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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 7 recites the limitation "the guess" in the line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1 is rejected under 35 U.S.C. 102(a) (1) as being anticipated by Allison Robin et al. [US 20050114829 A1].
Regarding claim 1, Allison teaches:
1. A system for detecting and responding to suspicious activity (i.e. FIG. 1 illustrates an example computing (or general device) operating environment 100 that is capable of (fully or partially) implementing at least one system, device, apparatus, component, arrangement, protocol, approach, method, procedure, media, API, data structure, some combination thereof, etc. for facilitating the process of designing and developing a project as described herein- ¶0080), the system comprising:
a monitoring device configured to generate a stream of activity data detailing activity within an environment (i.e. The inputs to the risk identification step are the available knowledge of general and project specific risk in relevant business, technical, organizational, and environmental areas. Additional considerations are the experience of the team, the current organizational approach toward risk in the forms of policies, guidelines, templates, and so forth, and information about the project as it is known at that time, including history and current state. The team may choose to draw upon other inputs-anything that the team considers relevant to risk identification should be considered- ¶1141… At the start of a project, it is useful to use group brainstorming, facilitated sessions, or even formal workshops to collect information on project team and stakeholder perceptions on risks and opportunities. Industry classification schemes such as the SEI Software risk taxonomy, project checklists, previous project summary reports, and other published industry sources and guides may also be helpful in assisting the team in identifying relevant project risks- 1142); and
a computer system in communication with the monitoring device, wherein the computer system comprises processors and memory storing instructions that, when executed, cause the processors to perform operations (i.e. Implementations for facilitating the process of designing and developing a project may be described in the general context of processor-executable instructions. Generally, processor-executable instructions include routines, programs, protocols, objects, interfaces, components, data structures, etc. that perform and/or enable particular tasks and/or implement particular abstract data types- ¶0084… Example operating environment 100 includes a general-purpose computing device in the form of a computer 102, which may comprise any (e.g., electronic) device with computing/processing capabilities. The components of computer 102 may include, but are not limited to, one or more processors or processing units 104, a system memory 106, and a system bus 108 that couples various system components including processor 104 to system memory 106- ¶0085) comprising:
receiving, from the monitoring device, the stream of activity data; determining, based on the stream of activity data, whether a risk event is associated with activity of a user in the environment (i.e. FIG. 23 is a block diagram depicting an exemplary risk identification paradigm that produces at least one or more risk statements. It graphically depicts the inputs, outputs, and activities for the risk identification step- ¶1137);
determining a risk impact score for the user based on a determination that the risk event is associated with the activity of the user (i.e. Risk impact is an estimate of the severity of adverse effects, or the magnitude of a loss, or the potential opportunity cost should a risk be realized within a project. It should be a direct measure of the risk consequence as defined in the risk statement. It can either be measured in financial terms or with a subjective measurement scale- ¶1192… risk exposure is calculated by multiplying risk probability and impact- ¶1198);
determining a risk confidence score for the user indicating a likelihood that the activity of the user is associated with the risk event (i.e. Risk probability is a measure of the likelihood that the state of affairs described in the risk consequence portion of the risk statement will actually occur. Using a numerical value for risk probability is desirable for ranking risks. Risk probability should be greater than zero, or the risk does not pose a threat. Likewise, the probability should be less than 100 percent or the risk is a certainty--in other words, it is a known problem. Probabilities are notoriously difficult for individuals to estimate and apply, although industry or enterprise risk databases may be helpful in providing known probability estimates based on samples of large numbers of projects- ¶1187);
