DETAILED ACTION
Claims 1-12 have been examined and are pending.
Claims 1-12 are rejected (Non-Final Rejection).
Notice of 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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 25 July 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner.
Drawings
New corrected drawings in compliance with 37 CFR 1.121(d) and 37 CFR 1.84 are required in this application because of the following informalities:
FIGS. 2A and 2B are blurry and each include at least some unreadable text, and hence do not comply with 37 CFR 1.84(l), which requires “[a]ll drawings must be made by a process which will give them satisfactory reproduction characteristics. Every line, number, and letter must be durable, clean, black (except for color drawings), sufficiently dense and dark, and uniformly thick and well-defined. The weight of all lines and letters must be heavy enough to permit adequate reproduction. This requirement applies to all lines however fine, to shading, and to lines representing cut surfaces in sectional views. Lines and strokes of different thicknesses may be used in the same drawing where different thicknesses have a different meaning.”
Appropriate correction is required.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). Identifying indicia such as the application number (see 37 CFR 1.84(c)) should be written on the drawings in the front (not the back) of each sheet. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
INFORMATION ON HOW TO EFFECT DRAWING CHANGES
Replacement Drawing Sheet: Drawing changes must be made by presenting a replacement sheet which incorporates the desired changes and which complies with 37 CFR 1.84. An explanation of the changes made must be presented either in the drawing amendments section, or remarks, section of the amendment paper. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). A replacement sheet must include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of the amended drawing(s) must not be labeled as “amended.” If the changes to the drawing figure(s) are not accepted by the examiner, applicant will be notified of any required corrective action in the next Office action. No further drawing submission will be required, unless applicant is notified.
Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawing is required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawing will not be held in abeyance.
Specification
Para. [00102], Line 4 recites “inforamtion", which appears to be an artifact of Applicant’s editing process. Appropriate correction is required.
Claim Objections
Claim 12 is objected to because of the following informalities: Claim 12 is not a method claim and should not recite “the method further comprises …”. Appropriate correction is required. Examiner suggests “wherein the program instructions further comprise instructions that when executed by the one or more processors, cause the apparatus to perform: …”.
Claim Rejections - 35 U.S.C. § 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.
Claims 1-12 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Claim 1 recites “… each time within an advanced first time length …”, which is unclear and appears to be a literal translation into English from a foreign document and includes grammatical and/or idiomatic errors. Specifically, “each time within a [time length]” could be infinite. It appears that what may be meant is “each time interval” or “each time segment” within a time length/duration. Additionally, it is not clear what “advanced” means in “advanced first time length”. Does “advanced first time length” mean a “future first time period”? That is, is “advanced” modifying/describing the start of the time length/period, or alternatively, is “advanced” modifying/describing the length of the time length/period?
In addition, claim 4 recites “an earliest test time” but it is not clear what the test time is earliest among. Normally, it would be expected that “earliest” would be “earliest” time from among a plurality/group of times.
Accordingly, claims 1 and 4 are rejected for being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. Claims 2, 4-6, 8-10 and 12 recite similar “advanced” time length language and are rejected for similar reasons as claim 1, while claim 12 recites similar “earliest” time language and is rejected for similar reasons as claim 4. Additionally, dependent claims 2-4, 6-8 and 10-12 depend (directly or indirectly) from one of the rejected claims 1, 5 or 9. Therefore, claims 2-4, 6-8 and 10-12 are also rejected under the same rationale since these claims inherit the deficiencies of claims 1, 5 or 9, while failing to cure the respective deficiencies of claims 1, 5 or 9.
For compact prosecution, Examiner has made an interpretation (as best understood), which is based on the assumptions described above. Most notably, Examiner is interpreting “advanced” time length as meaning a future time period.
Claim 4 and 12 are rejected under 35 U.S.C. § 112(b) because of lack of antecedent basis in the claim.
Claims 4 and 12 each recite the limitations “the advanced second time length” and “the test times”. There is insufficient antecedent basis for these limitations in the claim.
Claim Rejections - 35 U.S.C. § 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.
The following is an analysis based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
1. Determining if the claim falls within a statutory category;
2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and
2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception.
(See MPEP 2106).
Claims 1-12 Step 1, Statutory Category?:
Yes: Claims 1-8 are directed to the statutory category of a process. See MPEP § 2106.03.
Yes: Claims 9-12 are directed to the statutory category of a machine. See MPEP § 2106.03.
Claims 1-12 are rejected under 35 U.S.C. § 101 because the claimed inventions are directed to an abstract idea without significantly more. The claim(s) recite a mental process and a mathematical calculation. See MPEP § 2106.04(a)(2)(I) and MPEP § 2106.04(a)(2)(III).
Step 2A:
Step 2A is a two-prong inquiry. See MPEP § 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. See MPEP § 2106.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. See MPEP § 2106.04(d).
