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 .
Status of Claims
This FINAL action is in response to application No. 18/493,810 filed on 11/18/2025. Claims 1-2, 5-6, 9-10 are currently pending and have been examined. Claims 1-2, 5-6, 9-10 have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/09/2026 is being considered by the examiner.
Priority
Acknowledgment is made of applicant's claim priority for foreign applications JP2022-175747, filed on 11/01/2022.
Response to Arguments
Applicant's amendment and/or arguments with respect to the Claim Objections, Claim Interpretation, and rejection of claims under 35 USC 101 as set forth in the office action of 25 August 2023 have been considered and are NOT persuasive. Claim 1 remains an abstract idea to be expanded on below.
Applicant’s amendments and/or arguments with respect to the rejection of claims 1-2, 5-6, 9-10 under 35 USC 102 as set forth in the office action of 09/30/2025 have been considered but are moot because the new ground(s) of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-9 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
Claim 1 is directed to a method performed by an information processing apparatus (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, 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:
An information processing method performed by an information processing apparatus, the information processing method comprising;
driver information regarding a driver of [[the]]a delivery vehicle and delivery information regarding a package to be delivered by the delivery vehicle,
wherein the driver information comprises, as delivery work characteristics of the driver, ratings from third parties including a passerby or a resident around a location where the delivery vehicle was parked at the delivery destinations:
generating a learned model that outputs route information for the delivery vehicle upon inputting the driver information and the delivery information, by learning using training data,
the training data being generated by associating the driver information and the delivery information with actual data of routes or stop positions of the delivery vehicle and actual data of the ratings from the third parties: and
generating the route information by executing the learned model, wherein the route information indicates a route or a stop position, the route or the stop position providing the driver with experience to achieve improvement of the delivery work characteristics of the driver by increasing a number of routes on which the driver can drive the delivery vehicle or a number of stop positions at which the driver can stop the delivery vehicle, and
the improvement of the delivery work characteristics includes reduction in complaints or dissatisfaction from the third parties.
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, “generating...” in the context of this claim encompasses a person looking at data collected and forming a simple judgement for the route. 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”):
An information processing method performed by an information processing apparatus, the information processing method comprising;
driver information regarding a driver of [[the]]a delivery vehicle and delivery information regarding a package to be delivered by the delivery vehicle,
wherein the driver information comprises, as delivery work characteristics of the driver, ratings from third parties including a passerby or a resident around a location where the delivery vehicle was parked at the delivery destinations:
generating a learned model that outputs route information for the delivery vehicle upon inputting the driver information and the delivery information, by learning using training data,
the training data being generated by associating the driver information and the delivery information with actual data of routes or stop positions of the delivery vehicle and actual data of the ratings from the third parties: and
generating the route information by executing the learned model, wherein the route information indicates a route or a stop position, the route or the stop position providing the driver with experience to achieve improvement of the delivery work characteristics of the driver by increasing a number of routes on which the driver can drive the delivery vehicle or a number of stop positions at which the driver can stop the delivery vehicle, and
the improvement of the delivery work characteristics includes reduction in complaints or dissatisfaction from the third parties.
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 “The information processing method performed..” and “…information”, “..data”, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform data gathering.
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 an information processing method performed by an information processing method to perform the acquiring... 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 “acquiring route information,” the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “acquiring route information,” are well-understood, routine, and conventional activities because the background recites routing is a standard practice for delivery making. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The additional limitation of “acquiring a route...,” is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere acquiring data is a well understood, routine, and conventional function. Hence, the claim is not patent eligible.
Dependent Claims
Dependent claims 2-9, do not recite any further limitations that causes the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-9 are not patent eligible under the same rationale as provided for in the rejection of claims 1.
Therefore, claim(s) 1-9 are ineligible under 35 USC §101.
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.
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, 5 and 6 are rejected under 35 U.S.C 103 as being unpatentable over Tripathy (US 20200265366 A1) in view of Matsumoto (JP7317406B1) and Zhong (US 20100088146 A1).
