DETAILED ACTION
The following is a Final Office action. In response to Examiner’s communication of 10/27/25, Applicant, on 12/12/25, amended claims 1-9. Claims 1-9 are now pending and have been rejected as indicated below.
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 .
Response to Amendments
Applicant’s amendments are acknowledged.
The 35 USC 101 rejection of claims 1-9 in regard to abstract ideas has been maintained in light of Applicant’s amendments and explanations.
New 35 USC § 103 rejections of claims 1-9 are applied in light of Applicant’s amendments and explanation.
Claim Rejections - 35 USC§ 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a queue management system for monitoring the creation and status of a queue. Examiner formulates an abstract idea analysis, following the framework described in the MPEP, as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 8) and "system" (claims 1-7 and 9).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
… acquire moving information from a measurement system;
manage the moving information regarding movement of a moving body;;
plan an estimated queue line for guiding the moving body to the queue based on the moving information such that at least a part of the queue of the moving bodies crosses a main trajectory
predict trajectory data at a target time using measured trajectory data stored in a trajectory database and obstacle data stored in a map database
detect the main trajectory in a target area and a time at which the main trajectory is formed based on the measured trajectory data and the predicted trajectory data
calculate adjustment data for adjusting a guidance position of the moving body to be lined up on the queue using the main trajectory, queue state data, and the predicted trajectory data;
calculate a maximum number of moving bodies to be lined up on the queue in the target area at a target time
define the estimated queue line at the target time on a predetermined time basis
Independent claims 8 and 9 recites substantially similar claim language.
Dependent claims 2-7 recite the same or similar abstract idea(s) as independent claims 1, 8, and 9 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-9 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to a queue management system for monitoring the creation and status of a queue and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
Step 2A - Prong 2: Claims 1-9 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" A moving body queue management system that manages a queue of moving bodies, the system comprising one or more memory devices having a program stored thereon that, when executed by one or more processors, cause the one or more processors to / A moving body queue management method of managing a queue of moving bodies using one or more processors, the method comprising: by the computer / A computer program storable in a non-transitory memory comprising instructions for causing one or more processors to function as moving body queue management for managing a queue of moving bodies, the computer program causing the one or more processors to: " (claims 1, 8, and 9), “wherein the one or more processors are configured to provide at least a part of the information to a calculation system relating to a source from which the queue is formed.,” (claim 7), however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of receiving data from a generic "system" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPEP 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1-9 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of a queue management system for monitoring the creation and status of a queue via a “queue management system", as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to a queue management system for monitoring the creation and status of a queue.
Claims 1-9 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2009/0034846 to Senior et al. (hereafter referred to as Singh) in view of U.S. Patent Application Publication Number 2021/0019528 to Ghadyali et al. (hereafter referred to as Ghadyali).
As per claim 1, Senior teaches:
A moving body queue management system that manages a queue of moving bodies, the system comprising one or more memory devices having a program stored thereon that, when executed by one or more processors, cause the one or more processors to (Paragraph Number [0050] teaches the computer system 102 is shown including a processing component 114 (e.g., one or more processors), a storage component 116 (e.g., a storage hierarchy), an input/output (I/O) component 118 (e.g., one or more I/O interfaces and/or devices), and a communications pathway 120. In general, the processing component 114 executes program code, such as the feature extraction program 104, feature analysis program 108, and queue attribute program 110, which are at least partially stored in storage component 116. While executing program code, the processing component 114 can read and/or write data to/from the storage component 116 and/or the I/O component 118. The communication pathway 120 provides a communications link between each of the components in computer system 102. The I/O component 118 can comprise one or more human I/O devices, which enable a human user 122 to interact with the computer system 102 (e.g., via the GUI 30, FIG. 3), and/or one or more communications devices to enable other computer system(s) to communicate with the computer system 102 using any type of communications link).