Claim 1 Step 2A Prong One: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
A method of constructing a vehicle dynamics model, comprising:
obtaining sample historical state information and a sample control parameter sequence corresponding to each sample time of a target vehicle and label vehicle state information of each sample time, wherein the sample control parameter sequence corresponding to each sample time comprises control parameters of the sample time and each time within an advanced first time length;
for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into an initial vehicle dynamics model to determine sample prediction state information corresponding to the sample time;
for each sample time, by using the sample prediction state information corresponding to the sample time and the label vehicle state information of the sample time, determining a current loss value corresponding to the initial vehicle dynamics model;
based on the current loss value, adjusting model parameters of the initial vehicle dynamics model until the initial vehicle dynamics model reaches a preset convergence state so as to obtain a pre-constructed vehicle dynamics model.
The limitations “constructing a vehicle dynamics model”, “for each sample time, by using the sample prediction state information corresponding to the sample time and the label vehicle state information of the sample time, determining a current loss value corresponding to the initial vehicle dynamics model” and “based on the current loss value, adjusting model parameters of the initial vehicle dynamics model until the initial vehicle dynamics model reaches a preset convergence state so as to obtain a pre-constructed vehicle dynamics model” can be performed using mathematical calculations/equations and therefore encompass mathematical concepts. See MPEP 2106.04(a)(2)(I). The broadest reasonable interpretation, in light of the specification, of the “determining a current loss value” requires mathematical calculations. For example, Para. [0086]-[0088] of the specification indicates the “determined” current loss value is “the calculated distance” or a sum or average of the calculated distances. In addition, Para. [0090] of the specification indicates adjusting model parameters is via an optimization algorithm (mathematical calculation(s)). In addition, the limitations of “reaches a convergence state” are abstract ideas because they are directed to mental processes, observations, evaluations, judgments, and/or opinions. The limitations, as drafted and under broadest reasonable interpretation, “can be performed in the human mind or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, a human could compare information to a predefined threshold as a pre-requisite for updating/adjusting information. Thus, claim 1 recites an abstract idea(s).
Claim 1 Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “obtaining sample historical state information and a sample control parameter sequence corresponding to each sample time of a target vehicle and label vehicle state information of each sample time, wherein the sample control parameter sequence corresponding to each sample time comprises control parameters of the sample time and each time within an advanced first time length” and “for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into an initial vehicle dynamics model to determine sample prediction state information corresponding to the sample time” (insignificant extra-solution activity – mere data gathering/inputting – see MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data gathering/inputting in conjunction with the abstract idea (see MPEP § 2106.05(g)).
Claim 1 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there is one type of additional element. The type of additional element (“obtaining … information and a …. sequence” and “inputting” the same), as explained previously, are insignificant extra-solution activity (mere data inputting/gathering and/or data outputting). These recitations are recited at a high level of generality, and are also well-known. These limitations therefore remain insignificant extra-solution activity even upon reconsideration. Thus, these limitations do not amount to significantly more.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or data gathering, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 1 does not include significantly more than the abstract idea. The claim 1 is not patent subject matter eligible. Dependent claims 2-4 are further addressed below after addressing each independent claim.
Claim 5 Step 2A Prong One: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
A method of predicting vehicle state information based on a vehicle dynamics model, comprising:
obtaining historical state information and current control parameter sequence of a target vehicle corresponding to a current time, wherein the current control parameter sequence comprises: control parameters of the current time and each time within an advanced first time length;
inputting the historical state information and the current control parameter sequence into a pre-constructed vehicle dynamics model to determine vehicle state information of the target vehicle at the current time, wherein the pre-constructed vehicle dynamics model is a recurrent neural network model obtained by training based on sample state information and sample control parameter sequence corresponding to each historical time of the target vehicle; wherein the vehicle dynamics model is constructed by the following method:
obtaining sample historical state information and a sample control parameter sequence corresponding to each sample time of a target vehicle and label vehicle state information of each sample time, wherein the sample control parameter sequence corresponding to each sample time comprises control parameters of the sample time and each time within an advanced first time length;
for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into an initial vehicle dynamics model to determine sample prediction state information corresponding to the sample time;
for each sample time, by using the sample prediction state information corresponding to the sample time and the label vehicle state information of the sample time, determining a current loss value corresponding to the initial vehicle dynamics model;
based on the current loss value, adjusting model parameters of the initial vehicle dynamics model until the initial vehicle dynamics model reaches a preset convergence state so as to obtain a pre-constructed vehicle dynamics model.
The limitations “predicting vehicle state information based on a vehicle dynamics model”, “for each sample time, by using the sample prediction state information corresponding to the sample time and the label vehicle state information of the sample time, determining a current loss value corresponding to the initial vehicle dynamics model” and “based on the current loss value, adjusting model parameters of the initial vehicle dynamics model until the initial vehicle dynamics model reaches a preset convergence state so as to obtain a pre-constructed vehicle dynamics model” can be performed using mathematical calculations/equations and therefore encompass mathematical concepts. See MPEP 2106.04(a)(2)(I). The broadest reasonable interpretation, in light of the specification, of the “determining a current loss value” requires mathematical calculations. For example, Para. [0086]-[0088] of the specification indicates the “determined” current loss value is “the calculated distance” or a sum or average of the calculated distances. In addition, Para. [0090] of the specification indicates adjusting model parameters is via an optimization algorithm (mathematical calculation(s)). In addition, the limitations of “reaches a preset convergence state” are abstract ideas because they are directed to mental processes, observations, evaluations, judgments, and/or opinions. The limitations, as drafted and under broadest reasonable interpretation, “can be performed in the human mind or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, a human could compare information to a predefined threshold as a pre-requisite for updating/adjusting information. Thus, claim 5 recites an abstract idea(s).
