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
Applicant’s communications filed on 11/26/2025 have been considered.
Claim 1 has been amended.
Claims 12-20 have previously been canceled.
Claims 1-11 are currently pending and have been examined.
Response to Arguments
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues that the amended claims provide a technical improvement to a technical problem by “enabl[ing] the machine learning model to produce ‘calculated order wait times for each of the candidate stores’… improv[ing] computer functionality by enabling predictive analysis of fulfillment capacity across networked locations… this represents an enhancement to how computers process and route orders through networks (Remarks Pages 5 and 6). The specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.04(d)(1). The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art, and conversely, if the specification explicitly sets forth an improvement but in a conclusory manner the examiner should not determine the claim improves technology. In the instant case, Applicant’s specification provides no explanation of an improvement to the functioning of a computer or other technology. Rather, the claims focus “on a process that qualifies as an ‘abstract idea’ for which computers are merely invoked as a tool”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016) citing Enfish at 1327, 1336. This is reflected in paragraphs [0001][0009] of Applicant’s specification, which identify problems including customers facing increasingly unreasonable wait times for an order as a result of an increase in online ordering and staffing shortages, particularly during rush hour, as well as paragraph [0011], which describes improvements such as load balancing online orders and in-store orders for selecting an alternative store with wait times that are acceptable to a consumer, and selecting an optimal store for placing an order based on a calculated estimated wait time to fulfill the order and based on a current location of the customer, associated with the least amount of time for order fulfillment. The statements in the specification merely represent conclusory statements, as they do not provide any detail regarding how the claimed invention is providing any improvement to the functioning of the computer or other technology. For example, the improvements regarding reduced wait time for a customer are not technological improvements. Although the claims include computer technology such as online order counts for online orders; an online order, the online order placed through an online ordering application; providing input to a machine-learning model (MLM); and receiving output from the MLM; training the MLM; obtaining images from in-store cameras; and obtaining the current online order counts from an online order system, such elements are merely peripherally incorporated in order to implement the abstract idea. This is unlike the improvements recognized by the Courts in cases such as Enfish and McRO. Unlike these precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The claimed process, while arguably resulting in improved wait times for customers, is not providing any improvement to the online ordering application, machine-learning model, or in-store cameras. While these additional elements are used, it is the reduced wait times and reduced abandonment rates, for example, that provide the improvement (see at least specification [0001-0002][0009-0011]). Additionally, there is no improvement to obtaining the current online order counts from an online order system. It is further noted that improving the type of analysis that is being performed, such as predictive analysis, does not necessarily improve the functioning of the computer itself. The claimed process is utilizing known online ordering systems, machine-learning methods, and in-store cameras to improve the ordering experience for a customer. As such, the claims do not recite technological improvements.
Applicant argues that the amended claims represent a technical improvement in that the claims align with the USPTO’s Examples 47 and 48 (Remarks Pages 6 and 7). This argument has been considered but is not persuasive. The subject matter eligibility examples are hypothetical and only intended to be illustrative of the claim analysis under the MPEP. These examples are to be interpreted based on the fact patterns set forth in each example, as other fact patterns may have different eligibility outcomes. Claim 1 of Example 47 was determined to be eligible because they did not recite any of the abstract ideas enumerated in the MPEP. However, as emphasized via bolding in the 101 rejection of the Office Action, below, the present claims recite a number of limitations that are directed to the “Certain Methods of Organizing Human Activity” subgrouping of abstract ideas, including determining current queue counts for in-store customers waiting to place in-store orders at the stores; determining current order counts for orders at the stores; receiving an order; filtering the stores to obtain candidate stores; and determining a particular store from the candidate stores to process the order based on the current queue counts and the current order counts for the candidate stores. This is unlike Example 47, where no abstract ideas were identified in the claim, despite the claim being directed towards an artificial neural network (ANN).
With regards to Example 48, the claims provided a technical improvement by reciting steps directed to creating a new speech signal that no longer contains extraneous speech signals from unwanted sources, which reflected the technical improvement described in the disclosure. The instant claims provide no analogous technical solution. As discussed above, Applicant’s specification does not describe an improvement to any of the claimed additional elements, such that one of ordinary skill in the art would recognize a technical improvement, and accordingly the claims do not reflect an improvement, as in Example 48. Accordingly, the rejection has been maintained.
Applicant argues that the claims are not merely directed to an abstract idea, but to “a specific technological solution for improving computer-network based order processing systems” (Remarks Page 7). This argument has been fully considered but is not persuasive. As discussed above, the additional elements of the instant claims do not recite an improvement to a computer or other technology, but rather amount to mere implementation of the abstract idea in a technological environment, and accordingly are insufficient to integrate the abstract idea into a practical application. Accordingly, the claims recite an abstract idea, and not a technological solution.
