DETAILED ACTION
This communication is in responsive to Application 14/070653 filed on 10/23/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims:
Claims 1-23 are presented for examination.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
The limitation “an environment characteristic detection element to sense an environment” of claim 1 because the limitation uses generic place holder “element” followed by functional language “to” where the “element” is not modified by sufficient structure. Note that “environment characteristic detection” does not connote hardware or sufficient structure to one with ordinary skill in the art.
The limitation “communication device…to transmit” of claim 1 because the limitation uses generic place holder “device” followed by functional language “to” where the “device” is not modified by sufficient structure. Note that “communication” does not connote hardware or sufficient structure to one with ordinary skill in the art.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-23 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 21 of U.S. Patent No. 12167505. Although the claims at issue are not identical, they are not patentably distinct from each other because the issued claims (independent claims 1 and 21) anticipate current claims (independent claims 1 and 21). In fact, the issued claims are narrower in scope than current claims -anticipation double patenting type rejection.
Claims 1-23 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 21 of U.S. Patent No. 11818801. Although the claims at issue are not identical, they are not patentably distinct from each other because the issued claims (independent claims 1 and 21) anticipate current claims (independent claims 1 and 21). In fact, the issued claims are narrower in scope than current claims -anticipation double patenting type rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-23 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 21 recites the limitation "… potential damage is the capacity for future damage…”. There is insufficient antecedent basis for this limitation in the claim. Claims 2-20 and 22-23 are rejected for depending on rejected claims 1 and 21.
Claim 1 limitations identified above under “claim interpretation” section, invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-23 are rejected under 35 U.S.C. 103 as being unpatentable over Przechocki et al. (hereinafter Przechocki) US 2019/0188797 in view of Alsubai et al. (hereinafter Alsubai) US 2019/0122129 A1.
Regarding Claim 1, Przechocki teaches a system associated with a site, comprising:
a plurality of risk relationship sensors (¶0032; risk data collected from sensors), including at least one image capturing sensor (¶0040 & ¶0050; infrared sensor, visual, audio, thermal, humidity, vibration sensors), each risk relationship sensor including:
an environment characteristic detection element to sense an environment characteristic (¶0040; environmental sensor e.g., temp sensor, radioactivity or voltage sensors), a power source (¶0040; inherent because all the active sensors include power source), and a communication device (¶0032; collecting information from sensors via Wi-Fi network), coupled to the environment characteristic detection element and the power source, to transmit data associated with a risk relationship at the site via communication network (¶0032; collect information from sensors via Wi-Fi network);
a risk relationship data store (Fig. 4 & ¶0045; data store 410. Also see Fig. 11 & ¶0063 where data store 1110 receives a stream of sensor data from a remote monitoring site 1140) containing electronic records associated with prior risk relationship events at other sites along with risk relationship sensor location data for those sites (¶0043; platform 320…review sensor data and historical data from insured party or other similar insured parties and third parties);
and an enterprise analytics platform (¶0041; system 300 provides information to a risk analytic platform 320. Also see ¶0063 & Fig. 11), coupled to the risk relationship data store and a third-party information interface (¶0045 & ¶0063; data store 110 contain and utilize third party data or corporate transactional systems), including a computer processor (Fig. 16; processor) programmed to:
(i) automatically analyze the electronic records in the risk relationship data store to create a predictive analytics algorithm (¶0053; Note that techniques such as machine learning may require large amounts of historical data to train models to make accurate predictions based on new data. The data that the robotic sensors 744 capture through can be fed back to an insurer's analytics environment to create beneficial predictive models associated with risk. By collecting data from many different sensors and aggregating data among multiple customers, an insurer may better predict impending risks, respond proactively, and/or model pricing and underwriting behaviors accordingly)),
(ii) receive the data associated with a risk relationship at the site (¶0063; As before, a risk monitoring data store 1110 receives a stream of sensor data from a remote monitoring site 1140 ... The risk monitoring data store 1110 may contain and utilize data such as historical data 1112 and derived analytic elements 1114, as well as support a streaming architecture 1116 to handle data in real-time (or near-real-time)),