acquire moving information from a measurement system; manage the moving information regarding movement of a moving body (Paragraph Number [0026] teaches the present invention provides a framework for determining, measuring, and/or estimating (hereafter referred to collectively as "measuring") attributes of queues acquired, for example, by direct overhead or oblique cameras with fixed settings. An illustrative queue 10 is depicted in FIG. 1. The queue 10 is defined as a series of ordered and connected locations 12 in the field-of-view (FOV) of a video camera 14, where the starting and ending locations represent the entry point 16 and exit point 18 of the queue 10. The queue 10 represents a physical span in a scene 20. The queue 10 usually contains objects 22, such as humans, shopping carts, animals, vehicles, and/or the like, moving in a particular direction from the entry point 16 to the exit point 18 of queue 10. Often, one or more attributes of the queue 10 are of interest, such as the speed of the traffic movement in the queue 10, the number of moving objects 22 in the queue, the density of the queue 10, the average waiting time to exit the queue 10, and/or the like. Herein, a "waiting line" 24 is defined as the actual line formed by the objects 22 present in the queue 10. The waiting line 24 can vary based on the number and density of the objects 22 in the queue 10, as well as other factors).
plan an estimated queue line for guiding the moving body to the queue based on the moving information (Paragraph Number [0042] teaches to estimate the above-listed queue attributes, the correspondences of foreground ground patches are established over time. For example, the feature of a color histogram can be extracted from each foreground ground patch to provide color statistics. This can be achieved using any solution. Each foreground ground patch keeps a history of its extracted features at different time points. If an object moves inside the queue, it will pass through consecutive foreground ground patches. Thus, by finding matches between the foreground ground patches over time, the trace of the moving object can be reconstructed. Furthermore, the speed of the object's movement can also be estimated using the distance it travels and the time it takes to travel).
such that at least a part of the queue of the moving bodies crosses a main trajectory (Paragraph Number [0047] teaches training can be performed, for example, when the moving objects can be clearly distinguished from each other (e.g., low density traffic) and can be correctly tracked using a suitable tracking algorithm. Once the training objects are tracked, their trajectories are grouped together to obtain a super-track, which lays out the path of the queue. As shown in FIGS. 10A-10C, a super-track is initialized using a single object trajectory 50. The super-track envelope 52 (the width-span of the super-track) is iteratively updated by including more and more training object trajectories 50. The queue path is defined as the major axis of the generated super-track, while taking the envelope as the queue width. The ground patches (or other forms of queue representation) are automatically selected by equally sampling along the queue path or by detecting prominent points along the queue where the spatiotemporal curvature (speed and acceleration) is significant).
predict trajectory data at a target time using measured trajectory data stored in a trajectory database and obstacle data stored in a map database (Paragraph Number [0047] teaches training can be performed, for example, when the moving objects can be clearly distinguished from each other (e.g., low density traffic) and can be correctly tracked using a suitable tracking algorithm. Once the training objects are tracked, their trajectories are grouped together to obtain a super-track, which lays out the path of the queue. As shown in FIGS. 10A-10C, a super-track is initialized using a single object trajectory 50. The super-track envelope 52 (the width-span of the super-track) is iteratively updated by including more and more training object trajectories 50. The queue path is defined as the major axis of the generated super-track, while taking the envelope as the queue width. The ground patches (or other forms of queue representation) are automatically selected by equally sampling along the queue path or by detecting prominent points along the queue where the spatiotemporal curvature (speed and acceleration) is significant. Paragraph Number [0034] teaches there are many choices of the image features. For instance, the image features can be either low level features (such as color histograms, edge histograms, color moments, etc.), or high-level semantic features (such as humans, animals, vehicles, shopping carts, etc.), that are generated using, for example, object detection methods. The option of using a specific feature(s) is based on a user's preference and application requirements. Simpler (low-level) features yield faster queue attribute estimation, while more complex (often high-level) features can be used to handle more sophisticated tasks. A combination of features can also be used, for example, color histograms and detected persons. A similarity match using color histograms can comprise a color histogram intersection).
define the estimated queue line at the target time on a predetermined time basis (Paragraph Number [0027] teaches the attributes of a queue (e.g., queue 10, FIG. 1) can be classified into a plurality of different categories based on their nature. For example, one category contains attributes that can be measured at a given point in time. These attributes are called "time-point based attributes". Examples of time-point based attributes of a queue can include, for instance, queue density and the number of objects in the queue. A second category contains attributes that can only be measured over a period of time and which cannot be measured using a single image frame. These attributes are called "duration-based attributes". Examples of duration-based attributes of a queue can include, for instance, the average moving speed at a given location in the queue and the overall speed of the entire queue. Due to the different nature of these categories, they are measured in two separate ways. To this extent, time-point based attributes are measured based on the analysis of single video frame, while duration-based attributes are measured based on an analysis of video features over time (e.g., by analyzing a plurality of sequential video frames)).
