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 .
DETAILED ACTION
Status of Claims
The following is a Final Office Action in response to Applicant’s amendment received 06/05/2026.
In accordance with Applicant’s amendment, claims 1-9 and 18-20 are amended. Claims 1-9 and 18-20 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 05/20/2026 has been considered.
Response to Amendment
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
The amendment to the Specification filed on 06/05/2026 (removing browser-executable code from pars. 166 and 179) has been entered and the objection to the Specification is withdrawn in response.
The 35 U.S.C. §112(f) interpretation applied to the claimed receiver (claims 1 and 18) and customer service determiner (claims 1, 4, 6, 9, and 18) in the previous office action is no longer applicable in view of the amendments removing the §112(f) interpretation invoked by the previous version of these claims.
The 35 U.S.C. §112(a) and §112(b) rejections of claims 1-9 and 18 are withdrawn in response to applicant’s amendment.
Response to Arguments
Response to §101 Arguments - Applicant's arguments with respect to the §101 rejection of claims 1-9 and 18-20 (Remarks at pg. 10) have been considered, but with the exception of the argument addressed below, are directed toward the amendments to the claims and are therefore addressed in the updated §101 rejection set forth in the instant office action.
Applicant refers to Example 39 of the Subject Matter Eligibility Examples and argues that “claim 1 relates to computer vision processing, and is therefore more analogous to Example 39 of the USPTO eligibility guidance” (Remarks at pg. 10). The Examiner respectfully disagrees.
In response to applicant’s reliance on Example 39, the Examiner first emphasizes that the analysis of Example 39 under Step 2A Prong 1 concluded that the claim was eligible because it “does not recite any of the judicial exceptions….the claim does not recite any mathematical relationships, formulas or equations….the claim does not recite a mental process…the claim does not recite any method of organizing human activity…Thus, the claim is eligible because it does not recite a judicial exception.” In contrast, when evaluated under Step 2A Prong 1, Applicant’s claims plainly recite steps for falling under the “Certain methods of organizing human activity” and “Mental Processes” abstract idea groupings, as explained in the Step 2A Prong One analysis of the §101 rejection. Therefore, in contrast to claim 1 of Example 39 that cited no abstract ideas, Applicant’s claims clearly recite one or more abstract ideas. Moreover, while claim 1 of Example 39 requires steps for creating a first training set from collected digital facial images, modified digital facial images, and digital non-facial images to train a neural network and thereby yield an improvement to the technological process of digital facial image detection, applicant’s claims merely involve a pre-trained neural network to perform a “determines” step that could otherwise be implemented mentally by a human observing/evaluating the captured image of customers, which is implemented by a generic computer and which furthermore adds nothing to the neural network, the computer, or any other technology, and which is not reasonably considered as being similar to Example 39’s transformation of digital facial images including mirroring, rotating, smoothing, or contrast to create a modified set of digital facial images to facilitate creation of the training set for training the neural network. Applicant’s claims have not been shown to yield any discernible transformation or yield any other technical improvement, but instead results in determining that customer service is necessary, which is implemented by the “second hardware processor” to apply this activity that otherwise falls under the scope of the abstract idea itself (as discussed below under the Step 2A1 eligibility analysis), which is devoid of any technical improvement comparable to the digital facial image detection of Example 39. Accordingly, Applicant’s reliance on the eligibility rationale of Example 39 is not persuasive.
Response to §103 Arguments - Applicant's arguments with respect to the §101 rejection of claims 1-9 and 18-20 (Remarks at pgs. 11-12) have been considered, but are primarily raised in support of the amendments to the claims and are therefore believed to be fully addressed in the new ground of rejection set forth below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-9 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. 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. The eligibility analysis in support of these findings is provided below, in accordance with the subject matter eligibility guidance set forth in MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106.03), it is first noted that the claimed apparatus (claims 1-9), system (claim 18), method (claim 19), non-transitory recording medium (claim 20) are each directed to a potentially eligible category of subject matter (i.e., machines, process, and article of manufacture). Accordingly, claims 1-9 and 18-20 satisfy Step 1 of the eligibility inquiry.