Regarding claim 1, Tripathy teaches acquiring driver information regarding a driver of [[the]]a delivery vehicle and delivery information regarding a package to be delivered by the delivery vehicle, (see at least [6]; "The computing device is also configured to determine, for each of the plurality of drivers, a plurality of familiarity values, each familiarity value corresponding to a delivery route of the plurality of delivery routes to be assigned, where each of the familiarity values is determined based on the historical route data corresponding to each driver. I…The inputs may act as constraints in an optimization framework for a set of orders to be delivered to generate one or more routes where each route is a sequence of orders placed in one delivery truck along with a plan of delivering those within the customer-provided delivery time windows. ")
generating a learned model that outputs route information for the delivery vehicle upon inputting the driver information and the delivery information, by learning using training data, (see at least [22, 48]; " The routes may be generated based on application of any suitable route planning model, where each route may identify one or more delivery locations for one or more purchase orders….By way of example, the machine learning model may be trained on a plurality of data sets. For example, delivery management computing device 102 may aggregate within a data repository, such as database 116, historical route data (e.g., driver's historical routes, customer feedback ratings, driver's own ratings of previous routes) over a period of time (e.g., the past 13 months) for each driver.") Tripathy describes generating a learned model from training data that outputs route information from driver and delivery information.
the training data being generated by associating the driver information and the delivery information with actual data of routes or stop positions of the delivery vehicle and actual data of the ratings from the third parties: and (see at least [48]; "By way of example, the machine learning model may be trained on a plurality of data sets. For example, delivery management computing device 102 may aggregate within a data repository, such as database 116, historical route data (e.g., driver's historical routes, customer feedback ratings, driver's own ratings of previous routes) over a period of time (e.g., the past 13 months) for each driver.") Tripathy describes training data being generated from associating driver and delivery information as well as ratings from third parties.
generating the route information by executing the learned model, (see at least [22]; "The routes may be generated based on application of any suitable route planning model, ")
wherein the route information indicates a route or a stop position, (see at least [22]; "The routes may be generated based on application of any suitable route planning model, where each route may identify one or more delivery locations for one or more purchase orders.") Tripathy describes delivery locations, which would be considered a stop along their route.
Tripathy does not explicitly disclose wherein the driver information comprises, as delivery work characteristics of the driver, ratings from third parties including a passerby or a resident around a location where the delivery vehicle was parked at the delivery destinations:, the route or the stop position providing the driver with experience to achieve improvement of the delivery work characteristics of the driver by increasing a number of routes on which the driver can drive the delivery vehicle or a number of stop positions at which the driver can stop the delivery vehicle, and the improvement of the delivery work characteristics includes reduction in complaints or dissatisfaction from the third parties.
However, Matsumoto teaches wherein the driver information comprises, as delivery work characteristics of the driver, ratings from third parties including a passerby or a resident around a location where the delivery vehicle was parked at the delivery destinations: (see at least [0065]; "Item "user evaluation": Whether the evaluation of the driver from the user of the delivery source or the delivery destination of the delivery for the past predetermined number of times by the driver is above a certain level") Matsumoto describes rating from third parties to include a delivery destination of the delivery.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tripathy to incorporate the teachings of Matsumoto which teaches the ratings from third parties to include a delivery destination in order to receive a somewhat unbiased opinion on the delivery driver.
Matsumoto does not explicitly disclose the route or the stop position providing the driver with experience to achieve improvement of the delivery work characteristics of the driver by increasing a number of routes on which the driver can drive the delivery vehicle or a number of stop positions at which the driver can stop the delivery vehicle, and the improvement of the delivery work characteristics includes reduction in complaints or dissatisfaction from the third parties.
However, Zhong teaches the route or the stop position providing the driver with experience to achieve improvement of the delivery work characteristics of the driver by increasing a number of routes on which the driver can drive the delivery vehicle or a number of stop positions at which the driver can stop the delivery vehicle, and (see at least [0079, 0048, 0109]; "A high visit frequency to a cell promotes driver familiarity within the cell and, in turn, improves driver performance overall…In one embodiment, the method of the present invention involves grouping nearby stops 42 into cells 40… Assume that a driver visits a cell according to a probability p. After a cell visit, driver performance will increase along the learning curve") Zhong describes a method which provides frequency of routes in order to improve the driver's work characteristics and experience of that route.
the improvement of the delivery work characteristics includes reduction in complaints or dissatisfaction from the third parties. (see at least [0120]; "Driver familiarity along a consistent route also provides more personalized service to customers along the route.") Zhong's method increases driver experience with a route as well as allows for more personalized service to the customers, which would reduce complaints and satisfaction from third parties.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tripathy to incorporate teachings of Zhong which teaches route generation based on increasing the efficiency of the driver in order to maximize the efficiencies of the delivery process.
Regarding claim 5, Tripathy, Matsumoto and Zhong, in combination, disclose limitations of claim 1 as discussed above, furthermore, Tripathy discloses The information processing method according to claim 1, further comprising accepting, from the driver of the delivery vehicle, a designation to notify the driver of the delivery vehicle of at least a part of items of the route information or a designation not to notify the driver of the delivery vehicle of at least a part of the items of the route information. (see a least [77], "In some examples, driver route assignment engine 406 packages assigned routes 306B within a message for transmission to another computing device, such as a computing device of a corresponding driver. For example, driver route assignment engine 406 may package assigned routes 306B within driver assignment data 407, and transmit driver assignment data 407 to the assigned driver.")
