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 . Claims 1-7, 9-16, and 18-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/10/2025 has been entered.
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 1-7, 9-16, and 18-20 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is directed to a method (i.e., a process), claim 10 is directed to a non-transitory computer-readable medium (i.e., machine or manufacture), and claim 19 is directed to a system (i.e., a machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 USC § 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea from claims 1, 10, and 19 (claim 1 being representative) is:
collecting data including current coordinates;
receiving data including the current coordinates of the device representing a current location of the device;
mapping the current geographic location of the device to a retail location by determining a nearest retail location for the current geographic location of the device;
applying a parking prohibition model to information about the current geographic location to identify that parking for the retail location at the current geographic location is restricted;
responsive to determining that the parking is restricted:
identifying a plurality of candidate parking locations for the retail location;
applying a parking quality model to one or more features for each candidate parking location of the plurality of candidate parking locations including a degree of anticipated traffic at each candidate parking location, a current degree of demand for pickers at the retail location, and a current average picker wait time at the retail location before receiving an order to generate a score for each candidate parking location that quantifies a favorability of each candidate parking location in relation to the retail location;
ranking, using the score for each candidate parking location, the plurality of candidate parking locations to generate ranked candidate parking locations; and
[displaying] a map with an indication of a geographic location of each of the ranked candidate parking locations, wherein the map is generated using the information about the current geographic location of the device.
The abstract idea steps italicized above are those which cover managing personal behavior relationships, interactions between people. The steps describe, at a high level, managing parking location selection to reduce congestion. This is further supported by paragraphs 0002-0003 of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation (BRI), covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim is directed to determining restrictions, scoring options, and providing suggestions. This is further supported by paragraphs 0002-0004 of applicant’s specification as filed. This constitutes a process that, under its BRI, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, therefore, it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: via sensors of a device associated with a picker; Global Positioning System (GPS); device; via a network; using a geographic database; machine-learned; causing a user interface of the device; electronic.
Claim 10 recites the following additional elements: non-transitory computer-readable storage medium; one or more computer processors; via sensors of a device associated with a picker; Global Positioning System (GPS); device; via a network; using a geographic database; machine-learned; causing a user interface of the device; electronic.
Claim 19 recites the following additional elements: computer system; one or more computer processors; computer-readable storage medium; via sensors of a device associated with a picker; Global Positioning System (GPS); device; via a network; using a geographic database; machine-learned; causing a user interface of the device; electronic.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraph [0067] and [0069] of applicant’s specification as filed, for example. Further, the combination of these elements is nothing more than a generic computing system.
Examiner interprets applying a machine-learned parking prohibition model and applying a machine-learned parking quality model described in paragraphs [0050], [0057], and [0060] of applicant’s specification as filed as an additional element. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, applying a machine-learned parking prohibition model and applying a machine-learned parking quality model is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Claims 2, 11, and 20, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: applying the machine-learned parking prohibition model. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claims 3 and 12, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: devices associated with pickers. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claims 4 and 13, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: training the machine-learned parking quality model using logistic regression applied to features derived from data about prior orders. Examiner notes that this results-oriented training step is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claims 5 and 14, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: via the network and from the device associated with the picker; retraining the machine-learned parking quality model using the feedback. Examiner notes that this results-oriented training step is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claims 6 and 15, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: applying the machine-learned parking quality model. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Regarding claims 7 and 16, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible.
Claims 9 and 18, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: via the network and from the device associated with the picker; displayed on the electronic map; via the electronic map on the user interface. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Accordingly, claims 1-7, 9-16, and 18-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
No Prior Art Applied to Claims 1-7, 9-16, and 18-20
Claims 1, 10, and 19
There is no prior art applied to claims 1, 10, and 19 because the cited prior art fails to disclose or suggest the complete feature set recited in the claims. Adelsberger, considered the closest art, discloses:
(claim 1) A method performed at a computer system comprising a processor and a computer-readable medium, the method for identifying shopping parking locations that reduce congestion and comprising {“The invention discloses a method for computing street parking occupancy for a street segment. [Also], a system for computing street parking occupancy for a street segment. The system comprises a server. The server comprises a memory component configured to store at least image data, map data and historical data. The server further comprises a processing component. The processing component is configured to compute a baseline occupancy module” (paragraph 0039).}
(claim 10) A non-transitory computer-readable storage medium containing instructions that when executed by one or more processors perform actions comprising: {“The invention discloses a system for computing street parking occupancy for a street segment. The system comprises a server. The server comprises a memory component configured to store at least image data, map data and historical data. The server further comprises a processing component. The processing component is configured to compute a baseline occupancy module” (paragraph 0039).}
(claim 19) A computer system comprising: one or more computer processors; and a computer-readable storage medium storing instructions that when executed by the one or more computer processors perform actions comprising: {“The system comprises a server. The server comprises a memory component configured to store at least image data, map data and historical data. The server further comprises a processing component.” (paragraph 0039).}
mapping, using a geographic database, the current geographic location of the device to a retail location by determining a nearest retail location for the current geographic location of the device {Map data is used as a geographic database (paragraph 0158) and current geographic location data is obtained from mobile devices (paragraph 0167). Geographic analysis based on proximity is used, including identification of closest locations relative to a given position (paragraph 0181).}
responsive to determining that the parking is restricted: identifying a plurality of candidate parking locations for the retail location {The system computes forecasted occupancy levels using real time and historical data (paragraph 0033 and 0035). Also, It triggers notifications when predicted parking occupancy exceeds a threshold (paragraph 0042). The system then identifies alternate parking segments for the user (paragraphs 0035 and 0043).}
However, Adelsberger does not disclose or suggest “collecting, via sensors of a device associated with a picker, sensor data including current Global Positioning System (GPS) coordinates of the device receiving, via a network and from the device the sensor data including the current GPS coordinates of the device representing a current geographic location of the device; applying a machine-learned parking prohibition model to information about the current geographic location of the device to identify that parking for the retail location at the current geographic location is restricted”; “applying a machine-learned parking quality model to one or more features for each candidate parking location of the plurality of candidate parking locations including a degree of anticipated traffic at each candidate parking location, a current degree of demand for pickers at the retail location, and a current average picker wait time at the retail location before receiving an order to generate a score for each candidate parking location that quantifies a favorability of each candidate parking location in relation to the retail location”; “ranking, using the score for each candidate parking location, the plurality of candidate parking locations to generate ranked candidate parking locations” or “causing a user interface of the device to display an electronic map with an indication of a geographic location of each of the ranked candidate parking locations, wherein the electronic map is generated using the information about the current geographic location of the device.”