Applicant further argues that the amended claims represent an improvement to computer functionality by the claimed MLM “calculating predicted fulfillment times across multiple networked locations using synchronized visual and order data. The improvement is evident in how the system operates… synchronized data collection and processing improves computer system functionality by enabling real-time predictive analysis that was not previously available” (Remarks Page 7). This argument has been fully considered but is not persuasive. It is noted that a new function is not necessarily a technical improvement, if it implements the abstract idea through the use of generic computing components. Paragraph [0027] of the specification describes the operation of balancer 133, including obtaining online order counts from the order system and images processed by MLM at the same intervals of time, and storing records in a database for each store and each interval of time. This database mapping may be used to filter candidate stores based on calculated wait times. It is noted that this portion of the specification does not identify a specific improvement to the claimed computer or other technology, and accordingly the claim amounts to using generic computing components (online ordering, machine learning, in-store cameras) to perform a known activity (order routing). For example, this portion of the specification discloses obtaining information (online order counts and images), processing information (via machine learning), storing information (in a database), and performing a filtering process of candidate stores according to the stored information, but does not identify how any of the claimed computing components has been improved. While the claimed machine learning model performs the business function of providing calculated order wait times for candidate stores corresponding to estimated times for each of the candidate stores to fulfill an order, there is no improvement to the machine learning model itself. Accordingly, the claims do not recite a technical improvement.
Applicant further argues that the claims recite a process implemented with a particular machine integral to the claimed invention, in a manner “analogous to the eligible claims in Core Wireless, where… claims improving ‘the efficiency of using the electronic device’ by displaying application data in a particular manner constituted patent-eligible improvements” (Remarks Pages 7 and 8). This argument has been considered but is not persuasive. With regards to Core Wireless, the claims were directed to an improved user interface that displays an application summary of launched applications, where the particular data in the summary is selectable by a user to launch the respective application. The instant claims provide no such analogous improvement. While the claims in Core Wireless represented an improvement in that they pertained to a particular manner of summarizing and presenting information within electronic devices, Applicant’s disclosure focuses on order processing and routing, an abstract idea, which is implemented in a generic computing environment, without effectuating a change or improvement to a computer or other technology. It is further noted that, while the application of a judicial exception by or with a particular machine is an important clue, it is not a stand-alone test for eligibility. MPEP 2106.05. As discussed above, it has been determined that the amended claims amount to mere instructions to apply an exception, and accordingly the claims do not recite a particular machine.
Applicant argues that the claims apply the judicial exception in a meaningful way beyond generic computer implementation because “the claims apply machine learning in a specific, meaningful way… [representing] a specific technological solution that improves how computer networks process orders – similar to the eligible claims in SRI International” (Remarks Page 8). This argument has been considered but is not persuasive. The cited portion of Applicant’s specification (see at least [0027]) discloses that the balancer may use a second MLM or heuristics to obtain an estimated order wait time for the current order for each store in the candidate records. While this portion of the disclosure addresses the MLM providing the estimated order wait times to the balancer, the estimation and provision of said wait times via the MLM amounts to mere implementation of the abstract idea in a particular technological environment. It is further noted that the disclosure of using the second MLM or heuristics to obtain the estimated wait times indicates that the second MLM is not required to obtain said estimated wait times.
With regards to SRI Int’l, claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology, as they recited limitations that could not be performed in the human mind. In the instant case, the independent claims recite the claimed machine learning model as receiving as input current queue counts and current online order counts, outputting a particular store identifier, and outputting calculated order wait times for each of the candidate stores corresponding to estimated times for each of the candidate stores to fulfill the online order. As discussed above, these functions amount to activities falling under the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, in that they recite analysis of queues in a store for the purpose of selecting a candidate store to process an order. Accordingly, the machine learning elements implemented in the claims do not meaningfully limit the abstract idea because they merely link the user of the abstract idea to a particular technological environment (i.e., “implementation via computers”).
Applicant further argues “the combination of claim elements… is not well-understood, routine, or conventional activity in the field of order processing” (Remarks Page 8). This argument has been considered but is not persuasive. It is noted that the 35 USC 101 rejection filed in the previous Non-Final Rejection (8/27/2025) did not make the statement that the combination of claimed elements of the independent claims amount to well-understood, routine, or conventional activity, and accordingly Applicant’s arguments regarding the claim limitations not being well-understood, routine or conventional is not applicable. Rather, the 101 rejection was made based on the claims amounting to mere instructions to implement the abstract idea in a particular computing environment (see Non-Final Rejection, Pages [4-8] and [29-31]). Accordingly, the claims do not provide an inventive concept, and the rejection has been maintained.