Senior teaches a queue management system for monitoring the creation and status of a queue but does not explicitly teach determining distance between two bodies in the queue and notifying queue participants when the distances change past a threshold as described by the following citations from Ghadyali:
detect the main trajectory in a target area and a time at which the main trajectory is formed based on the measured trajectory data and the predicted trajectory data (Paragraph Number [0293] teaches in image portion 2706, the control system has detected a group comprising two people in the image portion 2706. The distance between the two people is small and the trajectory of the two people is similar. It is possible that these people are the start of a group of a line forming to get food in the cafeteria. Paragraph Number [0294] teaches as shown in FIG. 26B, in an operation 2652, the method 2650 comprises tracking, by the control system, an aspect of a movement, of a given object, in the group (e.g., objects entering and leaving the group, objects stopping in the group, etc.). FIG. 28 illustrates an example of tracking objects' movement in a group. The images 2800 show a group forming at the process point no matter which direction the line forms (e.g., line 2810 in image 2800A curves in a different direction that line 2820 in image 2800B). A process point can be an area, line or point where objects move through (e.g., for providing payment or identification). Payment or identification could be performed by providing, for instance, a ticket, an RFID tag, a badge, a QR code, or biometric scan at the process point).
calculate adjustment data for adjusting a guidance position of the moving body to be lined up on the queue using the main trajectory, queue state data, and the predicted trajectory data (Paragraph Number [0223] teaches in an operation 1485, the method 1480 comprises generating a predicted change in the first data value for the second image. In an operation 1486, the method 1480 comprises generating a predicted change in the second data value for the second image. For instance, Kalman filtering can be used to predict temporal change. Paragraph Number [0404] teaches monitoring or tracking in the physical environment 4509 can be used to feedback an updated current state 4510 of the physical environment to the computing agent. For instance, monitoring the physical environment 4509 may comprise obtaining image data captured over time of the physical environment 4509 and detecting one or more objects in the image data as described herein (e.g., detecting a location and/or trajectory of an object). The proposed action 4508 may comprise issuing a recommendation to change a location or trajectory of the object or another different object in the physical environment 4509. For instance, in the queue scenario a first person may be used to track a first state of formation of a queue, but a second person entering the queue may have a changed location or trajectory by opening up a second process point location for the second person. (See also Paragraph Number [0298])).
calculate a maximum number of moving bodies to be lined up on the queue in the target area at a target time (Paragraph Number [0322] teaches multiple camera feeds are used to capture multiple images at a same point in time. For instance, another camera captures an entrance region not fully shown in the image 3300. Statistics 3370 are displayed capturing metrics from all the camera feeds at a point in time. For instance, counts at the food region, dessert region, entrance region and agent region are displayed without having to show these areas. Cluster information can also be provided in the statistics 3370 to show information on groups (e.g., a max size)).
Both Senior and Ghadyali are directed to queue management. Senior discloses a queue management system for monitoring the creation and status of a queue. Ghadyali improves upon Senior by disclosing determining distance between two bodies in the queue and notifying queue participants when the distances change past a threshold. One of ordinary skill in the art would be motivated to further include determining distance between two bodies in the queue and notifying queue participants when the distances change past a threshold, to efficiently determine the efficiency of the queue as well as how orderly a queue is and whether proper distancing is being observed. Accordingly, 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 system and method of a queue management system for monitoring the creation and status of a queue in Senior to further utilize determining distance between two bodies in the queue and notifying queue participants when the distances change past a threshold as disclosed in Ghadyali, since the claimed invention is merely a combination of old elements, and in 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.
As per claim 2, the combination of Senior and Ghadyali teaches each of the limitations of claim 1.
In addition, Senior teaches:
where the queue overlaps the main trajectory and an area other than the first area (Paragraph Number [0036] teaches the waiting line length can be computed as depicted in FIG. 5. Initially, each ground patch of the queue is examined (B1) to determine if it is a foreground ground patch or not. Starting from the ground patch at the entry point of the queue, the first foreground ground patch is located (B2) and its location is recorded (B3). Similarly, starting from the ground patch at the exit point of the queue, the first foreground ground patch is located (B4) and its location is recorded (B5). Then, the waiting line length inside the queue is determined (B6) based on the distance between the first foreground patch at the entry point of the queue and the first foreground patch at the exit point of the queue. This distance comprises the distance it takes to walk between the first foreground patch at the entry point of the queue to the first foreground patch at the exit point of the queue along the queue path (i.e., it is not the direct Euclidean distance between the two ground patches)).