Regarding claim 5, Tripathy, Matsumoto and Zhong, in combination, disclose limitations of claim 1 as discussed above, furthermore, Tripathy does not explicitly disclose Tripathy discloses The information processing method according to claim 2, further comprising accepting, from the driver of the delivery vehicle, a designation to notify the driver of the delivery vehicle of at least a part of items of the route information or a designation not to notify the driver of the delivery vehicle of at least a part of the items of the route information. (see a least [77], "In some examples, driver route assignment engine 406 packages assigned routes 306B within a message for transmission to another computing device, such as a computing device of a corresponding driver. For example, driver route assignment engine 406 may package assigned routes 306B within driver assignment data 407, and transmit driver assignment data 407 to the assigned driver.")
Claim 2 is rejected under 35 U.S.C 103 as being unpatentable over Tripathy (US 20200265366 A1) in view of Matsumoto and Zhong (US 20170221169 A1) and in further view of Zhong.
Regarding Claim 2, Tripathy, Matsumoto and Zhong, in combination, disclose limitations of claim 1 as discussed above, furthermore, Tripathy does not explicitly disclose The information processing method according to claim 1, wherein the delivery work characteristics of the driver of the delivery vehicle[[,]1 further comprise a difference between an average work and a delivery work performance of the driver.
However, Champa teaches The information processing method according to claim 1, wherein the delivery work characteristics of the driver of the delivery vehicle[[,]1 further comprise a difference between an average work and a delivery work performance of the driver.(see at least [0046]; "For example, to determine a stop difficulty, analytics functions component 120 may compare or correlate the number of steps 154 or average number of steps of fleet vehicle driver 150 to other drivers in order to determine whether a particular stop is more difficult.") Champa teaches a characteristic that measures the driver versus the average.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tripathy to incorporate teachings of Champa which teaches measuring the difference of a work characteristic between the average and the delivery drivers in order to measure how efficient the delivery driver is against the expected average.
Claims 9 and 10 are rejected under 35 U.S.C 103 as being unpatentable over Tripathy (US 20200265366 A1) in view of Matsumoto and Zhong (US 20170221169 A1) and in further view of Panagiotis.
Regarding Claim 9, Tripathy, Matsumoto and Zhong, in combination, disclose limitations of claim 1 as discussed above, furthermore, Tripathy does not explicitly disclose The information processing method according to claim 1, wherein the route information indicates, as the stop position, a location where the driver can stop the delivery vehicle with reduced impact on surrounding traffic.
However, Panagiotis teaches The information processing method according to claim 1, wherein the route information indicates, as the stop position, a location where the driver can stop the delivery vehicle with reduced impact on surrounding traffic.
(see at least [90]; "According to an embodiment of this service, the said vehicle parks at a specific space in a specific area and at a specific direction… Then, a guidance program is formed for these areas, which is provided in the form of instructions for accurate manoeuvres towards the desired destination, e.g. "Straight 150m - Left turn 300 Straight 200m - STOP". ")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tripathy to incorporate the teachings of Panagiotis which teaches directing the driver to a safe place to stop in order to keep the delivery driver and truck safe from traffic as well as prevent traffic interference.
Regarding Claim 10, Zhong discloses limitations of claim 2 as discussed above, furthermore, Tripathy does not explicitly disclose The information processing method according to claim 1, wherein the route information indicates, as the stop position, a location where the driver can stop the delivery vehicle with reduced impact on surrounding traffic.
However, Panagiotis teaches The information processing method according to claim 1, wherein the route information indicates, as the stop position, a location where the driver can stop the delivery vehicle with reduced impact on surrounding traffic.
(see at least [90]; "According to an embodiment of this service, the said vehicle parks at a specific space in a specific area and at a specific direction… Then, a guidance program is formed for these areas, which is provided in the form of instructions for accurate manoeuvres towards the desired destination, e.g. "Straight 150m - Left turn 300 Straight 200m - STOP". ")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tripathy to incorporate the teachings of Panagiotis which teaches directing the driver to a safe place to stop in order to keep the delivery driver and truck safe from traffic as well as prevent traffic interference.
Conclusion
THIS ACTION IS MADE FINAL. 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 HANA VICTORIA HALL whose telephone number is (571) 272-5289. The examiner can normally be reached M-F 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, Rachid Bendidi can be reached at 5712724896. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HANA VICTORIA HALL/Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664