Claim 5 Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “obtaining historical state information and current control parameter sequence of a target vehicle corresponding to a current time, wherein the current control parameter sequence comprises: control parameters of the current time and each time within an advanced first time length”, “inputting the historical state information and the current control parameter sequence into a pre-constructed vehicle dynamics model to determine vehicle state information of the target vehicle at the current time, wherein the pre-constructed vehicle dynamics model is a recurrent neural network model obtained by training based on sample state information and sample control parameter sequence corresponding to each historical time of the target vehicle”. “wherein the vehicle dynamics model is constructed by the following method: obtaining sample historical state information and a sample control parameter sequence corresponding to each sample time of a target vehicle and label vehicle state information of each sample time, wherein the sample control parameter sequence corresponding to each sample time comprises control parameters of the sample time and each time within an advanced first time length” and “for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into an initial vehicle dynamics model to determine sample prediction state information corresponding to the sample time” (insignificant extra-solution activity – mere data gathering/inputting – see MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data gathering/inputting in conjunction with the abstract idea (see MPEP § 2106.05(g)).
Claim 5 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there is one type of additional element. The type of additional element (“obtaining … information and a …. sequence” and “inputting” the same), as explained previously, are insignificant extra-solution activity (mere data inputting/gathering and/or data outputting). These recitations are recited at a high level of generality, and are also well-known. These limitations therefore remain insignificant extra-solution activity even upon reconsideration. Thus, these limitations do not amount to significantly more.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or data gathering, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 1 does not include significantly more than the abstract idea. The claim 5 is not patent subject matter eligible. Dependent claims 5-8 are further addressed below after addressing each independent claim.
Claim 9 Step 2A Prong One: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
An apparatus for constructing a vehicle dynamics model, comprising: one or more processors, and a non-transitory storage medium in communication with the one or more processors, the non-transitory storage medium configured to store program instructions, wherein, when executed by the one or more processors, the instructions cause the apparatus to perform:
obtaining sample historical state information and a sample control parameter sequence corresponding to each sample time of a target vehicle and label vehicle state information of each sample time, wherein the sample control parameter sequence corresponding to each sample time comprises control parameters of the sample time and each time within an advanced first time length;
for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into an initial vehicle dynamics model to determine sample prediction state information corresponding to the sample time;
for each sample time, by using the sample prediction state information corresponding to the sample time and the label vehicle state information of the sample time, determining a current loss value corresponding to the initial vehicle dynamics model;
based on the current loss value, adjusting model parameters of the initial vehicle dynamics model until the initial vehicle dynamics model reaches a preset convergence state, so as to obtain a pre-constructed vehicle dynamics model.
The limitations “constructing a vehicle dynamics model”, “for each sample time, by using the sample prediction state information corresponding to the sample time and the label vehicle state information of the sample time, determining a current loss value corresponding to the initial vehicle dynamics model” and “based on the current loss value, adjusting model parameters of the initial vehicle dynamics model until the initial vehicle dynamics model reaches a preset convergence state so as to obtain a pre-constructed vehicle dynamics model” can be performed using mathematical calculations/equations and therefore encompass mathematical concepts. See MPEP 2106.04(a)(2)(I). The broadest reasonable interpretation, in light of the specification, of the “determining a current loss value” requires mathematical calculations. For example, Para. [0086]-[0088] of the specification indicates the “determined” current loss value is “the calculated distance” or a sum or average of the calculated distances. In addition, Para. [0090] of the specification indicates adjusting model parameters is via an optimization algorithm (mathematical calculation(s)). In addition, the limitations of “reaches a convergence state” are abstract ideas because they are directed to mental processes, observations, evaluations, judgments, and/or opinions. The limitations, as drafted and under broadest reasonable interpretation, “can be performed in the human mind or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, a human could compare information to a predefined threshold as a pre-requisite for updating/adjusting information. Thus, claim 9 recites an abstract idea(s).
Claim 9 Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “one or more processors, and a non-transitory storage medium in communication with the one or more processors, the non-transitory storage medium configured to store program instructions, wherein, when executed by the one or more processors, the instructions cause the apparatus to perform” (mere instructions to apply an exception to a computer – see MPEP 2106.04(d) referencing MPEP 2106.05(f); these limitations can be viewed as nothing more than high level recitations of generic computer components or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a generic computer (see MPEP 2106.05(f)).
• “obtaining sample historical state information and a sample control parameter sequence corresponding to each sample time of a target vehicle and label vehicle state information of each sample time, wherein the sample control parameter sequence corresponding to each sample time comprises control parameters of the sample time and each time within an advanced first time length” and “for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into an initial vehicle dynamics model to determine sample prediction state information corresponding to the sample time” (insignificant extra-solution activity – mere data gathering/inputting – see MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data gathering/inputting in conjunction with the abstract idea (see MPEP § 2106.05(g)).