With respect to Step 2A Prong One of the eligibility inquiry (as explained in MPEP 2106.04), it is next noted that the claims recite an abstract idea that falls under the “Certain methods of organizing human activity” abstract idea grouping by reciting limitations that describe activities considered commercial interactions (sales or marketing activity, e.g., customer service) or managing personal behavior relationships or interactions (customer interactions), may be implemented as “Mental Processes” (e.g., observation, evaluation, judgment, or opinion). The limitations reciting the abstract idea as set forth in independent claim 1 are identified in bold text below, whereas the additional elements are presented in plain text and are separately evaluated under Step 2A Prong Two and Step 2B:
an input circuit that receives an image generated by image-capturing by an imaging device with a plurality of customers as subjects (This step is an additional element addressed below under Step 2A Prong Two and Step 2B);
a customer service determiner comprising a first hardware processor that determines, based on a posture of each of the customers included in the received image as determined by analysis of skeleton points detected from the received image using a trained neural network, whether or not customer service is necessary for each of the customers (This step describes commercial interactions (sales or marketing activity) or managing personal behavior relationships or interactions (customer interactions) because it describes activity that may encompass providing sales assistance to a customer seeking to make a purchase, and furthermore, but for the generic implementation by the determiner, may be implemented as “Mental Processes” such as via human evaluation, judgment, or opinion to make the determination by observing the image); and
a second hardware processor that determines, when it is determined that customer service is necessary for a plurality of customers, an order of customer service for the plurality of customers for which it is determined that customer service is necessary (This step describes commercial interactions (sales or marketing activity) or managing personal behavior relationships or interactions (customer interactions) because it describes activity that may encompass providing sales assistance to a customer seeking to make a purchase, and furthermore, but for the generic hardware processor, may be implemented as “Mental Processes” such as via human evaluation, judgment, or opinion to make the determination as to order of customer service, e.g., deciding placement of customers in a service queue).
Independent claims 18-20 recite similar limitations as those set forth in claim 1 as discussed above, and have therefore been determined to recite the same abstract idea as claim 1.
With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP 2106.04(d)), the judicial exception is not integrated into a practical application. Independent claims 1 and 18-20 include additional elements of an input circuit that receives an image generated by image-capturing by an imaging device with a plurality of customers as subjects, a customer service determiner comprising a first hardware processor, a second hardware processor, using a trained neural network, apparatus, and non-transitory recording medium. The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application. The computing elements (input circuit…, determiner comprising a first hardware processor, second hardware processor, apparatus, non-transitory recording medium) amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). The input circuit that receives an image generated by image-capturing by an imaging device with a plurality of customers as subjects at most amounts to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g). Although the trained neural network could be implemented via a mathematical algorithm (e.g., backpropagation/gradient descent), even if considered as an additional element, the trained neural network is recited at a high level of generality, is not actually trained within the scope of the claim, may encompass virtually any available pre-existing off-the-shelf neural network, and fails to provide an improvement to the functioning of a computer or to any other technology or technical field. In addition, these additional elements fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry (as explained in MPEP 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent claims 1 and 18-20 include additional elements of an input circuit that receives an image generated by image-capturing by an imaging device with a plurality of customers as subjects, a customer service determiner comprising a first hardware processor, a second hardware processor, using a trained neural network, apparatus, and non-transitory recording medium. These additional elements have been evaluated, but fail to add significantly more. The computing elements (input circuit…, determiner comprising a first hardware processor, second hardware processor, apparatus, non-transitory recording medium) amount to using generic computing elements or instructions/software to perform the abstract idea (See, e.g., Spec. at par. [0042]), which merely serves to tie the abstract idea to a particular technological environment (generic computing environment), similar to adding the words “apply it” (or an equivalent). Accordingly, the generic computer implementation merely serves to link the use of the judicial exception to a particular technological environment and therefore does not amount to significantly more than the abstract idea itself. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The input circuit that receives an image generated by image-capturing by an imaging device with a plurality of customers as subjects at most amounts to insignificant extra-solution data gathering activity accomplished via receiving/transmitting data, which is well-understood, routine, and conventional activity and thus insufficient to add significantly more to the claims. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Although the trained neural network could be implemented via a mathematical algorithm as discussed above (e.g., backpropagation/gradient descent), even if considered as an additional element, the trained neural network is recited at a high level of generality, is not actually trained within the scope of the claim, may encompass virtually any available pre-existing off-the-shelf neural network, and therefore similar to the generic computing elements addressed above, may also be reasonably understood as being similar to adding the words “apply it” which, as discussed above, does not add significantly more to the claims. Nevertheless, it is noted that the use of a neural network is well-understood, routine, and conventional activity in the art. See, e.g., Negishi, US Patent No. 5,444,819 (col. 13, lines 10-13), noting “predicting and analyzing system, using neural networks, according to the conventional art.” See also, Dailey et al., US Patent No. 6,917,952 (col. 10, lines 10-12), noting “The preferred embodiment uses neural networks, and conventional methods of training them as are known in the art.”
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-9 recite the same abstract idea(s) as recited in the independent claims, and have been determined to recite further details/steps falling under the “Certain methods of organizing human activity” and/or “Mental Processes” abstract idea groupings discussed above along with the same generic computing elements (input circuit, first/second hardware processor) as recited in the independent claims, which merely serve the purpose of tying the invention to a particular technological environment and which, as discussed above, is insufficient to integrate the abstract idea into a practical application or add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 and 18-20 are rejected under 35 U.S.C. §103 as unpatentable over Nadler et al. (US 2016/0189170, hereinafter “Nadler”) in view of Hussami et al. (US 2022/0269346, hereinafter “Hussami”) in view of Argue et al. (US 2015/0379434, hereinafter “Argue”).
