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 .
This office action is in response to continuing application filed 3/4/2024. Claims 1-21 are pending. Priority date: 11/22/2021
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 11367290. Although the claims at issue are not identical, they are not patentably distinct from each other. The main difference (underlined) includes simultaneously executing neural networks and using counts in control.
Instant Application
Patent No. 11367290
A controller for controlling one or more autonomous machines each coupled to
a plurality of sensors generating input data, the controller comprising:
a first neural network deployed on the autonomous machine, trained with a first
training data set and configured to generate first output data after processing a set of input data;
a second neural network deployed on the autonomous machine, trained with a
second training data set and configured to generate second output data after processing said set of input data; and
a comparator receiving and comparing the first output data and second output
data, the comparator configured to detect a difference between the first and second
output data and produce a result, wherein the controller controls the one or more
autonomous machine using input that includes the first output data and the result of the comparator.
1. A controller for an autonomous machine coupled to a plurality of sensors generating input data, the controller comprising:
a first neural network deployed on the autonomous machine, trained with a first
training data set and configured to generate first output data after processing a set of input data;
a second neural network deployed on the autonomous machine, trained with a
second training data set and configured to generate second output data after processing said set of input data, the first and second neural networks being executed simultaneously; and
a comparator receiving and comparing the first output data and second output data,
the comparator configured to detect a minimum difference between the first and second output data and produce a result,
a counter for counting instances of the comparator detecting the minimum
difference between the first and second output data, wherein the controller stops using the first output data to control the autonomous machine when the counter counts more than a predetermined number during a predetermined time period,
wherein the controller controls the autonomous machine using the first output data and the result of the comparator.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: a comparator and neural networks in claim 1 and neural networks in claim 17.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
1. In claim 17, the term, “detect”, in “a means for comparing the first output data and second output data and detect a minimum difference” may be changed to ‘detecting’.
2. Claims 6-7, 15 and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-9 and 17-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. In claim 1, neither the spec nor the claim provides structural description of a comparator. For example, the spec (e.g., [0030], [0171]) recites a comparator used for comparison, but fails to define it to be hardware or software. Note that receiving and detecting as broadly recited can be functions of a hardware device or a software program. In claim 17, the corresponding structure for the means-for step includes the comparator. Claims 2-9 and 18-21 are rejected for the same reason.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9, 17-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. It is not clear whether neural networks in claims 1 and 17 are software or hardware. In the spec, a neural network (e.g., [0067], “each node has a memory”) can be either. In claim 1, the structural information of a comparator is not defined. Claims 1 and 17 fail the definite requirement. Claims 2-9 and 18-21 are rejected for the same reason.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Step 1. The claim recites a controller.
Step 2a, prong 1. The claim recites an abstract idea. Steps, comparing the first output data and second output, to detect a difference between the first and second
output data and produce a result, to generate first output data after processing a set of input data, and to generate second output data after processing said set of input data, fall under mental processing. Detecting and producing, as broadly recited, amount to observing, evaluating and determining. Human can make observations, evaluation and determination. Generating outputs from inputs, as broadly recited, amounts to evaluating and determining a set of data.
Step 2a, prong 2. This judicial exception is not integrated into a practical application. Additional elements include a controller for controlling one or more autonomous machines each coupled to a plurality of sensors generating input data, a first neural network deployed on the autonomous machine, trained with a first
training data set, a second neural network deployed on the autonomous machine,
trained with a second training data set, a comparator receiving, the controller controls the one or more autonomous machine using input that includes the first output data and the result of the comparator. The controller recited at a high level of generality and amounts to mere instructions to apply the exception (e.g., [0030]). The autonomous machine is recited as a generic controlled machine (e.g., [0092]). Mere inclusion of such a machine without describing how it operates or causes a state change does not apply judicial exception beyond generally linking to a technological field. The neural networks are described at a high level and used to apply the abstract idea without placing limitations on how the neural networks operate. Generic sensors are included to perform data collection. comparing the first output data and second output data The claim recites trained with data or training, but provides nothing more than mere instructions to implement abstract idea of numerical calculation on a computer. The comparator, as recited at a high level, is merely used to perform the judicia exception. The function receiving, as recited at a high level, amounts to an extra solution data gathering activity. The claim limitation that the controller controls the autonomous machine using input that includes the first output data and the result of the comparator recites only a use of outcomes, but provides no technical details of how the use is implemented or effecting a state change of the machine and thus, does not apply the judicial exception beyond generally linking to a technological field.
