DETAILED ACTION
Status of the Claims
The following is a Non-final Office Action in response to amendments and remarks filed 10 December 2025.
Claims 1, 3, 5, 10, 12, 15, and 19 have been amended.
Claims 2 and 14 have been cancelled.
Claims 1, 3-13, and 15-20 are pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10 December 2025 has been entered.
Response to Arguments
Applicant's arguments with respect to the §112(b) rejections have been fully considered, and in light of the amendments, are persuasive. As such, the rejection has been withdrawn.
The Examiner notes that in order to be patent eligible under 35 U.S.C. 101, the claims must be directed towards a patent eligible concept, which, the instant claims are not directed. Contrary to Applicants’ assertion that the claims cannot be performed by the human brain due to the use of image acquisition devices and algorithms; however the Examiner notes that observing pictures/images/footage for suspicious behavior is a function that security, law enforcement, detectives, private investigators, etc. have traditionally performed as a specific method of organizing human activity (literally organizing human activity to find patterns/specific behavior). Next, the claims are not directed to a practical application of the concept. The claims do not result in improvements to the functioning of a computer or to any other technology or technical field. They do not effect a particular treatment for a disease. They are not applied with or by a particular machine. They do not effect a transformation or reduction of a particular article to a different state or thing. And they are not applied in some other meaningful way beyond generally linking the use of the judicial exception (i.e., determining that a specific person is dangerous in a case that the suspicious level exceeds a first threshold) to a particular technological environment (i.e., with the use of generic computing components such as image acquisition devices and algorithms). Here, again as noted in the previous rejection, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept - MPEP 2016.05(f). The claims recitation of the “facial recognition algorithm” only generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). The claim(s) is/are not patent eligible.
This argument appears to be whether or not the use of computer or computing components for increased speed and efficiency prevents the claims from being performed in the human mind and amounts to an improvement; however the Examiner respectfully disagrees. Nor, in addressing the second step of Alice, does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted)). As such, these arguments are not persuasive, and the rejection not withdrawn.
Applicant’s arguments with respect to the prior art have been fully considered but are moot on grounds of new rejection, as necessitated by amendments.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by the Applicants in regards to distinctly and specifically pointing out the supposed errors in the Examiner's prior office action (37 CFR 1.111). The Examiner asserts that the Applicants only argue that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
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, 3-13, and 15-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a process (an act, or series of acts or steps), a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices), and a manufacture (an article produced from raw or prepared materials by giving these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery). Thus, each of the claims falls within one of the four statutory categories (Step 1). However, the claim(s) recite(s) determining that a specific person is dangerous in a case that the suspicious level exceeds a first threshold which is an abstract idea of organizing human activities as well as a mental process.
The limitations of (claim 1 reproduced as an exemplary claim, claims 10-13 recite substantially similar subject matter) “extracting a face feature from the face image...; comparing an extracted face feature with face features of historical face images in a face database; generating a historical trajectory of a specific person according to historical data of the specific person acquired by the plurality of image acquisition devices within a designated time period, wherein the historical data comprises a person identifier of the specific person, an acquisition time and a device identifier; determining suspicious behaviors of the specific person appearing in the historical trajectory through analyzing behaviors of the specific person according to the historical trajectory of the specific person; determining a suspicious level of the specific person according to a frequency of at least one of the suspicious behaviors appearing in a corresponding historical trajectory; and determining that the specific person is dangerous in a case that the suspicious level exceeds a first set threshold, wherein the method further comprises: storing a face image of the specific person into the face database in response to comparison between the face image and the historical face images in the face database fails; assigning a person identifier to the face image of the specific person; determining whether the person identifier is in a pre-stored white list; wherein the pre- stored white list comprises: registered users, staff in a smart park, and foreign affair persons having fixed cooperation with the smart park; determining that a specific person corresponding to the person identifier is a non- suspicious person in response to the person identifier is in the pre-stored white list; determining whether the person identifier is in a black list; performing a short-term behavior analysis on a specific person corresponding to the person identifier in response to the person identifier is in the black list; wherein person identifiers in the black list are ex-offenders; wherein the generating the historical trajectory of the specific person according to the historical data of the specific person acquired by the plurality of devices within the designated time period, comprises: obtaining historical data of the person identifier within the designated time period according to the person identifier; forming a movement trajectory from the historical data contained in the designated time period according to device identifiers corresponding to a plurality of acquisition times in accordance with an order of the acquisition times of the historical data; and dividing the movement trajectory into a plurality of historical trajectories in a case that a time difference corresponding to two adjacent pieces of historical data in the movement trajectory is greater than a second set threshold, wherein the two adjacent pieces of historical data are located at one end of each of two different historical trajectories respectively” as drafted, is a process that, under its broadest reasonable interpretation, covers organizing human activities--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) and/or a mental process—concepts performed in the human mind (including an observation, evaluation, judgment, opinion) but for the recitation of generic computer components (Step 2A Prong 1). Method claim 1 is devoid of structure whatsoever and thus can only amount to an abstract idea. Next, “An apparatus for determining dangerousness of a person, comprising: a first obtaining unit,...a extracting unit...a comparing unit... a second obtaining unit...a generating unit…a selecting unit…a determining unit…,” in claim 10 “An apparatus for determining dangerousness of a person, comprising: at least one processor, and a memory connected with the at least one processor; wherein the memory stores instructions capable of being executed by the at least one processor, and the at least one processor, by executing the instructions stored in the memory, executes:” in claim 11, and “A readable storage medium, comprising a memory, wherein the memory is configured to store instructions, and the instructions, when executed by a processor, cause an apparatus comprising the readable storage medium to complete the method according to claim 1” in claim 13, nothing in the claim element precludes the step from the methods of organizing human interactions grouping or from practically being performed in the mind. For example, but for the “An apparatus for determining dangerousness of a person, comprising: a generating unit…a selecting unit…a determining unit…,” in claim 10 “An apparatus for determining dangerousness of a person, comprising: at least one processor, and a memory connected with the at least one processor; wherein the memory stores instructions capable of being executed by the at least one processor, and the at least one processor, by executing the instructions stored in the memory, executes:” in claim 11, and “A readable storage medium, comprising a memory, wherein the memory is configured to store instructions, and the instructions, when executed by a processor, cause an apparatus comprising the readable storage medium to complete the method according to claim 1” in claim 13” language, “extracting,” “comparing,” “generating,” “determining,” “determining,” “determining” “assigning,” “determining,” “determining,” “determining,” “performing,” “forming,” and “dividing” in the context of this claim encompasses the user manually collecting or observing user behavior and comparing to the threshold which is a mental process/judgement or business relation/fundamental economic practice/commercial or legal interaction/managing personal behavior in order to determine someone is suspicious or not based upon historical data such as movement. However, if possible, the Examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the limitations are considered together as a single abstract idea for further analysis. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations as a mathematical concept, while some of the limitations may be performed in the mind after certain limitations are performed, but for the recitation of generic computer components, then it falls within the grouping of abstract ideas. (Step 2A, Prong One: YES). Accordingly, the claim(s) recite(s) an abstract idea.
This judicial exception is not integrated into a practical application (Step 2A Prong Two). Method claim 1 is devoid of structure whatsoever and thus can only amount to an abstract idea. The “obtaining,” “storing,” steps and “image acquisition devices” are simply insignificant extrasolution data gathering activities. Next, the claims only recites one additional element – using an apparatus or processor to perform the steps. The apparatus or processor in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of electronic data, query, storage, retrieval/count of results) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Specifically the claims amount to nothing more than an instruction to apply the abstract idea using a generic computer or invoking computers as tools by adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d)(I) discussing MPEP 2106.05(f). The recitation of “facial recognition algorithm” in the limitations also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “facial recognition algorithm” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (algorithms/neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly, the combination of these additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea, even when considered as a whole (Step 2A Prong Two: NO).
The claim does not include a combination of additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Method claim 1 is devoid of structure whatsoever and thus can only amount to an abstract idea. As discussed above with respect to integration of the abstract idea into a practical application (Step 2A Prong 2), the combination of additional elements of using an apparatus or processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Reevaluating here in step 2B, the “obtaining” steps and “image acquisition devices” step(s) which are insignificant extrasolution activities are also determined to be well-understood, routine and conventional activity in the field. The Symantec, TLI, and OIP Techs court decisions in MPEP 2106.05(d)(II) indicate that the mere receipt or transmission of data over a network is well-understood, routine, and conventional function when it is claimed in a merely generic manner (as is here). Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim. As such, the claim(s) is/are not patent eligible, even when considered as a whole (Step 2B: NO).