Senior teaches a queue management system for monitoring the creation and status of a queue but does not explicitly teach determining distance between two bodies in the queue and notifying queue participants when the distances change past a threshold as described by the following citations from Ghadyali:
wherein the one or more processors are configured to plan the estimated queue line such that a distance between adjacent moving bodies varies between a first area (Paragraph Number [0218] teaches generating, based on the first data value and the second data value, initial relative information between the first object and the second object. This could indicate information such as a current relative position between the objects. Paragraph Number [0236] teaches a distance is shown between derived center points for boards 1502, 1504, 1702, 1704, 1706, 1708, and 1710. Tight polygons from key points prediction and Kalman filtering can be drawn around the objects and identifiers are assigned to objects to track them as the objects move (e.g., id1-id7). The lines connecting centers of the objects are used to measure distances between them. Paragraph Number [0291] teaches a method 2650 for executing a control system. In an operation 2651, the method 2650 comprises detecting, by the control system, a group forming in the plurality of images based on: detecting a distance between the first object and the second object that is below a threshold; and determining a trajectory indication of the first object compared to the second object).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
As per claim 3, the combination of Senior and Ghadyali teaches each of the limitations of claims 1 and 2.
Senior teaches a queue management system for monitoring the creation and status of a queue but does not explicitly teach determining distance between two bodies in the queue and notifying queue participants when the distances change past a threshold as described by the following citations from Ghadyali:
wherein the one or more processors are configured to set the distance between adjacent moving bodies in the first area to be longer than the distance between adjacent moving bodies in the area other than the first area. (Paragraph Number [0319] teaches a distance between objects detected in a group is tracked and updated as it changes until the object leaves a group. For instance, there is a distance of 4 feet between objects in group 3350. In this case, the computing system refrains from tracking a respective distance when an object of the pair of objects leaves the group. For instance, object 3364 with identifier 64 leaves group 3362 so that the distance between objects is only tracked in the group remaining (e.g., 2 feet between the remaining objects). In this case objects are also no longer tracked when they exceed a certain distance (e.g., 6 feet for social distancing). Colors can also be used in a display to indicate whether an action should be taken. For instance, red could be used to indicate a distance of four feet or less and yellow is used to show between 5 feet and 6 feet. In response to a red indication an alert could be sent to the individuals in the group, but yellow no action will be taken. Other criteria could be required before sending an alert or detecting a group (e.g., a positive virus test case for one of the members of the group)).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
As per claim 4, the combination of Senior and Ghadyali teaches each of the limitations of claim 1.
In addition, Senior teaches:
wherein the one or more processors are configured to predict a length of the queue of the moving bodies (Paragraph Number [0040] teaches using the measurement of the waiting line length inside the queue, another queue attribute, "queue fullness", can be determined (E1, FIG. 8) by comparing the waiting line length with the queue length. If the length of the waiting line is equal or near to the length of the queue (E2), then the queue is declared as full. This attribute can be relaxed to incorporate (E3) the density information, such as the queue is full only if the waiting line length is near or equal to the length of the queue, and the density of the queue is above a desired threshold. The "queue fullness" attribute is important in many situations for waiting line management, such as stores, parks, public facilities, etc.).
and plans the estimated queue line based on the predicted length of the queue of the moving bodies and a direction of the main trajectory (Paragraph Number [0040] teaches using the measurement of the waiting line length inside the queue, another queue attribute, "queue fullness", can be determined (E1, FIG. 8) by comparing the waiting line length with the queue length. If the length of the waiting line is equal or near to the length of the queue (E2), then the queue is declared as full. This attribute can be relaxed to incorporate (E3) the density information, such as the queue is full only if the waiting line length is near or equal to the length of the queue, and the density of the queue is above a desired threshold. The "queue fullness" attribute is important in many situations for waiting line management, such as stores, parks, public facilities, etc.).
As per claim 5, the combination of Senior and Ghadyali teaches each of the limitations of claim 1.
