a distance matrix including distance values between the candidate responses, respectively, and the input query; and a
results module configured to select one of the candidate responses as a response to the input query based on the distance values.
However, Lin teaches
wherein the encoder module is configured to identify a closest image to the one or more images captured from the camera, wherein the closet image is used to determine a present location of the navigating robot (i.e. para. [0013-0014], “obtaining a Next-Best-View and planning a global path from the robot to the Next-Best-View, which comprises: 2.1) obtaining an edge e closest to a current location of the robot and two nodes v.sub.e.sup.1 and v.sub.e.sup.2 of the e in the topological map G by taking all leaf nodes V.sub.leaf in the topological map G as initial candidate frontiers”, wherein the BRI for an encoding module encompasses how camera image data for an autonomous robot has features encoded as distance feature vectors to determine a robots current location when trying to find a current path with a number of candidate frontiers ) ;
a distance module configured to generate a distance matrix including distance values between the candidate responses, respectively, and the input query (i.e. para. [0012, 0018], “ recording edge lengths in a distance matrix M.sub.dist, to obtain a topological map G={V, E, M.sub.dist} of the passable region… valuating each candidate point in the candidate point set P.sub.candidate by a Multi-Criteria-Decision-Making approach based on a fuzzy measure function, taking the candidate point with the highest score as the Next-Best-View p.sub.NBV”, wherein a distance candidate point with a highest score is used to obtain a next best view when planning the global path); and
a results module configured to select one of the candidate responses as a response to the input query based on the distance values (i.e. para. [0018], obtaining
the global path R={r.sub.0, r.sub.1, r.sub.2, . . . , p.sub.NBV} from the current location of the robot to the Next-Best-View by tracing back in the result of in 2.2)).
It would have been obvious to one of ordinary skill in the art at the time of filing to add wherein the encoder module is configured to identify a closest image to the one or more images captured from the camera, wherein the closet image is used to determine a present location of the navigating robot; a distance module configured to generate a distance matrix including distance values between the candidate responses, respectively, and the input query; and a results module configured to select one of the candidate responses as a response to the input query based on the distance values, to Silander-Perez-Phan-Song’s hyperparameter optimization and image encoding with propulsion determination, with the distance matrix and distance calculations between potential candidate image data points that results in a candidate direction for the robot being selected as a result of the distance calculations, as taught by Lin. One would have been motivated to combine the distance matrix calculations of Lin and image machine learning model for recognition of Silander-Perez-Phan-Song in order to achieve further process different features found in input images by a navigational robot which can enable the indoor autonomous robotic exploration to be faster.
Claim 23:
Silander, Perez, Phan, Song, and Mohensi teach the navigating robot of claim 21.
Perez further teaches
wherein the encoder module includes a neural network that generates the encodings using the parameters trained using hyperparameters optimized using coordinate descent (i.e. para. [0030], “This distance based ranking loss aims at giving a higher score to sentences containing an answer to the given question than sentences not containing an answer to the given question, in order to use it to optimize the scoring model 205”, wherein the BRI for coordinate descent encompasses how model parameters are optimize by minimizing loss) and line searching (i.e. para. [0053], “retrieving documents 202 concerning the runtime question using search engine 24; identifying sentences 102, or more generally portions of text, in the retrieved documents; computing a runtime score 402 for the identified sentences using the neural network model”, wherein the BRI for line searching encompasses searching and identifying sentences containing portions of text).
Claim 26:
Claim 26 is the robot claim reciting similar limitations to Claim 1 and is rejected for similar reasons.
Claim 27:
Claim 27 is the robot claim reciting similar limitations to Claim 13 and is rejected for similar reasons.
Claim 28:
Claim 28 is the robot claim reciting similar limitations to Claim 14 and is rejected for similar reasons.
Claim 29:
Claim 29 is the robot claim reciting similar limitations to Claim 15 and is rejected for similar reasons.
Claim 30:
Claim 30 is the robot claim reciting similar limitations to Claim 16 and is rejected for similar reasons.
Claim(s) 7 & 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication NO. 20210174161 “Perez” and further in light of U.S. Patent Application Publication NO. 20210141383 “Silander”, in light of U.S. Patent Application Publication NO. 20220027757 “Phan”, in light of U.S. Patent Application Publication NO. 20200238074 “Song”, and in light of U.S. Patent Application Publication NO. 20210142160 “Mohensi”,, as applied to Claims 1 and 21 above, and further in light of U.S. Patent Application Publication NO. 20220235721 “Williams”.