Claims 3-9 and 15-20 recite the same abstract idea of “determining that a specific person is dangerous in a case that the suspicious level exceeds a first threshold.” The claim(s) recite(s) the additional limitation(s) further including mathematical concepts with respect to the data collected in order to determine the suspiciousness of a specific person which is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 10, and 12, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 11 and 13 recite the same abstract idea of “determining that a specific person is dangerous in a case that the suspicious level exceeds a first threshold.” The claim(s) recite(s) the additional limitation(s) further including generic computing elements to perform the previously identified abstract idea which is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 10, and 12, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 1, 3-13, and 15-20 are therefore not eligible subject matter, even when considered as a whole.
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.
The factual inquiries 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.
Claim(s) 1, 3-13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rabb et al. (US Patent No. 10,984,496) and further in view of Gordon et al. (US Patent No. 10,522,013).
As per claims 1 and 12-13, Rabb discloses a method, apparatus, and a readable storage medium, for determining dangerousness of a person, comprising (method for facilitating communication relating to threat assessment and trauma response using the threat assessment and response facilitation system is also provided, Rabb Abstract; computerized device comprising a processor and a memory, Col. 5 lines 37-56):
generating a historical trajectory of a specific person according to historical data of the specific person acquired by the plurality of image acquisition devices within a designated time period, wherein the historical data comprises a person identifier of the specific person, an acquisition time and a device identifier (In some cases, these tagged persons of concern and/or other persons subject to threat assessment may be flagged for revenge-seeking behavior or other behavior that may be at risk of perpetuating violence. In this example, the news aggregation component 414 may identify victims of a school shooting event and the profile of the perpetrator of such an event. The news aggregation component 414 may extract information about the race or other characteristic of the perpetrator, associates of the perpetrator, and other information that may be used by a person of concern seeking revenge in a follow-up violent act. Ideally, any planning or preparation for a retaliatory event can be detected prior to its occurrence. However, should a retaliatory violent event occur by a person of concern showing a revenge-seeking bias, correlations relating to such a retaliatory violent event may be drawn to help identify potential future risks of concern, possible counseling or other diversionary programs that may reduce the risk of this person a concern committing future acts, and identifying trends relating to an environment in which a violent act has previously occurred that could lead to other individuals or persons of concern committing similar and/or retaliatory acts of violence. In one embodiment, threat assessment may be complimented using micro and macro threat assessment models. For example, threats may be assessed using a micro assessment model, for example, determining if a threat maker poses an actual risk to carry out the current threat. Threats may also be assessed using a macro assessment model, for example, focusing on historical and foundational risk enhancers that may be contributing to overall levels of risk, Rabb Col. 23 line 47-Col. 24 line 10; An example of a risk enhancer may include temporal storage around an incident. In this example, interactions may be different between different people and different groups of people in the aftermath of an incident. The temporal component may consider days or weeks after the occurrence of an incident, such as a violent incident including a shooting or other serious harm to others, or on the anniversary of such an incident, without limitation. Those having skill in the art will appreciate other temporal milestones and periods of significance that may affect the level of risk associated with a location and incidence of a violent act. Skilled artisans will additionally appreciate that assessment of risk enhancers is not limited to shooting-based events or violent events related to mass-killing scenarios. For example, temporal storage may be considered when analyzing the risk of a violent act on the anniversary of a proceeding violent act at a location. In a more specific example, the risk of the future violent act may be elevated on the anniversary of a previous school shooting of high notoriety and the news cycle. Additional sensitivity may be given to the analysis of potential threats and observation of persons of concern during periods of elevated risk due to temporal storage of the impact of a preceding violent incident, Col. 27 line 64-Col. 28 line 20);
determining suspicious behaviors of the specific person appearing in the historical trajectory through analyzing behaviors of the specific person according to the historical trajectory of the specific person (Profile information may include history of incidents, prior events occurrences that could influence the behavior of the person of concern, and/or other behavioral information that may be relevant to understanding factors that may affect the behavior of the person of concern, Rabb Col. 23 lines 3-16; For example, threats may be assessed using a micro assessment model, for example, determining if a threat maker poses an actual risk to carry out the current threat. Threats may also be assessed using a macro assessment model, for example, focusing on historical and foundational risk enhancers that may be contributing to overall levels of risk, Rabb Col. 23 line 47-Col. 24 line 10) (Examiner notes the prior incidents/events as the equivalent to the historical trajectory of the specific person and the worrisome behaviors/risk enhancers as the suspicious behavior);
determining a suspicious level of the specific person according to a frequency of at least one of the suspicious behaviors appearing in a corresponding historical trajectory (For example, threats may be assessed using a micro assessment model, for example, determining if a threat maker poses an actual risk to carry out the current threat. Threats may also be assessed using a macro assessment model, for example, focusing on historical and foundational risk enhancers that may be contributing to overall levels of risk, Rabb Col. 23 line 47-Col. 24 line 10; The worrisome behavior component 422 may additionally include information about baseline indicators. To illustrate one example of baseline indicators, facts relating to the victim of the bullying may be assessed, for example, frequency of such events, observations by administrators or faculty, external influences on the individual, social isolation, disconnection from others, or other divergent behavior that may be indicative of a risk or threat of future retaliatory, violent, or otherwise divergent behavior as a result the being victimized. Those of skill in the art will appreciate that the above scenarios are given as examples to clearly communicate a possible use of a platform enabled by this disclosure, without limiting the functional capacity of such a platform to only these examples, Col 25 lines 20-33; An example of a risk enhancer may include temporal storage around an incident. In this example, interactions may be different between different people and different groups of people in the aftermath of an incident. The temporal component may consider days or weeks after the occurrence of an incident, such as a violent incident including a shooting or other serious harm to others, or on the anniversary of such an incident, without limitation. Those having skill in the art will appreciate other temporal milestones and periods of significance that may affect the level of risk associated with a location and incidence of a violent act. Skilled artisans will additionally appreciate that assessment of risk enhancers is not limited to shooting-based events or violent events related to mass-killing scenarios. For example, temporal storage may be considered when analyzing the risk of a violent act on the anniversary of a proceeding violent act at a location. In a more specific example, the risk of the future violent act may be elevated on the anniversary of a previous school shooting of high notoriety and the news cycle. Additional sensitivity may be given to the analysis of potential threats and observation of persons of concern during periods of elevated risk due to temporal storage of the impact of a preceding violent incident, Col. 27 line 64-Col. 28 line 20; Weights may be applied based on factors such as time, distance, communication, location, relevance, history, and other factors that would be appreciated by a person of skill and the art after having the benefit of this disclosure, Col. 32 lines 6-13) (Examiner notes the overall risk level as the equivalent to the determining of a suspicious level of the person); and
determining that the specific person is dangerous in a case that the suspicious level exceeds a first set threshold (threshold, determine worrisome behavior of a person becomes a concern that requires escalation, Rabb Col. 48 lines 24-36);
wherein the method further comprises:
determining whether the person identifier is in a pre-stored white list (approved guests, Rabb Col. 46 lines 7-23 ) (Examiner interprets the list of approved guests as a white list of registered users);
determining that a specific person corresponding to the person identifier is a non-suspicious person in response to the person identifier is in the pre-stored white list (approved guests, Rabb Col. 46 lines 7-23);
determining whether the person identifier is in a black list (In some cases, these tagged persons of concern and/or other persons subject to threat assessment may be flagged for revenge-seeking behavior or other behavior that may be at risk of perpetuating violence. In this example, the news aggregation component 414 may identify victims of a school shooting event and the profile of the perpetrator of such an event. The news aggregation component 414 may extract information about the race or other characteristic of the perpetrator, associates of the perpetrator, and other information that may be used by a person of concern seeking revenge in a follow-up violent act. Ideally, any planning or preparation for a retaliatory event can be detected prior to its occurrence. However, should a retaliatory violent event occur by a person of concern showing a revenge-seeking bias, correlations relating to such a retaliatory violent event may be drawn to help identify potential future risks of concern, possible counseling or other diversionary programs that may reduce the risk of this person a concern committing future acts, and identifying trends relating to an environment in which a violent act has previously occurred that could lead to other individuals or persons of concern committing similar and/or retaliatory acts of violence. In one embodiment, threat assessment may be complimented using micro and macro threat assessment models. For example, threats may be assessed using a micro assessment model, for example, determining if a threat maker poses an actual risk to carry out the current threat. Threats may also be assessed using a macro assessment model, for example, focusing on historical and foundational risk enhancers that may be contributing to overall levels of risk, Rabb Col. 23 line 47-Col. 24 line 10) (Examiner interprets the tagged persons of concern as the person identifier in the black list);
