DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1, 6, 7, 9-12, 16, 17, and 19-27 are pending and examined herein per Applicant’s 12/22/2025 filing with the Office. Claims 1 and 11 are amended. Claim 8 and 18 are canceled. Claims 2-5 and 13-15 were previously canceled. No claims are newly added or withdrawn.
Response to Amendment
Applicant’s amendments do not overcome the 35 USC 101 or 35 USC 103 rejection of the previous Office action.
Response to Arguments
Applicant's arguments filed with respect to the 35 USC 101 rejection of the previous Office action have been fully considered but they are not persuasive. Applicant argues:
Upon review, Applicant respectfully submits that claim 1 does not recite any of the types of activities recognized in the MPEP as managing personal behavior, Remarks p. 12.
Respectfully, The Office disagrees with Applicant’s position. Examples given in the MPEP are not meant to be limiting or exhaustive. Applicant’s invention monitors queue positions of a person in a physical location to determine a queue wait time in order to plan attendant/resource assignment. Under the abstract category of certain methods of organizing human activity falls the MPEP 2106.04(a)(2) provides, “managing personal behavior and relationships or interactions between people” include social activities, teaching, and following rules or instructions.
The Office finds that the instant claimed invention manages personal behavior and the following of instructions. Where adjusting the schedule of at least one attendant both manages the personal behavior (when the attendant works) and cause the attendant to following of instructions (adjusted schedule). For these reasons the rejection of the previous Office action is maintained as updated below.
A person skilled in the art would appreciate that a human mind does not perform machine learning operations, Remarks p. 12.
It is noted that in the updated rejection the abstract category of a mental process has been withdrawn.
Claim 1, expressly recites specific details regarding analysis of the content of the interaction, including analyzing, with a machine learning model, content of the interaction represented in the interaction data, including performing, with the machine learning model, speech recognition operations on the interaction data, and determining, with the machine learning model, whether the attendant spoke a set of content that satisfies a set of content compliance requirements that are defined as a function of a context and a time period in which the interaction occurred. Remarks p. 14.
Respectfully, Parker v. Flook is provides insight in to the abstract category of mathematical concepts. Nonetheless in MPEP 2106.05(d) Parker v. Flook provides things that courts have recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity - Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values). In the instant claims the machine learning model is applied to the sensor and interaction data to produce content of the interaction and if the content meets compliance requirements at high level of generality and without using or relating this data to the other limitations the claims.
The machine learning models are ancillary to the purpose of the claim. For these reasons the rejection of the previous Office action is maintained as updated below.
The prior art of record does not teach or suggest each of the features recited in any of the pending claims, as described in more detail with reference to the rejections under 103. Remarks p. 16.
Respectfully, art is not a consideration under 35 USC 101. It is noted that under Berkheimer Memorandum the elements of the claim when read in light of the specific would require the addition of 35 USC 112(a) if Applicant wants additional patentable weight applied to the elements. For example, the element of “pressure sensor” is described in the specification as “sensors 110 may be embodied as, or otherwise include, pressure sensors, optical sensors, light sensors, electromagnetic sensors, hall effect sensors, audio sensors (e.g., microphones), motion sensors, piezoelectric sensors, cameras, and/or other types of sensors” (Spec[41]) and “pressure sensors such that the client's position is detected and known when the client steps on one of the pressure sensors.” (Spec. [62]). In the disclosure one of ordinary skill in the art would understood any floor pressure sensor would meet the bounds of the claimed element. Therefore the element is well known in the art.
The machine learning models are ancillary to the purpose of the claim and do not provide significantly more or practical application to the identified abstract idea. For these reasons the rejection of the previous Office action is maintained as updated below.
Applicant's arguments filed with respect to the 35 USC 103 rejection are directed to newly added subject matter which is fully addressed in the updated rejection 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, 6-12, and 16-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. certain methods of organizing human activity) without practical application or significantly more when the elements are considered individually and as an ordered combination.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
Yes, the claims fall within at least one of the four categories of patent eligible subject. Claims 1, 6-10 and 24-27 are to a method (process) and claims 11, 12, 16-23 are to a system (machine).