(iii) automatically analyze, in substantially real-time, the data associated with the risk relationship at the site (A risk analytics platform 1120 uses data from the risk monitoring data store 1110 to calculate a result of a risk analysis that is provided to a risk operations platform 1130. The risk monitoring data store 1110 may contain and utilize data such as historical data 1112 and derived analytic elements 1114, as well as support a streaming architecture 1116 to handle data in real-time (or near-real-time)), including data from the at least one image capturing sensor (¶0040; an infrared sensor (to detect motion based on body heat); ¶0051; robotic sensors 744 at the customer site may utilize pattern recognition for various sensors (visual, audio, thermal, humidity, vibration, etc.) to perform baseline scans of the property), using the predictive analytics algorithm (¶0053; Note that techniques such as machine learning may require large amounts of historical data to train models to make accurate predictions based on new data. The data that the robotic sensors 744 capture through can be fed back to an insurer's analytics environment to create beneficial predictive models associated with risk. By collecting data from many different sensors and aggregating data among multiple customers, an insurer may better predict impending risks, respond proactively, and/or model pricing and underwriting behaviors accordingly)),
and (iv) transmit an indication of a result of the analysis (¶0041; The risk monitoring platform 320 may transmit a result of a risk analysis to a risk operations platform 330, which in turn may alter operation of the remote monitoring site 340; ¶ [0063]: A risk analytics platform 1120 uses data from the risk monitoring data store 1110 to calculate a result of a risk analysis that is provided to a risk operations platform 1130) including a map (¶0034; According to some embodiments, the risk analytics platform 120 communicates information associated with a simulator and/or a claims system to a remote operator and/or to an automated system, such as by transmitting an electronic file or template to an underwriter device, an insurance agent or analyst platform, an email server, a workflow management system, a predictive model, a map application, etc.) showing all of: 1. areas that signify particular levels of potential damage, (This limitation is obvious because Przechocki teaches that data is sent to a map application to pin point the exact event. See ¶0034 & ¶0112. In fig. 8 & ¶0055; Przechocki also teaches the display 800 may further include information about a potential alert 830 (e.g., including a location, a confidence level, a list of sensors that triggered the event, an event type, etc.). Moreover, the display 800 may include a “Transmit Alert” icon 840 that, when selected by an operator, may cause an alert message to be transmitted to a risk operations platform. See further in ¶0042; an enterprise may want to utilize devices that can monitor an insured object (e.g., premises, vehicles, or workers) to scan for hazards currently in progress or potential hazards avoid bodily injury and/or property damage. Note that hazards might be associated with various types of harm, including those that result in bodily injury, electricity, water, heat/overheating, fire, mechanical/vibration, over-use (a high cycle count), vandalism, intrusion, structural, environmental, etc. ¶0049; According to some embodiments, potential risks associated with equipment (prior to an actual failure) may be determined via machine learning based on data collected from similar equipment used by other insured customers. By utilizing the breadth of equipment data employed by a wide array of customers, events may be linked to particular equipment manufacturers and models. In addition, machine learning may match previously noted equipment failures and predict when such a failure is going happen for a different customer using similar equipment. For example, the system 700 may become aware that an insured pump is approaching failure (based on sensor data from a chronometer, thermometer, and accelerometer) because the insurance company has experiences failures for that type of pump with similar sensor signatures (and a risk operations platform may be alerted)) and the particular levels relate to a cost of the potential damage (obvious from ¶0108 because the output 1622 may be generated by the computer processor 1614 in response to applying the data for the current simulation to the trained predictive model component 1618. The output 1622 may, for example, be a potential alert, a monetary estimate, a risk level, and/or likelihood within a predetermined range of numbers. In some embodiments, the output 1622 may be implemented by a suitable program or program module executed by the computer processor 1614 in response to operation of the predictive model component 1618); and 2. at least one icon associated with an occurrence of a risk relationship event (¶0112 & Fig. 17; According to some embodiments, elements of the display 1710 are selectable (e.g., via a touchscreen) transmit an alert message and/or to adjust or see more information about a particular element. According to some embodiments, the display 1710 may include map information pinpointing the exact location of an event).