performing a short-term behavior analysis on a specific person corresponding to the person identifier in response to the person identifier is in the black list; wherein person identifiers in the black list are ex-offenders (In some cases, these tagged persons of concern and/or other persons subject to threat assessment may be flagged for revenge-seeking behavior or other behavior that may be at risk of perpetuating violence. In this example, the news aggregation component 414 may identify victims of a school shooting event and the profile of the perpetrator of such an event. The news aggregation component 414 may extract information about the race or other characteristic of the perpetrator, associates of the perpetrator, and other information that may be used by a person of concern seeking revenge in a follow-up violent act. Ideally, any planning or preparation for a retaliatory event can be detected prior to its occurrence. However, should a retaliatory violent event occur by a person of concern showing a revenge-seeking bias, correlations relating to such a retaliatory violent event may be drawn to help identify potential future risks of concern, possible counseling or other diversionary programs that may reduce the risk of this person a concern committing future acts, and identifying trends relating to an environment in which a violent act has previously occurred that could lead to other individuals or persons of concern committing similar and/or retaliatory acts of violence. In one embodiment, threat assessment may be complimented using micro and macro threat assessment models. For example, threats may be assessed using a micro assessment model, for example, determining if a threat maker poses an actual risk to carry out the current threat. Threats may also be assessed using a macro assessment model, for example, focusing on historical and foundational risk enhancers that may be contributing to overall levels of risk, Rabb Col. 23 line 47-Col. 24 line 10);
wherein the generating the historical trajectory of the specific person according to the historical data of the specific person acquired by the plurality of devices within the designated time period, comprises: obtaining historical data of the person identifier within the designated time period according to the person identifier (An example of a risk enhancer may include temporal storage around an incident. In this example, interactions may be different between different people and different groups of people in the aftermath of an incident. The temporal component may consider days or weeks after the occurrence of an incident, such as a violent incident including a shooting or other serious harm to others, or on the anniversary of such an incident, without limitation. Those having skill in the art will appreciate other temporal milestones and periods of significance that may affect the level of risk associated with a location and incidence of a violent act. Skilled artisans will additionally appreciate that assessment of risk enhancers is not limited to shooting-based events or violent events related to mass-killing scenarios. For example, temporal storage may be considered when analyzing the risk of a violent act on the anniversary of a proceeding violent act at a location. In a more specific example, the risk of the future violent act may be elevated on the anniversary of a previous school shooting of high notoriety and the news cycle. Additional sensitivity may be given to the analysis of potential threats and observation of persons of concern during periods of elevated risk due to temporal storage of the impact of a preceding violent incident, Col. 27 line 64-Col. 28 line 20; follow ups, Col. 31 lines 3-22);
Rabb does not expressly disclose obtaining an image of the specific person shot in real time by a plurality of image acquisition devices; obtaining a corresponding face image from the image; extracting a face feature from the face image by using facial recognition algorithms; comparing an extracted face feature with face features of historical face images in a face database; obtaining a person identifier of the specific person from the face database based on that the extracted face feature is successfully compared with the face features of historical face images in the face database; storing a face image of the specific person into the face database in response to comparison between the face image and the historical face images in the face database fails; forming a movement trajectory from the historical data contained in the designated time period according to device identifiers corresponding to a plurality of acquisition times in accordance with an order of the acquisition times of the historical data; and dividing the movement trajectory into a plurality of historical trajectories in a case that a time difference corresponding to two adjacent pieces of historical data in the movement trajectory is greater than a second set threshold, wherein the two adjacent pieces of historical data are located at one end of each of two different historical trajectories respectively
assigning a person identifier to the face image of the specific person.
However, Gordon teaches:
obtaining an image of the specific person shot in real time by a plurality of image acquisition devices (security cameras, images, Gordon Col. 5 line 53-Col. 6 line 8);
obtaining a corresponding face image from the image (facial scans, Gordon Col. 11 lines 1-16; facial recognition, Col. 16 lines 27-49; facial scan of the person, Col. 30 lines 5-34);
extracting a face feature from the face image by using facial recognition algorithms (facial recognition, Gordon Col. 16 lines 27-49) (Examiner interprets the facial recognition techniques to include a facial recognition algorithm);
comparing an extracted face feature with face features of historical face images in a face database (facial recognition, Gordon Col. 16 lines 27-49);
obtaining a person identifier of the specific person from the face database based on that the extracted face feature is successfully compared with the face features of historical face images in the face database (In another example, sensor linking module 415 may receive or obtain identification data related to a person and/or an event at another location. For example, a video camera may obtain a facial scan of the person who broke the window at house 220, but may also receive or obtain data from a local or remote database, such as a criminal database containing news photos and/or mugshots. Sensor linking module 415 may then compare the data received (both locally and from a remote source) to make a determination as to who and what has occurred. In this example, sensor linking module 415 may determine that a person matching a recent news report about burglaries has been identified at the porch of house 215 and is identified as the person that broke the window of house 220, thus sensor linking module 415 may determine a burglary is about to occur, Gordon Col. 30 lines 5-34).
storing a face image of the specific person into the face database in response to comparison between the face image and the historical face images in the face database fails (In another example, sensor linking module 415 may receive or obtain identification data related to a person and/or an event at another location. For example, a video camera may obtain a facial scan of the person who broke the window at house 220, but may also receive or obtain data from a local or remote database, such as a criminal database containing news photos and/or mugshots. Sensor linking module 415 may then compare the data received (both locally and from a remote source) to make a determination as to who and what has occurred. In this example, sensor linking module 415 may determine that a person matching a recent news report about burglaries has been identified at the porch of house 215 and is identified as the person that broke the window of house 220, thus sensor linking module 415 may determine a burglary is about to occur, Gordon Col. 30 lines 5-34);
assigning a person identifier to the face image of the specific person (In another example, sensor linking module 415 may receive or obtain identification data related to a person and/or an event at another location. For example, a video camera may obtain a facial scan of the person who broke the window at house 220, but may also receive or obtain data from a local or remote database, such as a criminal database containing news photos and/or mugshots. Sensor linking module 415 may then compare the data received (both locally and from a remote source) to make a determination as to who and what has occurred. In this example, sensor linking module 415 may determine that a person matching a recent news report about burglaries has been identified at the porch of house 215 and is identified as the person that broke the window of house 220, thus sensor linking module 415 may determine a burglary is about to occur, Gordon Col. 30 lines 5-34);
forming a movement trajectory from the historical data contained in the designated time period according to device identifiers corresponding to a plurality of acquisition times in accordance with an order of the acquisition times of the historical data (In one example, device 215-a obtains data from one or more sensors located at house 215 (block 310). In this example, the data obtained includes information indicating that the users of house 215 are scheduled to be out of the house based on their stored schedules (e.g., schedule information, activity information), the dog out with the dog walker (e.g., location information), a video of a woman approximately 5′ 11″ tall, dressed in black, and with blonde hair (e.g., physical characteristics, identification information), the device has obtained the sound of glass breaking near the living room (e.g., interior environment information), and a blue car drove by slowly five minutes before the sound of glass breaking (e.g., exterior environment information), Gordon Col. 18 lines 18-31; At block 715, the method 700 may include comparing the obtained data with the identification data. Based on the data obtained by the first device, and the identification data received, the device may compare the data to make a determination. For example, if it is detected that glass is breaking, but there is no person detected within a predetermined distance of the glass at the time of the breaking (e.g., within a five foot radius), the device may determine the glass broke due to a branch hitting the glass or a baseball hitting the glass. On the other hand, if the sound of glass breaking is obtained, as well as the presence of a person, and the person has characteristics which match a news report related to specific person breaking into houses nearby or if the person is unknown or cannot be recognized, the device may determine this is an intruder breaking into this house. The operation(s) at block 715 may be performed using the sensor linking module 415 and/or the identification module 505 and/or the event determination module 645 described with reference to FIGS. 4, 5, and 6 respectively, Col. 38 line 61-Col. 39 line 17) (Examiner notes schedules of users as the forming of the movement trajectories from historical data in designated time periods); and