With regards to Applicant’s argument that “Like Example 47, these claims recite specific steps for using machine learning output… to solve a technical problem” (Remarks Page 9), this argument has been considered but is not persuasive. As discussed above, the technical improvement in the claims of Example 47 is not analogous to the instant claims, in that the limitations of the instant claims have been identified as reciting abstract ideas falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the rejection has been maintained.
With regards to Applicant’s arguments that the claims improve computer functionality, with reference to Enfish LLC v. Microsoft Corp., McRO and Inc. v. Bandai Namco Games (Remarks Page 9), these arguments have been considered but are not persuasive. As discussed above, the improvements provided by the amended claims do not amount to technical improvements, unlike the improvements recognized by the Courts in cases such as Enfish and McRO. Unlike these precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities, but rather amount to an abstract improvement through reduced wait times and reduced abandonment rates (see at least specification ([0001][0009-0011]), which are activities falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the rejection has been maintained.
With regards to Applicant’s argument that the claims resemble the eligible claims in Amdocs (Israel) v. Openet Telecom, Inc. (Remarks Page 9), this argument has been considered but is not persuasive. Amdocs was directed to an inventive concept in the form of an unconventional technological solution (enhancing data in a distributed fashion) to a technological problem (massive record flows which previously required massive databases), wherein the claim’s enhancing limitation necessarily requires that generic components operate in an unconventional manner to achieve an improvement in computer functionality. As discussed above, the claims recite an abstract idea implemented in a particular computing environment, without effectuating any change or improvement to the claimed technology. Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include adding the words “apply it” (or an equivalent) with the judicial exception or mere instructions to implement an abstract idea on a computer. MPEP 2106.05. Accordingly, the instant claims are not analogous to those in Amdocs, in that the instant claims do not provide a technical solution to a technical problem, as the claims in Amdocs did. Accordingly, the rejection has been maintained.
Applicant further argues that the previous Non-Final Office Action misapprehends the specifications technical disclosure, and that the specification provides technical detail about how the system improves computer technology (Remarks Pages 9 and 10). This argument has been considered but is not persuasive. The cited portions of the specification recite technological implementations, but outside of implementing the claimed abstract idea via said technological implementations, the claims do not reflect a change or improvement to a computer or other technology. For example, Applicant’s specification [0033] describes that the claimed in-store cameras are configured to capture video feed snapshots of customer queues from stores, and transmit them at preconfigured intervals of time. While the obtaining of visual queue data is implemented via technology (in-store cameras), this portion of the specification does not provide any technical detail regarding how the claimed invention improves the functioning of said computer or other technology. Paragraph [0027] of the specification further discloses that the MLM is trained to receive images of the queues and produce, as output, a current customer queue count for each store, and balancer subsequently obtains the online order count and customer queue counts, stores these records in a database, and uses a second MLM or heuristics to obtain an estimated order wait time for the current order for each store. In this case, merely inputting information into a model and receiving information as output, and storing information in a database do not amount to technical improvements. This portion of the specification does not provide any detail regarding how the claimed invention is providing any improvement to the functioning of the computer/other technology. Paragraph [0025] of the specification describes functions of the MLM in obtaining input features, and outputting a sorted list including calculated order wait times for a customer’s order. Similar to the above limitations, and as discussed above, calculating an estimated order wait time for a store to fulfill an order amounts to abstract activity, and this portion of the disclosure amounts to mere implementation of abstract activity via a particular computing environment. Specifically, producing calculated wait times for an order, producing a sorted list according to the calculations, and sending the list to an order system, such that a user can select a desired store with which to place an order amount to, does not represent an improvement to the functioning of a computer. Paragraph [0028] of the specification describes the balancer selecting a candidate store according to the shortest calculated order wait time, and routing the order to an order manager of the store. Similar to the above cited portions of the Spec, paragraph [0028] further identifies functions of the MLM/balancer architecture, which amount to abstract activity (selecting a candidate store based on order wait times), but does not provide sufficient technical detail such that one of ordinary skill in the art would determine that a technical improvement is apparent. Accordingly, the specification’s disclosures at paragraphs [0025][0027-0028][0033] further amount to instructions for implementing the abstract idea in a particular technological environment. The rejection has been maintained.