Examiner also considered the following additional references:
Zhao (US 20180349792), which teaches using sensor and probe data altogether with machine learned models and a geographic database to predict and display parking occupancy or availability for road links and parking facilities in navigation and mapping applications.
Niemiec (US 20210033410), which teaches managing and selecting pick-up and drop-off zones for autonomous vehicles by identifying, ranking, and routing vehicles to usable locations based on map data, vehicle capabilities, legal restrictions, and probabilistic availability models.
Stenneth (US 20180150764), which teaches a mapping and navigation system that uses machine learning to automatically determine and label street level parking restrictions for road segments based on geographic and probe data, and to present those restrictions to users in navigation interfaces.
However, neither reference disclose or suggest “a current degree of demand for pickers at the retail location, and a current average picker wait time at the retail location before receiving an order to generate a score for each candidate parking location that quantifies a favorability of each candidate parking location in relation to the retail location”.
Accordingly, there is no prior art applied to claims 1, 10, and 19. Claims 2-7, 9, 11-16, 18, and 20, by virtue of their dependency, also have no prior art applied.
Response to Amendment
Applicant's arguments filed on November 10, 2025 have been fully considered but they are not persuasive.
Rejections under 35 U.S.C. §101
Claim 1 is still directed to an abstract idea, i.e., collecting location data, using machine-learned model to score or rank parking locations, and displaying the results on a map to help a user decide where to park. This is data collection, analysis, and presentation of information.
The additional limitations do not integrate the abstract idea into a practical application. Displaying ranked locations on an electronic map, even in real-time, merely uses generic computer components to show the results of an abstract idea. The claims do not improve how the computer, map, or machine-learning model itself operates.
The machine-or-transformation argument is not persuasive because the claim does not require a particular machine integral to the invention nor a qualifying transformation.
Accordingly, the rejection under 35 USC §101 is maintained.
Rejections under 35 USC §103
Arguments are moot under 35 USC §103 because there is no prior art applied to claims 1-7, 9-16, and 18-20. Accordingly, Examiner directs Applicant’s attention to the analysis above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional pertinent references can be found on attached form PTO-892):
US 20210133665, which teaches: An online concierge system receives an order from a customer. The online concierge system transmits a notification to the customer's client device indicating that the order is ready for pick up and receives location data from the customer's client device as the customer travels to a pickup location. In response to the online concierge system receiving a first indication that the customer has entered an outer geofence, the online concierge system transmits a second notification to a runner's client device that the customer is in transit. In response to the online concierge system receiving a second indication that the customer has entered an inner geofence, the online concierge system starts a timer. When the online system receives a confirmation that the order has been picked up by the customer, it stops the timer and computes a wait time for pick up of the order.
US 20240227785, which teaches: A method that includes obtaining a first image from a first camera disposed on a vehicle and generating a birds-eye view (BEV) image using the first image. The method further includes processing, with a machine-learned model, the BEV image to produce parking slot prediction data. The parking slot prediction data includes a first center coordinate for a first available parking slot, a first parking slot confidence, and a first corner displacement data. The first corner displacement data includes a first relative coordinate pair that locates a first corner relative to the first center coordinate and a second relative coordinate pair that locates a second corner relative to the first center coordinate. The method further includes determining a first location of the first available parking slot using the parking slot prediction data and parking the vehicle in the first available parking slot when the first parking slot confidence meets a threshold.
“Alibaba vehicle routing algorithms enable rapid pick and delivery" (NPL attached), which teaches: Alibaba Group pioneered integrated online and offline retail models to allow customers to place online orders of e-commerce and grocery products at its participating stores or restaurants for rapid delivery—in some cases, in as little as 30 minutes after an order has been placed. To meet these service commitments, quick online routing decisions must be made to optimize order picking routes at warehouses and delivery routes for drivers. The solutions to these routing problems are complicated by stringent service commitments, uncertainties, and complex operations in warehouses with limited space. Alibaba has developed a set of algorithms for vehicle routing problems (VRPs), which include an open-architecture adaptive large neighborhood search to support the solution of variants of routing problems and a deep learning-based approach that trains neural network models offline to generate almost instantaneous solutions online. These algorithms have been implemented to solve VRPs in several Alibaba subsidiaries, have generated more than $50 million in annual financial savings, and are applicable to the broader logistics industry. The success of these algorithms has fermented an inner-source community of operations researchers within Alibaba, boosted the confidence of the company’s executives in operations research, and made operations research one of the core competencies of Alibaba Group.
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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629