Senior teaches a queue management system for monitoring the creation and status of a queue but does not explicitly teach determining distance between two bodies in the queue and notifying queue participants when the distances change past a threshold as described by the following citations from Ghadyali:
wherein the one or more processors are configured to guide at least a next moving body to be lined up on the queue to a waiting position based on the estimated queue line. (Paragraph Number [0267] teaches derived metrics associated with these identified objects are displayed. Example metrics include information about a set of objects (e.g., a number of objects, distances between objects) or about an individual object (e.g., position of an object, angle of an objects, velocity of an object, acceleration, orientation, skew, alignment or misalignment). This is possible from the key points prediction. In one or more embodiments, the derived metrics can be used to trigger alerts to a controller or notify an operator. These metrics may be used to drive the manufacturing operation in real time. For example, they may help predict and alert about possible jams. In the event of an indicated jam, the controller or operator is notified. Paragraph Number [0297] teaches the method 2650 comprises evaluating, by the control system, simulated actions in the simulated environment for a predefined objective for the physical environment. The predefined objective is related to an interaction between objects in the group and is predefined by a first user of the control system. For instance, the predefined objective may include reducing time objects spend in a group (e.g., reducing the amount of time people wait in a queue line). Additionally, or alternatively, the predefined objective comprises augmenting distance between objects in the group. For instance, an operator of the control system may be concerned about the closeness of people for transmitting communicable diseases. The control system according to the objective may send an alert to devices (e.g., devices associated with close people) to communicate an action to increase distance. For instance, the alert may include a message to take a step back or it could include a message to open another process point to encourage a group of people to separate to different process points).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
As per claim 6, the combination of Senior and Ghadyali teaches each of the limitations of claims 1 and 5.
Senior teaches a queue management system for monitoring the creation and status of a queue but does not explicitly teach determining distance between two bodies in the queue and notifying queue participants when the distances change past a threshold as described by the following citations from Ghadyali:
wherein the one or more processors are configured to provide guidance to other moving bodies that are moving on the main trajectory about presence of the moving body that is waiting at the waiting position (Paragraph Number [0267] teaches derived metrics associated with these identified objects are displayed. Example metrics include information about a set of objects (e.g., a number of objects, distances between objects) or about an individual object (e.g., position of an object, angle of an objects, velocity of an object, acceleration, orientation, skew, alignment or misalignment). This is possible from the key points prediction. In one or more embodiments, the derived metrics can be used to trigger alerts to a controller or notify an operator. These metrics may be used to drive the manufacturing operation in real time. For example, they may help predict and alert about possible jams. In the event of an indicated jam, the controller or operator is notified. Paragraph Number [0298] teaches the method 2650 comprises generating, by the control system, based on evaluated simulated actions and autonomously from involvement by any user of the control system, an indication to augment the physical environment. For instance, the indication to augment the physical environment may comprise sending an alert to a given user of the control system. For example, a manager of the cafeteria does not need to watch a line forming or define a schedule for agents but can instead wait for an alert to send a second agent. Additionally, or alternatively the indication to augment the physical environment may comprise sending an alert to a given object in the group. For instance, a person in the queue could receive a text message to increase the space between them in line or receive a sound or light message alerting them another agent spot is opening).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 1.
As per claim 7, the combination of Senior and Ghadyali teaches each of the limitations of claims 1 and 5.
In addition, Senior teaches:
wherein the one or more processors are configured to provide at least a part of the information to a calculation system relating to a source from which the queue is formed. (Paragraph Number [0053] teaches when the computer system 102 includes multiple computing devices, the computing devices can communicate over any type of communications link. Further, while performing the process described herein, the computer system 102 can communicate with one or more other computer systems using any type of communications link. In either case, the communications link can comprise any combination of various types of wired and/or wireless links; comprise any combination of one or more types of networks; and/or utilize any combination of various types of transmission techniques and protocols. Paragraph Number [0054] teaches it is understood that each of the process flows shown and described herein is only illustrative. To this extent, numerous variations of these process flows are possible, and are included within the scope of this invention. Illustrative variations include performing one or more processes in parallel and/or a different order, performing additional processes, not performing some processes, and/or the like. To this extent, the computer system 102, feature extraction program 104, feature analysis program 108, and/or queue attribute program 110 can utilize multiple tasks/threads/processes to perform the actions of the processes described herein).