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Yes, the claims are found to recite an abstract idea. Specifically, the abstract idea of certain methods of organizing human activity. Where certain methods of organizing human activity include 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) (see MPEP § 2106.04(a)(2), subsection II); and
Claim 1 (as a representative claim) recites the following, where the limitations found to contain elements of the abstract idea are in bold italics:
1. A method for analysis of in-person attendant interactions, the method comprising:
receiving first sensor data generated by a first pressure sensor positioned at a start queue position of a queue within a monitored area;
determining that a person is physically located at the start queue position based on the first sensor data generated by the first pressure sensor;
determining a start queue time associated with a time at which the person is located at the start queue position of the queue within the monitored area in response to determining that the person is physically located at the start queue position;
receiving second sensor data generated by a second pressure sensor positioned at an end queue position of the queue within the monitored area;
determining that the person is physically located at the end queue position based on the second sensor data generated by the second pressure sensor;
determining an end queue time associated with a time at which the person is located at the end queue position of the queue within the monitored area in response to determining that the person is physically located at the end queue position;
recording interaction data of an interaction between the person and an attendant when the person is located at the end queue position;
analyzing with a machine learning model, content of the interaction represented in the interaction data, including performing, with the machine learning model, speech recognition operations on the interaction data, and determining with the machine learning model, whether the attendant spoke a set of content that satisfies a set of content compliance requirements that are defined as a function of a context and a time period in which the interaction occurred;
determining a wait time of the person in the queue based on the start queue time and the end queue time;
determining an interaction time of the interaction between the person and the attendant based on the interaction data; and
adjusting an attendant schedule of one or more attendants of the monitored area to improve one or more of the wait time and the interaction time.
The claims are found to be directed towards queue management when considered as an ordered combination and as individual limitations. The claims assist in determining an attendant’s work schedule based on observed/sensed queue data and interaction data. A schedule by its nature is a set of instruction – where and when a person is supposed to be at a given time. Where adjusting the schedule of at least one attendant both manages the personal behavior (when the attendant works) and cause the attendant to following of instructions (adjusted schedule). As such the claimed invention is found to be directed towards an abstract idea of methods of organizing human activity - managing personal behavior, which includes following instructions.
It is further noted the newly amended limitation drawn to the machine learning model is ancillary to the purpose of the claim, since it is not tied or relied on in any of the other limitations.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claimed invention does not recite additional elements that integrate the abstract idea into a practical application. Where a practical application is described as integrating the abstract idea by applying it, relying on it, or using the abstract idea in a manner that imposes a meaningful limit on it such that the claim is more than a drafting effort designed to monopolize it, see October 2019: Subject Matter Eligibility at p. 11.
The identified judicial exception is not integrated into a practical application. In particular, the claims recites the additional limitations see non-bold-italicized elements above. The “receiving” and “recording” elements are determined to be insignificant extra solution activity – steps of data gathering.
Where 2106.05(g) MPEP states, “term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.”
The Office finds that merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea; adding insignificant extra solution activity to the judicial exception; or only generally linking the use of the abstract idea to a particular technological environment or field is not sufficient to integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
No, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and as part of the ordered combination. It is further noted that the claimed invention uses generic computing components, see for examples the instant specification at [45] “the computing device 200 may be embodied as a server, desktop computer, laptop computer, tablet computer, notebook, netbook, UltrabookTM, cellular phone, mobile computing device, smartphone, wearable computing device, personal digital assistant, Internet of Things (IoT) device, processing system, wireless access point, router, gateway, and/or any other computing, processing, and/or communication device”.
Further it is found that the added machine learning steps for analyzing simply apply the model to the interaction data. The MPEP2106.05(f) provides, “the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.”
Where 2106.05(d)(I)(2) of the MPEP states, “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").”