generating a response friction level for the activity of the user based on a determination of whether at least one of the impact score or the confidence score satisfies risk criteria(i.e. Use of a catastrophic impact scored where an artificially high value such as 100 is assigned will ensure that a risk with even a very low probability will rise to the top of the risk list and remain there- ¶1196), wherein the response friction level corresponds to (i.e. Risk mitigation planning involves actions and activities performed ahead of time to either prevent a risk from occurring altogether or to reduce the impact or consequences of its occurring to an acceptable level- ¶1275… Risk contingency planning involves creation of one or more fallback plans that can be activated in case efforts to prevent the adverse event fail. Contingency plans are necessary for all risks, including those that have mitigation plans. They address what to do if the risk occurs and focus on the consequence and how to minimize its impact- ¶1279) an escalation of a type of response to be taken for the activity of the user (i.e. Risk Mitigation Strategy. A paragraph or two of text describing the team strategy for mitigating a specific risk, including any assumptions that have been made… Risk Mitigation Strategy Metrics. The metrics the team will use to determine whether the planned risk mitigation actions are achieving the desired results… Risk Action Items. A list of actions the team is taking to implement the strategy for a specific risk, including the due date for completion and the person responsible- ¶1295-1297);
selecting a response from candidate manual responses and candidate automated responses to the activity of the user based, at least in part, on the friction level satisfying at least one of manual response criteria or automated response criteria (i.e. top risks list, and information from the risk management knowledge base, but also the project plans and schedules (as shown in FIG. 26)- ¶1242… The output from the risk action planning should include specific risk action plans implementing one of the six approaches discussed above at a step-by-step level of detail- ¶1287); and
executing instructions to perform the selected response (i.e. The goal of the risk control step is successful execution of the contingency plans that the project team has created for top risks- ¶1355).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Allison Robin et al. [US 20050114829 A1] in view of Samuel English Anthony et al. [US 20210182604 A1].
Regarding claim 2, Allison teaches all the limitations of claim 1.
However, Allison do not teach explicitly:
wherein determining whether the risk event is associated with the activity of the user in the environment is based on applying a model to the stream of activity data to identify a portion of the stream of activity data corresponding to the activity of the user in the environment, the model being trained to identify features in the portion of the stream of activity data indicative of the risk event
In a related field of endeavor, Samuel teaches:
wherein determining whether the risk event is associated with the activity of the user in the environment is based on applying a model to the stream of activity data to identify a portion of the stream of activity data corresponding to the activity of the user in the environment, the model being trained to identify features in the portion of the stream of activity data indicative of the risk event (i.e. The learning algorithm is optimized by a process of progressively adjusting the parameters of that algorithm in response to the characteristics of the images and summary statistics given to it in the training phase to minimize the error in its predictions of the summary statistics for the training images in step 804- ¶0063… An autonomous robot may be deployed to perform hospitality or security related tasks in settings such as airports, malls, hotels, offices, warehouses, and stores. For example, the robot may perform tasks such as greeting customers, answering questions, directing customers to a location, retrieving objects for the customer, identifying suspicious activity, and contacting security personnel. The robot may predict behavior of people nearby and determine which task to perform to best assist the people. The robot may determine whether a person is approaching with intentions to interact with the robot or happens to be heading in the robot's direction to avoid harassing a person with no intentions to interact. To make such predictions, the robot may capture video segments of its surroundings and apply a machine learning based model to the captured video segments that predicts summary statistics about the state of mind of people in the video segments- ¶0096).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison and Greg with the teachings Samuel to determine an action to be performed for managing movement of people captured in the video segments (Samuel- ¶0071).
Regarding claim 3, Allison teaches all the limitations of claim 2 and Allison further teaches:
wherein determining the risk impact score for the user comprises: identifying, a safety threat in the activity of the user (i.e. FIG. 23 is a block diagram depicting an exemplary risk identification paradigm that produces at least one or more risk statements. It graphically depicts the inputs, outputs, and activities for the risk identification step- ¶1137); and assigning the risk impact score above a threshold impact value based on the identified safety threat satisfying one or more safety risk criteria (i.e. Risk impact is an estimate of the severity of adverse effects, or the magnitude of a loss, or the potential opportunity cost should a risk be realized within a project. It should be a direct measure of the risk consequence as defined in the risk statement. It can either be measured in financial terms or with a subjective measurement scale- ¶1192… risk exposure is calculated by multiplying risk probability and impact- ¶1198).