Claim 1: Nadler teaches a customer service management apparatus (par. 38 and Fig. 2: data processing apparatus) that manages a customer service method for a customer staying in a predetermined region, the apparatus comprising:
an input circuit that receives an image generated by image-capturing by an imaging device with a plurality of customers as subjects (pars. 43-44, 55, and Fig. 2: customer 210 at a store is tracked by sensor(s) 201 by detecting the customer and acquiring data of his location at different times … sensor may be…video capturing equipment such as a camera or a network of cameras [i.e., imaging device], motion detection sensor(s), proximity sensor, light flicker and/or any other sensor installed in the store; photograph of customer 210 may be acquired, for example, from the data collected by sensor(s) 201 when they include imaging sensors and/or an image of customer 210 may be included in the customer information stored in information dataset 205 and automatically extracted; See also, par. 36: electronic circuitry including, for example, programmable logic circuitry…may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention);
a customer service determiner comprising a first hardware processor that determines … whether or not customer service is necessary for each of the customers (pars. 19, 35, 38, 43, 49, 51, and 55-56: identify a potential requirement of assistance by the customer; movement pattern is analyzed by pattern analysis module 203 to identify a potential requirement of assistance by the customer; photograph of customer 210 may be acquired, for example, from the data collected by sensor(s) 201 when they include imaging sensors and/or an image of customer; Tracking module 202 may be, for example, a software module embedded in system 200, and/or a hardware device; recognizing customers requiring assistance; computing/ processing devices [e.g., first hardware processor]; processor of the computer or other programmable data processing apparatus); and
a second hardware processor that determines, when it is determined that customer service is necessary for a plurality of customers, … customer service for the plurality of customers for which it is determined that customer service is necessary (pars. 18-20, 29, 35, 38, 41, 45, 54, 56, 60, and Figs. 1, 2, and 4: recognizing customers requiring assistance…and triggering a providing of the assistance to the customer; providing of assistance is triggered; computing/ processing devices; assistance may be provided, for example, as part of the pages viewed by customer 210, by a message on the screen of terminal 211; terminal 211, which may be, for example, a personal computer, a mobile computer, a tablet computer and/or a mobile phone).
Nadler does not explicitly teach:
based on a posture of each of the customers included in the received image as determined by analysis of skeleton points detected from the received image using a trained neural network;
determines…an order of customer service.
Hussami teaches:
based on a posture of each of the customers included in the received image as determined by analysis of skeleton points detected from the received image using a trained neural network (pars. 150, 165, 292, 358, 411, 469, and 536: processor 822 may also receive inputs from other sensors (e.g., IMU sensor 818, an image sensor, etc.) that may be configured to track a position of a body part of the user; capture at least one image, and the at least one measurement of position is determined based, at least in part, on the at least one image; capture at least one image as the user performs the gesture as the neuromuscular signals are recorded, and the one or more measurements may be determined based on the captured image(s); statistical model is a neural network, the output layer of the neural network may provide a set of output values corresponding to a respective set of possible musculo-skeletal position characteristics (e.g., joint angles) [i.e., posture]. In this way, the neural network may operate as a non-linear regression model configured to predict musculo-skeletal position characteristics from raw or pre-processed sensor measurements; when the inferential model is a neural network, parameters of the neural network (e.g., weights) may be estimated from the training data; neural network model may be trained using mechanical motion data and, as noted above, the model may represent, implicitly, movement statistics and constraints due to the articulation of the user's arm relative to his or her torso, and the relation of the movements to the measured data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nadler with Hussami because the references are analogous since Nadler is directed to computer implemented features for managing the provision of assistance to customers, which is within Applicant’s field of endeavor of customer service management, whereas Hussami’s neural network based analysis of body state based on captured image data is reasonably pertinent to the problem with which applicant is concerned (automated gesture detection of subjects using image data and a trained neural network) and because modifying Nadler to incorporate Hussami’s usage of a neural network to determine posture using skeleton points from image data, as claimed, would serve the motivation to accurately estimate bodily pose or gesture of a human (Hussami at pa. 89), which would aid in tracking customer behavior (Nadler at par. 2), such as for detecting a need for customer assistance (Nadler at par. 3); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Nadler and Hussami do not teach:
determines…an order of customer service (Abstract
Argue teaches:
determines…an order of customer service (Abstract, pars. 43-44, 48, and claim 1: within a computerized processor, establishing a place in the queue for the first customer, wherein the queue operates to establish among a plurality of customers a customer currently being served by a store employee and an order in which other customers of the plurality will be served; customer location module 240 monitors and receives constant updates about the whereabouts of a shopper so that his or her proximity to the deli counter is known. This information can update a shopper's place in a virtual queue if the shopper misses his or her arrival time; At step 408, the deli counter ticket machine is updated so the next paper ticket that it prints will be the next number in the queue. At step 410, the server responds to the smartphone application with a message indicating a queue number of a customer currently being served and/or a queue number of the customer, e.g. “Now Serving X, You Are Y.” At step 412, the server uses historical shopper data, including individual shopper history, the time of day, the available deli counter staff, and other trends to estimate the waiting time for each shopper in the virtual queue).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nadler/Hussami with Argue because the references are analogous since Nadler/Argue are each directed to computer implemented features for managing the provision of assistance to customers, which is within Applicant’s field of endeavor of customer service management, whereas Hussami’s neural network based analysis of body state based on captured image data is reasonably pertinent to the problem with which applicant is concerned (automated gesture detection of subjects using image data and a trained neural network), and because modifying Nadler/Hussami to incorporate Argue’s feature for determining an order of customer service, as claimed, would serve the motivation to manage a customer service queue that helps shoppers achieve a faster shopping experience (Argue at par. 14) and because a customer’s order in a queue would provide the benefit of an estimated wait time related thereto (Argue at par. 21); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 18-20 are directed to a customer service management system, method, and non-transitory recording medium for performing substantially similar limitations as those recited in claim 1 and addressed above. Nadler, in view of Hussami/Argue, teaches a customer service management system, method, and non-transitory recording medium for performing the limitations discussed above (Nadler at pars. 33-34: system, a method, and/or a computer program product; computer readable storage medium; See also, Hussami at pars. 91, 174, 500, and 615; See also, Argue at pars. 36-37: device, process, or computer program product), and claims 18-20 are therefore rejected as obvious under §103 using the same references and for substantially the same reasons as set forth above.
Claim 2: Nadler further teaches wherein the second hardware processor further calculates a time during which each of the customers stays in the region (par. 47: identifying the movement pattern from the data provided by sensor(s) 201 and/or tracking module 202. The movement pattern may be, for example, extended dwell time of customer 210 at a specific location or different locations in the store, repeat visits to a particular location in the store), and determines…customer service for the plurality of customers for which it is determined that customer service is necessary (par. 57: determine which store employee is sent to assist customer 210. For example, a closest store employee to customer 210 is sent or a store employee that has the most knowledge of the products in the area of the store where customer 210 dwells), but does not teach determines, using the calculated time, the order of customer service.
Argue teaches determines, using the calculated time, the order of customer service (pars. 15-17: e.g., If the customer is on the other side of a large store or stepped out of the store, then the interval can be set at a larger time in order to permit the customer to traverse the large distance. If the customer is too far away for an estimated time until the customer's number is going to be called, the customer can be bumped down the queue).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Nadler/Hussami/Argue such that Nadler’s dwell time is used for Argue’s feature for determining the order of customer service, as claimed, in pursuit of reducing customer waiting time and thereby providing a faster shopping experience (Argue at par. 14); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 3: Nadler further teaches wherein the second hardware processor gives priority … to a customer staying in the region for a time longer than a predetermined threshold value among the plurality of customers for which it is determined that customer service is necessary (pars. 47-49: identifying the movement pattern from the data provided by sensor(s) 201 and/or tracking module 202. The movement pattern may be, for example, extended dwell time of customer 210 at a specific location [wherein Nadler’s “extended dwell time” is a predetermined threshold value]; movement pattern is analyzed by pattern analysis module 203 to identify a potential requirement of assistance by the customer), but does not teach gives priority, in the order of customer service.
Argue teaches gives priority, in the order of customer service (par. 17: A warning interval can be adjusted based upon a location of the customer in relation to the counter. If the customer is thirty feet away from the counter, the interval can be set at one minute. If the customer is on the other side of a large store or stepped out of the store, then the interval can be set at a larger time in order to permit the customer to traverse the large distance. If the customer is too far away for an estimated time until the customer's number is going to be called, the customer can be bumped down the queue [i.e., customer’s priority in the queue may be assigned/changed]. In such an instance, the customer can be notified that the assigned spot in the queue has been lost based upon the location of the customer; The bumped customer can be re-entered at the end of the queue with a new number. In the alternative, the bumped customer can be provided with a new space in the queue between two existing spaces in the queue).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Nadler/Hussami/Argue such that Nadler’s staying time for determining customer service is used in conjunction with Argue’s priority ordering of customer service, as claimed, in pursuit of reducing customer waiting time and thereby providing a faster shopping experience (Argue at par. 14); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 4: Nadler does not teach the limitations of claim 4.
