DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
2. The Amendment filed on September 19, 2025 has been entered. The examiner acknowledges the amendments to claims 1-4, 6-9, 11, 13-15, and 17-20 the cancellation of claims 10-12, and the addition of claims 21-22.
Rejections under 35 U.S.C. § 101: Applicant argues that the claims do not cite a
judicial exception. The examination process for 35 U.S.C. § 101 takes an independent claim and removes all hardware, processing and IT components or elements to reveal the underlying activities in the claim. This is where underlying abstract ideas are evaluated. The examination reveals how the activities follow sets of rules or instructions, employ processes that have as their basis mental processes, or perform economic activities to include cost considerations or risk. In this way, abstract ideas are revealed and those are evident in the documented analysis.
Applicant also argues that additional elements constitute a technical
improvement in how computers process and present multi-dimensional risk data, and that claims provide an improved user interface that displays a specific risk heat map that improves the user interface enabling faster recognition of entity asset risk and navigating risk scores using computer-unique techniques, and that the structure listed in the claims improves the way computers convey risk information to users, thereby reducing the cognitive load on user and facilitating decision-making in ways that conventional reports could not.
The Examiner points out that by definition additional elements are well-
understood, routine, conventional activities previously known to the industry, [MPEP 2106.05(d)], so a claim that an additional element constitutes a technical improvement here appears suspect. Heat maps are similarly known entities and a conclusion that a heat map provides for faster recognition seems plausible, but lacks evidence. The final points addressing computer-unique techniques, and structure improving the way computers convey risk information lack support, and support the observation that the invention provides a wealth of risk analysis and presents information to users for action. It is not apparent that the invention takes any action beyond reporting to a user on a convention display using a conventional interface, on the knowledge gained from the data processing an analysis, in which case, the invention appears to be software on a generic computer, invoking the “Apply it” moniker, as discussed in MPEP § 2106.05(f).
Lacking a practical application and without evidence of improvement to the computer technology, the rejections under 35 U.S.C. § 101 will not be withdrawn.
Rejections under 35 U.S.C. § 103: Applicant’s arguments in favor of claims appear to center around the presence and application of heat maps for display. Applicant argued prior art merely describes generating forensic heat maps for IT resource metrics. Additional art was found applying heat map techniques to risk. Examiner notes that a heat map is not a new display function but exists in many fields and applications.
Applicant argues that prior art does not teach elements analogous to the claimed invention. Examiner disagrees and notes that descriptions and processes reviewed can be reasonably interpreted as performing like functions. Although terminology variants are noted, the foundational aspects for risk evaluation proved consistent.
Applicant’s arguments for withdrawing art rejections are not compelling and the rejections under 3 U.S.C. § 103 will not be withdrawn.
Claim Rejections – 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9, 11, 13-22 are rejected under 35 U.S.C. § 101 because the claimed invention is
directed to non-statutory subject matter. The claims, 1-9, 11, 13-22 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-9, 11, 13-22 are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1-9, 11, 13-22 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of providing insight into the risks inherent in an organizations’ entities and assets, and their underlying causes, leading to recommendations for those assets.
Claim 1 discloses a method, comprising:
A method for generating an insight report for an entity, the method
comprising:
identifying, an entity asset set for the entity, (following rules or instructions, observation, evaluation, judgement, opinion)
wherein (i) the entity asset set comprises a plurality of entity assets associated with the entity, (ii) each entity asset is associated with an entity asset type, and (iii) each entity asset is further associated with a geographic area;
determining, based on a corresponding entity asset type and a corresponding geographic area, an entity physical nature risk score and an entity transitional nature risk score for each entity asset, (economic principles and practices calculating costs, mitigating risk, following rules or instructions, observation, evaluation, judgement, opinion)
generating, based on a corresponding entity physical nature risk score and a corresponding entity transitional nature risk score, an insight for each entity asset wherein each insight is indicative of an inferred cause for at least one of the corresponding entity physical nature risk score and the corresponding entity transitional nature risk score associated with the entity asset; (economic principles and practices calculating costs, mitigating risk, following rules or instructions, observation, evaluation, judgement, opinion)
generating, based on the entity physical nature risk score and the entity transitional nature risk score for each entity asset, a risk heat map, wherein (a) the risk heat map is associated with a risk score type and (b) the risk heat map is a visual representation of at least one of the entity physical nature risk score and the entity transitional nature risk score for each entity asset; (economic principles and practices calculating costs, mitigating risk, following rules or instructions, observation, evaluation, judgement, opinion)
generating, the insight report for the entity, wherein the insight report comprises the insight for each entity asset and the risk heat map;
generating the insight report; and outputting to a user, (economic principles and practices calculating costs, mitigating risk, following rules or instructions, observation, evaluation, judgement, opinion).
