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 Claim
Claims 5, 12, and 18 have been amended.
Claims 1-20 are currently pending and are rejected as described below.
Response to Amendment/Argument
35 USC § 112
Applicant’s amendments to claims 5, 12, and 18 are sufficient to overcome the 35 U.S.C. 112. Accordingly, the previous rejection of claims 5, 12, and 18 under 35 U.S.C 112 is withdrawn.
35 USC § 102/103
Applicant asserts that Estes contains no description of receiving or processing records of service provider visits, fuel purchases, maintenance transactions, or similar service-related entries. The examiner respectfully disagrees. Under BRI, “data log entries” are merely pieces of information entered/recorded on a table/database. The independent claims do not define the “data log entries” as records of service provider visits, fuel purchases, or maintenance transactions. “Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment." Superguide. Dependent Claims 3, 10, and 17 don’t necessarily define the term, instead provides a narrower definition of what “computing an alternate data log entry” is comprised of which is taught by the Niu reference in combination with the primary reference Estes. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Applicant asserts that Estes does not describe identifying which specific vehicle is associated with a particular data entry. Knowing that a gig worker "typically drives" a certain vehicle type is categorically different from determining that a specific incoming data log entry corresponds to Vehicle #47 in a fleet database. The examiner respectfully disagrees. Once again, the applicant is importing into a claim limitations that are not part of the claim. The claims do not disclose a fleet, instead the claims disclose a driver of a vehicle and various data associated with the vehicle which under BRI is not confined to the applicant’s disclosure of a fleet. In fact, all art rejection related arguments follow the same fact pattern with applicant importing into a claim limitations that are not part of the claim and the previously disclosed rationale hold true in rebutting applicant’s arguments. The examiner highly encourages the applicant to consider adding these terms into the claim language in order to differentiate it from the prior art references used in the rejection.
35 USC § 101
Applicant asserts that none of these limitations sets forth or describes a mathematical relationship, formula, or equation. The claims do not recite the internal workings of the machine learning model or the optimization algorithm-they recite applying these technological tools to specific data structures to achieve a technical result. As the August 2025 Memorandum explains, citing USPTO Example 39, a limitation such as "training the neural network" does not recite a judicial exception even though neural network training "may involve or rely upon mathematical concepts," because the limitation "does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols". The examiner respectfully disagrees. Computing (e.g. calculate) an alternate data log so an optimization algorithm (e.g. a mathematical method) can be applied to a historical service provider falls within the abstract idea of a mathematical calculation. MPEP 2106.04(a)(2)(C) describes it in the following manner. “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation Therefore, the invention remains an abstract idea under 2A prong I.
Applicant asserts that the claims do not recite managing any relationship or interaction between people. That the ultimate output-a recommendation-may be useful to a human user does not transform technical data processing into "organizing human activity." The examiner respectfully disagrees. Identifying a driver and/or a vehicle associated with said driver and loading information onto a log with a timestamp is a practice that existed long before the advent of computers or the internet and therefore not rooted in computer technology. These activities have been done in the past with communications between dispatcher and driver, regardless if it was done manual, via phone, or with the aid of a smart device.
Although the examiner never branded the claims as a Mental Process, the examiner disagrees with the applicant’s categorization of the human mind. The human mind performs complex tasks and calculations, often in real-time, and is precisely the benchmark for all AI models. The examiner further notes that the many of the applicant’s 101 arguments follow the same fact pattern as the applicant’s art rejection where the claimed language is much broader and often devoid of applicant’s terms found in the specification and used to narrow claim interpretation. While the specification may help illuminate the true focus of a claim, when analyzing patent eligibility, reliance on the specification must always yield to the claim language in identifying that focus." Id. at 766; see also Trinity Info Media, 72 F.4th at 1363 ("Our focus is on the claims, as informed by the specification."). At bottom, we must "articulate what the claims are directed to with enough specificity to ensure the step one inquiry is meaningful." Thales Visionix Inc. v. United States, 850 F.3d 1343, 1347 (Fed. Cir. 2017). Therefore, the invention remains an observation (i.e. a mental process) of determining the recommendation information corresponding to the user identifier based on skills and a mathematical calculation (i.e. a mathematical concept) of determining a scored value via a scoring model merely applied by generic computer components disclosed at a high level of generality and do not satisfy the Alice Test.