However, Allison and Greg do not teach explicitly:
based on applying the model to the stream of activity data.
In a related field of endeavor, Samuel teaches:
based on applying the model to the stream of activity data (i.e. The learning algorithm is optimized by a process of progressively adjusting the parameters of that algorithm in response to the characteristics of the images and summary statistics given to it in the training phase to minimize the error in its predictions of the summary statistics for the training images in step 804- ¶0063… An autonomous robot may be deployed to perform hospitality or security related tasks in settings such as airports, malls, hotels, offices, warehouses, and stores. For example, the robot may perform tasks such as greeting customers, answering questions, directing customers to a location, retrieving objects for the customer, identifying suspicious activity, and contacting security personnel. The robot may predict behavior of people nearby and determine which task to perform to best assist the people. The robot may determine whether a person is approaching with intentions to interact with the robot or happens to be heading in the robot's direction to avoid harassing a person with no intentions to interact. To make such predictions, the robot may capture video segments of its surroundings and apply a machine learning based model to the captured video segments that predicts summary statistics about the state of mind of people in the video segments- ¶0096).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison and Greg with the teachings Samuel to determine an action to be performed for managing movement of people captured in the video segments (Samuel- ¶0071).
Claims 4-6, 8-11 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Allison Robin et al. [US 20050114829 A1] in view of Greg King et al. [US 20190088096 A1].
Regarding claim 4, Allison teaches all the limitations of claim 1.
However, Allison does not teach explicitly:
wherein executing the instructions to perform the selected response comprises at least one of (i) transmitting instructions to a display device configured to display information to the user to implement an automated response or (ii) transmitting instructions to the display device to implement a manual response.
In a related field of endeavor, Greg teaches:
wherein executing the instructions to perform the selected response comprises at least one of (i) transmitting instructions to a display device configured to display information to the user to implement an automated response or (ii) transmitting instructions to the display device to implement a manual response (i.e. Smart Video Monitoring System: The Trigger causes the appropriate video segment (incident location and time frame) to be pushed to a monitoring station, portable smart device, or other destination for immediate review. For example, a Loss Prevention professional in the store could receive the video on his/her mobile device; assess the situation; identify the suspect; and quickly take remedial action- ¶0135).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 5, Allison teaches all the limitations of claim 1.
However, Allison does not teach explicitly:
wherein executing the instructions to perform the selected response causes an automated response to be provided using a display device configured to display information to the user.
In a related field of endeavor, Greg teaches:
wherein executing the instructions to perform the selected response causes an automated response to be provided using a display device configured to display information to the user (i.e. Smart Video Monitoring System: The Trigger causes the appropriate video segment (incident location and time frame) to be pushed to a monitoring station, portable smart device, or other destination for immediate review. For example, a Loss Prevention professional in the store could receive the video on his/her mobile device; assess the situation; identify the suspect; and quickly take remedial action- ¶0135… This empowers these resources to proactively take appropriate actions to deter or apprehend- ¶0035… It is yet another object of the present invention to provide real time awareness of an actual theft in progress that can enable loss prevention professionals to apprehend a suspect and/or to increase the probability of conviction through the use of video push (to mobile devices and monitoring stations) and video capture triggered by MAS detected merchandise activity- ¶0042).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 6, Allison teaches all the limitations of claim 1.
However, Allison does not teach explicitly:
wherein executing the instructions to perform the selected response causes a manual response to be outputted by a display device to prompt an employee to perform the manual response with respect to the user.