With regards to Applicant’s argument that the Examiner’s analysis conflates the field of application with the nature of the technological improvement, as the claims improve how computers process orders (Remarks Pages 10 and 11), this argument has been considered and is not persuasive. The rejection does not merely rely on identification of the field of use of the claimed invention. Rather, the amended claims are directed to the abstract idea of distributing/routing and processing orders, implemented using generic computing components, including online orders, an online ordering application, a machine-learning model (MLM), training the MLM, and in-store cameras. While the claims are implemented utilizing computer components, limiting an abstract idea to a particular technological environment does not render the claim eligible. Accordingly, the rejection has been maintained.
Applicant argues that the claims do not recite an abstract idea because “the claims recite… [a] combination of claim elements [that] is unconventional in the field of order processing” (Remarks Page 11). This argument has been considered and is not persuasive. While Applicant’s arguments characterize the claim limitations as unconventional, the specification ([0033]) describes obtaining and processing video feed snapshots from in-store cameras via a machine learning model in order to receive a store identifier and customer queue count for the corresponding store as output, as well as receiving online current online order counts for the stores, and storing both types of information in a cloud data store. This portion of the specification focuses on the input/output aspects of utilizing the machine learning model, rather than technical implementation details behind obtaining the data or machine learning processing that are evident of a technical improvement. The abstract idea is further implemented in a particular technological environment via the subsequent storage of the obtained information in a cloud data store. It is further noted that “coordinated, time-synchronized data collection from multiple sources” at preconfigured intervals of time does not necessarily indicate an improvement to technology, as it describes how data is gathered for analysis, rather than how a computer or other technology is being improved. The claims do not recite an unconventional combination of claim elements in the field of order processing, but rather amount to implementation of an abstract idea (distributing/routing and processing orders) in a particular computing environment. Accordingly, the claims do not recite an inventive concept, and the rejection has been maintained.
Applicant argues that the claims recite a specific machine learning application that improves technology “by enabling predictive analysis capabilities not present in conventional ordering processing systems… this is analogous to the inventive concept recognized in BASCOM” (Remarks Page 11). This argument has been considered and is not persuasive. With regards to BASCOM, the non-conventional and non-generic technological arrangement of elements amounted to a technical improvement. In the instant case, rather than reciting a specific technological arrangement, the claims recite abstract steps performed using generic computing components. As discussed above, the amended claims amount to mere implementation of the abstract idea in a particular technological environment, and accordingly are insufficient to amount to significantly more than the abstract idea. It is further noted that improving the type of analysis performed, such as predictive analysis, does not necessarily improve the functioning of the computer itself. Furthermore, as discussed above, paragraph [0027] discusses the machine learning aspect of the claimed invention, but does not provide a sufficient technical description such that one of ordinary skill in the art would recognize and improvement to technology. Accordingly, the rejection has been maintained.
Applicant further argues that the combination of claimed elements “works together to improve computer network functionality in a manner not achievable through conventional implementations… similar to the inventive concept found in Ancora Technologies v. HTC America, where the Federal Circuit recognized that using ‘a particular method that improved the functionality of the computer’ constituted an inventive concept” (Remarks Page 12). This argument has been considered but is not persuasive. The claims of Ancora indicated an improvement in computer functionality in that they recited specific method of restricting software operation within a license, “improving security against a computer’s unauthorized use of a program”, and addressing the technical problem of vulnerability of license-authorization software to hacking. The claims achieved this by storing a license verification structure in a BIOS memory, as opposed to a typical memory, in order to prevent unauthorized software use. See Ancora. On the other hand, as discussed above, the instant claims do not recite an analogous technological improvement to a computer or network functionality, but rather amount to mere implementation of an abstract idea in a particular technological environment. Applicant’s cited improvements, including reduced wait time and reduced abandonment rates for customers (see at least Specification [0001][0009-0011]), do not amount to technical improvements, as these activities fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and implement said abstract idea using a particular technological environment. The cited portions of Applicant’s specification do not provide sufficient technical detail such that one of ordinary skill in the art would recognize a technical improvement analogous to the improvement addressed in Ancora. Accordingly, the rejection has been maintained.
With regards to Applicant’s argument that the claims share characteristics with the Office’s Example 34 in that “[the] claims recite a non-conventional combination of synchronized data collection, machine learning training, and calculated wait time output for routing decisions” (Remarks Page 12). This argument has been fully considered but is not persuasive. The subject matter eligibility examples are hypothetical and only intended to be illustrative of the claim analysis under the MPEP. These examples are to be interpreted based on the fact patterns set forth in each example, as other fact patterns may have different eligibility outcomes. The claims in Example 34 were determined as amounting to significantly more than the judicial exception because they recited a combination of limitations resulting in an improvement in the technology of filtering content on the Internet, in that they offered “the benefits of a filter on the local computer, and the benefits of a filter on the ISP server”. These limitations recited “a technology-based solution” of filtering content on the Internet that overcame the advantages of prior art filtering systems. On the other hand, as discussed above, the claimed combination does not improve computer functionality, but instead uses a computer as a tool to implement the abstract idea. It is further noted that combining different steps regarding data processing does not necessarily qualify the claims as non-conventional, particularly in view of the instant claims amounting to mere implementation of an abstract idea using generic computing components. Accordingly, the rejection has been maintained.