Additional limitations employ the method to determine entity management policies and one or more operating rules to determine insights, (following rules and instructions, observation, evaluation, judgement, opinion- claim 2), to determine optimal entity management policies and operating rules to result in a maximum risk score for the entity asset, and one or more optimal management policies that are missing from the entity policy management set, and generating a recommended policy set of one or more policies that were not included in the policy management set and the including this in the insight report, (following rules and instructions, observation, evaluation, judgement, opinion, mitigating risk - claim 3), to determine an implementation cost estimate for optimal entity policy, generating an implementation service offer including a financial instrument offer covering at least a portion of the cost estimate and providing an offer message including each generated implemented service offer for the optimal entity management policies, (following rules and instructions, observation, evaluation, judgement, opinion, marketing or sales activities, - claim 4), identifying candidate entity assets, determining geographic area associated with the asset, and generating the entity asset set for the entity, (following rules and instructions, observation, evaluation, judgement, opinion- claim 5), receiving resource consumption metrics for the entity where determining a risk score is based on the consumption set for the asset, (following rules and instructions, observation, evaluation, judgement, opinion, mitigating risk - claim 6), receiving an indication of an asset by type, determining candidate geographic areas for the future asset, determining the risk score for the geographic area, determining based on the risk score, recommended areas and providing a future entity asset recommendation response by geographic area, (following rules and instructions, observation, evaluation, judgement, opinion, mitigating risk- claim 7), receiving a scenario request a predicted risk score for each entity, predicted insights for each entity with cause for the risk, and providing a scenario response that includes the insights, (following rules and instructions, observation, evaluation, judgement, opinion, mitigating risk- claim 8), determining based on risk scores, a global risk score, and global insights for the entity, with the insight indicative of the inferred risk for the entity, (following rules and instructions, observation, evaluation, judgement, opinion, mitigating risk- claim 9), determining comparative entries in an industry and based on risk score, ranking the entity risks, to be included in the report, (following rules and instructions, observation, evaluation, judgement, opinion, mitigating risk- claim 11), where the risk heat map is presented to users, (following rules and instructions, observation, evaluation, judgement, opinion – claim 21), and wherein the information comprises at least one of an associated entity physical nature risk score, an entity transitional nature risk score, a type of entity asset, an associated geographic area, an insight, an entity policy management set, a recommended policy set, associated implementation offers, and an entity asset type requirement set, (following rules and instructions, observation, evaluation, judgement, opinion, mitigating risk – claim 22).
Each of these claimed limitations employ: organizing human activity in the form of fundamental economic principles and practices based on mitigating risk and calculating costs, following rules or instructions, performing mental processes including, observation, evaluation, judgement, and opinion.
Claims 13-20 recite similar abstract ideas as those identified with respect to claims 1-9, 11, 21-22.
Thus, the concepts set forth in claims 1-9, 11, 13-22, recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-9, 11, 13-22 recite additional limitations which are hardware or software elements such as entity analysis circuitry, risk scoring circuitry, insight circuitry, risk scoring circuitry, entity policy identification circuitry, entity improvement circuitry, a graphical user interface, communications hardware, a user device, a computer program product, and a non-transitory computer-readable storage medium, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-9, 11, 13-22 are directed to abstract ideas.
Step 2B
Claims 1-9, 11, 13-22 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-9, 11, 13-22 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 7-8, 13, 16, 18-22 are rejected under 35 U.S.C. § 103 as
being anticipated by Lu, (US 20190197442 A1), hereafter Lu, “Artificial Intelligence Based Risk and Knowledge Management,” in view of Joseph, (US-10860742-B2), hereafter Joseph, “Privacy Risk Information Display.”