In a related field of endeavor, Greg teaches:
wherein executing the instructions to perform the selected response causes a manual response to be outputted by a display device to prompt an employee to perform the manual response with respect to the user (i.e. Smart Video Monitoring System: The Trigger causes the appropriate video segment (incident location and time frame) to be pushed to a monitoring station, portable smart device, or other destination for immediate review. For example, a Loss Prevention professional in the store could receive the video on his/her mobile device; assess the situation; identify the suspect; and quickly take remedial action- ¶0135… This empowers these resources to proactively take appropriate actions to deter or apprehend- ¶0035… It is yet another object of the present invention to provide real time awareness of an actual theft in progress that can enable loss prevention professionals to apprehend a suspect and/or to increase the probability of conviction through the use of video push (to mobile devices and monitoring stations) and video capture triggered by MAS detected merchandise activity- ¶0042).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 8, Allison teaches all the limitations of claim 1.
However, Allison does not teach explicitly:
further comprising a display device configured to present information about the selected response to the user.
In a related field of endeavor, Greg teaches:
further comprising a display device configured to present information about the selected response to the user (i.e. Smart Video Monitoring System: The Trigger causes the appropriate video segment (incident location and time frame) to be pushed to a monitoring station, portable smart device, or other destination for immediate review. For example, a Loss Prevention professional in the store could receive the video on his/her mobile device; assess the situation; identify the suspect; and quickly take remedial action- ¶0135… This empowers these resources to proactively take appropriate actions to deter or apprehend- ¶0035… It is yet another object of the present invention to provide real time awareness of an actual theft in progress that can enable loss prevention professionals to apprehend a suspect and/or to increase the probability of conviction through the use of video push (to mobile devices and monitoring stations) and video capture triggered by MAS detected merchandise activity- ¶0042).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 9, Allison teaches all the limitations of claim 1.
However, Allison does not teach explicitly:
wherein the environment is a retail environment and the display device is a point of sale (POS) terminal, wherein the POS terminal further comprises at least one of (i) a scanner configured to scan item identifiers during a checkout process, (ii) a display device configured to display information during the checkout process, or (iii) a payment terminal configured to receive and process payment information during the checkout process.
In a related field of endeavor, Greg teaches:
wherein the environment is a retail environment and the display device is a point of sale (POS) terminal, wherein the POS terminal further comprises at least one of (i) a scanner configured to scan item identifiers during a checkout process, (ii) a display device configured to display information during the checkout process, or (iii) a payment terminal configured to receive and process payment information during the checkout process (i.e. Apparatus and systems using merchandise activity sensors for increasing the awareness of interactivity with merchandise on retail store displays (shelves, peg hooks, merchandise pushers, and other Point of Purchase displays) in order to facilitate more effective customer service, reduce theft and to provide additional analysis data related to merchandise/shopper interaction. Additionally, apparatus and systems for the conversion of cameras from passive to active deterrence devices aware of events occurring the environment. More particularly, the present invention pertains to awareness of behaviors often related to potential theft activity- Abstract… Many retailers anticipate adopting shopper self-checkout at the point of display using smart phones. Under this concept, a shopper scans merchandise before placing it in their shopping cart or bag. This provides a time savings for the shopper, who no longer needs to wait in line for a cashier or even a self-checkout POS station- ¶0134).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 10, Allison teaches:
10. A system for detecting and responding to suspicious activity (i.e. FIG. 1 illustrates an example computing (or general device) operating environment 100 that is capable of (fully or partially) implementing at least one system, device, apparatus, component, arrangement, protocol, approach, method, procedure, media, API, data structure, some combination thereof, etc. for facilitating the process of designing and developing a project as described herein- ¶0080), the system comprising:
one or more processors; and memory storing instructions that, when executed, causes the one or more processors to perform operations (i.e. Implementations for facilitating the process of designing and developing a project may be described in the general context of processor-executable instructions. Generally, processor-executable instructions include routines, programs, protocols, objects, interfaces, components, data structures, etc. that perform and/or enable particular tasks and/or implement particular abstract data types- ¶0084… Example operating environment 100 includes a general-purpose computing device in the form of a computer 102, which may comprise any (e.g., electronic) device with computing/processing capabilities. The components of computer 102 may include, but are not limited to, one or more processors or processing units 104, a system memory 106, and a system bus 108 that couples various system components including processor 104 to system memory 106- ¶0085) comprising:
receiving a stream of activity data; determining, based on the stream of activity data, whether a risk event is associated with activity of a user (i.e. FIG. 23 is a block diagram depicting an exemplary risk identification paradigm that produces at least one or more risk statements. It graphically depicts the inputs, outputs, and activities for the risk identification step- ¶1137);
determining a risk impact score for the user based on a determination that the risk event is associated with the activity of the user (i.e. Risk impact is an estimate of the severity of adverse effects, or the magnitude of a loss, or the potential opportunity cost should a risk be realized within a project. It should be a direct measure of the risk consequence as defined in the risk statement. It can either be measured in financial terms or with a subjective measurement scale- ¶1192… risk exposure is calculated by multiplying risk probability and impact- ¶1198);
determining (i) a manual response to the activity of the user and (ii) an automated response to the activity of the user based, at least in part, on the risk impact score satisfying response criteria(i.e. top risks list, and information from the risk management knowledge base, but also the project plans and schedules (as shown in FIG. 26)- ¶1242… The output from the risk action planning should include specific risk action plans implementing one of the six approaches discussed above at a step-by-step level of detail- ¶1287); and
executing instructions to perform the manual response and the automated response (i.e. The goal of the risk control step is successful execution of the contingency plans that the project team has created for top risks- ¶1355).
However, Allison does not teach explicitly:
from a sensor.
In a related field of endeavor, Greg teaches:
from a sensor (i.e. The SmartDome embodiment of the invention consists of a housing which is identifiable as a video surveillance camera, which may be in one of many forms. The invention will also operate equally well integrated into a housing of a real video dome camera. The SmartDome further consists of sensing technology capable of detecting real time shopper behaviors using one or more of the following: presence of one or more persons in an area of interest; actual or probable product removal from shelves; and door opening. Another key feature is local audio in the form of one or more sound effects, including voice, to draw attention to the camera when it has “gone active” due to detected suspicious activity and the ability to “escalate” audio notification based on perceived risk level- ¶0206).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 11, Allison teaches all the limitations of claim 10.
However, Allison does not teach explicitly:
further comprising a display device configured to present information about the selected response to the user.
In a related field of endeavor, Greg teaches:
further comprising a display device configured to present information about the selected response to the user (i.e. Smart Video Monitoring System: The Trigger causes the appropriate video segment (incident location and time frame) to be pushed to a monitoring station, portable smart device, or other destination for immediate review. For example, a Loss Prevention professional in the store could receive the video on his/her mobile device; assess the situation; identify the suspect; and quickly take remedial action- ¶0135).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 13, Allison teaches all the limitations of claim 10.
However, Allison does not teach explicitly:
wherein the risk event is associated with activity of a user during a checkout process in a retail environment.
In a related field of endeavor, Greg teaches:
wherein the risk event is associated with activity of a user during a checkout process in a retail environment (i.e. Many retailers anticipate adopting shopper self-checkout at the point of display using smart phones. Under this concept, a shopper scans merchandise before placing it in their shopping cart or bag. This provides a time savings for the shopper, who no longer needs to wait in line for a cashier or even a self-checkout POS station. It also reduces in-store labor, decreases the amount of in-store POS technology, and frees floor space previously used by POS stations for merchandising purposes. However, these benefits will no doubt be somewhat offset by those taking the increased opportunity to steal merchandise- ¶0134).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 14, Allison teaches all the limitations of claim 10.
However, Allison does not teach explicitly:
wherein executing the instructions to perform the automated response causes the automated response to be provided using a display device.