Claim 9 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there are two types of additional elements. The first type of additional element is the generic computer components (“processors”, “non-transitory storage medium”), which are high level recitations of generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer. See MPEP § 2106.05(f). Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP § 2106.05(f).
The second type of additional element (“obtaining … information and a …. sequence” and “inputting” the same), as explained previously, are insignificant extra-solution activity (mere data inputting/gathering and/or data outputting). These recitations are recited at a high level of generality, and are also well-known. These limitations therefore remain insignificant extra-solution activity even upon reconsideration. Thus, these limitations do not amount to significantly more.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or data gathering, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f). Considering the claim limitations as an ordered combination, claim 9 does not include significantly more than the abstract idea. The claim 9 is not patent subject matter eligible.
Dependent Claims 2-4, 6-8 and 10-12
Regarding claims 2-4 and 8, claim 2 depends from claim 1 and further recites: “wherein the sample historical state information corresponding to the sample time is vehicle state information of the target vehicle at a time corresponding to an advanced second time length of the sample time, wherein the second time length is less than the first time length”, claim 3 depends from claim 1 and further recites: “wherein for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into the initial vehicle dynamics model to determine the sample prediction state information corresponding to the sample time comprises: for each sample time, inputting the sample historical state information corresponding to the sample time into a feature coding layer of the initial vehicle dynamics model to obtain an implicit vector corresponding to the sample historical state information corresponding to the sample time; for each sample time, inputting the implicit vector corresponding to the sample historical state information corresponding to the sample time and the sample control parameter sequence corresponding to the sample time into a state recurrent prediction layer of the initial vehicle dynamics model to obtain an implicit vector corresponding to the vehicle state information corresponding to the sample time; for each sample time, inputting the implicit vector corresponding to the vehicle state information corresponding to the sample time into a feature decoding layer of the initial vehicle dynamics model to determine the sample prediction state information corresponding to the sample time”, claim 4 depends from claim 1 and further recites: “wherein after the step of obtaining the pre-constructed vehicle dynamics model when determining the initial vehicle dynamics model converges, the method further comprises: obtaining raw test data of the target vehicle, wherein the raw test data comprises: test historical state information, a test control parameter sequence and test vehicle state information corresponding to each test time generated during a travel process of the target vehicle, the test control parameter sequence corresponding to each test time comprises: control parameters of the test time and each time within the advanced first time length, and the test historical state information corresponding to the test time is: vehicle state information of a time corresponding to the advanced second time length of the test time; inputting test historical state information and a test control parameter sequence corresponding to a first test time into the pre-constructed vehicle dynamics model to determine test prediction state information corresponding to the first test time, wherein the first test time comprises an earliest test time and each time prior to the time corresponding to the second time length after the earliest test time; inputting prediction historical state information and a test control parameter sequence corresponding to a second test time into the pre-constructed vehicle dynamics model to determine test prediction state information corresponding to the second test time, wherein the second test time is a time other than the first test time in the test times, and the prediction historical state information corresponding to the second test time is test prediction state information corresponding to a time corresponding to the advanced second time length of the second test time; by using the test prediction state information and the test vehicle state information corresponding to each test time, determining a test result of the pre-constructed vehicle dynamics model” and claim 8 depends from claim 5 and further recites: “further comprising: obtaining current control parameters determined by a preset control parameter determining model based on the vehicle state information of the current time; inputting a target control parameter sequence comprising the current control parameters and historical state information corresponding to a next time of the current time into the pre-constructed vehicle dynamics model to determine vehicle state information of the target vehicle at the next time of the current time, wherein the target control parameter sequence further comprises: control parameters of various times between the current time and a previous time of a time corresponding to the advanced first time length”.
These features have been considered in combination with the features required by the claim(s) from which these claims depend. The bolded portion of the additional features are considered to further clarify the details of the mathematical concepts and/or the human’s mental activity (e.g., with pen and paper). See MPEP §§ 2106.04(a)(2)(I) and (III). In addition, the not bolded features of claims 2-4 and 8 are considered to be insignificant extra-solution activity of data gathering/inputting, which cannot provide an inventive concept, and is well-understood, routine and conventional. See MPEP 2106.05(g); See also MPEP § 2106.05(d)(II) (“The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”)). Therefore, these features are considered to be drawn to the abstract idea without adding significantly more, and hence claims 2-4 and 8 are considered to be ineligible under 35 U.S.C. § 101.
Claims 6 and 10 have substantially similar limitations as recited in claim 2; therefore, they are rejected under 35 U.S.C. § 101 for the same reasons.
Claims 7 and 11 have substantially similar limitations as recited in claim 3; therefore, they are rejected under 35 U.S.C. § 101 for the same reasons.
Claim 12 has substantially similar limitations as recited in claim 4; therefore, it is rejected under 35 U.S.C. § 101 for the same reasons.
For the foregoing reasons, claims 1-12 are rejected under 35 U.S.C. § 101 as being directed to patent ineligible subject matter.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-12 are rejected under 35 U.S.C. § 103 as being unpatentable over VILLEGAS et al. (U.S. Patent Application Publication No. 2020/0082248 A1) in view of BAGNELL et al. (U.S. Patent No. 11,989,020 B1).
