DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis-Step 1
Claims 1, 7,8 are directed to a system, process, and product. Therefore, claims 1, 7, 8 are within at least one of the four statutory categories.
101 Analysis-Step2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
1. A remote operation system of remote operation of a plurality of types of mobilities, the remote operation system comprising one or more processors configured to:
acquire operator license information indicating a license each of a plurality of remote operators has for operation of the plurality of types of mobilities;
acquire required capability information indicating at least a required license required for the remote operation of a target mobility; and
assign a first remote operator who has the required license out of the remote operators to the remote operation of the target mobility, based on the operator license information and the required license.
wherein the one or more processors are further configured to:
acquire operator state information indicating a state of a remote operator,
calculate an operator score indicating suitability of the remote operator for the remote operation, based on the operator state information, and
select the first remote operator based on the operator score.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “assign a first remote operator…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
1. A remote operation system of remote operation of a plurality of types of mobilities, the remote operation system comprising one or more processors configured to:
acquire operator license information indicating a license each of a plurality of remote operators has for operation of the plurality of types of mobilities;
acquire required capability information indicating at least a required license required for the remote operation of a target mobility; and
assign a first remote operator who has the required license out of the remote operators to the remote operation of the target mobility, based on the operator license information and the required license.
assign a first remote operator who has the required license out of the remote operators to the remote operation of the target mobility, based on the operator license information and the required license.
wherein the one or more processors are further configured to:
acquire operator state information indicating a state of a remote operator,
calculate an operator score indicating suitability of the remote operator for the remote operation, based on the operator state information, and
select the first remote operator based on the operator score.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “acquire operator license information…, acquire required capability information…” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (controller) to perform the process. In particular, the acquiring steps are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The remote operation system is recited at a high level of generality and merely automates the evaluating step.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a controller to perform the assigning… amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “acquire operator license information…, acquire required capability information…” the examiner submits that these limitations are insignificant extra-solution activities.
Hence, the claim is not patent eligible.
Dependent claims 2-6 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-6 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1-8 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 7-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Igarashi (US 2022/0317683)
As to claim 1 Igarashi discloses a remote operation system of remote operation of a plurality of types of mobilities, the remote operation system comprising one or more processors configured to:
acquire operator license information indicating a license each of a plurality of remote operators has for operation of the plurality of types of mobilities (Paragraph 28 “The operator database 32 is a database in which operator attributes are registered for each operator. The operator attributes include, for example, the age and sex of the operator, and types of licenses the operator has. The types of licenses include, for example, a standard-sized license, a medium-sized license, a large-sized licenses, a large-sized special license, a traction vehicle license, and the like.”);
acquire required capability information indicating at least a required license required for the remote operation of a target mobility(Paragraph 28 “The operator database 32 is a database in which operator attributes are registered for each operator. The operator attributes include, for example, the age and sex of the operator, and types of licenses the operator has. The types of licenses include, for example, a standard-sized license, a medium-sized license, a large-sized licenses, a large-sized special license, a traction vehicle license, and the like.”); and
assign a first remote operator who has the required license out of the remote operators to the remote operation of the target mobility, based on the operator license information and the required license(Paragraph 28-29 “The operator database 32 is a database in which operator attributes are registered for each operator. The operator attributes include, for example, the age and sex of the operator, and types of licenses the operator has. The types of licenses include, for example, a standard-sized license, a medium-sized license, a large-sized licenses, a large-sized special license, a traction vehicle license, and the like.” he matching model 33 is a regression model using evaluation results of operators by users as objective functions and is created based on co-occurrence of user attributes and operator attributes as feature quantities. In the matching model 33, a relationship between user attributes and operator attributes satisfied by a large number of users is statistically modeled. The matching model 33 is updated on the basis of evaluation results of operators by users every time an evaluation result is newly obtained or when predetermined amounts of evaluation results are accumulated.).