Step 2b. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The controller is used as a tool to perform the judicial exception. The comparator and neural networks are equivalent of adding the words “apply it” to the judicial exception. Deploying neural networks on a machine or using sensors for data collection is well understood, conventional or routine activity (e.g., Stein). Receiving, as recited at a high level, amounts to receiving or transmitting data over a network (Berkheimer). Training neural networks without technical details is a well-understood or WURC activity (e.g., Stein). Controlling a machine with outcomes form neural network is a well-understood or WURC activity (e.g., Stein). The additional elements taken individually or in an ordered combination represent mere instructions to apply an exception, insignificantly extra solution activity or well understood, conventional or routine activities and thus, do not provide an inventive concept. Claim 1 is not eligible.
Claim 10:
Step 1. The claim recites a process.
Step 2a, prong 1. The claim recites an abstract idea. Steps, detecting a minimum difference between the first output data and second output data and producing a result, inferencing to generate first output data, inferencing to generate second output data, fall under mental processing. Detecting, as broadly recited, amounts to observing and evaluating. Human can make observation and evaluation. Inferencing, as broadly recited, amounts to evaluating, calculating and determining. Human can perform evaluation or calculation.
Step 2a, prong 2. This judicial exception is not integrated into a practical application. Additional elements include an autonomous machine coupled to a plurality of
sensors generating input data, a first neural network deployed on the autonomous machine and trained with a first training data set, on a second neural network deployed
on the autonomous machine and trained with a second training data set, controlling the autonomous machine using input that includes the first output data and the result of the detecting step. The autonomous machine is recited as a generic controlled machine (e.g., [0092]). Mere inclusion of such a machine without describing how it operates or causes a state change of a particular machine does not apply judicial exception beyond generally linking to a technological field. Generic sensors are included to perform data collection. The neural networks are described at a high level and used to apply the abstract idea without placing limitations on how the neural networks operate. The claim recites trained with data or training, but provides nothing more than mere instructions to implement abstract idea of numerical calculation on a computer. The claim limitation that controlling the autonomous machine using input that includes the first output data and the result of the detecting step recites only a use of outcomes, but provides no technical details of how the use is implemented or effecting a state change of the machine and thus, does not apply the judicial exception beyond generally linking to a technological field.
Step 2b. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The neural networks are equivalent of adding the words “apply it” to the judicial exception. Deploying neural networks on a machine or using sensors for data collection is well understood, conventional or routine activity (e.g., Stein). Training neural networks without technical details is a well-understood or WURC activity (e.g., Stein). Controlling a machine with outcomes from neural network is a well-understood or WURC activity (e.g., Stein). The additional elements taken individually or in an ordered combination represent mere instructions to apply an exception or are URC activities and thus, do not provide an inventive concept. Claim 10 is not eligible.
Claim 17:
Step 1. The claim recites a controller.
Step 2a, prong 1. The claim recites an abstract idea. Steps, comparing the first output data and second output, detect a minimum difference between the first and second
output data and produce a result, to generate first output data after processing a set of input data, and to generate second output data after processing said set of input data, fall under mental processing. Detecting and producing, as broadly recited, amount to observing, evaluating and determining. Human can make observations, evaluation and determination. Generating outputs from inputs, as broadly recited, amounts to evaluating and determining a set of data.