Regarding claim 1, VILLEGAS discloses a method of constructing a vehicle dynamics model (for training (e.g., for training model 200 of FIG. 2A), the sensor data 102 generated by one or more vehicles may be used, Para. [0028] of VILLEGAS), comprising: obtaining sample historical state information (generate a spatial encoding corresponding to a spatial arrangement of the objects in the environment … the spatial arrangement is representative of a current arrangement of the objects in the environment—relative to the ego-vehicle, in embodiments—and historical state information of the objects as represented by the encoded states of the objects, Para. [0038] of VILLEGAS) and a sample control parameter sequence corresponding to each sample time of a target vehicle (the encoded state feature 210D, the first encoded spatial feature 214, and the second encoded spatial feature 216 may be concatenated (block 218D) and applied to a maneuver classifier 220D, Para. [0057] of VILLEGAS; [the maneuver classifier is interpreted as corresponding to a control parameter]; Regarding “each sample time”, VILLEGAS also teaches “encoded state of the object may represent a state of motion of the object at the current interval, frame, or time step … [t]he encoded state may be updated at each interval, frame, or time step as the updated object information for the current instance is provided to the temporal encoder 106, Para. [0031] of VILLEGAS) and label vehicle state information of each sample time (an additional object class label may be associated with each object when positioning the encoded state features 210 in the grid 212, Para. [0033] of VILLEGAS), wherein the sample control parameter sequence corresponding to each sample time comprises control parameters of the sample time and each time within an advanced first time length (encoded state, spatial encoding, and maneuver predictions) for each time instance within the rolling period of time, Para. [0061] of VILLEGAS); for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into an initial vehicle dynamics model to determine sample prediction state information corresponding to the sample time (predicting a future location of the object in the environment using the encoded spatial feature and the first encoded state feature, Claim 10 of VILLEGAS; See also Para. [0080] of VILLEGAS; [Examiner’s Note: the “to determine” language is not given patentable weight because it is not positively recited]); for each sample time, by using the sample prediction state information corresponding to the sample time and the label vehicle state information of the sample time, determining a current loss value corresponding to the initial vehicle dynamics model (to train the machine learning models (e.g., LSTMs) for sequence encoding 110, a loss function may be used that computes a loss over the state information of each of the objects, rather than only having a single loss function for each object (as in conventional systems) … for example, there may be a loss function corresponding to each of the objects, and each of the loss functions may be aggregated into a single loss function … inn some examples, without limitation, the loss function used may be an adversarial loss function … different loss functions may be used at different phases of training … a first loss function (e.g., root mean square error (RMSE) loss) may be used for initial training of maneuver predictions (e.g., explained with respect to FIGS. 2F and 2G), and a second loss function (e.g., negative log-likelihood loss) may be used for training of maneuver predictions after the initial training, Para. [0041] of VILLEGAS); based on the current loss value, adjusting model parameters of the initial vehicle dynamics model (first and/or second loss function are used for training, Para. [0041] of VILLEGAS; See also parameters may be learned during training, Para. [0053] of VILLEGAS).
VILLEGAS does not appear to explicitly disclose until the initial vehicle dynamics model reaches a preset convergence state so as to obtain a pre-constructed vehicle dynamics model.
BAGNELL, however, is in the field of training a machine learning model for use by an autonomous vehicle (Col. 1, Lines 49-51, of BAGNELL) and teaches based on the current loss value, adjusting model parameters of the initial vehicle dynamics model until the initial vehicle dynamics model reaches a preset convergence state so as to obtain a pre-constructed vehicle dynamics model (in response to determining the one or more conditions are satisfied: iteratively updating the model based on yet further losses, each of the yet further losses based on comparing one or more corresponding yet further predictions to the ground truth labels, and the one corresponding yet further predictions to the ground truth labels, and the one or more yet further corresponding predictions each based on a corresponding yet further simulated episode that is also initialized based on the initial state instance, but is progressed for a second quantity of time instances using the ML model as most recently updated … determining that the machine learning model, as most recently updated, has converged, may include determining that a corresponding one of the further losses satisfies a threshold, Col. 2, Line 30 – Col. 3, Line 3, of BAGNELL).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the method of constructing a vehicle dynamics model of VILLEGAS with the convergence-based ending point of model training as in BAGNELL for the purpose of having autonomous vehicles be able to reliably handle a wider variety of situations and accommodate both expected and unexpected situations (Col. 1, Lines 36-45, of BAGNELL).
Regarding claim 2, VILLEGAS as modified by BAGNELL discloses the method of claim 1 (as shown above), wherein the sample historical state information corresponding to the sample time is vehicle state information of the target vehicle at a time corresponding to an advanced second time length of the sample time, wherein the second time length is less than the first time length (inputting, at a second time instance and to the LSTM network, second data representative of an updated past location(s), a new current location, and the future location … for example, the prediction(s) 226 of the model 200 may be fed back to the state information 206 for the object, and the prediction(s) 226 in addition to a new current location, and updated past location(s) may be provided as input to the instantiation of the encoder LSTM 208, Para. [0081] of VILLEGAS).