dividing the movement trajectory into a plurality of historical trajectories in a case that a time difference corresponding to two adjacent pieces of historical data in the movement trajectory is greater than a second set threshold, wherein the two adjacent pieces of historical data are located at one end of each of two different historical trajectories respectively (Based on determining an identification of a person and/or an event, sensor linking module 415 may adjust a parameter associated with house 215 or take an action. The adjustment may be pre-programmed (e.g., a user profile which establishes actions and reactions), or may be based on artificial intelligence and learning based on previous events and actions both at the location and based on events and actions at other networked locations. In some examples, the actions may be based on a category of the request. For example, a user may set up a preference that in response to a request, the sensor linking module 415 will be configured to send identification of the person and/or the event without user approval, if the category associated with the request satisfies a threshold. Thus, in one example, an adjustment may be made automatically; however, in another example, the sensor linking module 415 may send a notification to a user to ask what the request adjustment should be based on the data and the comparison, Gordon Col. 30 lines 25-43; At block 715, the method 700 may include comparing the obtained data with the identification data. Based on the data obtained by the first device, and the identification data received, the device may compare the data to make a determination. For example, if it is detected that glass is breaking, but there is no person detected within a predetermined distance of the glass at the time of the breaking (e.g., within a five foot radius), the device may determine the glass broke due to a branch hitting the glass or a baseball hitting the glass. On the other hand, if the sound of glass breaking is obtained, as well as the presence of a person, and the person has characteristics which match a news report related to specific person breaking into houses nearby or if the person is unknown or cannot be recognized, the device may determine this is an intruder breaking into this house. The operation(s) at block 715 may be performed using the sensor linking module 415 and/or the identification module 505 and/or the event determination module 645 described with reference to FIGS. 4, 5, and 6 respectively, Col. 38 line 61-Col. 39 line 17; In one example, the device 215-b queries a local database associated with device 215-b (i.e., associated with house 215) and determines that a child approximately 4′ 4″ tall, dressed in a red coat, and with brown hair and blue eyes was seen near a particular house in the neighborhood and may, in some cases, note a time when the child having at least some similar characteristics was captured by the device 215-b. Device 215-b may also determine whether the child was seen anywhere within a specified geographic area (e.g., based on the devices included in the street watch group, within a radius or a distance of one or more locations (e.g., a location of device 215-b, a location of the last location where the child was captured on camera or detected, a location associated with the child such as his home), some combination, or other information. If the child was spotted at multiple places at the same, a similar time, within a time period, etc., then it is likely that the recognition data is not relevant. In one example, the device 215-b queries a local database associated with other devices (i.e., devices associated with houses in the same neighborhood watch group as house 215) and determines that a child approximately 4′ 4″ tall, dressed in a red coat, and with brown hair and blue eyes was seen near a particular house in the neighborhood. In some examples, the neighborhood watch group associated with house 215 may be different from the neighborhood watch group associated with house 220. The device 215-b may be configured to query devices associated with both neighborhood watch groups, Col. 23 line 53-Col. 24 line 9) (Examiner interprets the ability to use thresholds with scheduled historic events and the ability to narrow a geographic area for a child that is missing as the ability to divide the movement trajectories based upon historical information and designated time periods).
Both the Gordon and the Rabb references are analogous in that both are directed towards/concerned with personal safety/security. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Gordon’s ability to obtain facial images and establish timelines of events related to short term events for threats in Rabb’s system to improve the system and method with reasonable expectation that this would result in a safety management system that is able to connect multiple systems and automate security actions.
The motivation being that there is a need for a more networked security solution as multiple audio or video devices, such as security cameras may be networked together to receive and transmit data related to the location and/or the association of the devices. In some examples, the network of devices may be created and maintained based on a predetermined proximity of the devices or a device to a location, such as devices associated with a house or houses in a neighborhood. In other examples, the network of devices may be based on each device's association with a group, such as a community network, or a group of devices running the same software application (Gordon Col. 1 lines 29-39).
While Rabb and Gordon disclose the safety management system above, the combination of Gordon and Rabb do not expressly disclose the “wherein the pre- stored white list comprises: registered users, staff in a smart park, and foreign affair persons having fixed cooperation with the smart park,” “black list,” “ex-offenders.”
However, the Examiner asserts that the data identifying the users such as registered users, staff in a smart park, foreign persons having fixed cooperation with the smart park and ex-offenders and a list thereof (whitelist, black list) is simply a label for the users and how the users are grouped or categorized, and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific types of users or lists) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability (see MPEP 2144.04).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the user data to include a user type and a list thereof since the specific type of user or list does not functionally alter or relate to the steps of the method and merely labeling the information differently from that in the prior art does not patentably distinguish the claimed invention.
As per claims 3 and 15, Rabb and Gordon disclose as shown above with respect to claims 1 and 12. Rabb further discloses wherein determining the suspicious behaviors of the specific person according to the historical trajectory of the specific person, comprises: determining, in a first period, short-term suspicious behaviors of the specific person existing in the historical trajectory and a number of times of the short-term suspicious behaviors through counting a frequency and behavior patterns of specific person appearing at each device; and determining, in a second period, periodic suspicious behaviors of the specific person existing in the historical trajectory and a number of times of the periodic suspicious behaviors through counting the short-term suspicious behaviors of the specific person appearing in the historical trajectory, and determining, wherein the first period is shorter than the second period (By monitoring baseline indicators, information relating to incidents and worrisome behavior may be discovered including a fluidity of suicide-to-homicide variation, change in the baseline behavior of an observed person, a derived behavior relating to additional factors observed via the threat assessment assistance module 400, and/or other interactions between the suicidal and homicidal domains. For the purpose of this disclosure, fluidity is intended to include interactions between the suicidal and the homicidal domains, such as including transitions between intense emotional pain relating to periods of wanting to kill oneself and periods of wanting to kill others, the convergence between such periods, and the struggles in a person of concern between the suicidal domain, the homicidal domain, or a combination of elements from suicidal and homicidal domains, Rabb Col. 26 lines 6-20; see also Col. 27 lines 64-Col. 28 line 20) (Examiner notes the ability to monitor baseline indicators with respect to behavior over periods as the equivalent to being able to count different terms of suspicious behaviors of persons appearing in historical trajectories).
As per claims 4 and 16, Rabb and Gordon disclose as shown above with respect to claims 3 and 15. Rabb does not expressly disclose wherein the short-term suspicious behaviors comprise: a staying behavior, a hovering behavior, a passing behavior and a behavior of appearing in a specific time period.
However, Gordon teaches wherein the short-term suspicious behaviors comprise: a staying behavior, a hovering behavior, a passing behavior and a behavior of appearing in a specific time period (In one example, device 215-a obtains data from one or more sensors located at house 215 (block 310). In this example, the data obtained includes information indicating that the users of house 215 are scheduled to be out of the house based on their stored schedules (e.g., schedule information, activity information), the dog out with the dog walker (e.g., location information), a video of a woman approximately 5′ 11″ tall, dressed in black, and with blonde hair (e.g., physical characteristics, identification information), the device has obtained the sound of glass breaking near the living room (e.g., interior environment information), and a blue car drove by slowly five minutes before the sound of glass breaking (e.g., exterior environment information), Gordon Col. 18 lines 18-31; At block 715, the method 700 may include comparing the obtained data with the identification data. Based on the data obtained by the first device, and the identification data received, the device may compare the data to make a determination. For example, if it is detected that glass is breaking, but there is no person detected within a predetermined distance of the glass at the time of the breaking (e.g., within a five foot radius), the device may determine the glass broke due to a branch hitting the glass or a baseball hitting the glass. On the other hand, if the sound of glass breaking is obtained, as well as the presence of a person, and the person has characteristics which match a news report related to specific person breaking into houses nearby or if the person is unknown or cannot be recognized, the device may determine this is an intruder breaking into this house. The operation(s) at block 715 may be performed using the sensor linking module 415 and/or the identification module 505 and/or the event determination module 645 described with reference to FIGS. 4, 5, and 6 respectively, Col. 38 line 61-Col. 39 line 17).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Gordon’s ability to establish timelines of events related to short term events for threats in Rabb’s system to improve the system and method with reasonable expectation that this would result in a safety management system that is able to connect multiple systems and automate security actions.