With regards to Applicant’s argument that the claims are analogous to those in Data Engine Technologies LLC v. Google LLC, since the claims recite “a specific method of calculating wait times and routing orders that improves computer network functionality” (Remarks Page 12), this argument has been considered but is not persuasive. The claims in Data Engine Technologies amounted to an improvement in computer functionality because they recited a specific improvement to technology, specifically an interface and implementation for navigating/organizing complex three-dimensional spreadsheets using techniques unique to computers. However, the instant claims provide no analogous improvement to interface navigation technology. Rather, the claims use a computer as a tool to implement the abstract idea, which is insufficient to amount to a technical improvement. Furthermore, the improvement addressed by the claims is not technical in nature, but improves the claimed abstract process, as discussed above. Accordingly, the rejection has been maintained.
Applicant further argues “the claims do not need to improve machine learning technology itself to be patent-eligible… the claims improve order processing technology… by applying machine learning in an unconventional manner” with reference to McRO (Remarks Page 13). This argument has been considered but is not persuasive. While an improvement to machine learning is not explicitly required, the claims must recite an improvement to computer functionality or another technology, rather than implementing the abstract idea in a particular computing environment, with no change or improvement to the claimed technology. MPEP 2106.05. With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. On the other hand, the instant case, the claims recite an improved business outcome (reduced wait times) rather than improving the functioning of the computer or other technology. It is further noted that “improving order processing technology” amounts to a conclusory statement, as the claims implement the abstract idea in a generic computing environment, and the specification does not describe a technical improvement, as discussed above. With regards to the application of machine learning in the claims, receiving information (current queue counts and current online order counts) as input, receiving output (calculated order wait times for each of the candidate stores; a particular store identifier for the particular store), and training the model (to select a particular store that is able to fulfill an online order in a shortest amount of time) does not amount to applying machine learning in an unconventional manner, but rather to a generic implementation of the abstract idea in a computing environment. Accordingly, the rejection has been maintained.
Applicant further argues “the claims… recite specifical technological mechanisms for achieving the improvement… distinguishable from cases like Electric Power Group v. Alstom S.A., the claims improve computer functionality by enabling [activities] not previously available in order processing systems” (Remarks Page 13). With regards to Electric Power Group, the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis merely indicated a technological environment in which to apply a judicial exception. As discussed above, the claimed process does not provide an improvement to the claimed technology, including the online ordering application, machine-learning model, or in-store cameras, but rather utilizes the claimed additional elements to implement the abstract idea of distributing/routing and fulfilling customer orders. While these additional elements are utilized, it is the reduced wait times and reduced abandonments that provide the improvement (see at least Specification [0001-0002][0009-0011]). Furthermore, regardless of whether or not such capabilities were not previously available, this would not in itself provide a technical improvement, as neither the claims nor the specification describe an improvement to the functioning of a computer or other technology. Accordingly, the claims do not amount to an inventive concept, and the rejection has been maintained.
Applicant argues that the Examiner does not provide a proper claim-by-claim analysis of dependent claims 2-11 (Remarks Pages 13 and 14). This argument has been considered but is not persuasive. With respect to the additional elements recited in the dependent claims, an analysis can be found on [Pages 7 and 8] of the Non-Final Rejection, filed 8/27/2025, where it is discussed that the dependent claims further define the abstract idea noted in independent claim 1, and recite additional limitations that amount to no more than instructions to apply the judicial exception in a generic technological environment.
Applicant argues that Claim 2 “adds a specific technological function that completes the improved order processing system by automating the routing action based on the calculated wait times” (Remarks Page 13). This argument has been considered and is not persuasive. Automating an action using a computer amounts to mere implementation using generic computing components in order to perform the abstract idea (order distribution/routing). The claims represent an abstract improvement rather than a technical improvement. Accordingly, claim 2 does not affect the 101 analysis of independent claim 1, and the rejection has been maintained.
Applicant further argues that Claim 3 “adds elements related to data storage… specif[ying] how the system maintained synchronized data for processing” (Remarks Page 14). This argument has been considered and is not persuasive. The courts have found that collecting data and storing data in a memory” amounts to an abstract idea because “data collection and storage is undisputedly well-known”. See BASCOM Global Internet v. AT&T Mobility LLC, citing Content Extraction, 776 F.3d at 1347. Claiming mere data storage in a data store amounts to implementing the abstract idea in a particular computing environment, without effectuating any change or improvement to the claimed technology. Accordingly, claim 3 does not affect the 101 analysis of independent claim 1, and the rejection has been maintained.