Regarding claim 3, VILLEGAS as modified by BAGNELL discloses the method of claim 1 (as shown above), wherein for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into the initial vehicle dynamics model to determine the sample prediction state information corresponding to the sample time (addressed in rejection of claim 1) comprises: for each sample time, inputting the sample historical state information corresponding to the sample time into a feature coding layer of the initial vehicle dynamics model to obtain an implicit vector corresponding to the sample historical state information corresponding to the sample time (temporal encoder 106 and spatial encoder 108, Paras. [0029]-[0031] of VILLEGAS; See also regarding vector usage in VILLEGAS: Claim 1 and Paras. [0054], [0118], [0121] & [0122]); for each sample time, inputting the implicit vector corresponding to the sample historical state information corresponding to the sample time and the sample control parameter sequence corresponding to the sample time into a state recurrent prediction layer of the initial vehicle dynamics model to obtain an implicit vector corresponding to the vehicle state information corresponding to the sample time (the temporal encoder 106 may include a recurrent neural network, such as a long short-term memory (LSTM) network, that may receive object information for an object of the objects—provided as information relative to the vehicle 500, in embodiments—and generate an encoded state for each object, Para. [0031] of VILLEGAS); for each sample time, inputting the implicit vector corresponding to the vehicle state information corresponding to the sample time into a feature decoding layer of the initial vehicle dynamics model to determine the sample prediction state information corresponding to the sample time (trajectory decoder, Paras. [0061] & [0062] of VILLEGAS).
Regarding claim 4, VILLEGAS as modified by BAGNELL discloses the method of claim 1 (as shown above), wherein after the step of obtaining the pre-constructed vehicle dynamics model when determining the initial vehicle dynamics model converges, the method further comprises: obtaining raw test data of the target vehicle (for testing and training purposes, the virtual ego-vehicle may be tested in an environment where surrounding virtual objects are controlled according to these more accurate, more realistic trajectories predicted by the decoder LSTMs 224, Para. [0063] of VILLEGAS), wherein the raw test data comprises: test historical state information (generate a spatial encoding corresponding to a spatial arrangement of the objects in the environment … the spatial arrangement is representative of a current arrangement of the objects in the environment—relative to the ego-vehicle, in embodiments—and historical state information of the objects as represented by the encoded states of the objects, Para. [0038] of VILLEGAS), a test control parameter sequence (the encoded state feature 210D, the first encoded spatial feature 214, and the second encoded spatial feature 216 may be concatenated (block 218D) and applied to a maneuver classifier 220D, Para. [0057] of VILLEGAS; [the maneuver classifier is interpreted as corresponding to a control parameter]; Regarding “each sample time”, VILLEGAS also teaches “encoded state of the object may represent a state of motion of the object at the current interval, frame, or time step … [t]he encoded state may be updated at each interval, frame, or time step as the updated object information for the current instance is provided to the temporal encoder 106, Para. [0031] of VILLEGAS) and test vehicle state information corresponding to each test time generated during a travel process of the target vehicle (an additional object class label may be associated with each object when positioning the encoded state features 210 in the grid 212, Para. [0033] of VILLEGAS), the test control parameter sequence corresponding to each test time comprises: control parameters of the test time and each time within the advanced first time length (encoded state, spatial encoding, and maneuver predictions for each time instance within the rolling period of time, Para. [0061] of VILLEGAS), and the test historical state information corresponding to the test time is: vehicle state information of a time corresponding to the advanced second time length of the test time (encoded state, spatial encoding, and maneuver predictions for each time instance within the rolling period of time, Para. [0061] of VILLEGAS); inputting test historical state information and a test control parameter sequence corresponding to a first test time into the pre-constructed vehicle dynamics model to determine test prediction state information corresponding to the first test time (predicting a future location of the object in the environment using the encoded spatial feature and the first encoded state feature, Claim 10 of VILLEGAS; See also Para. [0080] of VILLEGAS; [Examiner’s Note: the “to determine” language is not given patentable weight because it is not positively recited]), wherein the first test time comprises an earliest test time and each time prior to the time corresponding to the second time length after the earliest test time (each prior prediction (e.g., corresponding to an earlier future frame or time) may be applied as another input to the decoder LSTM 224D for predicting a next or each next future prediction after the prior prediction(s), Para. [0063] of VILLEGAS); inputting prediction historical state information and a test control parameter sequence corresponding to a second test time into the pre-constructed vehicle dynamics model to determine test prediction state information corresponding to the second test time (each prior prediction (e.g., corresponding to an earlier future frame or time) may be applied as another input to the decoder LSTM 224D for predicting a next or each next future prediction after the prior prediction(s), Para. [0063] of VILLEGAS), wherein the second test time is a time other than the first test time in the test times, and the prediction historical state information corresponding to the second test time is test prediction state information corresponding to a time corresponding to the advanced second time length of the second test time (each prior prediction (e.g., corresponding to an earlier future frame or time) may be applied as another input to the decoder LSTM 224D for predicting a next or each next future prediction after the prior prediction(s), Para. [0063] of VILLEGAS); by using the test prediction state information and the test vehicle state information corresponding to each test time, determining a test result of the pre-constructed vehicle dynamics model (to train the machine learning models (e.g., LSTMs) for sequence encoding 110, a loss function may be used that computes a loss over the state information of each of the objects, rather than only having a single loss function for each object (as in conventional systems) … for example, there may be a loss function corresponding to each of the objects, and each of the loss functions may be aggregated into a single loss function … inn some examples, without limitation, the loss function used may be an adversarial loss function … different loss functions may be used at different phases of training … a first loss function (e.g., root mean square error (RMSE) loss) may be used for initial training of maneuver predictions (e.g., explained with respect to FIGS. 2F and 2G), and a second loss function (e.g., negative log-likelihood loss) may be used for training of maneuver predictions after the initial training, Para. [0041] of VILLEGAS).