The motivation being that there is a need for a more networked security solution as multiple audio or video devices, such as security cameras may be networked together to receive and transmit data related to the location and/or the association of the devices. In some examples, the network of devices may be created and maintained based on a predetermined proximity of the devices or a device to a location, such as devices associated with a house or houses in a neighborhood. In other examples, the network of devices may be based on each device's association with a group, such as a community network, or a group of devices running the same software application (Gordon Col. 1 lines 29-39).
As per claims 5 and 17, Rabb and Gordon disclose as shown above with respect to claims 4 and 16. Gordon further teaches wherein the determining the short-term suspicious behaviors of the specific person existing in the historical trajectory and the number of times of the short-term suspicious behaviors through counting the frequency and behavior patterns of specific person appearing at each device, comprises: counting a staying duration of the specific person appearing at a device corresponding to each device identifier, determining the short-term suspicious behavior corresponding to the staying duration exceeding a first threshold as the staying behavior, and accumulating an appearing number of times of the staying behavior by 1; counting an order of the specific person continuously appearing among the plurality of devices and coverage rates of the devices in the order, determining the short-term suspicious behavior corresponding to the coverage rate less than a second threshold as the hovering behavior, and accumulating an appearing number of times of the hovering behavior by 1, wherein the coverage rate is a ratio of a total number of the devices existing in the order to a total number of times of sequentially passing the devices; counting an order of the specific person appearing among the plurality of devices and an average movement speed of passing the plurality of devices, determining that the corresponding short- term suspicious behavior is a passing behavior in a case that the specific person sequentially appears among the plurality of devices without shuttling and the corresponding average movement speed is less than a third threshold, and accumulating an appearing number of times of the passing behavior by 1; and counting the staying behavior, the hovering behavior and the passing behavior appearing in a specific time period, determining that the corresponding short-term suspicious behavior is the behavior of appearing in the specific time period if any one of the staying behavior, the hovering behavior and the passing behavior exists in the specific time period, and accumulating an appearing number of times of the behavior of appearing in the specific time period by 1 (In an example, device 220-b may transmit the relevant data if the data received from device 215-b is identified as being indicative of a crime, but device 220-b may disregard an alert if the data from device 215-b is related only to a person walking through the neighborhood that is not identified as a threat. For example, device 215-b may categorize data as indicative of a crime if the data (e.g., video footage) includes footage that can be categorized as a threat. For example, the device 215-b may categorize the data as a crime based on objects (e.g., gun, knife), clothing item (e.g., masks) or people (e.g., recognized as a criminal) identified in the footage. A person walking down the street with a mask on, or a person walking down the street holding a knife, may be classified as data indicative of threat. In some examples, the device 215-b may receive sounds related to tires screeching near house 220-b during afternoon. The schedule preference for house 220-b may indicate that no member is scheduled to be home at that time of the day. Device 215-b may further capture video footage of people wearing masks coming out of the car and may conclude that these footages indicate a burglary. Alternatively, in some examples, device 220-b may transmit an alert or take an action if the data and/or request received from device 215-b is identified as being indicative of a security alert (e.g., a security status change, an arm or disarm event), but may not transmit an alert or take action if the data received from device 215-b is not related to or correlated to the request 365-b received from device 220-b. It should be understood that these are merely examples, and any security action may be contemplated. In some examples, the device 215-b may send a request to device 220-b to initiate a security action (at block 390), such as turning on a security camera at house 220 which has a view of house 215 across the street, Gordon Col. 24 line 45-Col. 25 line 10; In another example embodiment, house 215 may be occupied by a single man who is suspicious of any amount of detected activity or unknown visitors. The man has programmed his security systems and sensors to determine that any person and/or any vehicle which comes within a predetermined distance of his house is of the highest threat. The man at house 215 desires to be notified of every movement and every noise that is detected by device 215-a. In addition, the man desires to be notified of all people coming and going at his nearby neighbors houses, and he wants to be notified of all cars driving by that do not explicitly belong to someone in the neighborhood. The man's neighbors, however, do not want to receive every single alert and action transmitted from device 215-a, lest they be inundated with alerts and security action adjustment requests, Col. 19 lines 9-24).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Gordon’s ability to establish timelines of events related to short term events for threats in Rabb’s system to improve the system and method with reasonable expectation that this would result in a safety management system that is able to connect multiple systems and automate security actions.
The motivation being that there is a need for a more networked security solution as multiple audio or video devices, such as security cameras may be networked together to receive and transmit data related to the location and/or the association of the devices. In some examples, the network of devices may be created and maintained based on a predetermined proximity of the devices or a device to a location, such as devices associated with a house or houses in a neighborhood. In other examples, the network of devices may be based on each device's association with a group, such as a community network, or a group of devices running the same software application (Gordon Col. 1 lines 29-39).
The Examiner also notes that the method by which to accumulate behaviors and coverage rate (i.e. count, tally, average, amount of time, rate, ratio, or weighted) are simply a design choice which would be obvious to try as there is a finite number of predictable types of accumulating some sort of number (See MPEP 2144.05.B).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention/before the effective filing date of the claimed invention, to include the accumulation of an appearing number of times of the behavior of the specific time period by 1 as this is simply a design choice from a finite number of ways to accumulate or account for something such as an activity occurrence commonly used or are marketed successful.
As per claims 6 and 18, Rabb and Gordon disclose as shown above with respect to claims 4 and 16. Gordon further teaches wherein the determining the periodic suspicious behaviors of the specific person existing in the historical trajectory through counting the short-term suspicious behaviors of the specific person appearing in the historical trajectory, comprises: counting a first total number of times of the staying behavior or the hovering behavior appearing in each set time period within one second period, and determining that a behavior appearing regularly exists in the corresponding set time period in a case that the first total number of times exceeds a fourth threshold; and counting a second total number of times of the staying behavior or the hovering behavior of the specific person appearing at each device within the one second period, and determining that a behavior appearing at a fixed position exists at the corresponding device in a case that the second total number of times exceeds a fifth threshold (In another example embodiment, house 215 may be occupied by a single man who is suspicious of any amount of detected activity or unknown visitors. The man has programmed his security systems and sensors to determine that any person and/or any vehicle which comes within a predetermined distance of his house is of the highest threat. The man at house 215 desires to be notified of every movement and every noise that is detected by device 215-a. In addition, the man desires to be notified of all people coming and going at his nearby neighbors houses, and he wants to be notified of all cars driving by that do not explicitly belong to someone in the neighborhood. The man's neighbors, however, do not want to receive every single alert and action transmitted from device 215-a, lest they be inundated with alerts and security action adjustment requests. Thus, for example, the users at house 220 have set their system to receive all alerts and requests from devices at houses 225, 235, 240 and 245 as described in the previous example. With regard to alerts from house 215, the users at house 220 have programmed their system to only receive alerts and requests from house 215 if the device 220-a receives the alert and/or data and determines that the threat level, alert, and/or request satisfies a predetermined threshold. For example, device 220-a may transmit an alert or take an action if the data and/or request received from device 215-a is indicative of a crime, but device 220-a may disregard an alert received from device 215-a if device 220-a determines the alert is related only to a person walking through the neighborhood that is unlikely to pose a threat. Alternatively, device 220-a may transmit an alert or take an action if the data and/or request received from device 215-a is indicative of security alert (e.g., a security status change, an arm or disarm event), but may not transmit an alert or take action based only on sensor data alone. It should be understood that these are merely examples, and any security action may be contemplated Gordon Col. 19 lines 9-45).
Furthermore, one of ordinary skill, before the effective filing date of the claimed invention, would have found it obvious to repeat the processes in claim 1 for additional thresholds because duplication is obvious, MPEP 2144.04.VI.B. The duplication of parts (or steps) has no patentable significance unless a new and unexpected result is produced. Examiner finds no evidence that performing the processes in claim 1 for additional thresholds would produce new and unexpected results as compared to performing the processes in claim 1 for only a first threshold of a first time period.