Argue teaches wherein the second hardware processor further calculates, for each of the customers for which it is determined that customer service is necessary, a time during which the customer stays in the region from a time point when it is determined, by the customer service determiner, that customer service is necessary (pars. 17-18, 21, 48 and Figs. 1 and 4: e.g., determine that a location of the customer within the store or in an area proximate to the store…interval can be adjusted based upon a location of the customer in relation to the counter; server uses historical shopper data, including individual shopper history, the time of day, the available deli counter staff, and other trends to estimate the waiting time for each shopper in the virtual queue; displaying a “Start Virtual Queue” button 11, a queue number status indication 13, an estimated waiting time; server sends this estimated waiting information to the smartphone of the waiting shopper), and determines the order of customer service using the calculated time for the plurality of customers for which it is determined that customer service is necessary (pars. 16, 18, 45, 48 and Fig. 1: e.g., Judy is using the queue software, customers behind Judy can include longer warning intervals even though Judy has not yet actually placed an order; customer in the queue can wait until his or her number is called, and then can proceed; customer's device can be activated at the appropriate time by queue place-keeping module; server sends this estimated waiting information to the smartphone of the waiting shopper, and periodically updates it. This step includes a “X” minute warning that the server sends to the shopper prior to the scheduled time. Step 416 is the scheduled time for the shopper to be at the deli counter. At step 418 the shopper shows his or her smartphone screen or presents his or her paper ticket, and is served. At step 420, if the shopper is delayed, the deli has the option of “bumping” the shopper back in the queue so that the shopper does not lose his or her place [Examiner’s Note: It is further noted that the customers’ wait times in the queue are necessarily used for determining order because a queue embodies a standard first-in first-out principle, i.e., first come first serve such that order and ascending wait times are necessarily interdependent by virtue of a queue’s logical FIFO arrangement]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Nadler/Hussami/Argue to include Argue’s calculation of stay time and determination of the order of customer service using the stay time, as claimed, in pursuit of reducing customer waiting time and thereby providing a faster shopping experience (Argue at par. 14) and to ensure the shopper is aware when customer service will be provided (Argue at par. 46: e.g., alerting the shopper that he or she should go to the deli counter); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 5: Nadler further teaches wherein the second hardware processor gives priority … to a customer staying in the region for a time longer than a predetermined threshold value among a plurality of customers for which it is determined that customer service is necessary (pars. 47-49: identifying the movement pattern from the data provided by sensor(s) 201 and/or tracking module 202. The movement pattern may be, for example, extended dwell time of customer 210 at a specific location [wherein Nadler’s “extended dwell time” is a predetermined threshold value]; movement pattern is analyzed by pattern analysis module 203 to identify a potential requirement of assistance by the customer), but does not teach gives priority, in the order of customer service.
Argue teaches gives priority, in the order of customer service (par. 17: A warning interval can be adjusted based upon a location of the customer in relation to the counter. If the customer is thirty feet away from the counter, the interval can be set at one minute. If the customer is on the other side of a large store or stepped out of the store, then the interval can be set at a larger time in order to permit the customer to traverse the large distance. If the customer is too far away for an estimated time until the customer's number is going to be called, the customer can be bumped down the queue [i.e., customer’s priority in the queue may be assigned/changed]. In such an instance, the customer can be notified that the assigned spot in the queue has been lost based upon the location of the customer; The bumped customer can be re-entered at the end of the queue with a new number. In the alternative, the bumped customer can be provided with a new space in the queue between two existing spaces in the queue).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Nadler/Hussami/Argue such that Nadler’s staying time for determining customer service is used in conjunction with Argue’s priority ordering of customer service, as claimed, in pursuit of reducing customer waiting time and thereby providing a faster shopping experience (Argue at par. 14); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 6-9 are rejected under 35 U.S.C. §103 as unpatentable over Nadler et al. (US 2016/0189170, hereinafter “Nadler”) in view of Hussami et al. (US 2022/0269346, hereinafter “Hussami”) in view of Argue et al. (US 2015/0379434, hereinafter “Argue”), as applied to claim 1 above, and further in view of Yoshitake et al. (US 2015/0199698, hereinafter “Yoshitake”).
Claim 6: Nadler further teaches the second hardware processor further calculates a time during which each of the customers stays in the region (par. 47: identifying the movement pattern from the data provided by sensor(s) 201 and/or tracking module 202. The movement pattern may be, for example, extended dwell time of customer 210 at a specific location or different locations in the store, repeat visits to a particular location in the store), and determines…customer service for the plurality of customers for which it is determined that customer service is necessary (par. 57: determine which store employee is sent to assist customer 210. For example, a closest store employee to customer 210 is sent or a store employee that has the most knowledge of the products in the area of the store where customer 210 dwells), but does not teach wherein an order of customer service is determined according to an order in which each of the customers enters the region, when it is determined, by the customer service determiner, that customer service is necessary for one customer, the second hardware processor determines to increase the order of customer service by one for the customer, and the second hardware processor determines whether or not the calculated time is equal to or more than a predetermined threshold value for the customer for which it is determined that customer service is necessary, and when it is determined that the calculated time is equal to or more than the predetermined threshold value, the second hardware processor determines to increase the order of the customer service by one.