Regarding claim 5, VILLEGAS discloses a method of predicting vehicle state information based on a vehicle dynamics model (predicting a future location of the object in the environment using the encoded spatial feature and the first encoded state feature, Claim 10 of VILLEGAS), comprising: obtaining historical state information (generate a spatial encoding corresponding to a spatial arrangement of the objects in the environment … the spatial arrangement is representative of a current arrangement of the objects in the environment—relative to the ego-vehicle, in embodiments—and historical state information of the objects as represented by the encoded states of the objects, Para. [0038] of VILLEGAS) and current control parameter sequence of a target vehicle corresponding to a current time (the encoded state feature 210D, the first encoded spatial feature 214, and the second encoded spatial feature 216 may be concatenated (block 218D) and applied to a maneuver classifier 220D, Para. [0057] of VILLEGAS; [the maneuver classifier is interpreted as corresponding to a control parameter]; VILLEGAS also teaches “encoded state of the object may represent a state of motion of the object at the current interval, frame, or time step … [t]he encoded state may be updated at each interval, frame, or time step as the updated object information for the current instance is provided to the temporal encoder 106, Para. [0031] of VILLEGAS), wherein the current control parameter sequence comprises: control parameters of the current time and each time within an advanced first time length (encoded state, spatial encoding, and maneuver predictions) for each time instance within the rolling period of time, Para. [0061] of VILLEGAS); inputting the historical state information and the current control parameter sequence into a pre-constructed vehicle dynamics model to determine vehicle state information of the target vehicle at the current time (predicting a future location of the object in the environment using the encoded spatial feature and the first encoded state feature, Claim 10 of VILLEGAS; See also Para. [0080] of VILLEGAS; [Examiner’s Note: the “to determine” language is not given patentable weight because it is not positively recited]), wherein the pre-constructed vehicle dynamics model is a recurrent neural network model obtained by training based on sample state information and sample control parameter sequence corresponding to each historical time of the target vehicle (the temporal encoder 106 may include a recurrent neural network (RNN), such as a long short-term memory (LSTM) network, Para. [0031] of VILLEGAS); wherein the vehicle dynamics model is constructed by the following method: obtaining sample historical state information (generate a spatial encoding corresponding to a spatial arrangement of the objects in the environment … the spatial arrangement is representative of a current arrangement of the objects in the environment—relative to the ego-vehicle, in embodiments—and historical state information of the objects as represented by the encoded states of the objects, Para. [0038] of VILLEGAS) and a sample control parameter sequence corresponding to each sample time of a target vehicle (the encoded state feature 210D, the first encoded spatial feature 214, and the second encoded spatial feature 216 may be concatenated (block 218D) and applied to a maneuver classifier 220D, Para. [0057] of VILLEGAS; [the maneuver classifier is interpreted as corresponding to a control parameter]; Regarding “each sample time”, VILLEGAS also teaches “encoded state of the object may represent a state of motion of the object at the current interval, frame, or time step … [t]he encoded state may be updated at each interval, frame, or time step as the updated object information for the current instance is provided to the temporal encoder 106, Para. [0031] of VILLEGAS) and label vehicle state information of each sample time (an additional object class label may be associated with each object when positioning the encoded state features 210 in the grid 212, Para. [0033] of VILLEGAS), wherein the sample control parameter sequence corresponding to each sample time comprises control parameters of the sample time and each time within an advanced first time length (encoded state, spatial encoding, and maneuver predictions) for each time instance within the rolling period of time, Para. [0061] of VILLEGAS); for each sample time, inputting the sample historical state information and the sample control parameter sequence corresponding to the sample time into an initial vehicle dynamics model to determine sample prediction state information corresponding to the sample time (predicting a future location of the object in the environment using the encoded spatial feature and the first encoded state feature, Claim 10 of VILLEGAS; See also Para. [0080] of VILLEGAS; [Examiner’s Note: the “to determine” language is not given patentable weight because it is not positively recited]); for each sample time, by using the sample prediction state information corresponding to the sample time and the label vehicle state information of the sample time, determining a current loss value corresponding to the initial vehicle dynamics model (to train the machine learning models (e.g., LSTMs) for sequence encoding 110, a loss function may be used that computes a loss over the state information of each of the objects, rather than only having a single loss function for each object (as in conventional systems) … for example, there may be a loss function corresponding to each of the objects, and each of the loss functions may be aggregated into a single loss function … inn some examples, without limitation, the loss function used may be an adversarial loss function … different loss functions may be used at different phases of training … a first loss function (e.g., root mean square error (RMSE) loss) may be used for initial training of maneuver predictions (e.g., explained with respect to FIGS. 2F and 2G), and a second loss function (e.g., negative log-likelihood loss) may be used for training of maneuver predictions after the initial training, Para. [0041] of VILLEGAS); based on the current loss value, adjusting model parameters of the initial vehicle dynamics model (first and/or second loss function are used for training, Para. [0041] of VILLEGAS; See also parameters may be learned during training, Para. [0053] of VILLEGAS).