Regarding claim 1, A method for generating an insight report for an entity, Lu teaches, (an artificial intelligence-based risk and knowledge management analysis are described, [Abstract]), the method comprising:
identifying, by entity analysis circuitry, an entity asset set for the entity, (a data analyzer may obtain entity data pertaining to an entity associated with a risk management instrument, [Abstract], wherein (i) the entity asset set comprises a plurality of entity assets associated with the entity, (ii) each entity asset is associated with an entity asset type, (the entity may be, for instance, a property, such as a building and a house, a vehicle, such as a car, an appliance, such as a television, and the like [0017]), and (iii) each entity asset is further associated with a geographic area; (the sensors 120 connects to a single or multiple entities 110 in one or multiple domains (for example, in different distances), while gathering information such as, geographical location, signal frequency, sensors attributes, and entity category, [0040]),
determining, by risk scoring circuitry and based on a corresponding entity asset type and a corresponding geographic area, an entity physical nature risk score and an entity transitional risk score for each entity asset;
The specification of a risk entity is a design choice with no benefit to the utility of the invention. Regardless, Lu teaches, (the intelligent risk control and knowledge management agent 135 identifies the potential and emerging risks and markets by the techniques, such as, dynamic AI data analysis, observable trends predictions, and fraudulent areas. The risk identifications, in the area of such as cyber, climate, nanotechnology, self-diving cars, and drones, may be conducted based on the received sensor data of multiple entities, such as climate, location and entity category, Lu, [0044]), It would be a design choice to include physical nature and transitional risk scores and it would be obvious for one of ordinary skill in the art to rearrange parts of an invention, in this case choose appropriate entities for risk evaluation, so that the entity data may be processed by an intelligent risk management agent to perform a variety of risk control and knowledge management tasks, Lu, [Abstract], (MPEP 2144.04 I, 2144.04 VI (C)).
generating, by insight circuitry and based on a corresponding entity physical nature risk score and a corresponding entity transitional nature risk score, an insight for each entity asset, wherein each insight is indicative of an inferred cause for at least one of the corresponding entity physical nature risk score and the corresponding entity transitional nature risk score associated with the entity asset; (the intelligent risk control and knowledge management agent 135 includes notification generator (shown in FIG. 2) to notify the users existing and potential damages to the covered entities, and to provide advices on how to react
to the alerts and damages. The agent 135 adopts the proactive approaches rather than reactive to the damages caused by the reasons such as natural disasters, human mistakes, equipment failures, and all kind of chaos situations, which prevents the potential damages and losses by alarming and advising the users before the damages happen and ahead of time, [0045]),
generating, by the risk scoring circuitry and based on the entity physical nature risk score and the entity transitional nature risk score for each entity asset, a risk heat map, wherein (a) the risk heat map is associated with a risk score type and (b) the risk heat map is a visual representation of at least one of the entity physical nature risk score and the entity transitional nature risk score for each entity asset; Lu does not teach, Joseph teaches, (In some implementations, the privacy profile 500 may comprise a heat map that utilizes a color-coded background 544 on which the graphical representations are displayed, Joseph [13: 30-32]).
generating, by the insight circuitry, the insight report for the entity, wherein the insight report comprises the insight for each entity asset and the risk heat map: Lu teaches, (the entity data may be used to identify potential or emerging risks, generate alerts, and provide the advices on how to react on the alerts by the intelligent risk control and knowledge management agent to prevent or lower the damage to the entity proactively, [0028]).
generating, by the insight circuitry, a graphical user interface (GUI) that represents the
insight report; Lu does not teach, Joseph teaches, (Display 224 may be any type of display device that presents information, such as via a user interface that captures privacy risk information, from client device 220. Interface 234 may be any combination of hardware and/or programming that facilitates the exchange of data between the internal components of client device 220 and external components, such as privacy risk assessment device 210, Joseph, [4: 19-25]), and
outputting, by communications hardware, the GUI to a user device, Lu does not teach, Joseph teaches, (The privacy risk assessment device 210 may cause a graphical element representing a combination of the privacy attention score 252 and the aggregated privacy assessment score 256 to be displayed via a display device, such as display 224 of client device 220.), [3:48-52]).
Lu and Joseph are both considered to be analogous to the claimed invention because they are both in the field of risk identification and mitigation. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the risk insight development method of Lu with the information displays of Joseph to allow a decision maker to view the privacy profile and follow privacy risk trends among a portfolio of applications, [13: 59-61]).