As per claims 7 and 19, Rabb and Gordon disclose as shown above with respect to claims 5 and 17. Gordon further teaches wherein a determining method of the second threshold comprises: determining a coverage rate mean value and a coverage rate standard error of coverage rates corresponding to the hovering behaviors of all specific persons in a selected historical time period through analyzing distribution of the coverage rates corresponding to the hovering behaviors of all specific persons in the selected historical time period; and determining a difference value of the coverage rate mean value and N times of the coverage rate standard error as the second threshold (In another example embodiment, house 215 may be occupied by a single man who is suspicious of any amount of detected activity or unknown visitors. The man has programmed his security systems and sensors to determine that any person and/or any vehicle which comes within a predetermined distance of his house is of the highest threat. The man at house 215 desires to be notified of every movement and every noise that is detected by device 215-a. In addition, the man desires to be notified of all people coming and going at his nearby neighbors houses, and he wants to be notified of all cars driving by that do not explicitly belong to someone in the neighborhood. The man's neighbors, however, do not want to receive every single alert and action transmitted from device 215-a, lest they be inundated with alerts and security action adjustment requests. Thus, for example, the users at house 220 have set their system to receive all alerts and requests from devices at houses 225, 235, 240 and 245 as described in the previous example. With regard to alerts from house 215, the users at house 220 have programmed their system to only receive alerts and requests from house 215 if the device 220-a receives the alert and/or data and determines that the threat level, alert, and/or request satisfies a predetermined threshold. For example, device 220-a may transmit an alert or take an action if the data and/or request received from device 215-a is indicative of a crime, but device 220-a may disregard an alert received from device 215-a if device 220-a determines the alert is related only to a person walking through the neighborhood that is unlikely to pose a threat. Alternatively, device 220-a may transmit an alert or take an action if the data and/or request received from device 215-a is indicative of security alert (e.g., a security status change, an arm or disarm event), but may not transmit an alert or take action based only on sensor data alone. It should be understood that these are merely examples, and any security action may be contemplated Gordon Col. 19 lines 9-45).
The Examiner also notes that the method by which to establish thresholds (i.e. count, tally, average, or weighted) are simply a design choice which would be obvious to try as there is a finite number of predictable types of accumulating some sort of number (See MPEP 2144.05.B).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention/before the effective filing date of the claimed invention, to include the coverage rate difference value of the coverage rate mean value and N times the coverage rate standard error, as this is simply a design choice from a finite number of ways to accumulate or account for something such as an activity occurrence commonly used or are marketed successful.
As per claims 8 and 20, Rabb and Gordon disclose as shown above with respect to claims 5 and 17. Gordon further teaches wherein the determining the suspicious level of the specific person according to the frequency of at least one of the suspicious behaviors appearing in the corresponding historical trajectory, comprises: determining an initial suspicious level value for a suspicious level of each suspicious behavior; accumulating, every time one suspicious behavior appears, the corresponding suspicious level by a first set value in a case that the one suspicious behavior is the short-term suspicious behavior; and accumulating, every time one suspicious behavior appears, the corresponding suspicious level by a second set value in a case that the one suspicious behavior is a long-term suspicious behavior, wherein the second set value is greater than the first set value; decreasing a value of the suspicious level corresponding to the one short-term suspicious behavior by a third set value, in a case that one short-term suspicious behavior does not appear again within a duration corresponding to one second period after the one short-term suspicious behavior of the specific person appears; and determining a sum of the suspicious levels corresponding to all the suspicious behaviors currently contained by the specific person as a current value of the suspicious level of the specific person (In another example embodiment, house 215 may be occupied by a single man who is suspicious of any amount of detected activity or unknown visitors. The man has programmed his security systems and sensors to determine that any person and/or any vehicle which comes within a predetermined distance of his house is of the highest threat. The man at house 215 desires to be notified of every movement and every noise that is detected by device 215-a. In addition, the man desires to be notified of all people coming and going at his nearby neighbors houses, and he wants to be notified of all cars driving by that do not explicitly belong to someone in the neighborhood. The man's neighbors, however, do not want to receive every single alert and action transmitted from device 215-a, lest they be inundated with alerts and security action adjustment requests. Thus, for example, the users at house 220 have set their system to receive all alerts and requests from devices at houses 225, 235, 240 and 245 as described in the previous example. With regard to alerts from house 215, the users at house 220 have programmed their system to only receive alerts and requests from house 215 if the device 220-a receives the alert and/or data and determines that the threat level, alert, and/or request satisfies a predetermined threshold. For example, device 220-a may transmit an alert or take an action if the data and/or request received from device 215-a is indicative of a crime, but device 220-a may disregard an alert received from device 215-a if device 220-a determines the alert is related only to a person walking through the neighborhood that is unlikely to pose a threat. Alternatively, device 220-a may transmit an alert or take an action if the data and/or request received from device 215-a is indicative of security alert (e.g., a security status change, an arm or disarm event), but may not transmit an alert or take action based only on sensor data alone. It should be understood that these are merely examples, and any security action may be contemplated Gordon Col. 19 lines 9-45).
The Examiner also notes that the method by which to accumulate behaviors and coverage rate (i.e. count, tally, average, amount of time, rate, ratio, or weighted) are simply a design choice which would be obvious to try as there is a finite number of predictable types of accumulating some sort of number (See MPEP 2144.05.B).
Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention/before the effective filing date of the claimed invention, to include the accumulation of an appearing number of times of the behavior of the specific time period by 1 as this is simply a design choice from a finite number of ways to accumulate or account for something such as an activity occurrence commonly used or are marketed successful.
As per claim 9, Rabb and Gordon disclose as shown above with respect to claim 1. Gordon further teaches wherein the method further comprises: storing the face image into the face database and assigning a corresponding person identifier to the face image, in a case that the extracted face feature fails to match the face features of the historical face images in the face database; determining whether the person identifier of the specific person is a person identifier in a white list; and determining the person identifier of the specific person as a person identifier of a suspicious person in a case that the person identifier of the specific person is not a person identifier in a white list (In addition, the database 305-a may store identification information about frequency and/or allowed and/or expected guests (e.g., extended family, friends, nanny, delivery people, neighbors), Gordon Col. 22 lines 16-19; Facial recognition, Col. 8 lines 21-39) (Examiner interprets the list of allowed/expected guests as the white-list).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Gordon’s ability to establish timelines of events related to short term events for threats in Rabb’s system to improve the system and method with reasonable expectation that this would result in a safety management system that is able to connect multiple systems and automate security actions.
The motivation being that there is a need for a more networked security solution as multiple audio or video devices, such as security cameras may be networked together to receive and transmit data related to the location and/or the association of the devices. In some examples, the network of devices may be created and maintained based on a predetermined proximity of the devices or a device to a location, such as devices associated with a house or houses in a neighborhood. In other examples, the network of devices may be based on each device's association with a group, such as a community network, or a group of devices running the same software application (Gordon Col. 1 lines 29-39).