Applicant further argues that claims 4-11 further specify how the improved system operations (Remarks Page 14). This argument has been considered but is not persuasive. In light of the 101 rejection to independent 1 being maintained due to the claims utilizing the claimed additional elements as a tool to perform the abstract idea, as discussed in the above paragraphs, merely specifying how the system operates is not sufficient to integrate the judicial exception into a practical application or provide an inventive concept. As discussed in the Non-Final Rejection, the additional elements of claims 4-11 do amount to no more than instructions to apply the judicial exception in a generic technological environment. Accordingly, the rejection has been maintained.
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 103 have been fully considered and are persuasive.
Applicant’s amendments to independent claim 1, as well as Applicant’s Remarks (see Remarks Page 15), have been considered and are persuasive. Independent claim 1 has been indicated as subject matter free of the cited prior art, and would be allowable if amended to overcome the 101 rejection set forth in this Office Action, above. Accordingly, the previously filed 103 rejection of claims 1-11 has been withdrawn.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. See MPEP 2106.03. Claims 1-11 are directed towards a process. Therefore, claims 1-11 are directed to one of the four statutory categories (Step 1: YES, regarding claims 1-11).
Under Step 2A of the MPEP, it is determined whether the claims are directed to a judicially recognized exception. See MPEP 2106.04. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception.
Taking Claim 1 as representative, claim 1 recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
A method, comprising:
determining, from images captured of customer queues in stores, current queue counts for in-store customers waiting to place in-store orders at the stores;
determining current order counts for unfulfilled orders at the stores;
receiving an order from a customer;
filtering the stores to obtain candidate stores based on a threshold distance between a current location of the customer and known locations of the stores; and
determining a particular store from the candidate stores to process the order based on the current queue counts and the current order counts for the candidate stores, wherein the particular store is identified as capable of fulfilling the order in the shortest amount of time relative each other candidate store;
wherein determining further comprises: passing the current queue counts and the current order counts for the candidate stores as input to a model; and
receiving as output a particular store identifier for the particular store;
receiving as output, calculated order wait times for each of the candidate stores, wherein the calculated order wait times correspond to estimated times for each of the candidate stores to fulfill the online order;
identify[ing], from images of the queues, a current count of customers in a queue to place an order at each store and uses the current customer counts along with the current order counts to select, as the particular store, a particular store that is able to fulfill the order in the shortest amount of time;
obtaining the images at preconfigured intervals of time; and
obtaining the current order counts from the particular store at the preconfigured intervals of time.
Claim 1, as exemplary, recites the abstract idea of distributing and fulfilling customer orders. These recited limitations fall within the "Certain Methods of Organizing Human Activities" Grouping of abstract ideas as it relates to commercial interactions and sales activities/behaviors.
Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claim 1 recites an abstract idea (Step 2A, Prong One: YES).
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception.
Claim 1 recites additional elements beyond the judicial exception(s), including online order counts for online orders; an online order, the online order placed through an online ordering application; providing input to a machine-learning model (MLM); and receiving output from the MLM; training the MLM; obtaining images from in-store cameras; and obtaining the current online order counts from an online order system.
These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Claim 1 specifying that the abstract idea of distributing and fulfilling customer orders is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the Alice/Mayo test, when considered both individually and as a whole, the limitations of claim 1 is not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claim 1 recites an abstract idea and fail to integrate the abstract idea into a practical application, claim 1 is “directed to” an abstract idea (Step 2A: YES). Accordingly, the judicial exception is not integrated into a practical application.
Next, under Step 2B, the instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements of online order counts for online orders; an online order, the online order placed through an online ordering application; providing input to a machine-learning model (MLM); and receiving output from the MLM information; training the MLM; obtaining images from in-store cameras; and obtaining the current online order counts from an online order system amount to no more than mere instructions to apply the exception using generic computer components. For the same reason these elements are not sufficient to provide an inventive concept. Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible (Step 2B: NO).
Dependent claims 2-11, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. As for dependent claims 3, 8, and 9, these claims recite limitations that further define the same abstract idea noted in independent claim 1. Therefore, claims 3, 8, and 9 are considered patent ineligible for the reasons given above.