In a related field of endeavor, Greg teaches:
wherein executing the instructions to perform the automated response causes the automated response to be provided using a display device(i.e. Smart Video Monitoring System: The Trigger causes the appropriate video segment (incident location and time frame) to be pushed to a monitoring station, portable smart device, or other destination for immediate review. For example, a Loss Prevention professional in the store could receive the video on his/her mobile device; assess the situation; identify the suspect; and quickly take remedial action- ¶0135).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 15, Allison teaches all the limitations of claim 10.
However, Allison does not teach explicitly:
wherein executing the instructions to perform the manual response causes a manual response to be outputted by a display device to prompt an employee to perform the manual response with respect to the user.
In a related field of endeavor, Greg teaches:
wherein executing the instructions to perform the manual response causes a manual response to be outputted by a display device to prompt an employee to perform the manual response with respect to the user (i.e. The correlation system immediately pushes a notification to store personnel who may then take actions to observe and deter or apprehend- ¶0200).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 16, method claim 16 corresponds to apparatus claim 10, and therefore is also rejected for the same reasons of obviousness as listed above.
Regarding claim 17, Allison teaches all the limitations of claim 10 and Allison further teaches:
further comprising: determining a risk confidence score for the user indicating a likelihood that the activity of the user is associated with the risk event(i.e. Risk probability is a measure of the likelihood that the state of affairs described in the risk consequence portion of the risk statement will actually occur. Using a numerical value for risk probability is desirable for ranking risks. Risk probability should be greater than zero, or the risk does not pose a threat. Likewise, the probability should be less than 100 percent or the risk is a certainty--in other words, it is a known problem. Probabilities are notoriously difficult for individuals to estimate and apply, although industry or enterprise risk databases may be helpful in providing known probability estimates based on samples of large numbers of projects- ¶1187);
generating a response friction level for the activity of the user based on a determination of whether at least one of the impact score or the confidence score satisfies risk criteria (i.e. Use of a catastrophic impact scored where an artificially high value such as 100 is assigned will ensure that a risk with even a very low probability will rise to the top of the risk list and remain there- ¶1196), wherein the response friction level corresponds to an escalation of a type of response to be taken for the activity of the user (i.e. Risk mitigation planning involves actions and activities performed ahead of time to either prevent a risk from occurring altogether or to reduce the impact or consequences of its occurring to an acceptable level- ¶1275… Risk contingency planning involves creation of one or more fallback plans that can be activated in case efforts to prevent the adverse event fail. Contingency plans are necessary for all risks, including those that have mitigation plans. They address what to do if the risk occurs and focus on the consequence and how to minimize its impact- ¶1279) an escalation of a type of response to be taken for the activity of the user (i.e. Risk Mitigation Strategy. A paragraph or two of text describing the team strategy for mitigating a specific risk, including any assumptions that have been made… Risk Mitigation Strategy Metrics. The metrics the team will use to determine whether the planned risk mitigation actions are achieving the desired results… Risk Action Items. A list of actions the team is taking to implement the strategy for a specific risk, including the due date for completion and the person responsible- ¶1295-1297); and
selecting a response from candidate manual responses and candidate automated responses to the activity of the user based, at least in part, on the friction level satisfying at least one of manual response criteria or automated response criteria (i.e. top risks list, and information from the risk management knowledge base, but also the project plans and schedules (as shown in FIG. 26)- ¶1242… The output from the risk action planning should include specific risk action plans implementing one of the six approaches discussed above at a step-by-step level of detail- ¶1287).
Regarding claim 18, Allison teaches all the limitations of claim 10.
However, Allison does not teach explicitly:
wherein executing instructions to perform the selected response comprises transmitting instructions that cause at least one of (i) an automated response to be provided using a display device or (ii) a manual response to be performed by an employee.