Argue teaches wherein:
an order of customer service is determined according to an order in which each of the customers enters the region (Abstract, pars. 14, 22, 43-44, 48, and claim 1: e.g., within a computerized processor, establishing a place in the queue for the first customer, wherein the queue operates to establish among a plurality of customers a customer currently being served by a store employee and an order in which other customers of the plurality will be served; customer location module 240 monitors and receives constant updates about the whereabouts of a shopper so that his or her proximity to the deli counter is known. This information can update a shopper's place in a virtual queue; “Start Virtual Queue” button 11. Selecting this connects the user's portable computerized device 10 in direct communication with the store's remote server 30. The remote server 30 then enters the shopper into a virtual queue),
when it is determined, by the customer service determiner, that customer service is necessary for one customer, the second hardware processor determines to increase the order of customer service by one for the customer (pars. 17-18, 21, 48 and Figs. 1 and 4: e.g., determine that a location of the customer within the store or in an area proximate to the store…interval can be adjusted based upon a location of the customer in relation to the counter; server uses historical shopper data, including individual shopper history, the time of day, the available deli counter staff, and other trends to estimate the waiting time for each shopper in the virtual queue; displaying a “Start Virtual Queue” button 11, a queue number status indication 13, an estimated waiting time; server sends this estimated waiting information to the smartphone of the waiting shopper; If the customer is too far away for an estimated time until the customer's number is going to be called, the customer can be bumped down the queue; deli has the option of “bumping” the shopper back in the queue so that the shopper does not lose his or her place [wherein bumping customers within the queue increases the order of all customers in the deli queue as a result]), and
the second hardware processor determines to increase the order of the customer service by one (pars. 17-18, 21, 48 and Figs. 1 and 4: e.g., determine that a location of the customer within the store or in an area proximate to the store…interval can be adjusted based upon a location of the customer in relation to the counter; If the customer is too far away for an estimated time until the customer's number is going to be called, the customer can be bumped down the queue; deli has the option of “bumping” the shopper back in the queue so that the shopper does not lose his or her place [wherein bumping customers within the queue increases the order of all customers in the deli queue as a result]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Nadler/Hussami/Argue to include Argue’s features for determining an order for customer service and increasing an order of customer serviced by one, as claimed, in pursuit of adjusting a queue based on customer location (Argue at par. 17) or to aid a customer in keeping their place in line after previously losing their place (Argue at par. 48), which would help to reduce overall customer waiting time and thereby provide a faster shopping experience (Argue at par. 14); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Nadler, Hussami, and Argue do not teach the second hardware processor determines whether or not the calculated time is equal to or more than a predetermined threshold value for the customer for which it is determined that customer service is necessary, and when it is determined that the calculated time is equal to or more than the predetermined threshold value.
Yoshitake teaches the second hardware processor determines whether or not the calculated time is equal to or more than a predetermined threshold value for the customer for which it is determined that customer service is necessary, and when it is determined that the calculated time is equal to or more than the predetermined threshold value (pars. 63, 141, 151, 154-155, and 279: determines appropriate threshold values in accordance with the duration; classifying the numbers of persons staying in each area calculated at the predetermined time intervals by using the determined threshold values; calculates an average value of reference values for the respective time groups to obtain a threshold value; determining different threshold values for the time periods during which a store is crowded).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nadler/Hussami/Argue with Yoshitake because Nadler/Argue/Yoshitake are analogous since they are each directed to computer implemented features for evaluating customer behavior to manage the provision of customer service, which is within Applicant’s field of endeavor of customer service management, whereas Hussami’s neural network based analysis of body state based on captured image data is reasonably pertinent to the problem with which applicant is concerned (automated gesture detection of subjects using image data and a trained neural network), and because modifying Nadler/Hussami/Argue with Yoshitake’s calculated time based threshold values, as claimed, would provide customer service intelligence such as to help shoppers achieve a faster shopping experience (Argue at par. 14); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 7: Nadler, in view of Hussami/Argue, teaches the limitations of claim 1 as set forth above. Nadler does not teach the limitations of claim 7.
Argue teaches the second hardware processor determines the order of customer service in the sub region (Abstract, pars. 14, 43-44, 48, and claim 1: operating a computerized queue for customers seeking service from the deli counter [i.e., a sub region]; within a computerized processor, establishing a place in the queue for the first customer, wherein the queue operates to establish among a plurality of customers … an order in which other customers of the plurality will be served; server uses historical shopper data, including individual shopper history, the time of day, the available deli counter staff, and other trends to estimate the waiting time for each shopper in the virtual queue).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Nadler/Hussami/Argue to include Argue’s ordering of customer service, as claimed, in pursuit of reducing customer waiting time and thereby providing a faster shopping experience (Argue at par. 14); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Nadler, Hussami, and Argue do not teach wherein the region includes a plurality of sub regions, the customer service management apparatus further comprising a number calculator that calculates, for each of the sub regions, a number of customers staying in the sub region based on the received image, and the second hardware processor … using the number of customers calculated for each of the sub regions.