wherein the one or more processors are further configured to:
acquire operator state information indicating a state of a remote operator (Paragraph 28 “The operator database 32 is a database in which operator attributes are registered for each operator. The operator attributes include, for example, the age and sex of the operator, and types of licenses the operator has. The types of licenses include, for example, a standard-sized license, a medium-sized license, a large-sized licenses, a large-sized special license, a traction vehicle license, and the like.”),
calculate an operator score indicating suitability of the remote operator for the remote operation, based on the operator state information (Paragraph 31-32 “The collaborative filtering model 34 is a model that recommends an operator using collaborative filtering. The collaborative filtering model 34 is created on the basis of evaluation results of operators collected from a large number of users. Here, an outline of the collaborative filtering model 34 will be described with reference to FIGS. 3 and 4. In the steps of creating and updating the collaborative filtering model 34, after the end of remote operation, the evaluation of the operator by the user is performed on, for example, a five-level scale from 1 of the lowest evaluation to 5 of the highest evaluation. The evaluation results are summarized in a table as shown in FIG. 3. FIG. 3 shows, as an example, evaluation results in the case where the number of users is five and the number of operators is five. The cell labeled “-” in the user x operator matrix shown in FIG. 3 represents an operator who has not been used by the user. For example, for User II, Operator A and Operator E have not been used, and for User V, Operator A and Operator C have not been used.”), and
select the first remote operator based on the operator score (Paragraph 46 “The requesting user selects an operator to whom the requesting user requests remote operation from among the recommended operators recommended from the server 30. In step S8, the server 30 receives the operator selection result by the requesting user.”).
As to claim 2 Igarashi discloses a remote operation wherein the one or more processors are configured to
receive a remote operation request indicating a type of the target mobility (Paragraph 30), and
recognize the required license required for the remote operation of the target mobility, based on the type of the target mobility(Paragraph 30).
As to claim 7 the claim is interpreted and rejected as in claim 1.
As to claim 8 the claim is interpreted and rejected as in claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4-6, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Igarashi (US 2022/0317683) in view of Hirai (US 2024/0391112)
As to claim 4 Hirai teaches a remote operation system wherein:
the required capability information further indicates a required terminal specification that is a specification required for the remote operation of the target mobility (Paragraph 129, 210); and
the one or more processors are further configured to
acquire terminal specification information indicating specifications of each of a plurality of remote operator terminals(Paragraph 129, 210), and
assign a first remote operator terminal that meets the required terminal specification out of the remote operator terminals to the remote operation of the target mobility, based on the terminal specification information and the required terminal specification(Paragraph 129, 210). It would have been obvious to one of ordinary skill to modify Igrashi to include the teachings of determining terminal specification for the purpose of selecting the appropriate terminal.
As to claim 5 Hirai teaches a remote operation system wherein the one or more processors are further configured to
receive a remote operation request indicating either or both of a type of the target mobility and content of the remote operation of the target mobility (Paragraph 129, 210), and
recognize the required terminal specification required for the remote operation of the target mobility, based on the remote operation request (Paragraph 129, 210).
As to claim 6 Hirai teaches a remote operation system wherein the one or more processors are further configured to
acquire terminal state information indicating a state of a remote operator terminal (Paragraph 129, 210).
calculate a terminal score indicating suitability of the remote operator terminal for the remote operation, based on the terminal state information(Paragraph 116), and
select the first remote operator terminal based on the terminal score (Paragraph 116).
As to claim 11 Igarashi teaches a remote operation system wherein the remote operation system, wherein the one or more processors are further configured to calculate an integrated score by integrating the operator score and a terminal score indicating suitability of the remote operator terminal, and select a combination of the first remote operator and the first remote operator terminal based on the integrated score (Paragraph 33).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Igarashi (US 2022/0317683) in view of Brooks (US 2017/0308080)
As to claim 9 Brooks teaches a remote operation system wherein the operator state information includes a health state of the remote operator, and the one or more processors are configured to calculate the operator score based on the health state of the remote operator (Paragraph 66). It would have been obvious to one of ordinary skill to modify Igarashi to include the teaching of determining the alertness of the operator for the purpose of safely controlling the vehicle from a remote location.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Igarashi (US 2022/0317683) in view of Stenneth (US 2015/0346718)
As to claim 10 Stenneth teaches a remote operation system, wherein the operator state information includes an amount of working time the remote operator was engaged in remote operation over a past period, and the one or more processors are configured to calculate the operator score based on the amount of working time (Paragraph 46). It would have been obvious to one of ordinary skill to modify Igarashi to include the teachings of using the work schedule of the remote operator for the purpose of improving safety by not overworking over a predetermined number of hours.
Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable over Igarashi (US 2022/0317683)
As to claim 12 Igarashi does not explicitly discloses a remote operation wherein when there is a plurality of remote operators who have the required license, the one or more processors are configured to select a remote operator having fewest types of licenses as the first remote operator. Igrarashi does teach of selecting remote operators with various types of licenses (Paragraph 28). It would have been an obvious design choice to choose the remote operator having the fewest type of licenses for the purpose of managing and delegating tasks to different operators.
Claim is 13 is rejected under 35 U.S.C. 103 as being unpatentable over Igarashi (US 2022/0317683) in view of Hirai (US 2024/0391112), as applied to claim 6 above, and in further view of Brooks (US 2017/0308080)
As to claim 13 Brooks teaches a remote operation system wherein the terminal state information includes a communication state of the remote operator terminal, and the one or more processors are configured to calculate the terminal score based on the communication state (Paragraph 28). It would have been obvious to one of ordinary skill to modify Igarashi to include the teaching of determining a communication state of the remote operator terminal for the purpose of providing control remotely from an remote facility.
Response to Arguments
Applicant's arguments filed 4/8/2026 have been fully considered but they are not persuasive.
On page 9 of the applicants arguments applicants argue that the claim does not recite an abstract idea. Specifically applicants argue that the amended claims recite assigning an operator to perform remote operation of a physical mobility device and not merely making a mental judgement.
The examiner respectfully disagrees with the applicants arguments. As mentioned the above the claims recite a mental processes. Assigning an operator and evaluating license information of a plurality of operators is a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “assign a first remote operator…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
On page 10 of the applicants arguments applicants argue that Igarashi does not disclose “acquire required capability information indicating at least required license required for the remote operation of a target mobility”
The examiner respectfully disagrees with the applicants arguments. The applicant is reminded that the examiner interprets the claim with the broadest reasonable interpretation. Igarashi teaches of obtaining information of different operations and of a database which contains information of each of the registered operators. This information include the type of licenses the operator has and the type of vehicles in which they are allowed to operate(Paragraph 28-29 “The operator database 32 is a database in which operator attributes are registered for each operator. The operator attributes include, for example, the age and sex of the operator, and types of licenses the operator has. The types of licenses include, for example, a standard-sized license, a medium-sized license, a large-sized licenses, a large-sized special license, a traction vehicle license, and the like.” he matching model 33 is a regression model using evaluation results of operators by users as objective functions and is created based on co-occurrence of user attributes and operator attributes as feature quantities. In the matching model 33, a relationship between user attributes and operator attributes satisfied by a large number of users is statistically modeled. The matching model 33 is updated on the basis of evaluation results of operators by users every time an evaluation result is newly obtained or when predetermined amounts of evaluation results are accumulated.). Clearly an operator with the required license for the particular vehicle would be selected to operate that particular class of vehicle[i.e. an operator who doesn’t have a license for a large vehicle or a traction vehicle license would not be selected to operate that vehicle since they aren’t qualified to legally allowed to operate such a vehicle. The system of Igarashi further teaches of selecting an operator based on the operator score (Paragraph 31-32 “The collaborative filtering model 34 is a model that recommends an operator using collaborative filtering. The collaborative filtering model 34 is created on the basis of evaluation results of operators collected from a large number of users. Here, an outline of the collaborative filtering model 34 will be described with reference to FIGS. 3 and 4. In the steps of creating and updating the collaborative filtering model 34, after the end of remote operation, the evaluation of the operator by the user is performed on, for example, a five-level scale from 1 of the lowest evaluation to 5 of the highest evaluation. The evaluation results are summarized in a table as shown in FIG. 3. FIG. 3 shows, as an example, evaluation results in the case where the number of users is five and the number of operators is five. The cell labeled “-” in the user x operator matrix shown in FIG. 3 represents an operator who has not been used by the user. For example, for User II, Operator A and Operator E have not been used, and for User V, Operator A and Operator C have not been used.”)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMRAN K MUSTAFA whose telephone number is (571)270-1471. The examiner can normally be reached Mon-Fri 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James J Lee can be reached at 571-270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
IMRAN K. MUSTAFA
Primary Examiner
Art Unit 3668
/IMRAN K MUSTAFA/ Primary Examiner, Art Unit 3668
6/25/2026
Prosecution Insights