Claim 7:
Perez, Silander, Phan, Song, and Mohensi teach the search system of claim 6.
Perez may not explicitly teach
wherein the line searching includes bounded golden section line searching.
However, Williams teaches
wherein the line searching includes bounded golden section line searching (i.e. para. [0102], “a golden-section line search method may be used to locate the minima. A golden-section line search is a form of sectioning algorithm wherein the golden ratio ((1+√5)/2) is used to select the next point (group of actuator setpoints) to be evaluated”, wherein the BRI for bounded golden section line searching encompasses how an optimizer module may have a line searching direction or vector within a setpoint search space and execute golden line search method).
It would have been obvious to one of ordinary skill in the art at the time of filing to add wherein the line searching includes bounded golden section line searching, to Perez-Silander-Phan-Song-Mohensi’s contrastive loss optimization, with wherein the line searching includes bounded golden section line searching, as taught by Williams. One would have been motivated to combine the optimization searching techniques of Williams and the line searching of Perez-Silander-Phan-Song-Mohensi in order to optimize line searching in a computationally efficient manner.
Claim 22:
Claim 22 is the robot claim reciting similar limitations to Claim 7 and is rejected for similar reasons.
Claim(s) 9-10 & 24-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication NO. 20210174161 “Perez” and further in light of U.S. Patent Application Publication NO. 20210141383 “Silander”, in light of U.S. Patent Application Publication NO. 20220027757 “Phan”, in light of U.S. Patent Application Publication NO. 20200238074 “Song”, and in light of U.S. Patent Application Publication NO. 20210142160 “Mohensi”, as applied to claim 6 above, and further in light of U.S. Patent Application Publication NO. 20220114444 “Weinzaepfel”.
Claim 9:
Perez, Silander, Phan, Song, and Mohensi teach the search system of claim 6.
Perez further teaches wherein the neural network is a convolutional neural network (i.e. para. [0035], “FIG. 2 is adapted from the Weaver model for machine reading, where the answering parts are removed and a different pooling layer is added for reducing a variable-size tensor into a fixed-size tensor, which is followed by a fully connected neural network (FCNN), for example a multilayer perceptron (MLP)”, wherein Perez sets the stage for a convolutional neural network as the scoring model multi-layered neural network for classifying and optimizing lines of data).
While Perez teaches a training system with a neural network utilizing contrastive losses, Perez may not explicitly teach that the neural network is a
convolutional neural network
However, Weinzaepfel teaches that the neural network is a
Convolutional neural network (i.e. para. [0110], “Regarding object detection, the SuperLoss function may be applied on the box classification component of two object detection frameworks, such as the faster recursive convolutional neural network (Faster R-CNN) framework”, wherein a convolutional neural network may be used for faster classification).
It would have been obvious to one of ordinary skill in the art at the time of filing to add a convolutional neural network, to Perez-Silander-Phan-Song-Mohensi’s contrastive loss optimization, with using a CNN, as taught by Weinzaepfel. One would have been motivated to combine the use of a CNN of Weinzaepfel and the contrastive loss optimization of Perez-Silander-Phan-Song-Mohensi in order to achieve faster object detection in the field of image recognition.
Claim 10:
Perez, Silander, Phan, Song, Mohensi, and Weinzaepfel teach the search system of claim 8.
Weinzaepfel further teaches wherein the neural network includes a ResNet-18 neural network (i.e. para. [0118], A ResNet-18 model (with a single output) is used, initialized on ImageNet as predictor and trained for 100 epochs using SGD).
Claim 24:
Claim 24 is the robot claim reciting similar limitations to claim 9 and is rejected for similar reasons.
Claim 25:
Claim 25 is the robot claim reciting similar limitations to claim 10 and is rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication NO. 20110099131 “Sellamanickam”, teaches in para. [0012], The hyperparameter and threshold parameter are selected or optimized so as to constrain positive examples to be labeled correctly while minimizing the number of unlabeled examples labeled as positive. In some embodiments, optimizing the objective function includes minimizing the objective function.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID H TAN whose telephone number is (571)272-7433. The examiner can normally be reached M-F 7:30-4:30.
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/D.T./Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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