These limitations do NOT offer an improvement to another technology or technical field; improvements to the functioning of the computer itself; apply the judicial exception with, or by use of, a particular machine; effect a transformation or reduction of a particular article to a different state or thing; add a specific limitation other than what is well-understood, routine and conventional in the field, or add unconventional steps that confine the claim to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, these additional limitations when considered individually or in combination do not provide an inventive concept that can transform the abstract idea into patent eligible subject matter.
The other independent claims recite similar limitations and are rejected for the same reasoning given above.
The dependent claims do not further limit the claimed invention in such a way as to direct the claimed invention to statutory subject matter.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6, 9, 11-16, 19, 21-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hammoud (US 2013/0117695 A1) in view of Demir et al (US 2018/0330815 A1) and Haukioja et al (US 2019/0253558 A1).
Claims 1 and 11
Hammoud A method for analysis of in-person attendant interactions (Hammoud [7] “method includes estimating a wait time to reach a service point for a user at a first position of a physical queue”), the method comprising:
receiving first sensor data generated by a first pressure sensor positioned at a start queue position of a queue within a monitored area (Hammoud [44] “one or more cameras at physical queue 160 may capture images of physical queue 160, and movement of physical queue 160 may be detected based on the captured images. In some examples, a motion sensor may be placed at physical queue 160 to detect movement of physical queue 160. In some examples, weight sensors may be placed at physical queue 160 to detect movement of physical queue”, [76] “user detection module 422 may determine the location of a user within a physical queue based on sensors at positions within the physical queue that senses an NFC-enabled mobile device associated with the user”, and [77] “sensors at physical queues may be able to sense an NFC-enabled mobile device associated with the user if the user enters a physical queue.” It is noted that Hammoud’s example use NFC sensors, however the Hammoud also teaches the sensors could be weight sensors, which are a type of pressure sensor. The Office finds the NFC sensor to serve the same function as the claimed pressure sensor as a matter of design choice);
determining that a person is physically located at the start queue position based on the first sensor data generated by the first pressure sensor (Hammoud [27] “Physical queue 160 may be divided up into one or more successive positions. Second user 102 may be located in first position 110 of physical queue 160. Third user 104 may be located in second position 112 of physical queue 160 behind first position 110. Fourth user 108 may be located in third position 114 of physical queue 160 behind second position 112. Each of the positions 110, 112, and 114 within physical queue 160 may have a terminal associated with the position.” And [79] “system may determine that a user has entered a particular queue at position N (302)”, where entering the queue is the equivalent of the claimed start queue position also see fig. 5);
determining a physical location of a person within a monitored area based on sensor data generated by one or more sensors (Hammond [76] “user detection module 422 may determine the location of a user within a physical queue based on sensors at positions within the physical queue that senses an NFC-enabled mobile device associated with the user”);
determining a start queue time associated with a time at which the person is located at the start queue position of the queue within the monitored area in response to determining that the person is physically located at the start queue position (Hammoud [79] “system may determine that a user has entered a particular queue at position N (302), and may estimate a wait time for that user at location N (304). The system may also set the value of a variable S to zero (306) to help track the position of the user in the queue by subtracting the value of S from the value of position N to determine the position of the user in the queue”);
receiving second sensor data generated by a second pressure sensor positioned at an end queue position of the queue within the monitored area (Hammoud [26] “Second user 104 may be considered to be at the head of physical queue 160 because second user 104 is at a position in physical queue 160 that is closest to service point 150, while fourth user 108 may be considered to be at the tail of queue physical 160 because fourth user 108 is the user in physical queue 160 that is farthest from service point 150” and [49] “ mobile device 240 associated with second user 204 may be tracked to locate second user 204. For example, mobile device 240 may be an NFC-enabled mobile phone, and NFC sensors at first physical queue 260 and second physical queue 280 may be able to sense mobile device 240 if second user 204 enters first physical queue 260 or second physical queue”);