Applicant asserts that the specification identifies a specific technical problem and a specific technical solution. The technical problem is that "transactions are not tied to vehicles or drivers. Thus, in a conventional system there is no technical way to automatically associate a given payment transaction with a specific driver or vehicle. Instead, many systems rely pre-programmed, associations or manual input." See Specification at page 5. The claims solve this problem through a system that applies machine learning to resolve vehicle identity from incoming data log entries, correlates those entries with vehicle location logs, and computes alternate data log entries using optimization algorithms applied to historical service provider data. The examiner respectfully disagrees. To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Mere automation of a manual process or claiming the improved speed or efficiency inherent with applying the abstract idea on a computer where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to transform an abstract idea into a patent-eligible invention. See MPEP 2106.04(a); MPEP 2106.05(a); MPEP 2106.05(f); FairWarning IP, LLC v. Iatric Sys., 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Credit Acceptance Corp. v. Westlake Services, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017); Intellectual Ventures I LLC v. Capital One Bank (USA), 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Applicant asserts that Ex parte Desjardins, Appeal 2024-000567 (PTAB Sept. 26, 2025), is instructive. In that case, the Appeals Review Panel vacated a§ 101 rejection of claims directed to training machine learning models. The Panel criticized the original board panel's reasoning as "overbroad" for "essentially equat[ing] any machine learning with an unpatentable 'algorithm' and the remaining additional elements as 'generic computer components,' without adequate explanation." The Panel held that "Examiners and panels should not evaluate claims at such a high level of generality." The Panel found the claims eligible because they reflected improvements identified in the specification-specifically, improvements in how the machine learning model itself operates. The examiner respectfully disagrees. Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. In summary, Desjardins discloses the improvement in the specification and the claim language reflects said improvement which is not what is happening in the claims of the instant application. There is no reflection of the improvement mentioned by the applicant. The instant application as the claims fail to integrate the abstract idea into a practical application. Considered as an ordered combination, the generic computer components of applicant’s claimed invention add nothing that is not already present when the limitations are considered separately. For example, claim 1 does not purport to improve the functioning of the computer components themselves. Nor does it affect an improvement in any other technology or technical field. Instead, claim 1 amounts to nothing significantly more than an instruction to apply the abstract ideas using generic computer components performing routine computer functions. That is not enough to transform an abstract idea into a patent-eligible invention. See Alice, 573 U.S. at 225-26.
Claim Rejections - 35 USC § 101
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machines, article of manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014). See MPEP 2106.03(II).
The claims are then analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
With respect to 2A Prong 1, claim 15 recites “a processor; and a storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising steps for: receiving, by the processor, a data log entry associated with a driver of a vehicle, the data log entry including a service provider location and a timestamp; identifying, by the processor, a vehicle associated the data log entry, wherein identifying the vehicle comprises applying a machine learning model to the data log entry and a vehicle database; loading, by the processor from a location database, a vehicle location log associated with the identified vehicle, the vehicle location log including a plurality of location data points and associated timestamps; computing, by the processor, an alternate data log entry based on the data log entry and the vehicle location log, wherein computing the alternate data log entry comprises applying a rule-based optimization algorithm to a historical service provider database; and transmitting, by the processor to a user device, a recommendation based on the alternate data log entry, wherein the recommendation includes a geospatial visualization of the alternate data log entry”. Claims 1 and 8 disclose similar limitations as Claim 15 as disclosed, and therefore recites an abstract idea. More specifically, claims 1, 8, and 15 are directed to “Mathematical Concept” in particular “mathematical calculations” and Certain Methods of Organizing Human Activity in particular “managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” as discussed in MPEP §2106.04(a)(2), and in the 2019-01-08 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claims recite an abstract idea.
Dependent claims 2-7, 9-15, and 16-20 further recite abstract idea(s) contained within the independent claims, and do not contribute to significant more or enable practical application. Thus, the dependent claims are rejected under 101 based on the same rationale as the independent claims.
Under Prong Two of Step 2A of the Alice/Mayo test, the examiner acknowledges that Claims 1, 8, and 15 recite additional elements yet the additional elements do not integrate the abstract idea into a practical application. In order for the judicial exception to be “integrated into a practical application”, an additional element or a combination of additional elements in the claim “will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” PEG, 84 Fed. Reg. 54 (Jan. 7, 2019). The courts have identified examples in which a judicial exception has not been integrated into a practical application when “an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.” PEG, 84 Fed. Reg. 55 (Jan. 7, 2019); MPEP § 2106.05(h). The claims are directed to an abstract idea.