Step 2a, prong 2. This judicial exception is not integrated into a practical application. Additional elements include a controller for controlling one or more autonomous machines each coupled to a plurality of sensors generating input data, a first neural network deployed on the autonomous machine, trained with a first
training data set, a second neural network deployed on the autonomous machine,
trained with a second training data set, the controller controls the one or more autonomous machine using input that includes the first output data and the result of the comparator. The controller recited at a high level of generality and amounts to mere instructions to apply the exception (e.g., [0030]). The autonomous machine is recited as a generic controlled machine (e.g., [0092]). Mere inclusion of such a machine without describing how it operates or causes a state change does not apply judicial exception beyond generally linking to a technological field. The neural networks are described at a high level and used to apply the abstract idea without placing limitations on how the neural networks operate. The claim recites trained with data or training, but provides nothing more than mere instructions to implement abstract idea of numerical calculation on a computer. The claim limitation that the controller controls the autonomous machine using input that includes the first output data and the result of the comparator recites only a use of outcomes, but provides no technical details of how the use is implemented or effecting a state change of the machine and thus, does not apply the judicial exception beyond generally linking to a technological field.
Step 2b. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The controller is used as a tool to perform the judicial exception. The neural networks are mere instructions to apply an exception. Deploying neural networks on a machine or using sensors for data collection is well understood, conventional or routine activity (e.g., Stein). Training neural networks without descriptions of technical details is a well-understood or WURC activity (e.g., Stein). Controlling a machine with outcomes from neural network is a well-understood or WURC activity (e.g., Stein). The additional elements taken individually or in an ordered combination represent mere instructions to apply an exception, insignificantly extra solution activity or well understood, conventional or routine activities and thus, do not provide an inventive concept. Claim 17 is not eligible.
Dependent claims 2-5, 8-9, 11-14, 16 and 18-20 recite further claim limitations, in claim 2 and 18, the first neural network and the second neural network are deployed in different memory spaces (field of use, as the claim limitation recites storage), in claims 3 and 19, the first and second neural networks are deployed on different virtual machines (field of use, as the claim limitation recites a generic virtual machine), in claims 4, 13 and 20, the one or more autonomous machines includes multiple autonomous machines (WURC, e.g., Stein, multiple modules of processors), and the first neural network is deployed on one of the autonomous machines and the second neural network is deployed on another of the autonomous machines (WURC, e.g., Stein, neural networks run by different modules), in claim 5, the comparator is further configured to detect a minimum difference between the first output data and the second output data and produce the result (mental processing, as detecting amounts to observing and evaluating), in claims 8 and 14, the first and second training data sets are identical to each other (field of use), in claims 9 and 16, wherein each of the one or more autonomous machines is an autonomous land vehicle (field of use), in claim 11, wherein the first neural network and the second neural network are deployed in different memory spaces on the autonomous machine (field of use, as the claim limitation recites storage), in claim 12, the first and second neural networks are deployed on different virtual machines on the autonomous machine (field of use, as the claim limitation recites a generic virtual machine). Adding an abstract idea to another abstract idea (e.g., independent claims) does not make the claim non-abstract. Claims 2-5, 8-9, 11-14, 16 and 18-20 are not eligible.
Claim Rejections - 35 USC § 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.
Claims 1-5, 8-14, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Stein et al. (US 10872433), in view of Gang et al. (CN 113111814A) (in English translation).
1. A controller for controlling one or more autonomous machines each coupled to a plurality of sensors generating input data (Stein: e.g., col 3, lines 23-27, vehicles with sensors, where Fig 2, 21 or 28, a module in the computing device or the image and control processor is an example of a controller), the controller comprising:
a first neural network deployed on the autonomous machine, trained with a first training data set and configured to generate first output data after processing a set of input data (Stein: e.g., col 49, lines 35-38, col 10, lines 44-46, col 4, lines 4-7, a first ANN to produce a first output based on a provided data set with training using training data sets including, col 6, lines 49-50, 57-59, sensor data or images, where the training data for training the first ANN is an example of a first training data set);
a second neural network deployed on the autonomous machine, trained with a second training data set and configured to generate second output data after processing said set of input data (Stein: e.g., col 49, lines 40-42, col 10, lines 44-46, col 4, lines 4-7, a second ANN to produce a second output based on a provided data set with training using training data sets including, col 6, lines 49-50, 57-59, sensor data or images, where the training data for training the second ANN is an example of a second training data set); and
a comparator receiving and comparing the first output data and second output data, theStein: e.g., col 49, lines 42-43, comparing the first and the second output to determine a feature of the surface, where the module that implements the comparison is an example of a comparator and Fig 21, a vehicle control processor is an example of a controller that controls the autonomous machine, and where determining a feature of the surface based on the first output and the result obtained from comparison interprets using input that includes the first output data and the result of the comparator).