determining that the person is physically located at the end queue position based on the second sensor data generated by the second pressure sensor (Hammoud [38] “First user 102 (not shown) has finished his transactions at service point 150 and has left service point 150. Second user 104, who was previously at the head of physical queue 160 at first position 110, has now left physical queue 160 and has advanced to service point 150. Third user 106, who was previously at second position 112 of physical queue 160, has now advanced to the head of physical queue 160 at first position 110. Fourth user 108, who was previously at the tail of physical queue 160 at third position 114, has now advanced to second position 112. Fifth user 110 has now entered physical queue 160 at the tail of queue 160 in third position 114.”, [44] “ one or more cameras at physical queue 160 may capture images of physical queue 160, and movement of physical queue 160 may be detected based on the captured images. In some examples, a motion sensor may be placed at physical queue 160 to detect movement of physical queue 160. In some examples, weight sensors may be placed at physical queue 160 to detect movement of physical queue 160.”);
determining an end queue time associated with a time at which the person is located at the end queue position of the queue within the monitored area in response to determining that the person is physically located at the end queue position (Hammoud [31] “Once a transaction time has been estimated for each user in front of the user, an estimated wait time may be calculated for the user by adding up the estimated transaction times for all of the users in front of the user in physical queue”);
recording interaction data of an interaction between the person and an attendant when the person is located at the end queue position (Hammoud [31] “the wait time for a user at physical queue 160 waiting to reach service point 150 may be estimated. Estimating the wait time for the user may include estimating the transaction time for each person waiting in physical queue 160 in front of the user. In the context of a checkout line at a store, a user's transaction time, or the time it takes the person to finish performing a transaction at service point 150, may be estimated based on estimating the amount of items the user has in his shopping cart.” And [69] “may save and retrieve data entered by a user during the course of user interactions with an activity”);
determining a wait time of the person in the queue based on the start queue time and the end queue time (Hammoud [31] “Once a transaction time has been estimated for each user in front of the user, an estimated wait time may be calculated for the user by adding up the estimated transaction times for all of the users in front of the user in physical queue”);
determining an interaction time of the interaction between the person and the attendant based on the interaction data (Hammoud [72] “In the context of a checkout line at a store, a user's transaction time, or the time it takes the user to finish performing a transaction at a service point”); and
Hammoud does not expressly teach the limitation of analyzing with a machine learning model, content of the interaction represented in the interaction data, including performing, with the machine learning model, speech recognition operations on the interaction data, and determining with the machine learning model, whether the attendant spoke a set of content that satisfies a set of content compliance requirements that are defined as a function of a context and a time period in which the interaction occurred;
Haukioja in the analogous art of service level agreement compliance teaches the claimed limitation of analyzing with a machine learning model, content of the interaction represented in the interaction data, including performing, with the machine learning model, speech recognition operations on the interaction data, and determining with the machine learning model, whether the attendant spoke a set of content that satisfies a set of content compliance requirements that are defined as a function of a context and a time period in which the interaction occurred (Haukioja [47] “ artificial intelligence, the system interprets the script to generate a referential model for analyzing customer calls for performance metrics and SLA compliance . . . agent/customer interaction will be sampled for patterns matching the informational content and emotional overlay patterns with the referential model and weighted average SLA metrics will be computed for performance, compliance, resolution and satisfaction” and [57] “an output of the application specific SLA metrics in a reporting cycle, whether that is real-time live data, or daily, weekly, or monthly reporting. The system receives input from the environment”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Hammoud the an output of the application specific SLA metrics in a reporting cycle, whether that is real-time live data, or daily, weekly, or monthly reporting. The system receives input from the environment as taught by Haukioja since 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.
Hammoud does not expressly teach the limitation of adjusting an attendant schedule of one or more attendants of the monitored area to improve one or more of the wait time and the interaction time.
Demir, in the analogous art of monitoring and interacting with occupants of a health care facility teaches the claimed limitation of adjusting an attendant schedule of one or more attendants of the monitored area to improve one or more of the wait time and the interaction time (Demir [7] “processing hub may transmit a notification to an electronic device associated with facility staff, in response to determination that the number of people waiting at the reception desk exceeds a directed threshold. This information may be utilized to, e.g., allow facility staff to quickly redeploy resources or otherwise address unexpectedly high check in times.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Hammoud the adjusting an attendant schedule of one or more attendants of the monitored area to improve one or more of the wait time and the interaction time as taught by Demir since 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.