Yoshitake teaches wherein the region includes a plurality of sub regions, the customer service management apparatus further comprising a number calculator that calculates, for each of the sub regions, a number of customers staying in the sub region based on the received image, and the second hardware processor … using the number of customers calculated for each of the sub regions (pars. 6, 50-51, 69, and 119: numbers of persons staying in the plurality of areas within the store; capturing an image of inside of the store; generating stay information in which a location at which a person stays; presenting the states of customers who have stopped in each area within a store by displaying the states of customers who have stopped in each area within the store in a distinguishable manner by using a different display style in accordance with the number of customers staying in the area; stay information D600 stores the number of stayers, which is the number of customers who stayed in each area, the number of new stayers, which is the number of customers who moved to each area from a different location, and date and time in association with one another; may be performed by a processor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nadler/Hussami/Argue with Yoshitake because the references are analogous since they are each directed to computer implemented features for evaluating customer behavior to manage the provision of customer service, which is within Applicant’s field of endeavor of customer service management, whereas Hussami’s neural network based analysis of body state based on captured image data is reasonably pertinent to the problem with which applicant is concerned (automated gesture detection of subjects using image data and a trained neural network), and because modifying Nadler/Hussami/Argue to incorporate Yoshitake’s features for calculating using information about the number of customers staying in subregions of a store, as claimed, in order to provide marketing intelligence that would be helpful for decisions such as allocation of resources (e.g., employees) or placement of advertising displays (e.g., in high-traffic areas); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 8: Nadler does not teach the limitations of claim 8.
Argue teaches the second hardware processor increases the order of customer service by one for all of the customers staying in the sub region (pars. 17 and 48: If the customer is too far away for an estimated time until the customer's number is going to be called, the customer can be bumped down the queue; deli has the option of “bumping” the shopper back in the queue so that the shopper does not lose his or her place [wherein the bumping of customers in the queue increases the order of all customers in the deli queue as a result]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Nadler/Hussami/Argue/Yoshitake to include Argue’s increasing of the order of customer service for customers staying in a sub region, as claimed, in pursuit of adjusting a queue based on customer location (Argue at par. 17) or to aid a customer in keeping their place in line after previously losing their place (Argue at par. 48), which would help to reduce overall customer waiting time and thereby provide a faster shopping experience (Argue at par. 14); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Nadler, Hussami, and Argue do not teach wherein the second hardware processor determines whether or not the number of customers calculated for each of the sub regions is equal to or more than a predetermined threshold value, and when it is determined that the calculated number of customers is equal to or more than the predetermined threshold value.
Yoshitake teaches wherein the second hardware processor determines whether or not the number of customers calculated for each of the sub regions is equal to or more than a predetermined threshold value, and when it is determined that the calculated number of customers is equal to or more than the predetermined threshold value (pars. 132-133, 136, 141, 179, 190, 213, and 279: threshold values are used to classify the numbers of stayers in respective areas; The condition saving unit 404 saves condition information that specifies the condition for determining threshold values used to classify the numbers of stayers in the respective areas; determines appropriate threshold values in accordance with the duration indicated by the display target period, and classifies the numbers of stayers in the respective areas by using the threshold values; classifying the numbers of stayers in each area calculated by the acquisition unit 403 at predetermined time intervals by using threshold values, and display the generated map screens in chronological order).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Nadler/Hussami/Argue/Yoshitake by increasing the order of customer service using Yoshitake’s predetermined threshold value, as claimed, in order to provide marketing intelligence that would be helpful for decisions such as dynamic queue management based on customer priority, load balancing, or the like; and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 9: Nadler further teaches the second hardware processor calculates, for each of the customers who need customer service, a time during which the customer stays … from a time point when it is determined, by the customer service determiner, that customer service is necessary (par. 47: identifying the movement pattern from the data provided by sensor(s) 201 and/or tracking module 202. The movement pattern may be, for example, extended dwell time of customer 210 at a specific location or different locations in the store, repeat visits to a particular location in the store) and determines, by using the calculated time, the … customer service by giving priority to a customer staying longer than a predetermined threshold value … (pars. 47-49: identifying the movement pattern from the data provided by sensor(s) 201 and/or tracking module 202. The movement pattern may be, for example, extended dwell time of customer 210 at a specific location [wherein Nadler’s “extended dwell time” is a predetermined threshold value]; movement pattern is analyzed by pattern analysis module 203 to identify a potential requirement of assistance by the customer), but does not teach the region includes a plurality of sub regions in each of which a plurality of the customers stays, the customer service management apparatus further comprises a number calculator that calculates, for each of the sub regions, a number of customers who stay in the sub region and need customer service, based on the received image, … customer stays in each of the sub regions…, and determines…the order of customer service by giving priority…for each of the sub regions.