In a related field of endeavor, Greg teaches:
wherein executing instructions to perform the selected response comprises transmitting instructions that cause at least one of (i) an automated response to be provided using a display device or (ii) a manual response to be performed by an employee (i.e. Smart Video Monitoring System: The Trigger causes the appropriate video segment (incident location and time frame) to be pushed to a monitoring station, portable smart device, or other destination for immediate review. For example, a Loss Prevention professional in the store could receive the video on his/her mobile device; assess the situation; identify the suspect; and quickly take remedial action- ¶0135, fig. 7… The correlation system immediately pushes a notification to store personnel who may then take actions to observe and deter or apprehend- ¶0200).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison with the teachings of Greg for improved merchandise activity sensing (Greg- ¶0007).
Regarding claim 19, Allison teaches all the limitations of claim 10.
further comprising aggregating, based on the stream of activity data, data associated with the activity of the user within the environment into an activity profile associated with the user (i.e. The design process gives the team a systematic way to work from abstract concepts down to specific technical detail. This begins with a systematic analysis of user profiles (also called "personas") which describe various types of users and their job functions (operations staff are users too). Much of this is often done during the envisioning phase. These are broken into a series of usage scenarios, where a particular type of user is attempting to complete a type of activity, such as front desk registration in a hotel or administering user passwords for a system administrator. Finally, each usage scenario is broken into a specific sequence of tasks, known as use cases, which the user performs to complete that activity. This is called "story-boarding."- ¶0426, 1760-1763).
Claims 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Allison Robin et al. [US 20050114829 A1] in view of Greg King et al. [US 20190088096 A1] and further in view of Samuel English Anthony et al. [US 20210182604 A1].
Regarding claim 12, Allison and Greg all the limitations of claim 10 and Allison further teaches:
wherein determining the risk impact score for the user comprises:
identifying, a safety threat in the activity of the user (i.e. FIG. 23 is a block diagram depicting an exemplary risk identification paradigm that produces at least one or more risk statements. It graphically depicts the inputs, outputs, and activities for the risk identification step- ¶1137), and
assigning the risk impact score above a threshold impact value based on the identified safety threat satisfying one or more safety risk criteria (i.e. Risk impact is an estimate of the severity of adverse effects, or the magnitude of a loss, or the potential opportunity cost should a risk be realized within a project. It should be a direct measure of the risk consequence as defined in the risk statement. It can either be measured in financial terms or with a subjective measurement scale- ¶1192… risk exposure is calculated by multiplying risk probability and impact- ¶1198).
However, Allison and Greg do not teach explicitly:
based on applying a model to the stream of activity data, the model being trained to identify features in the portion of the stream of activity data indicative of the risk event.
In a related field of endeavor, Samuel teaches:
based on applying a model to the stream of activity data, the model being trained to identify features in the portion of the stream of activity data indicative of the risk event (i.e. The learning algorithm is optimized by a process of progressively adjusting the parameters of that algorithm in response to the characteristics of the images and summary statistics given to it in the training phase to minimize the error in its predictions of the summary statistics for the training images in step 804- ¶0063… An autonomous robot may be deployed to perform hospitality or security related tasks in settings such as airports, malls, hotels, offices, warehouses, and stores. For example, the robot may perform tasks such as greeting customers, answering questions, directing customers to a location, retrieving objects for the customer, identifying suspicious activity, and contacting security personnel. The robot may predict behavior of people nearby and determine which task to perform to best assist the people. The robot may determine whether a person is approaching with intentions to interact with the robot or happens to be heading in the robot's direction to avoid harassing a person with no intentions to interact. To make such predictions, the robot may capture video segments of its surroundings and apply a machine learning based model to the captured video segments that predicts summary statistics about the state of mind of people in the video segments- ¶0096).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Allison and Greg with the teachings Samuel to determine an action to be performed for managing movement of people captured in the video segments (Samuel- ¶0071).
Regarding claim 20, method claim 20 corresponds to apparatus claim 12, and therefore is also rejected for the same reasons of obviousness as listed above.
Conclusion
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CLIFFORD HILAIRE
Primary Examiner
Art Unit 2488
/CLIFFORD HILAIRE/Primary Examiner, Art Unit 2488
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