Regarding claim 5, the method of claim 1, further comprising:
identifying, by the entity analysis circuitry, entity asset information from one or more
entity data sources; Lu teaches, (the data pertaining to the entity may be gathered and analyzed by a data analyzer. The entity data may include data obtained from sensors and data pertaining to a risk management instrument covering the entity. Moreover, this system applies the Internet of things techniques, which may gather the live data simultaneously via remote sensing and/or IoT monitoring, [0021]),
identifying, by the entity analysis circuitry, one or more candidate entity assets from the entity asset information; Lu teaches, (FIG. 5, illustrates an example of Internet of things (IoT) and AI based risk management analysis, according to an example of the present subject matter. As can be observed, using the sensors 120 and IoT devices 505 data pertaining to entities 510-1 . . . 510-N may be gathered. Consider an example, where the entity 510-1 may be a building and the entity 510-N may be a vehicle. For the entity 510-1, the data may be gathered and AI based risk control may be performed for, for example, energy management and fire safety, [0096]),
determining, by the entity analysis circuitry, a geographic area associated with one or
more of the one or more candidate entity assets; (the sensors 120 connects to a single or multiple entities 110 in one or multiple domains (for example, in different distances), while gathering information such as, geographical location, signal frequency, sensors attributes, and entity category, [0040]), and
generating, by the entity analysis circuitry and based on the one or more identified
candidate entity assets and corresponding determined geographic location, the entity asset set for the entity, (the system 105 may include a data analyzer 130 and an intelligent risk management agent 135. The data analyzer 130 and the intelligent risk management agent 135may be in communication with each other to perform the functionalities of the system 105. The sensors 120 may send the data to the system 105 and database 115 simultaneously as it gathers the signals over time, [0039]), moreover, in certain circumstances, the data analyzer 130 gathers data from the sensors 120 pertaining to an entity 110. The data analyzer 130 usually gathers the data simultaneously via such as remote sensing and IoT monitoring over time, where the received signals and data are communicated with related components and processed instantaneously. For instance, an agent may need the received data to build risk control models and conduct the damage loss payment estimation. Meanwhile, the risk control and database may receive and store the sensing data as knowledge for usage. The sensors 120 connects to a single or multiple entities 110 in one or multiple domains (for example, in different distances), while gathering information such as, geographical location, signal frequency, sensors attributes, and entity category, [0040]).
Regarding claim 7, The method of claim 1, further comprising:
receiving, by the communications hardware, an indication of a future entity asset, wherein the future entity asset is associated with an entity asset type; Lu teaches, (the entity data may be processed to formulize a risk control and knowledge management instrument. For instance, the entity data pertaining to the multiple domains, such as location, entity type, and climate, may be processed to determine a probable demand for a new risk control model or a knowledge management instrument in future, [0111]),
determining, by the risk scoring circuitry and based on a corresponding entity asset type associated with the future entity asset, one or more candidate geographic areas for the future entity asset; (Upon the analysis, the intelligent risk control and knowledge management agent 135 determines the emerging risk domains and markets as explained in detail with reference to description of FIG. 2, [0044]),
determining, by the risk scoring circuitry and based on the entity asset type associated with the future entity asset, an entity physical nature risk score and an entity transitional nature risk score for the one or more candidate geographic areas; Lu teaches, (boundaries and bottlenecks, which the current risk control and knowledge management may have, the intelligent risk control and knowledge management agent 135 identifies the potential and emerging risks and markets by the techniques, such as, dynamic AI data analysis, observable trends predictions, and fraudulent areas. The risk identifications, in the area of such as cyber, climate, nanotechnology, self-diving cars, and drones, may be conducted based on the received sensor data of multiple entities, such as climate, location and entity category. Upon the analysis, the intelligent risk control and knowledge management agent 135 determines the emerging risk domains and markets as explained in detail with reference to description of FIG. 2. [0044]),
determining, by the risk scoring circuitry and based on the entity physical nature risk score and an entity transitional nature risk score associated with the one or more candidate geographic areas, one or more recommended geographic areas; (to identify the emerging risk markets, a RCF 230 develops a formulizer to extract from the data analyzer 130 about information, including the entity data within one or more domains, such as entity categories (e.g., property, vehicle, or an appliance), location/territory, and weather. The entity data may include details pertaining to the entities and their environment, [0069]), and
providing, by the communications hardware, a future entity asset recommendation response, (the output of the RCF230 communicates within intelligent agent 135, which including to provide to internal and external agents for further usages, [0070], wherein the future entity asset recommendation response comprises the one or more recommended geographic areas, (Simultaneously, the RCF 230 identifies the protection against the damages caused by rainfall as a new emerging market, where the corresponding products may be created, [0070]).