Przechocki does not expressly teach “…wherein potential damage is the capacity for future damage…”
Alsubai teaches “…wherein potential damage is the capacity for future damage…” (¶0072 & figs. 3, 19; A function of the output device 1522 may be to provide an output that is indicative of (as determined by the trained predictive model component 1518) particular water damage risk maps, events, insurance underwriting parameters, and recommendations. The output may be generated by the computer processor 1514 in accordance with program instructions stored in the program memory 1516 and executed by the computer processor 1514. More specifically, the output may be generated by the computer processor 1514 in response to applying the data for the current simulation to the trained predictive model component 1518. The output may, for example, be a monetary estimate, a water damage risk level, and/or likelihood within a predetermined range of numbers. In some embodiments, the output device may be implemented by a suitable program or program module executed by the computer processor 1514 in response to operation of the predictive model component 1518)
It would have been obvious to one with ordinary skill in the art before the effective filling date of the claimed limitation to incorporate the teachings of Alsubai into the system of Przechocki in order to automatically analyze the electronic records in the water impact data store to create a predictive analytics algorithm. The data associated with potential water-related data at the site and the third-party information may then be automatically analyzed, in substantially real-time, using the predictive analytics algorithm, and a result of the analysis may then be transmitted (e.g., to a party associated with the site and/or an on-site water shut-off valve) (abstract).
Regarding claim 2, Przechocki in view of Alsubai teaches the system of claim 1, Przechocki further discloses wherein the image capturing sensor comprises at least one of: (i) a camera, (ii) a video camera, (iii) an infrared camera, (iv) an autonomous platform, (v) a drone, and (vi) a wearable device (see examples in ¶0040, ¶0048 & ¶0051).
Regarding claim 3, Przechocki in view of Alsubai teaches the system of claim 2, Przechocki further discloses wherein the data from the at least one image capturing sensor is used to estimate an occupancy (¶0040; an infrared sensor (to detect motion based on body heat); ¶0051; Note that buildings may typically have security sensors, such as ... infrared sensors to detect intruders).
Regarding claim 4, Przechocki in view of Alsubai teaches the system of claim 3, Przechocki further discloses wherein the site is associated with a construction site, the risk relationship at the site is associated with at least one of fire, theft, vandalism, and water damage insurance, and a higher occupancy level (¶ [0038]: the term “risk” might be associated with bodily injury (e.g., to a factory worker), electrical damage to equipment, water damage, heat damage (e.g., a machine is overheated), fire damage, vibrational damage, over-use damage, vandalism, intrusion damage, structural damage, environmental damage, a cyber-security threat, etc.; ¶ [0042]: For example, an enterprise may want to utilize devices that can monitor an insured object (e.g., premises, vehicles, or workers) to scan for hazards currently in progress or potential hazards avoid bodily injury and/or property damage. Note that hazards might be associated with various types of harm, including those that result in bodily injury, electricity, water, heat/overheating, fire, mechanical/vibration, over-use (a high cycle count), vandalism, intrusion, structural, environmental, etc.) results in a reduced insurance premium ([0113]: the system may input a risk rate to an insurance underwriting model that generates at least one insurance based parameter (e.g., an insurance premium amount or discount, an insurance deductible, etc.)).
Regarding claim 5, Przechocki in view of Alsubai teaches the system of claim 3, Przechocki further discloses wherein the site is associated with a retail store or office (¶0048), risk relationship at the site is associated with at least one of personal injury, general liability, and workers’ compensation insurance, and a lower occupancy level (¶ [0038]: the term “risk” might be associated with bodily injury (e.g., to a factory worker), electrical damage to equipment, water damage, heat damage (e.g., a machine is overheated), fire damage, vibrational damage, over-use damage, vandalism, intrusion damage, structural damage, environmental damage, a cyber-security threat, etc.; ¶ [0040]: an accelerometer sensor (to detect a slip and/or fall when the sensor is worn by an employee; ¶ [0042]: For example, an enterprise may want to utilize devices that can monitor an insured object (e.g., premises, vehicles, or workers) to scan for hazards currently in progress or potential hazards avoid bodily injury and/or property damage. Note that hazards might be associated with various types of harm, including those that result in bodily injury, electricity, water, heat/overheating, fire, mechanical/vibration, over-use (a high cycle count), vandalism, intrusion, structural, environmental, etc.) results in a reduced insurance premium (¶ [0113]: the system may input a risk rate to an insurance underwriting model that generates at least one insurance based parameter (e.g., an insurance premium amount or discount, an insurance deductible, etc.)).
Regarding claim 6, Przechocki in view of Alsubai teaches the system of claim 3, Przechocki further discloses wherein at least one of the plurality of risk relationship sensors is associated with (i) a door sensor (¶0040), (ii) a chair sensor, (iii) a floor sensor (¶0052), (iv) an elevator sensor, (v) a motion detector (¶0040), (vi) a wireless network utilization sensor, and (vii) a WiFi utilization sensor (¶0032 & ¶0040).