In particular, claims 1, 8, and 15 recite additional elements boldened and underlined above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Further, the remaining additional element directed at receiving/transmitting data (italicized above) reflects insignificant extra solution activities to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Dependent claims 4-6, 11-13, and 18-20 recite additional elements “dashboard user interface”, “telematics device”, and “neural network”. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
With respect to step 2B, claims 1, 4-6, 8, 11-13, 15, and 18-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claim recites the additional elements described above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process, as evidenced by at least ¶163 “the CPU 1202 may comprise a general-purpose CPU. The CPU 1202 may comprise a single-core or multiple-core CPU. The CPU 1202 may comprise a system-on-a-chip (SoC) or a similar embedded system. In some embodiments, a graphics processing unit (GPU) may be used in place of, or in combination with, a CPU 1202. Memory 1204 may comprise a memory system including a dynamic random-access memory (DRAM), static random-access memory (SRAM), Flash (e.g., NAND Flash), or combinations thereof. In one embodiment, the bus 1214 may comprise a Peripheral Component Interconnect Express (PCIe) bus. In some embodiments, the bus 1214 may comprise multiple busses instead of a single bus”.
As a result, claims 1, 4-6, 8, 11-13, 15, and 18-20 do not include additional elements, when recited alone or in combination, that amount to significantly more than the above-identified judicial exception (the abstract idea). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Claims2-3, 7, 9-10, 14, and 16-17 do not disclose additional elements, further narrowing the abstract ideas of the independent claims and thus not practically integrated under prong 2A as part of a practical application or under 2B not significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Claim Rejections - 35 USC § 102
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, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 102 as being anticipated by US 11928738 to Estes et. al. (hereinafter referred to as “Estes”).
(A) As per Claims 1, 8, and 15:
Estes expressly discloses:
receiving, by a processor, a data log entry associated with a driver of a vehicle, the data log entry including a service provider location and a timestamp; (Estes Col. 33 Lines 47-56 in any event, as data 702 is received (block 706), the method 700 continues by processing the incoming data 702 for storage in a database 708 (block 710), which database 708 may comprise one or more databases 46. In one example, for each person, data is stored in the database 708 as a sequence of entries, where each entry includes (i) an identifier for a traveled segment, intersection, path, location, etc., (ii) a start time, (iii) an end time, (iv) a type (e.g., gig or non-gig) for the entry, if known, etc., and/or (v) an identifier for the gig-economy worker associated with the entry).
identifying, by the processor, a vehicle associated the data log entry…; (Estes Col. 32-33 Lines 64-1 further example behavioral data 702C includes, but is not limited to, the speed at which the gig-economy worker typically drives when performing gig-related activities; the type of vehicle that the gig-economy worker typically drives during gig-related or non-gig-related activities).
…wherein identifying the vehicle comprises applying a machine learning model to the data log entry and a vehicle database; (Estes Col. Lines , the risk model may utilize machine learning to adaptively determine risk scores for each driving behavior. Generally, machine learning may involve identifying and recognizing patterns in existing data (such as autonomous vehicle system, feature, or sensor data; autonomous vehicle system control signal data; vehicle-mounted sensor data; mobile device sensor data; and/or telematics, image, or radar data) in order to facilitate making predictions for subsequent data).
loading, by the processor from a location database, a vehicle location log associated with the identified vehicle, the vehicle location log including a plurality of location data points and associated timestamps; computing, by the processor, an alternate data log entry based on the data log entry and the vehicle location log, wherein computing the alternate data log entry comprises applying a rule-based optimization algorithm to a historical service provider database; (Estes Col. 10 Lines 16-26 the digital map server 43 may store geocoded map data regarding locations and transit pathways through geographic areas, which map data may be used to determine effective distances or travel times between locations, as well as for determining optimal or alternative routes. The digital map server 43 may store geocoded map data regarding locations and transit pathways through geographic areas, which map data may be used to determine effective distances or travel times between locations, as well as for determining optimal or alternative routes. In some embodiments, the digital map server 43 may provide real-time or historical traffic data relating to one or more route segments within the geographic area, which may include indications of traffic congestion, traffic flow, construction, lane closures, road closures, accidents, blockages, or other traffic-related conditions).
transmitting, by the processor to a user device, a recommendation based on the alternate data log entry, wherein the recommendation includes a geospatial visualization of the alternate data log entry; (Estes Cols. 12, 16 Lines 46-50, 13-18 such gig work management application 236 may be configured to generate or present gig optimization recommendations or other gig optimization data to a user of the mobile computing device 110 (e.g., a gig-economy worker 17). Such gig-related information may include timing instructions or predictions, recommended travel routes, or delivery instructions. In some embodiments, the server 40 may provide sequential gig-related information at a plurality of stages of the gig, such as directions to a pick-up location followed by directions to a drop-off location).