Stein does not expressly disclose, but Gang, an analogous art, directed to training recognition neural network models, discloses “difference” in “configured to detect a difference between the first and second output data” (Gang: e.g., page 8, par 5, minimizing the difference between the output of the first neural network and the output of the second neural network based on the labeled image and non-tagged image data). (Note: the cited page 8, paragraph 5 corresponding to [0055] in CN 11311814A). Nonetheless, optimization of a cost or loss is a common practice in machine learning. It would have been obvious for one of ordinary skill in the art, having Stein and Gang before the effective filing date, to combine Stein with Gang for a motivation generating a target function of a re-identification model based on the first and the second neural networks (Gang: page 8, par 10) and avoiding the influence of jitter in the training process of the first neural network (page 9, par 6) to improve the error based NN model learning in Stein.
10. A method of controlling an autonomous machine coupled to a plurality of sensors generating input data (Stein: e.g., col 3, lines 23-27, vehicles using control processors with sensors), comprising the steps of:
inferencing to generate first output data on a first neural network deployed on the autonomous machine and trained with a first training data set (Stein: e.g., col 49, lines 35-38, col 10, lines 44-46, col 4, lines 4-7, a first ANN to produce a first output based on a provided data set with training using training data sets including, col 6, lines 49-50, 57-59, sensor data or images, where the training data for training the first ANN is an example of a first training data set);
inferencing to generate second output data on a second neural network deployed on the autonomous machine and trained with a second training data set (Stein: e.g., col 49, lines 40-42, col 10, lines 44-46, col 4, lines 4-7, a second ANN to produce a second output based on a provided data set with training using training data sets including, col 6, lines 49-50, 57-59, sensor data or images, where the training data for training the second ANN is an example of a second training data set);
Stein: e.g., col 49, lines 42-43, comparing the first and the second output to determine a feature); and
controlling the autonomous machine using input that includes the first output data and the result of the detecting step (Stein: e.g., col 49, lines 42-43, comparing the first and the second output to determine a feature of the surface, where the module that implements the comparison is an example of a comparator and Fig 21, a vehicle control processor is an example of a controller that controls the autonomous machine, and where determining a feature of the surface based on the first output and the result obtained from comparison interprets using input that includes the first output data and the result of the comparator).
Stein does not expressly disclose, but Gang, an analogous art, directed to training recognition neural network models, discloses “a minimum difference” in “detecting a minimum difference between the first output data and second output data” (Gang: e.g., page 8, par 5, minimizing the difference between the output of the first neural network and the output of the second neural network based on the labeled image and non-tagged image data). (Note: the cited page 8, paragraph 5 corresponding to [0055] in CN 11311814A). Nonetheless, optimization of a cost or loss is a common practice in machine learning. It would have been obvious for one of ordinary skill in the art, having Stein and Gang before the effective filing date, to combine Stein with Gang for a motivation generating a target function of a re-identification model based on the first and the second neural networks (Gang: page 8, par 10) and avoiding the influence of jitter in the training process of the first neural network (page 9, par 6) to improve the error based NN model learning in Stein.