As per claims 10-11, Rabb discloses an apparatus and system for determining dangerousness of a person, comprising; and image acquisition device, (method for facilitating communication relating to threat assessment and trauma response using the threat assessment and response facilitation system is also provided, Rabb Abstract; computerized device comprising a processor and a memory, Col. 5 lines 37-56; photo matching, Col. 34 lines 28-35):
a generating unit, configured to generate a historical trajectory of a specific person according to historical data of the specific person acquired by a plurality of devices within a designated time period, wherein the historical data comprises a person identifier of the specific person, an acquisition time and a device identifier (In some cases, these tagged persons of concern and/or other persons subject to threat assessment may be flagged for revenge-seeking behavior or other behavior that may be at risk of perpetuating violence. In this example, the news aggregation component 414 may identify victims of a school shooting event and the profile of the perpetrator of such an event. The news aggregation component 414 may extract information about the race or other characteristic of the perpetrator, associates of the perpetrator, and other information that may be used by a person of concern seeking revenge in a follow-up violent act. Ideally, any planning or preparation for a retaliatory event can be detected prior to its occurrence. However, should a retaliatory violent event occur by a person of concern showing a revenge-seeking bias, correlations relating to such a retaliatory violent event may be drawn to help identify potential future risks of concern, possible counseling or other diversionary programs that may reduce the risk of this person a concern committing future acts, and identifying trends relating to an environment in which a violent act has previously occurred that could lead to other individuals or persons of concern committing similar and/or retaliatory acts of violence. In one embodiment, threat assessment may be complimented using micro and macro threat assessment models. For example, threats may be assessed using a micro assessment model, for example, determining if a threat maker poses an actual risk to carry out the current threat. Threats may also be assessed using a macro assessment model, for example, focusing on historical and foundational risk enhancers that may be contributing to overall levels of risk, Rabb Col. 23 line 47-Col. 24 line 10; An example of a risk enhancer may include temporal storage around an incident. In this example, interactions may be different between different people and different groups of people in the aftermath of an incident. The temporal component may consider days or weeks after the occurrence of an incident, such as a violent incident including a shooting or other serious harm to others, or on the anniversary of such an incident, without limitation. Those having skill in the art will appreciate other temporal milestones and periods of significance that may affect the level of risk associated with a location and incidence of a violent act. Skilled artisans will additionally appreciate that assessment of risk enhancers is not limited to shooting-based events or violent events related to mass-killing scenarios. For example, temporal storage may be considered when analyzing the risk of a violent act on the anniversary of a proceeding violent act at a location. In a more specific example, the risk of the future violent act may be elevated on the anniversary of a previous school shooting of high notoriety and the news cycle. Additional sensitivity may be given to the analysis of potential threats and observation of persons of concern during periods of elevated risk due to temporal storage of the impact of a preceding violent incident, Col. 27 line 64-Col. 28 line 20; The system may include modules, wherein each module of the modules may include a component, a database, and an application programming interface (API), Col. 3 lines 28-30);
a selecting unit, configured to determine suspicious behaviors of the specific person appearing in the historical trajectory by means of analyzing behaviors of the specific person according to the historical trajectory of the specific person (; The system may include modules, wherein each module of the modules may include a component, a database, and an application programming interface (API), Col. 3 lines 28-30); and
a first determining unit, configured to determine a suspicious level of the specific person according to a frequency of at least one of the suspicious behaviors appearing in a corresponding historical trajectory (Profile information may include history of incidents, prior events occurrences that could influence the behavior of the person of concern, and/or other behavioral information that may be relevant to understanding factors that may affect the behavior of the person of concern, Rabb Col. 23 lines 3-16; For example, threats may be assessed using a micro assessment model, for example, determining if a threat maker poses an actual risk to carry out the current threat. Threats may also be assessed using a macro assessment model, for example, focusing on historical and foundational risk enhancers that may be contributing to overall levels of risk, Rabb Col. 23 line 47-Col. 24 line 10; The system may include modules, wherein each module of the modules may include a component, a database, and an application programming interface (API), Col. 3 lines 28-30); and
determine that the specific person is dangerous in a case that the suspicious level exceeds a first set threshold and perform early-warning (The response coordination module may include a scheduling component for organizing responders for deployment to a site of a traumatic event. The response coordination module may include a resources component for requesting resources to support the responders. The response coordination module may include an information management component for managing the information sharable among the responders, Rabb Col. 4 lines 4-11; The response coordination module 700 may include various components to assist with the management and distribution of assets for responders to a violent, crisis, or trauma related event. The response coordination module 700 may include a request for support component 712, a scheduling component 714, a view resources component 722, a stipend management component 724, an information management component 732, and an efficacy component 734, without limitation. Those having skill in the art will appreciate that additional components may be included within the response coordination module 700 after having the benefit of this disclosure. The various components of the response coordination module 700 may be communicably connected to a response coordination database 702. Additionally, the response coordination module 700 and the response coordination database 702 may be communicably connected to exterior modules and/or databases via API, Col. 42 lines 49-65);
a second determining unit, configured to determine whether the person identifier is in a pre-stored white list; wherein the pre-stored white list comprises: registered users, staff in a smart park, and foreign affair persons having fixed cooperation with the smart park ;the second determining unit, further configured to determine that a specific person corresponding to the person identifier is a non-suspicious person in response to the person identifier is in the pre-stored white list (approved guests, Rabb Col. 46 lines 7-23 ) (Examiner interprets the list of approved guests as a white list of registered users;
a third determining unit, configured to determine whether the person identifier is in a black list (In some cases, these tagged persons of concern and/or other persons subject to threat assessment may be flagged for revenge-seeking behavior or other behavior that may be at risk of perpetuating violence. In this example, the news aggregation component 414 may identify victims of a school shooting event and the profile of the perpetrator of such an event. The news aggregation component 414 may extract information about the race or other characteristic of the perpetrator, associates of the perpetrator, and other information that may be used by a person of concern seeking revenge in a follow-up violent act. Ideally, any planning or preparation for a retaliatory event can be detected prior to its occurrence. However, should a retaliatory violent event occur by a person of concern showing a revenge-seeking bias, correlations relating to such a retaliatory violent event may be drawn to help identify potential future risks of concern, possible counseling or other diversionary programs that may reduce the risk of this person a concern committing future acts, and identifying trends relating to an environment in which a violent act has previously occurred that could lead to other individuals or persons of concern committing similar and/or retaliatory acts of violence. In one embodiment, threat assessment may be complimented using micro and macro threat assessment models. For example, threats may be assessed using a micro assessment model, for example, determining if a threat maker poses an actual risk to carry out the current threat. Threats may also be assessed using a macro assessment model, for example, focusing on historical and foundational risk enhancers that may be contributing to overall levels of risk, Rabb Col. 23 line 47-Col. 24 line 10) (Examiner interprets the tagged persons of concern as the person identifier in the black list):
a performing unit, configured to perform a short-term behavior analysis on a specific person corresponding to the person identifier in response to the person identifier is in the black list; wherein person identifiers in the black list are ex-offenders (In some cases, these tagged persons of concern and/or other persons subject to threat assessment may be flagged for revenge-seeking behavior or other behavior that may be at risk of perpetuating violence. In this example, the news aggregation component 414 may identify victims of a school shooting event and the profile of the perpetrator of such an event. The news aggregation component 414 may extract information about the race or other characteristic of the perpetrator, associates of the perpetrator, and other information that may be used by a person of concern seeking revenge in a follow-up violent act. Ideally, any planning or preparation for a retaliatory event can be detected prior to its occurrence. However, should a retaliatory violent event occur by a person of concern showing a revenge-seeking bias, correlations relating to such a retaliatory violent event may be drawn to help identify potential future risks of concern, possible counseling or other diversionary programs that may reduce the risk of this person a concern committing future acts, and identifying trends relating to an environment in which a violent act has previously occurred that could lead to other individuals or persons of concern committing similar and/or retaliatory acts of violence. In one embodiment, threat assessment may be complimented using micro and macro threat assessment models. For example, threats may be assessed using a micro assessment model, for example, determining if a threat maker poses an actual risk to carry out the current threat. Threats may also be assessed using a macro assessment model, for example, focusing on historical and foundational risk enhancers that may be contributing to overall levels of risk, Rabb Col. 23 line 47-Col. 24 line 10);
wherein the generating unit is further configured to: obtain historical data of the person identifier within the designated time period according to the person identifier (An example of a risk enhancer may include temporal storage around an incident. In this example, interactions may be different between different people and different groups of people in the aftermath of an incident. The temporal component may consider days or weeks after the occurrence of an incident, such as a violent incident including a shooting or other serious harm to others, or on the anniversary of such an incident, without limitation. Those having skill in the art will appreciate other temporal milestones and periods of significance that may affect the level of risk associated with a location and incidence of a violent act. Skilled artisans will additionally appreciate that assessment of risk enhancers is not limited to shooting-based events or violent events related to mass-killing scenarios. For example, temporal storage may be considered when analyzing the risk of a violent act on the anniversary of a proceeding violent act at a location. In a more specific example, the risk of the future violent act may be elevated on the anniversary of a previous school shooting of high notoriety and the news cycle. Additional sensitivity may be given to the analysis of potential threats and observation of persons of concern during periods of elevated risk due to temporal storage of the impact of a preceding violent incident, Col. 27 line 64-Col. 28 line 20; follow ups, Col. 31 lines 3-22);