Regarding claim 7, Przechocki in view of Alsubai teaches the system of claim 2, Przechocki further discloses wherein the transmitted indication of the result of the analysis is associated with an equipment use characterization (¶ [0049]: the system 700 may become aware that an insured pump is approaching failure (based on sensor data from a chronometer, thermometer, and accelerometer) because the insurance company has experiences failures for that type of pump with similar sensor signatures (and a risk operations platform may be alerted)).
Regarding claim 8, Przechocki in view of Alsubai teaches the system of claim 2, Przechocki further discloses wherein the transmitted indication of the result of the analysis is associated with a site space characterization (¶ [0042]: the risk analytics platform 120 may automatically generate and transmit electronic alert messages (e.g., when a dangerous condition is detected) and/or site remediation recommendations (e.g., “water to the fourth floor should be turned off immediately”)).
Regarding claim 9, Przechocki in view of Alsubai teaches the system of claim 2, Przechocki further discloses wherein the transmitted indication of the result of the analysis is associated with an employee behavior characterization (¶ [0040]: an accelerometer sensor (to detect a slip and/or fall when the sensor is worn by an employee).
Regarding claim 10, Przechocki in view of Alsubai teaches the system of claim 1, Przechocki further discloses wherein different risk relationship sensors sense different environment characteristics, including at least three of: (i) moisture (¶0040), (ii) water flowing through a pipe, (iii) a temperature, (iv) a thermal image, (v) mold, (vi) an image captured by a camera, (vii) video streamed from a camera, (viii) audio information detected by a microphone (¶0051 & ¶0049), (ix) a water flow volume or rate determined by a smart a risk relationship meter (¶0040 & ¶0051), (x) an alarm system, and (xi) a smoke detector (¶0049).
Regarding claim 11, Przechocki in view of Alsubai teaches the system of claim 1, Przechocki further discloses further comprising: an on-site a risk relationship information hub to: (i) receive data from the plurality of risk relationship sensors via the communication network (¶0061), and (ii) transmit indications associated with the received data via another communication network (¶0028).
Regarding claim 12, Przechocki in view of Alsubai teaches the system of claim 11, Przechocki further discloses wherein the communication network is a wireless communication network and the other communication network is the Internet (¶0033 & ¶0061).
Regarding claim 13, Przechocki in view of Alsubai teaches the system of claim 11, Przechocki further discloses wherein the enterprise analytics platform is associated with a cloud-based computing architecture (¶ [0061]: The predictive analytics database 1000 may be periodically created and updated, for example, based on information electrically received from sensors and/or a sensor hub via a cloud-based application).
Regarding claim 14, Przechocki in view of Alsubai teaches the system of claim 13, Przechocki further discloses wherein the predictive analytics algorithm is associated with at least one of: (i) cognitive learning, (ii) pattern recognition, (iii) an early detection algorithm, (iv) a risk analysis (¶ [0038]: the system may analyze the received sensor data, using at least one risk analytics algorithm, to detect an abnormal pattern associated with a predicted elevated level of risk at the risk monitoring site. For example, a risk analytics platform computer might execute pattern matching, incorporate a machine learning process, and/or incorporate an artificial intelligence process looking for a deviation from a “normal” signal signature that might indicate an elevated level of risk; ¶ [0047]: embodiments may facilitate early detection of an adverse event or condition to improve loss prevention;), and (v) a risk score (¶ [0105]: A function of the predictive model component 1618 may be to determine ... scores (e.g., a rating indicating how likely a loss event is based on prior data from similar sites).
Regarding claim 15, Przechocki in view of Alsubai teaches the system of claim 13, Przechocki further discloses wherein the indication transmitted by the enterprise analytics platform comprises an electronic alert signal (¶ [0029]: the risk analytics platform 120 utilizes a computer 122 and/or an algorithm 124 to transmit a result of a risk analysis (e.g., to a risk operations platform and/or administrator). For example, a Graphical User Interface (“GUI”) or other module of the risk analytics platform 120 might transmit information via the Internet to facilitate a rendering of an interactive graphical operator interface display and/or the creation of electronic alert messages, automatically created site recommendations, etc.).