(B) As per Claims 2, 9, and 16:
Estes expressly discloses:
comparing the timestamp of the data log entry with a plurality of predefined driving periods associated with the driver; determining, based on the comparison, a driving period that encompasses the timestamp of the data log entry, wherein the driving period is associated with the identified vehicle; (Estes Cols. 33-34 Lines 47-56, 10-15 in any event, as data 702 is received (block 706), the method 700 continues by processing the incoming data 702 for storage in a database 708 (block 710), which database 708 may comprise one or more databases 46. In one example, for each person, data is stored in the database 708 as a sequence of entries, where each entry includes (i) an identifier for a traveled segment, intersection, path, location, etc., (ii) a start time, (iii) an end time, (iv) a type (e.g., gig or non-gig) for the entry, if known, etc., and/or (v) an identifier for the gig-economy worker associated with the entry. In some examples, the gig or non-gig type of activity (or a likelihood of gig or non-gig activity) is determined using the behavioral data 702C. The classifier 53 utilizes, applies, or implements a machine-learning model 54 to process data stored in the database 708 to determine (e.g., estimate, generate, calculate, etc.) likelihoods that movements are gig-related. The machine-learning model 54 may utilize deep learning algorithms that are primarily focused on, for example, pattern recognition, and may be trained by processing example data).
(C) As per Claims 4, 11, and 20:
Estes expressly discloses:
wherein the geospatial visualization of the alternate data log entry is displayed within a dashboard user interface, the dashboard user interface displaying a plurality of service providers and associated data log entries; (Estes Col. 43 Lines 8-18 the representation may include a visual presentation of the gig optimization data outputs relative to input values, such as a tabular presentation of predicted profit for each a plurality of hours or a heat map showing the predicted profitability or waiting time for gigs at various locations within a geographic area. In some embodiments, a dashboard of gig optimization data may be presented to the user to make multiple decisions, such as a gig-economy platform to use and a location at which to offer gig-economy services).
(D) As per Claims 5, 12, and 18:
Estes expressly discloses:
receiving, from a telematics device associated with the identified vehicle, real-time vehicle data including at least one of a fuel level, a location, or a driver behavior metric; (Estes Col. 19 Lines 3-6 the set of data may facilitate mood detection in real-time or building an average mood profile for the gig-economy worker. The mood of a gig-economy worker may impact driving behavior).
predicting, using a second machine learning model, a future transaction based on the real-time vehicle data and the data log entry; (Estes Col. 34 Lines 33-37 machine-learning may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data 702.)
transmitting, to the user device, a proactive recommendation based on the predicted future transaction; (Estes Col. 43 Lines 52-59 the user input may be used to generate updated gig optimization data according to the previously identified gig-economy data models or additional gig-economy data models. In some embodiments, the user input may be used to generate one or more gig optimization recommendations for the user, which may be the same as or differ from previously generated gig optimization recommendations).
(E) As per Claims 6, 13, and 19:
Estes expressly discloses:
generating a message using a neural network, the message including a personalized feedback based on the alternate data log entry; transmitting the message to a messaging application installed on the user device; (Estes Cols. 55-56 Lines 63-48 the message provider device 1216 may transmit targeted recommendation data to the electronic device 1214 and/or external processing server 1215 through the network 1218. The recommendations are also more relevant than the static, general messages on conventional display devices because they are dynamically updated and targeted for the gig-economy worker and/or customer, and can be based upon travel routes extracted from the gig-economy applications).
(F) As per Claims 7, and 14:
Estes expressly discloses:
identifying, based on the data log entry and the vehicle location log, a driver behavior pattern associated with the driver; (Estes Col. 16 Lines 32-37 the insurance provider may use telematics and/or other data to monitor or identify driving behaviors of a gig-economy worker (e.g., gig-economy worker 17) during a gig).
comparing the driver behavior pattern with a plurality of historical driver behavior patterns associated with a plurality of drivers; (Estes Col. 20 Lines 57-62 the risk model may include pre-determined risk values for each driving behavior, the model may compare each driving behavior to a set of known driving behaviors of other gig-economy workers, and/or the model may utilize machine learning to adaptively determine risk scores for each driving behavior).
generating, based on the comparison, a driver-specific incentive to modify the driver behavior pattern; (Estes Col. 30 Lines 40-43 the method 600 may facilitate better driving habits, and the gig-economy worker's subsequent gig performance and overall profitability may be improved, by linking good driving behavior with monetary incentives).