Rabb does not expressly disclose a first obtaining unit, configured to obtain an image of the specific person shot in real time by a plurality of image acquisition devices; a extracting unit, configured to obtain a corresponding face image from the image, and extract a face feature from the face image by using facial recognition algorithms; a comparing unit, configured to compare an extracted face feature with face features of historical face images in a face database; a second obtaining unit, configured to obtain the person identifier of the specific person from the face database based on that the extracted face feature is successfully compared with the face features of historical face images in the face database; a storing unit, configured to store a face image of the specific person into the face database in response to comparison between the face image and the historical face images in the face database fails; an assigning unit, configured to assign a person identifier to the face image of the specific person; form a movement trajectory from the historical data contained in the designated time period according to device identifiers corresponding to a plurality of acquisition times in accordance with an order of the acquisition times of the historical data; and divide the movement trajectory into a plurality of historical trajectories in a case that a time difference corresponding to two adjacent pieces of historical data in the movement trajectory is greater than a second set threshold, wherein the two adjacent pieces of historical data are located at one end of each of two different historical trajectories respectively
However, Gordon teaches:
a first obtaining unit, configured to obtain an image of the specific person shot in real time by a plurality of image acquisition devices (security cameras, images, Gordon Col. 5 line 53-Col. 6 line 8; facial scans, Col. 11 lines 1-16; facial recognition, Col. 16 lines 27-49; facial scan of the person, Col. 30 lines 5-34; software modules that perform certain tasks, Col. 46 lines 9-22);
a extracting unit, configured to obtain a corresponding face image from the image, and extract a face feature from the face image by using facial recognition algorithms (facial recognition, Gordon Col. 16 lines 27-49; software modules that perform certain tasks, Col. 46 lines 9-22) (Examiner interprets the facial recognition techniques to include a facial recognition algorithm);
a comparing unit, configured to compare an extracted face feature with face features of historical face images in a face database (facial recognition, Gordon Col. 16 lines 27-49; software modules that perform certain tasks, Col. 46 lines 9-22);
a second obtaining unit, configured to obtain the person identifier of the specific person from the face database based on that the extracted face feature is successfully compared with the face features of historical face images in the face database (In another example, sensor linking module 415 may receive or obtain identification data related to a person and/or an event at another location. For example, a video camera may obtain a facial scan of the person who broke the window at house 220, but may also receive or obtain data from a local or remote database, such as a criminal database containing news photos and/or mugshots. Sensor linking module 415 may then compare the data received (both locally and from a remote source) to make a determination as to who and what has occurred. In this example, sensor linking module 415 may determine that a person matching a recent news report about burglaries has been identified at the porch of house 215 and is identified as the person that broke the window of house 220, thus sensor linking module 415 may determine a burglary is about to occur, Gordon Col. 30 lines 5-34; software modules that perform certain tasks, Col. 46 lines 9-22);
a storing unit, configured to store a face image of the specific person into the face database in response to comparison between the face image and the historical face images in the face database fails (In another example, sensor linking module 415 may receive or obtain identification data related to a person and/or an event at another location. For example, a video camera may obtain a facial scan of the person who broke the window at house 220, but may also receive or obtain data from a local or remote database, such as a criminal database containing news photos and/or mugshots. Sensor linking module 415 may then compare the data received (both locally and from a remote source) to make a determination as to who and what has occurred. In this example, sensor linking module 415 may determine that a person matching a recent news report about burglaries has been identified at the porch of house 215 and is identified as the person that broke the window of house 220, thus sensor linking module 415 may determine a burglary is about to occur, Gordon Col. 30 lines 5-34);
an assigning unit, configured to assign a person identifier to the face image of the specific person (In another example, sensor linking module 415 may receive or obtain identification data related to a person and/or an event at another location. For example, a video camera may obtain a facial scan of the person who broke the window at house 220, but may also receive or obtain data from a local or remote database, such as a criminal database containing news photos and/or mugshots. Sensor linking module 415 may then compare the data received (both locally and from a remote source) to make a determination as to who and what has occurred. In this example, sensor linking module 415 may determine that a person matching a recent news report about burglaries has been identified at the porch of house 215 and is identified as the person that broke the window of house 220, thus sensor linking module 415 may determine a burglary is about to occur, Gordon Col. 30 lines 5-34);
form a movement trajectory from the historical data contained in the designated time period according to device identifiers corresponding to a plurality of acquisition times in accordance with an order of the acquisition times of the historical data (In one example, device 215-a obtains data from one or more sensors located at house 215 (block 310). In this example, the data obtained includes information indicating that the users of house 215 are scheduled to be out of the house based on their stored schedules (e.g., schedule information, activity information), the dog out with the dog walker (e.g., location information), a video of a woman approximately 5′ 11″ tall, dressed in black, and with blonde hair (e.g., physical characteristics, identification information), the device has obtained the sound of glass breaking near the living room (e.g., interior environment information), and a blue car drove by slowly five minutes before the sound of glass breaking (e.g., exterior environment information), Gordon Col. 18 lines 18-31; At block 715, the method 700 may include comparing the obtained data with the identification data. Based on the data obtained by the first device, and the identification data received, the device may compare the data to make a determination. For example, if it is detected that glass is breaking, but there is no person detected within a predetermined distance of the glass at the time of the breaking (e.g., within a five foot radius), the device may determine the glass broke due to a branch hitting the glass or a baseball hitting the glass. On the other hand, if the sound of glass breaking is obtained, as well as the presence of a person, and the person has characteristics which match a news report related to specific person breaking into houses nearby or if the person is unknown or cannot be recognized, the device may determine this is an intruder breaking into this house. The operation(s) at block 715 may be performed using the sensor linking module 415 and/or the identification module 505 and/or the event determination module 645 described with reference to FIGS. 4, 5, and 6 respectively, Col. 38 line 61-Col. 39 line 17) (Examiner notes schedules of users as the forming of the movement trajectories from historical data in designated time periods); and
divide the movement trajectory into a plurality of historical trajectories in a case that a time difference corresponding to two adjacent pieces of historical data in the movement trajectory is greater than a second set threshold, wherein the two adjacent pieces of historical data are located at one end of each of two different historical trajectories respectively (Based on determining an identification of a person and/or an event, sensor linking module 415 may adjust a parameter associated with house 215 or take an action. The adjustment may be pre-programmed (e.g., a user profile which establishes actions and reactions), or may be based on artificial intelligence and learning based on previous events and actions both at the location and based on events and actions at other networked locations. In some examples, the actions may be based on a category of the request. For example, a user may set up a preference that in response to a request, the sensor linking module 415 will be configured to send identification of the person and/or the event without user approval, if the category associated with the request satisfies a threshold. Thus, in one example, an adjustment may be made automatically; however, in another example, the sensor linking module 415 may send a notification to a user to ask what the request adjustment should be based on the data and the comparison, Gordon Col. 30 lines 25-43; At block 715, the method 700 may include comparing the obtained data with the identification data. Based on the data obtained by the first device, and the identification data received, the device may compare the data to make a determination. For example, if it is detected that glass is breaking, but there is no person detected within a predetermined distance of the glass at the time of the breaking (e.g., within a five foot radius), the device may determine the glass broke due to a branch hitting the glass or a baseball hitting the glass. On the other hand, if the sound of glass breaking is obtained, as well as the presence of a person, and the person has characteristics which match a news report related to specific person breaking into houses nearby or if the person is unknown or cannot be recognized, the device may determine this is an intruder breaking into this house. The operation(s) at block 715 may be performed using the sensor linking module 415 and/or the identification module 505 and/or the event determination module 645 described with reference to FIGS. 4, 5, and 6 respectively, Col. 38 line 61-Col. 39 line 17).
Both the Gordon and the Rabb references are analogous in that both are directed towards/concerned with personal safety/security. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Gordon’s ability to obtain facial images and establish timelines of events related to short term events for threats in Rabb’s system to improve the system and method with reasonable expectation that this would result in a safety management system that is able to connect multiple systems and automate security actions.
The motivation being that there is a need for a more networked security solution as multiple audio or video devices, such as security cameras may be networked together to receive and transmit data related to the location and/or the association of the devices. In some examples, the network of devices may be created and maintained based on a predetermined proximity of the devices or a device to a location, such as devices associated with a house or houses in a neighborhood. In other examples, the network of devices may be based on each device's association with a group, such as a community network, or a group of devices running the same software application (Gordon Col. 1 lines 29-39).
However, the Examiner asserts that the data identifying the users such as registered users, staff in a smart park, foreign persons having fixed cooperation with the smart park and ex-offenders and a list thereof (whitelist, black list) is simply a label for the users and how the users are grouped or categorized, and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific types of users or lists) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability (see MPEP 2144.04).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the user data to include a user type and a list thereof since the specific type of user or list does not functionally alter or relate to the steps of the method and merely labeling the information differently from that in the prior art does not patentably distinguish the claimed invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ANDREW B WHITAKER whose telephone number is (571)270-7563. The examiner can normally be reached on M-F, 8am-5pm, EST.
If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629