With respect to independent system claim 11 recites substantially similar limitations as those rejected above is also rejected for the same reasons given above. Further Hammoud in view of Demir teach the additional limitations:
11. A system for analysis of in-person attendant interactions (Hammoud [51] “an activities management system 300 may include an activities management server”), the system comprising:
a plurality of sensors positioned at a monitored area and configured to generate sensor data (Hammoud [49] “NFC sensors at first physical queue 260 and second physical queue 280 may be able to sense mobile device 240 if second user 204 enters first physical queue 260 or second physical queue 280”), wherein the plurality of sensors comprise a first pressure sensor positioned at a start queue position of a queue within the monitored area and a second pressure sensor positioned at the end queue position of the queue (Hammoud [44] “a motion sensor may be placed at physical queue 160 to detect movement of physical queue 160. In some examples, weight sensors may be placed at physical queue 160 to detect movement of physical queue 160” see weight sensors and [76] “determine the location of a user within a physical queue based on sensors at positions within the physical queue that senses an NFC-enabled mobile device associated with the user”);
at least one processor (Hammoud [8] “computing device includes one or more processors” also see fig.4 at 402); and
at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor (Hammoud [65] “ executable software application running on one or more processors 402 and stored in memory 404 or one or more storage devices” also see fig.4 at 408), causes the system to:
Claims 6 and 16
Hammoud in view of Demir and Haukioja teach all the limitations of the method of claim 1, further comprising determining a physical location of the attendant based on third sensor data generated by one or more additional sensors (Hammoud [25] and [46]); and
wherein recording the interaction data of the interaction between the person and the attendant comprises recording the interaction data when the person is located at the end queue position and the attendant is located at a queue handling position (Hammoud [39] and [69]).
Claims 9 and 19
Hammoud in view of Demir and Haukioja teach all the limitations of the method of claim 1, further comprising transmitting the first sensor data and the second sensor data to a cloud-based computing system via an Application Programming Interface (API) (Hammoud [21] and [57], see remote server);
wherein determining the wait time of the person comprises determining the wait time of the person by the cloud-based computing system (Hammoud [31]); and
wherein determining the interaction time of the interaction comprises determining the interaction time of the interaction by the cloud-based computing system (Hammoud [72]).
Claim 12
Hammoud in view of Demir and Haukioja teach all the limitations of the system of claim 11, Hammoud does not teach but Demir in an analogous art does teach wherein to adjust the attendant schedule of the one or more attendants of the monitored area comprises to increase a number of attendants scheduled for a predefined shift (Demir [55] and [57]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Hammoud the wherein to adjust the attendant schedule of the one or more attendants of the monitored area comprises to increase a number of attendants scheduled for a predefined shift as taught by Demir since 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 21 and 25
Hammoud in view of Demir and Haukioja teach all the limitations of the system of claim 11, further comprising receiving third sensor data generated by one or more other sensors of the plurality of sensors within the monitored area, the third sensor data is indicative of one or more of a physical location of the person within the monitored area or the interaction between the person and the attendant (Hammoud [39] and fig 1A and 1B).
Claims 22 and 26
Hammoud in view of Demir and Haukioja teach all the limitations of the system of claim 21, wherein the one or more other sensors of the plurality of sensors comprises a camera (Hammoud [32], [51], [55] and [77]).
Claims 23 and 27
Hammoud in view of Demir and Haukioja teach all the limitations of the system of claim 11, wherein the plurality of sensors further comprises a third pressure sensor positioned at an intermediate queue position of the queue within the monitored area, wherein the intermediate queue position is between the start queue position and the end queue position (Hammoud [55] where the weight sensor is a type of pressure sensor [44]); and
wherein the plurality of instructions further causes the system to: receive third sensor data generated by the third pressure sensor, and determine that a person is physically located at the intermediate queue position based on the third sensor data generated by the third pressure sensor (Hammoud fig 1A and 1B, [25-27], and [79] where the system of Hammoud disclose track the user at multiple queue positions).