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 3, 10, and 17 are rejected under 35 U.S.C. 103 as being obvious by the combination of US 11928738 to Estes et. al. (hereinafter referred to as “Estes”) in view of US 20250181593 to Niu et. al. (hereinafter referred to as “Niu”) in further view of US 20240420045 to Garg et. al. (hereinafter referred to as “Garg”) and in even further view of US 20220084155 to Frederick et. al. (hereinafter referred to as “Frederick”).
(A) As per Claims 3, 10, and 17:
Although Estes teaches systems and methods relating to improving the experience of gig-economy workers, it doesn’t expressly disclose identifying alternate service providers within a radius, however Niu teaches:
identifying, based on the vehicle location log, a plurality of alternate service providers within a predefined radius of the service provider location; (Niu ¶52 the server 100 may communicate with the service provider device 170 to check the service provider's availability. In some embodiments, the server 100 may communicate with the service provider device 170 to aggregate information including, but not limited to, a location of the service provider, a type of services available, estimated fees, and other information, in order to produce the list of service providers within the delivery radius).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Estes’ data gathering including the identification of type of vehicle that the gig-economy worker typically drives during gig-related activities and communicate with the service provider device to aggregate information including, but not limited to, a location of the service provider, a type of services available, estimated fees of Niu as both are analogous art which teach solutions to having the digital map server store geocoded map data regarding locations and transit pathways through geographic areas as taught in Estes and produce the list of service providers within the delivery radius as taught in Niu.
Although Estes in view of Niu teaches systems and methods relating to improving the experience of gig-economy workers, it doesn’t expressly disclose filtering alternate service based on vehicle type, however Garg teaches:
filtering the plurality of alternate service providers based on at least one of a fuel type, a vehicle type, or a driver preference; (Garg ¶36 user interface 200 may also allow a provider/administrator/requestor user to select a desired vehicle type such as a bus, a van, a short bus, a city bus, a truck, or any other type of fleet vehicle for the particular route).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Estes in view of Niu’s data gathering including the identification of type of vehicle that the gig-economy worker typically drives during gig-related activities and have user interface also allow a provider/administrator/requestor user to select a desired vehicle type of Garg as both are analogous art which teach solutions to having the digital map server store geocoded map data regarding locations and transit pathways through geographic areas as taught in Estes in view of Niu and have a bus, a van, a short bus, a city bus, a truck, or any other type of fleet vehicle for the particular route as taught in Garg.
Although Estes in view of Niu and in further view of Garg teaches systems and methods relating to improving the experience of gig-economy workers, it doesn’t expressly disclose selecting alternate service provider based on cost optimization, however Frederick teaches:
selecting an alternate service provider from the filtered plurality of alternate service providers based on a cost optimization algorithm; (Frederick ¶24, 49 the transportation matching system can then analyze the set of requestor devices to generate a transportation group. For example, the transportation matching system can apply a cost function to a set of requestor devices to generate one or more transportation groups. More specifically, in some embodiments, the transportation matching system applies the cost function to identify transportation groups that minimize a cost metric subject to a constraint of satisfying each arrival deadline within the transportation group. the term “cost function” can include a computer-implemented algorithm that assigns costs to characteristics corresponding to a transportation group and selects requestor devices to join the transportation group based on the cost metrics (e.g., to minimize the cost metrics)).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Estes in view of Niu and in further view of Garg’s data gathering including the identification of type of vehicle that the gig-economy worker typically drives during gig-related activities and have the transportation matching system analyze the set of requestor devices to generate a transportation group of Frederick as both are analogous art which teach solutions to having the digital map server store geocoded map data regarding locations and transit pathways through geographic areas as taught in Estes in view of Niu and in further view of Garg and have the transportation matching system applies the cost function to identify transportation groups that minimize a cost metric subject to a constraint of satisfying each arrival deadline within the transportation groupas taught in Frederick.
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 extension fee 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 MATHEUS R STIVALETTI whose telephone number is (571)272-5758. The examiner can normally be reached on M-F 8:30-5:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao (Rob) Wu can be reached on (571)272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1822.
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/MATHEUS RIBEIRO STIVALETTI/Primary Examiner, Art Unit 3623 02/13/2026