Regarding claim 16, Przechocki in view of Alsubai teaches the system of claim 15, Przechocki further discloses wherein the electronic alert signal is associated with at least one of: (i) an automated telephone call, (ii) an email message, and (iii) a text message (¶ [0032]: the risk analytics platform 120 may automatically generate and transmit electronic alert messages (e.g., when a dangerous condition is detected); ¶ [0083]: Such rigor is not necessary because the data science workflow 1410 can use something as simple as email to receive the alerts).
Regarding claim 17, Przechocki in view of Alsubai teaches the system of claim 16, Przechocki further discloses wherein the electronic alert signal includes a potential cause of a risk relationship event and a recommended remedial action (¶ [0032]: the risk analytics platform 120 may automatically generate and transmit electronic alert messages (e.g., when a dangerous condition is detected) and/or site remediation recommendations (e.g., “water to the fourth floor should be turned off immediately”)).
Regarding claim 18, Przechocki in view of Alsubai teaches the system of claim 1, Przechocki further discloses wherein at least one of the power sources is associated with at least one of: (i) a battery, (ii) a re-chargeable battery, and (iii) an Alternating Current (“AC”) power adapter (¶ [0040]: Note that the set of sensor systems at the risk monitoring site might include a static sensor (e.g., a thermometer), a mobile robotic sensor (e.g., a robotic sensor that roams around at the site being monitored), a software sensor (e.g., to monitor operation of a computer or network), a vehicle sensor, a drone sensor, a wearable sensor, etc; The power source of each of the aforementioned sensors inherently belongs to one of battery, re-chargeable battery, or AC power adapter).
Regarding claim 19, Przechocki in view of Alsubai teaches the system of claim 1, Przechocki further discloses wherein the result of the analysis is to be used to calculate an event damage rating for an enterprise associated with the site (¶ [0108]: The output 1622 may, for example, be a potential alert, a monetary estimate, a risk level, and/or likelihood within a predetermined range of numbers; ¶ [0109]: the risk analytics platform 1624 may direct workflow by referring, to an enterprise analytics platform 1626, alerts generated by the predictive model component 1618 and found to be associated with various results or scores).
Regarding claim 20, Przechocki in view of Alsubai teaches the system of claim 19, Przechocki further discloses wherein the event damage rating is associated with at least one of: an insurance premium adjustment, a deductible value, a co-payment, an insurance policy endorsement, and an insurance limit value (¶ [0056]: The risk analytics platform 900 further includes ... an output device 950 (e.g., to output reports regarding ... insurance policy premiums); ¶ [0113]: the system may input a risk rate to an insurance underwriting model that generates at least one insurance based parameter (e.g., an insurance premium amount or discount, an insurance deductible, etc.).
Regarding claim 21, Przechocki discloses a computerized method associated with a site, comprising: collecting, from a plurality of risk relationship sensors, data associated with a risk relationship at the site via communication network (¶ [0032]: One function of the risk monitoring data store 110 may be to collect information from sensors via a wireless Wi-Fi network),
wherein each risk relationship sensor includes: (i) an environment characteristic detection element to sense an environment characteristic (¶ [0040]: An environmental sensor device might include a temperature sensor (to measure an ambient temperature, prevent freezing water pipes, detect an open door, identify a fire), a particulate sensor, a radioactivity sensor, and/or a voltage sensor (to detect power surges)), (ii) a power source (¶ [0040]: Note that the set of sensor systems at the risk monitoring site might include a static sensor (e.g., a thermometer), a mobile robotic sensor (e.g., a robotic sensor that roams around at the site being monitored), a software sensor (e.g., to monitor operation of a computer or network), a vehicle sensor, a drone sensor, a wearable sensor, etc.; Each of the aforementioned sensors inherently includes power source respectively), and (iii) a communication device (¶ [0032]: ] One function of the risk monitoring data store 110 may be to collect information from sensors via a wireless Wi-Fi network), coupled to the environment characteristic detection element and the power source (¶ [0040]: Note that the set of sensor systems at the risk monitoring site might include a static sensor (e.g., a thermometer), a mobile robotic sensor (e.g., a robotic sensor that roams around at the site being monitored), a software sensor (e.g., to monitor operation of a computer or network), a vehicle sensor, a drone sensor, a wearable sensor, etc.; Each of the aforementioned sensors inherently includes power source respectively), to transmit the data associated with a risk relationship (¶ [0032]: ] One function of the risk monitoring data store 110 may be to collect information from sensors via a wireless Wi-Fi network);