As per claim 8, Senior teaches:
A moving body queue management method of managing a queue of moving bodies using one or more processors, the method comprising: by the computer (Paragraph Number [0050] teaches the computer system 102 is shown including a processing component 114 (e.g., one or more processors), a storage component 116 (e.g., a storage hierarchy), an input/output (I/O) component 118 (e.g., one or more I/O interfaces and/or devices), and a communications pathway 120. In general, the processing component 114 executes program code, such as the feature extraction program 104, feature analysis program 108, and queue attribute program 110, which are at least partially stored in storage component 116. While executing program code, the processing component 114 can read and/or write data to/from the storage component 116 and/or the I/O component 118. The communication pathway 120 provides a communications link between each of the components in computer system 102. The I/O component 118 can comprise one or more human I/O devices, which enable a human user 122 to interact with the computer system 102 (e.g., via the GUI 30, FIG. 3), and/or one or more communications devices to enable other computer system(s) to communicate with the computer system 102 using any type of communications link).
The remainder of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claim 9, Senior teaches:
A computer program storable in a non-transitory memory comprising instructions for causing one or more processors to function as moving body queue management for managing a queue of moving bodies, the computer program causing the one or more processors to (Paragraph Number [0050] teaches the computer system 102 is shown including a processing component 114 (e.g., one or more processors), a storage component 116 (e.g., a storage hierarchy), an input/output (I/O) component 118 (e.g., one or more I/O interfaces and/or devices), and a communications pathway 120. In general, the processing component 114 executes program code, such as the feature extraction program 104, feature analysis program 108, and queue attribute program 110, which are at least partially stored in storage component 116. While executing program code, the processing component 114 can read and/or write data to/from the storage component 116 and/or the I/O component 118. The communication pathway 120 provides a communications link between each of the components in computer system 102. The I/O component 118 can comprise one or more human I/O devices, which enable a human user 122 to interact with the computer system 102 (e.g., via the GUI 30, FIG. 3), and/or one or more communications devices to enable other computer system(s) to communicate with the computer system 102 using any type of communications link).
The remainder of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
Response to Arguments
Applicant’s arguments filed 12/12/2025 have been fully considered but they are not fully persuasive.
Applicant argues that the claims are eligible under 35 USC 101. (See Applicant’s Remarks, 12/12/2025, pgs. 6-11). Examiner respectfully disagrees. As noted in the 35 USC 101 analysis presented above, the claims recite an abstract concept that is encapsulated by decision making analogous to a method of organizing human activity. Examiner notes that each of the limitations that encapsulate the abstract concepts are recited in the above 35 USC 101. Implementing a queue tracking and management method and improving its functionality are abstract concepts. Being able to understand, add to, and manipulate data related to a queue is additionally an abstract concept. Other than storing the data related to the queue in a computer database, the queue data and its associated manipulations are wholly independent from computer technology. Additionally, the claims do not recite a practical application of the abstract concepts in that there is no specific use or application of the method steps other than to make conclusory determinations and provide for direction for either a person or machine to follow at some future time. The claims do not recite any particular use for these determinations and directions that improve upon the underlying computer technology (in this instance the computer software, processor, and memory). Instead, Examiner asserts that the additional elements in the claim language are only used as implementation of the abstract concepts utilizing technology. The concepts described in the limitations when taken both as a whole and individually are not meaningfully different than those found by the courts to be abstract ideas and are similarly considered to be certain methods of organizing human activity such as managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions. The steps are then encapsulated into a particular technological environment by executing these steps upon a computer processor and utilizing features such as a computer interface or sending and receiving data over a network or displaying information via a computerized graphical user interface. However, sending and receiving of information over a network and execution of algorithms on a computer are utilized only to facilitate the abstract concepts (i.e. selecting data on an interface, publishing/displaying information, etc.). As such, Examiner asserts that the implementation of the abstract concepts recited by the claims utilize computer technology in a way that is considered to be generally linking the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). Accordingly, Examiner does not find that the claims recite a practical application of the abstract concepts recited by the claims.
Applicant argues that the previously cited reference does not teach the newly amended portions including the new limitations recited by the independent claims. (See Applicant’s Remarks, 12/12/2025, pgs. 12-13). Examiner respectfully disagrees. Examiner notes that new citations from the previously cited references have been applied to the newly presented claim limitations as indicated in the above in the new 35 USC 103 rejection. Examiner has added and emphasized specific portions of the Senior and Ghadyali references to read on the new independent claims. As such, Applicant’s arguments directed towards the previous rejection are moot. In response to Applicant’s arguments, Examiner directs Applicant to review the new citations and explanations provided in the new 35 USC 103 rejection presented above..
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Conclusion
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 concerning this communication or earlier communications from the examiner should be directed to MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787.
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/M.H.D/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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