Claim 24
Hammoud in view of Demir and Haukioja teach all the limitations of the method of claim 1, Hammoud does not teach but Demir in an analogous art does teach wherein adjusting the attendant schedule of the one or more attendants of the monitored area comprises increasing a number of attendants scheduled for a predefined shift (Demir [56-57], see additional resources).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Hammoud the adjusting the attendant schedule of the one or more attendants of the monitored area comprises increasing a number of attendants scheduled for a predefined shift as taught by Demir since 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(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hammoud (US 2013/0117695 A1) in view of Demir et al (US 2018/0330815 A1) and Haukioja et al (US 2019/0253558 A1) in further view of McCord et al (US 9,654,633 B2).
Claims 7 and 17
Hammoud in view of Demir and Haukioja teach all the limitations of the method of claim 1, however neither Hammoud, Demir, nor Haukioja teach the method further comprising the following limitations. McCord in an analogous art teaches:
analyzing a plurality of wait times including the wait time of the person in the queue based on a predefined optimal wait time, wherein the predefined optimal wait time is defined by a minimum acceptable queue duration and a maximum acceptable queue duration (McCord 25:24-27, 31:12-15, and 31:33-39); and
analyzing a plurality of interaction times including the interaction time of the interaction between the person and the attendant based on a predefined optimal interaction time, wherein the predefined optimal interaction time is defined by a minimum acceptable interaction duration and a maximum acceptable interaction duration. (McCord 29:37-42 and 32:36-55)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Hammoud in view of Demir and Haukioja the analyzing a plurality of wait times including the wait time of the person in the queue based on a predefined optimal wait time, wherein the predefined optimal wait time is defined by a minimum acceptable queue duration and a maximum acceptable queue duration; and analyzing a plurality of interaction times including the interaction time of the interaction between the person and the attendant based on a predefined optimal interaction time, wherein the predefined optimal interaction time is defined by a minimum acceptable interaction duration and a maximum acceptable interaction duration as taught by McCord since 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(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hammoud (US 2013/0117695 A1) in view of Demir et al (US 2018/0330815 A1) and Haukioja et al (US 2019/0253558 A1) and in further view of Friio (US 2023/0186317 A1)
Claims 10 and 20
Hammoud in view of Demir teach all the limitations of the method of claim 1, however neither Hammoud nor Demir teach the method further comprising the following limitations. Friio in an analogous art teaches: analyzing the wait time of the person in the queue and the interaction time of the interaction based on a machine learning model of a contact center system. (Friio [38-39]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Hammoud in view of Demir the further comprising analyzing the wait time of the person in the queue and the interaction time of the interaction based on a machine learning model of a contact center system as taught by Friio since 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.
Napoli (US 2022/0414566 A1) teaches workflow distributor may send via an API or function call via an interface one or more command or instructions to a robot or MHE to cause such robot or MHE to perform a function or task corresponding to or in accordance with the work assignment. Responsive to such command or instructions, the robot or MHE may automatically execute or perform the task or function. The workflow distributor may send via an API or function call via an interface one or more command or instructions to a user interface of the system on the client device that prompts a person corresponding to the work assignment to initiate performing the work assignment.
Tran (US 12,469,483) teaches the use of end-to-end voice data encryption to secure communications, the employment of biometric voice authentication to verify the identity of users based on unique vocal characteristics, and the implementation of regulatory compliance monitoring to ensure that voice communication and data processing operations adhere to applicable legal and technical standards.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FOLASHADE ANDERSON whose telephone number is (571)270-3331. The examiner can normally be reached Monday to Thursday 12:00 P.M. to 6:00 P.M. CST.
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, Rutao Wu can be reached at (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FOLASHADE ANDERSON/Primary Examiner, Art Unit 3623