As for dependent claims 2, 4-7, and 10-11, these claims recite limitations that further define the abstract idea noted in independent claim 1. Additionally, they recite the following additional limitations:
automatically routing the online order to an order manager of the particular store on behalf of the customer and the online ordering application;
obtaining updated current online order counts from online order systems associated with the stores at the preconfigured intervals of time;
wherein maintaining the current queue counts further includes passing the images to the MLM or another MLM, and receiving as output the current queue counts;
wherein maintaining the current online order counts further includes obtaining the current online order counts from corresponding order systems associated with the stores;
wherein receiving further includes receiving a store identifier for a customer-selected store associated with the online order, order details for the online order, and the current location for customer from the online order system associated with the online ordering application;
providing the particular store identifier to the online order system associated with the online ordering application such that details for the particular store are presented in a user interface of the online ordering application.
The additional elements of automatically routing the online order, updated current online order counts from online order systems, another MLM, order systems associated with the stores, the online order system associated with the online ordering application, and a user interface of the online ordering application are all recited at a high level of generality such that they amount to no more than instructions to apply the judicial exception in a generic technological environment. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Accordingly, under the Alice/Mayo test, claims 1-11 are ineligible.
Subject Matter Free of Prior Art
Claim 1 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office Action. Claims 2-11 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter:
Upon review of the evidence at hand, it is concluded that the totality of the evidence in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention as the noted features amount to more than a predictable user of elements in the prior art. The allowable features are as follows:
determining, from images captured of customer queues in stores, current queue counts for in-store customers…;
determining current online order counts for online and unfulfilled orders at the stores;
receiving as output, from the MLM, calculated order wait times for each of the candidate stores, wherein the calculated order wait times correspond to estimated times for each of the candidate stores to fulfill the online order; and
training, the MLM to identify, from images of the customer queues, a current count of customers in a queue to place an order at each store and uses the current queue counts along with the current online order counts to select, as the particular store, a particular candidate store that is able to fulfill the online order in the shortest amount of time.
The most relevant prior art made of record includes previously cited Nemati et al. (US 20180314999 A1), hereinafter Nemati, previously cited Hoang et al. (US 11,948,110 B2), hereinafter Hoang, previously cited Taylor et al. (US 20160258762 A1), hereinafter Taylor, newly cited Sulejmani (US 11,127,054 B1), hereinafter Sulejmani, and newly cited NPL Reference U (“Predicting Waiting Time in Customer Queuing Systems”, Carvalho et al., 2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA), September 28-30, 2016).
Nemati teaches systems and methods for fulfilling an online order, where a distribution center and retail store to fulfill the online order are selected based on a calculated cost to fulfill the order (Nemati: [abstract]). Nemati further teaches determining, in stores, current demand at the store (see at least [0033][0047] “receiving real-time historical online order data at one or more predefined times”). Nemati further teaches determining current online order counts for online and unfulfilled orders at the stores (see at least [0047-0048]). Nemati teaches receiving an online order from a customer, the online order placed through an online ordering application (see at least [0028-0029]). Nemati teaches filtering the stores to obtain candidate stores based on a threshold distance between a current location of the customer and known locations of the stores (see at least [0035] “the central server 104 can identify one or more distribution centers and/or retail stores… within a defined radius from the delivery destination). Nemati further teaches determining a particular store from the candidate stores to process the online order based on the current demand and the current online order counts for the candidate stores, wherein the particular store is identified as capable of fulfilling the online order in a shortest amount of time relative each other candidate store (see at least [0032][0038][0047-0048]). Nemati further teaches passing the current online order counts for the candidate stores as input to a machine-learning model (MLM), and receiving as output from the MLM a particular store identifier for the particular store (see at least [0047][0048]). Nemati further teaches wherein the MLM uses the current online order counts to select, as the particular store, a particular candidate store that is able to fulfill the online order in the shortest amount of time (see at least [0032][0047][0049]). Nemati further teaches obtaining information from in-store at preconfigured intervals of time, and obtaining the current online order counts from an online order system associated with the particular store at the preconfigured intervals of time (see at least [0030][0047][0048]). Yet Nemati does not explicitly disclose determining, from images captured of customer queues in stores, current queue counts for in-store customers; receiving as output, from the MLM, calculated order wait times for each of the candidate stores, wherein the calculated order wait times correspond to estimated times for each of the candidate stores to fulfill the online order; and training, the MLM to identify, from images of the customer queues, a current count of customers in a queue to place an order at each store.