17. A controller for controlling one or more autonomous machines each coupled to a plurality of sensors generating input data (Stein: e.g., col 3, lines 23-27, vehicles with sensors, where Fig 2, 21 or 28, a module in the computing device or the image and control processor is an example of a controller), the controller comprising:
a first neural network deployed on the autonomous machine, trained with a first training data set and configured to generate first output data after processing a set of input data (Stein: e.g., col 49, lines 35-38, col 10, lines 44-46, col 4, lines 4-7, a first ANN to produce a first output based on a provided data set with training using training data sets including, col 6, lines 49-50, 57-59, sensor data or images, where the training data for training the first ANN is an example of a first training data set);
a second neural network deployed on the autonomous machine, trained with a second training data set and configured to generate second output data after processing said set of input data (Stein: e.g., col 49, lines 40-42, col 10, lines 44-46, col 4, lines 4-7, a second ANN to produce a second output based on a provided data set with training using training data sets including, col 6, lines 49-50, 57-59, sensor data or images, where the training data for training the second ANN is an example of a second training data set); and
a means for comparing the first output data and second output data Stein: e.g., col 49, lines 42-43, comparing the first and the second output to determine a feature of the surface, where the module that implements the comparison is an example of a comparator and Fig 21, a vehicle control processor is an example of a controller that controls the autonomous machine, and where determining a feature of the surface based on the first output and the result obtained from comparison interprets using input that includes the first output data and the result of the comparator).
Stein does not expressly disclose, but Gang, an analogous art, directed to training recognition neural network models, discloses “a minimum difference” in “detect a minimum difference between the first and second output data and produce a result” (Gang: e.g., page 8, par 5, minimizing the difference between the output of the first neural network and the output of the second neural network based on the labeled image and non-tagged image data). (Note: the cited page 8, paragraph 5 corresponding to [0055] in CN 11311814A). Nonetheless, optimization of a cost or loss is a common practice in machine learning. It would have been obvious for one of ordinary skill in the art, having Stein and Gang before the effective filing date, to combine Stein with Gang for a motivation generating a target function of a re-identification model based on the first and the second neural networks (Gang: page 8, par 10) and avoiding the influence of jitter in the training process of the first neural network (page 9, par 6) to improve the error based NN model learning in Stein.
2 and 18, wherein the first neural network and the second neural network are deployed in different memory spaces (Stein: e.g., col 15, lines 5-10, Fig 4 or 11, the first neural networks and the second neural network (e.g., DNN) determining, respectively, the gamma and motion of an object).
11, wherein the first neural network and the second neural network are deployed in different memory spaces on the autonomous machine (Stein: e.g., col 15, lines 5-10, Fig 4 or 11, the first neural networks and the second neural network (e.g., DNN) on the vehicle determining, respectively, the gamma and motion of an object).
3 and 19, wherein the first and second neural networks are deployed on different virtual machines (Stein: e.g., col 15, lines 5-10, Fig 4 or 11, the first neural networks and the second neural network (e.g., DNN) executed on separate modules for accomplishing different tasks interprets the first and second neural networks are deployed on different virtual machines).
12, wherein the first and second neural networks are deployed on different virtual machines on the autonomous machine (Stein: e.g., col 15, lines 5-10, Fig 4 or 11, the first neural networks and the second neural network (e.g., DNN) on the vehicle executed on separate modules for accomplishing different tasks interprets the first and second neural networks are deployed on different virtual machines).
4, 13 and 20, wherein the one or more autonomous machines includes multiple autonomous machines, and the first neural network is deployed on one of the autonomous machines and the second neural network is deployed on another of the autonomous machines (Stein: e.g., col 15, lines 5-10, Fig 4 or 11, the first neural networks , block 412, and the second neural network, block 1112, executed by different modules on one or more processors, where a module on a processor is an example of an autonomous machine).
5. The controller of claim 1, wherein the comparator is further configured to detect a minimum difference between the first output data and the second output data and produce the result (Gang: e.g., page 8, par 5, minimizing the difference between the output of the first neural network and the output of the second neural network).
8 and 14, wherein the first and second training data sets are identical to each other (Stein: e.g., col 49, lines 33-35, obtained sequence of images to the first and the second neural networks).
9 and 16, wherein each of the one or more autonomous machines is an autonomous land vehicle (Stein: e.g., Fig 2, a land vehicle).
Conclusion
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/LI WU CHANG/ Primary Examiner, Art Unit 2124 November 29, 2025