Regarding claim 8, The method of claim 1, further comprising:
receiving, by the communications hardware, a scenario request, wherein the scenario
request comprises a set a scenario type and a scenario parameter set; Lu teaches, (Thus, the analysis may be performed based on a goal to be achieved, and the analyzed data may be processed by the intelligent risk control and knowledge management agent to provide a desired output, such as claim adjudication, fraud detection, alert and advice generation, formulization of new risk management instruments or policies, and renewing of existing risk management instruments or existing contracts and policies. The data analyzer and the intelligent risk control and knowledge management agent implement a combination of Artificial Intelligence (AI) and machine learning techniques to analyze and further subsequent process of the analyzed data, [0024]),
generating, by the risk scoring circuitry and based on a corresponding entity asset type, a corresponding geographic area, and the scenario request, a predicted entity physical nature risk score and a predicted entity transitional nature risk score for each entity asset;
generating, by the insight circuitry and based on, a corresponding entity physical nature risk score and a corresponding entity transitional nature risk score, one or more predicted insights for each entity asset included in the entity asset set, wherein each predicted insight is indicative of an inferred cause for at least one of the predicted
entity physical nature risk score and the predicted entity transitional nature risk score associated with the entity asset; (Additionally, the entity data may be also used to identify emerging risk zones and formulate risk management instruments, which includes risk model creations, policies and contracts, renewing and revising the existing ones, [0027] and the entity data may be used to identify potential or emerging risks, generate alerts, and provide the advices on how to react on the alerts by the intelligent risk control and knowledge management agent to prevent or lower the damage to the entity proactively. Such a proactive approach may help in minimizing losses and enhancing safe measure indexes, [0028]) and
providing, by the communications hardware, a scenario response, wherein the scenario response comprises the one or more predicted insights, (the system may be communicatively coupled to one or more Internet of things (loT) devices, such as sensors, and a risk management database through one or more communication links, where the sensors can be upgraded or replaced along with the Internet of things technologies. Moreover, the sensors may associate with an entity to be covered by a risk management instrument, [0020] and the entity data may be used to identify potential or emerging risks, generate alerts, and provide the advices on how to react on the alerts by the intelligent risk control and knowledge management agent to prevent or lower the damage to the entity proactively. Such a proactive approach may help in minimizing losses and enhancing safe measure indexes, Lu, [0028]).
Regarding claim 21, The method of claim 1, wherein (a) the risk heat map is configured to be interacted with by a user to display one or more of the plurality of entity assets and (b) in response to a user interacting with a displayed entity asset, information associated with the displayed entity asset is presented via the GUI, Lu does not teach, Joseph teaches, (in some implementations, the privacy profile 500 may comprise a heat map that utilizes a color-
coded background 544 on which the graphical representations are displayed, [13: 30-32], The privacy risk assessment device 210 may cause a graphical element representing a combination of the privacy attention score 252 and the aggregated privacy assessment score 256 to be displayed via a display device, such as display 224 of client device 220, [4: 19-25], FIGs 1-2, and
in the example shown in FIG. 2, client device 220 may include a processor226, a machine-readable storage medium 228, display device 224, and an interface 234, [4:3-6]).
Lu and Joseph are both considered to be analogous to the claimed invention because they are both in the field of risk identification and mitigation. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the risk insight development method of Lu with the information displays of Joseph to allow a decision maker to view the privacy profile and follow privacy risk trends among a portfolio of applications, [13: 59-61]).
Regarding claim 22, The method of claim 21, wherein the information comprises at least one of an associated entity physical nature risk score, an entity transitional nature risk score, a type of entity asset, an associated geographic area, an insight, an entity policy management set, a recommended policy set, associated implementation offers, and an entity asset type requirement set. Lu teaches, (the entity may be, for instance, a property, such as a building and a house, a vehicle, such as a car, an appliance, such as a television, and the like [0017], and (the sensors 120 connects to a single or multiple entities 110 in one or multiple domains (for example, in different distances), while gathering information such as, geographical location, [0040].
Claims 13, 16, 18, 19, and 20 are rejected for reasons corresponding to those provided for Claims 1, 5, 7, and 8. In these claims, the addition of an apparatus containing memory and at least one processor, or a non-transitory computer-readable medium, does not change the rational for the rejections under 35 U.S.C § 102 or the referenced prior art (Lu teaches The computer system 600 may execute, by a processor (e.g., a single or multiple processors) or other hardware processing circuit, the methods, functions and other processes illustrated herein. These methods, functions and other processes may be embodied as machine readable instructions stored on a computer readable medium, which may be non-transitory, such as hardware storage devices (e.g., RAM (random access memory), ROM (read only memory), EPROM (erasable, programmable ROM),EEPROM (electrically erasable, programmable ROM), hard drives, and flash memory), [0099], and [FIG. 6.]).