VILLEGAS does not appear to explicitly disclose until the initial vehicle dynamics model reaches a preset convergence state so as to obtain a pre-constructed vehicle dynamics model.
BAGNELL, however, is in the field of training a machine learning model for use by an autonomous vehicle (Col. 1, Lines 49-51, of BAGNELL) and teaches based on the current loss value, adjusting model parameters of the initial vehicle dynamics model until the initial vehicle dynamics model reaches a preset convergence state so as to obtain a pre-constructed vehicle dynamics model (in response to determining the one or more conditions are satisfied: iteratively updating the model based on yet further losses, each of the yet further losses based on comparing one or more corresponding yet further predictions to the ground truth labels, and the one corresponding yet further predictions to the ground truth labels, and the one or more yet further corresponding predictions each based on a corresponding yet further simulated episode that is also initialized based on the initial state instance, but is progressed for a second quantity of time instances using the ML model as most recently updated … determining that the machine learning model, as most recently updated, has converged, may include determining that a corresponding one of the further losses satisfies a threshold, Col. 2, Line 30 – Col. 3, Line 3, of BAGNELL).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the method of constructing a vehicle dynamics model of VILLEGAS with the convergence-based ending point of model training as in BAGNELL for the purpose of having autonomous vehicles be able to reliably handle a wider variety of situations and accommodate both expected and unexpected situations (Col. 1, Lines 36-45, of BAGNELL).
Claim 9 has substantially similar limitations as recited in method claim 1 in terms of an apparatus; therefore, it is rejected under 35 U.S.C. § 103, mutatis mutandis, for the same reasons. See also VILLEGAS as modified by BAGNELL teaches one or more processors, and a non-transitory storage medium in communication with the one or more processors, the non-transitory storage medium configured to store program instructions, wherein, when executed by the one or more processors, the instructions cause the apparatus to perform (various functions may be carried out by a processor executing instructions stored in memory … the methods 300 and 400 may also be embodied as computer-usable instructions stored on computer storage media, Para. [0072] of VILLEGAS).
Claims 6 and 10 have substantially similar limitations as recited in claim 2; therefore, they are rejected under 35 U.S.C. § 103 for the same reasons.
Claims 7 and 11 have substantially similar limitations as recited in claim 3; therefore, they are rejected under 35 U.S.C. § 102 for the same reasons.
Regarding claim 8, VILLEGAS as modified by BAGNELL discloses the method of claim 5 (as shown above), further comprising: obtaining current control parameters determined by a preset control parameter determining model based on the vehicle state information of the current time (control layer of the drive stack 124 may use information from the perception layer, the world model manager, the planning layer, and/or other layers of the drive stack 124 to determine one or more controls for the vehicle 500 to control the vehicle according to a determined trajectory or path for the vehicle 500. As such, the predicted trajectories of the objects in the environment may be useful for any of a number of operations of the vehicle 500 corresponding to any of a number of different layers of the drive stack 124 of the vehicle 50, Para. [0070] of VILLEGAS); inputting a target control parameter sequence comprising the current control parameters and historical state information corresponding to a next time of the current time into the pre-constructed vehicle dynamics model to determine vehicle state information of the target vehicle at the next time of the current time (each prior prediction (e.g., corresponding to an earlier future frame or time) may be applied as another input to the decoder LSTM 224D for predicting a next or each next future prediction after the prior prediction(s), Para. [0063] of VILLEGAS), wherein the target control parameter sequence further comprises: control parameters of various times between the current time and a previous time of a time corresponding to the advanced first time length (each prior prediction (e.g., corresponding to an earlier future frame or time) may be applied as another input to the decoder LSTM 224D for predicting a next or each next future prediction after the prior prediction(s), Para. [0063] of VILLEGAS).
Claim 12 has substantially similar limitations as recited in claim 4; therefore, it is rejected under 35 U.S.C. § 103 for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: YE et al. (U.S. Patent Publication No. 2021/0319261) teaches, at Para. [0125], “where a sum of the loss function of the first target detection model and the loss function of the second target detection model is greater than a preset threshold value, adjusting a parameter of the first target detection model and a parameter of the second target detection model according to the loss function of the first target detection model and the loss function of the second target detection model, and returning to the calculating the loss function of the first target detection model and the loss function of the second target detection model until the sum of the loss function of the first target detection model and the loss function of the second target detection model is less than or equal to the preset threshold value, to obtain a converged first target detection model and a converged second target detection model”.
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/JOHN P HOCKER/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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