Hoang teaches a system for managing performance of a service establishment, wherein the establishment has servers capable of taking orders from customers when they reach the head of a queue associated with a server (Hoang: [abstract]). Hoang further teaches determine, from images captures of customer queues in stores, current queue counts for in-store customers waiting to place in-store orders at the stores (see at least [Col 4 Ln 31-51][Col 6 Ln 43-49][Col 6 Ln 52-58]). Hoang further teaches making a determination based on the current queue counts (see at least [Col 6 Ln 32-42]). Hoang further teaches passing the current queue counts as input to a machine learning model (see at least [Col 4 Ln 31-56][Col 6 Ln 31-58]). Hoang further teaches receiving as output, from the MLM, calculated order wait times for different locations (server queues), wherein the calculated order wait times correspond to estimated times for each of the locations to fulfill the online order (see at least [Col 4 Ln 41-Col 5 Ln 3][Col 5 Ln 13-26]); and training the MLM to identify, from images of the customer queues, a current count of customers in a queue to place an order at each store (see at least [Col 4 Ln 31-56][Col 4 Ln 60-Col 5 Ln 3][Col 6 Ln 31-58]). Hoang further teaches obtaining the images from in-store cameras (see at least [Col 4 Ln 41-51]). Yet Hoang does not explicitly disclose the limitations regarding determining current online order counts for online and unfulfilled orders at the stores; receiving calculated order wait times for each of the candidate stores, wherein the calculated order wait times correspond to estimated times for each of the candidate stores to fulfill the online order; and using the current queue counts along with the current online order counts to select, as the particular store, a particular candidate store that is able to fulfill the online order in the shortest amount of time.
Taylor teaches systems and methods for providing route guidance to an in-store store user, where an optimized, fastest route can be provided, and a check-out lane may be selected based on cashier performance and/or current queue length determined based on video analysis (Taylor: [abstract][0110]). Taylor teaches using the current customer counts along with additional information to select a candidate location (see at least [0070][0110] the fastest route determination may take into account congested areas based upon real-time video analysis of store condition). Taylor further teaches shopping route provision based on an optimization process to minimize the time it takes to get through the store (see at least [0090][0094]). Yet Taylor does not explicitly disclose the limitations regarding determining current queue counts for in-store customer from images captured of customers in queues, determining current online order counts, receiving, from the MLM, calculated order wait times for each of the candidate stores, wherein the calculated order wait times correspond to estimates times for each of the candidate stores to fulfill the online order; and training, the MLM… and using the current queue counts along with the current online order counts to select… a particular candidate store.
Sulejmani teaches a queue wait time estimation system capable of estimating queue wait times at various establishments based on sensor feedback. The wait times are then communicated to users, enabling them to make an informed decision as to whether they’d like to visit a particular establishment based on the estimated queue wait time (Sulejmani [Col 2 Ln 34-42][Col 6 Ln 40-59]). Yet Sulejmani does not explicitly disclose the limitations regarding determining, from images captured of customer queues in stores, current queue counts for in-store customers; and using the current queue counts along with the current online order counts to select, as the particular store, a particular candidate store that is able to fulfill the online order in the shortest amount of time.
NPL Reference U teaches a queuing management system for predicting waiting time in customer queuing systems ([Title][Page 1, Paragraph 1]). U acknowledges common issues with customer queue management, including growing impatience of customers and potential loss of customers ([Page 1, Paragraph 1]). U further teaches implementing an adjustable predictive model in order to predict the waiting time in a queueing system, as a way to improve the attendance service of a specific store (Page 1, Paragraph 2]). U further discloses that the calculated waiting time is calculated according to various factors that may have influenced the prediction process in the past and at the present time, such as the time of day ([Page 2, Paragraph 2, Table 1]). Yet NPL Reference U does not explicitly disclose the limitations regarding determining current queue counts for in-store customer from images captured of customer queues in stores, determining current online order counts for online and unfulfilled orders at the stores; and training, the MLM to identify, from images of the customer queues, to use the current queue counts along with the current online order counts to select, as the particular store, a particular candidate store that is able to fulfill the online order in the shortest amount of time.
While these references arguably teach the claimed limitations using a piecemeal analysis, these references would only be combined and deemed obvious based on knowledge gleaned from the applicant’s disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). Accordingly, claim 1, taken as a whole, is indicated to be allowable over the cited prior art. The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free from prior art. Claims 2-11 depend from claim 1 and are therefore indicated as containing subject matter free from prior art.
Additionally, the Examiner further emphasizes the claims as a whole and herby asserts the totality of the evidence neither anticipates nor renders obvious the particular combination of elements as claimed. That is, the Examiner emphasized the claims as a whole and hereby asserts that the totality of evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for combining or otherwise modifying the available prior art to arrive at the claimed invention. The combination of features as claimed would not be modifying the available prior art to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art because any combination of the evidence at hand to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
It is hereby asserted by the Examiner that, in light of the above and in further deliberation over all of the evidence at hand, that the claims have subject matter free of prior art as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
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.
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/ZACHARY RYAN DONAHUE/Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689