storing, in a risk relationship data store (Fig. 4, ¶ [0045]: FIG. 4 illustrates a system 400 with a risk monitoring data store 410; Fig. 11, ¶ [0063]: a risk monitoring data store 1110 receives a stream of sensor data from a remote monitoring site 1140), electronic records associated with prior risk relationship events at other sites along with risk relationship sensor location data for those sites (¶ [0043]: the insurance company's risk analytics platform 320 may: (1) employ sophisticated algorithms, including machine learning and artificial intelligence, to review sensor data and historical data (from the insured party, other similar insured parties, and third parties));
automatically analyzing, by a computer processor of an enterprise analytics platform, the electronic records in the risk relationship data store to create a predictive analytics algorithm ¶ [0053]: Note that techniques such as machine learning may require large amounts of historical data to train models to make accurate predictions based on new data. The data that the robotic sensors 744 capture through can be fed back to an insurer's analytics environment to create beneficial predictive models associated with risk. By collecting data from many different sensors and aggregating data among multiple customers, an insurer may better predict impending risks, respond proactively, and/or model pricing and underwriting behaviors accordingly));
automatically analyzing, by the computer processor of the enterprise analytics platform in substantially real-time (¶ [0063]: A risk analytics platform 1120 uses data from the risk monitoring data store 1110 to calculate a result of a risk analysis that is provided to a risk operations platform 1130. The risk monitoring data store 1110 may contain and utilize data such as historical data 1112 and derived analytic elements 1114, as well as support a streaming architecture 1116 to handle data in real-time (or near-real-time)), the data associated with a risk relationship at the site using the predictive analytics algorithm (¶ [0053]: Note that techniques such as machine learning may require large amounts of historical data to train models to make accurate predictions based on new data. The data that the robotic sensors 744 capture through can be fed back to an insurer's analytics environment to create beneficial predictive models associated with risk. By collecting data from many different sensors and aggregating data among multiple customers, an insurer may better predict impending risks, respond proactively, and/or model pricing and underwriting behaviors accordingly)); and
transmitting, from the enterprise analytics platform, an indication of a result of the analysis (¶ [0041]: The risk monitoring platform 320 may transmit a result of a risk analysis to a risk operations platform 330, which in turn may alter operation of the remote monitoring site 340; ¶ [0063]: A risk analytics platform 1120 uses data from the risk monitoring data store 1110 to calculate a result of a risk analysis that is provided to a risk operations platform 1130) the result including a map (¶0034; According to some embodiments, the risk analytics platform 120 communicates information associated with a simulator and/or a claims system to a remote operator and/or to an automated system, such as by transmitting an electronic file or template to an underwriter device, an insurance agent or analyst platform, an email server, a workflow management system, a predictive model, a map application, etc.) showing all of: 1. areas signifying particular levels of potential damage, wherein potential damage is the capacity for future damage and the particular levels are related to a cost of the potential damage; and 2. at least one icon associated with an occurrence of a risk relationship event (¶0112 & Fig. 17; According to some embodiments, elements of the display 1710 are selectable (e.g., via a touchscreen) transmit an alert message and/or to adjust or see more information about a particular element. According to some embodiments, the display 1710 may include map information pinpointing the exact location of an event).
Regarding claim 22, Przechocki in view of Alsubai teaches wherein the plurality of image capturing sensors include at least one image capturing sensor comprising: (i) a camera, (ii) a video camera, or (iii) an infrared camera (¶ [0040]: an infrared sensor (to detect motion based on body heat); ¶ [0048]: the use of artificial intelligence and/or robotics may be fully autonomous and/or be integrated with human teamwork; ¶ [0051]: robotic sensors 744 at the customer site may utilize pattern recognition for various sensors (visual, audio, thermal, humidity, vibration, etc.) to perform baseline scans of the property).
Regarding claim 23, Przechocki in view of Alsubai teaches wherein the data from the at least one image capturing sensor is used to estimate an occupancy (¶ [0040]: an infrared sensor (to detect motion based on body heat); ¶ [0051]: Note that buildings may typically have security sensors, such as ... infrared sensors to detect intruders).
Conclusion
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MAHRAN ABU ROUMI
Primary Examiner
Art Unit 2455
/MAHRAN Y ABU ROUMI/Primary Examiner, Art Unit 2455
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