Argue teaches determines…the order of customer service by giving priority (par. 17: A warning interval can be adjusted based upon a location of the customer in relation to the counter. If the customer is thirty feet away from the counter, the interval can be set at one minute. If the customer is on the other side of a large store or stepped out of the store, then the interval can be set at a larger time in order to permit the customer to traverse the large distance. If the customer is too far away for an estimated time until the customer's number is going to be called, the customer can be bumped down the queue [i.e., customer’s priority in the queue may be assigned/changed]. In such an instance, the customer can be notified that the assigned spot in the queue has been lost based upon the location of the customer; The bumped customer can be re-entered at the end of the queue with a new number. In the alternative, the bumped customer can be provided with a new space in the queue between two existing spaces in the queue).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Nadler/Hussami/Argue such that Nadler’s staying time for determining customer service is used in conjunction with Argue’s priority ordering of customer service, as claimed, in pursuit of reducing customer waiting time and thereby providing a faster shopping experience (Argue at par. 14); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Nadler and Argue do not teach he region includes a plurality of sub regions in each of which a plurality of the customers stays, the customer service management apparatus further comprises a number calculator that calculates, for each of the sub regions, a number of customers who stay in the sub region and need customer service, based on the received image, … customer stays in each of the sub regions, and for each of the sub regions.
Yoshitake teaches he region includes a plurality of sub regions in each of which a plurality of the customers stays, the customer service management apparatus further comprises a number calculator that calculates, for each of the sub regions, a number of customers who stay in the sub region and need customer service, based on the received image, … customer stays in each of the sub regions …, and …for each of the sub regions (pars. 6, 50-51, 69, and 119: numbers of persons staying in the plurality of areas within the store; capturing an image of inside of the store; generating stay information in which a location at which a person stays; presenting the states of customers who have stopped in each area within a store by displaying the states of customers who have stopped in each area within the store in a distinguishable manner by using a different display style in accordance with the number of customers staying in the area; stay information D600 stores the number of stayers, which is the number of customers who stayed in each area, the number of new stayers, which is the number of customers who moved to each area from a different location, and date and time in association with one another; may be performed by a processor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Nadler/Hussami/Argue with Yoshitake because the references are analogous since they are each directed to computer implemented features for evaluating customer behavior to manage the provision of customer service, which is within Applicant’s field of endeavor of customer service management, and because modifying Nadler/Hussami/Argue with Yoshitake’s calculated customers with respect to sub regions, as claimed, would provide customer service intelligence such as to help shoppers achieve a faster shopping experience (Argue at par. 14); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sorensen (US 2004/0111454): discloses shopping environment analysis techniques, including features for exposing areas in which staff are insufficiently allocated to help shoppers (par. 64).
Derza (US Patent No. 11,763,366): discloses automatic customer assistance initialization based on computer vision video analysis.
Oh et al. (US 2022/0180640): discloses an electronic device for providing a response for a customer (e.g., a mobile robot that responds to a customer, Spec. at par. 54).
D. A. Mora Hernandez, O. Nalbach and D. Werth, "How Computer Vision Provides Physical Retail with a Better View on Customers," 2019 IEEE 21st Conference on Business Informatics (CBI), Moscow, Russia, 2019, pp. 462-471: discloses technologies employing computer vision to assist retailers in improving customers’ shopping experience.
C. H. Cheng, C. Y. Chen, J. J. Liang, T. N. Tsai, C. Y. Liu and T. H. S. Li, "Design and implementation of prototype service robot for shopping in a supermarket," 2017 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan, 2017, pp. 46-51: discloses a service robot system to create flexible interaction with customers in a retail shopping environment.
L. Jeanpierre et al., "COACHES: An assistance multi-robot system in public areas," 2017 European Conference on Mobile Robots (ECMR), Paris, France, 2017, pp. 1-6: discloses techniques for using fixed cameras and mobile robots to assist humans in public spaced.
I. Kramer, R. Memmesheimer and D. Paulus, "Customer Interaction of a Future Convenience Store with a Mobile Manipulation Service Robot," 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), Barcelona, Spain, 2021, pp. 1-7: discloses features for integrating mobile manipulation service robots to support customer interaction in convenience stores.
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 date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Timothy A. Padot whose telephone number is 571.270.1252. The Examiner can normally be reached on Monday-Friday, 8:30 - 5:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian Epstein can be reached at 571.270.5389. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/TIMOTHY PADOT/
Primary Examiner, Art Unit 3625
06/29/2026
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