Claims 2-4, 6, 9, 11, 14-15, 17 are rejected under 35 U.S.C. § 103 as being anticipated by
Lu, (US 20190197442 A1), hereafter Lu, “Artificial Intelligence Based Risk and Knowledge Management,” in view of Joseph, (US-10860742-B2), hereafter Joseph, “Privacy Risk Information Display,” in further view of Burns, (US 11232383 B1), hereafter Burns, “Systems and Methods for Transformative Corporate Formation and Automated Technology Assessment.”
Regarding claim 2, The method of claim 1, further comprising;
for an entity asset in the entity asset set:
determining, by entity policy identification circuitry, an entity policy management
set for the entity asset, wherein (i) the entity policy management set comprises one or more
entity management policies and (ii) each entity management policy is indicative of one or more operating rules that the entity asset follows, Lu does not teach, Burns teaches, (The predictive analytics includes business planning, technical risks, secondary and tertiary effects of trade-offs, model course of action (COA) in real time, real-time market trends, and the cross-track impacts of these elements. The TCF platform is preferably operable to recommend and/or perform actions based on historical data, external data sources, ML, AI, NNs, and/or other learning techniques, [81:40-47]),
wherein determining an insight for the entity asset is further based on the entity policy management set, (The Transformative Corporate Formation (TCF) platform is further operable for prescriptive analytics to recommend the best course of action (COA) based on prescribed policy or common risks associated with specific COAs, [81:53-57]),
Lu and Burns are both considered to be analogous to the claimed invention because they are both in the field of risk identification, insight, and mitigation. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the AI risk-based knowledge management of Lu with the course of action analysis of Burns to provide corporate management and structure for commercialization for improved impact and return on investment , Burns [4:39-42]).
Regarding claim 3, The method of claim 2, further comprising:
for the entity asset:
determining, by the entity policy identification circuitry and based on the geographic area associated with the entity asset, an optimal entity policy management set, wherein (i) the optimal entity policy management set comprises one or more optimal entity management policies and (ii) each optimal entity management policy is indicative of one or more operating rules determined to result in at least one of a maximum entity physical nature risk score and a maximum entity transitional nature risk score for the entity asset, Lu does not teach, Burns teaches, (Transformative Corporate Formation (TCF) includes evaluating the viability of an idea/product. The TCF platform considers the estimated maturity of the technology, the source of the technology, and encumbrances associated with it. [54:63-66], [ ] TCF evaluates the cost of insurance, the location or geographic factors of a product, [55:12-14], at least one server platform is configured to generate at least one confidence score for the input data and for the progress of the startup company and/or the plurality of startup companies, wherein the at least one confidence score indicates the risk of the startup company, [6:16-21], [ ], wherein the first phase includes creating a management team and performing automatic assessment of the startup company and/or the plurality of startup companies, wherein the at least one server platform is configured to provide at least one recommendation for the management team, wherein the management team initially includes employees predominantly external to the startup company and employs a specialized mentored management strategy that transitions executive management roles to the startup company by the end of the fourth phase, [6:26-35]),
identifying, by the entity policy identification circuitry and based on the entity policy management set and the optimal entity policy management set associated with the entity asset, one or more optimal entity management policies that are missing from the entity policy management set, and in an instance in which one or more optimal entity management policies are identified, generating, by the entity policy identification circuitry, a recommended policy set, wherein (i) the recommended policy set comprises the one or more optimal entity management policies that were determined to not be included in the entity policy management set and (ii) the insight report further comprises the recommended policy set, (The predictive analytics includes business planning, technical risks, secondary and tertiary effects of trade-offs, model course of action (COA) in real time, real-time market trends, and the cross-track impacts of these elements. The TCF platform is preferably operable to recommend and/or perform actions based on historical data, external data sources, ML, AI, NNs, and/or other learning techniques. [81:40-47], The TCF platform is further operable for prescriptive analytics to recommend the best course of action (COA) based on prescribed policy or common risks associated with specific COAs, [81:53-57], under the innovation track, the at least one server platform is configured to provide at least one recommendation for securing or generating new intellectual property to meet the market need with coordination across the other functionality tracks of the plurality of functionality tracks, wherein under the business operations track, the at least one server platform includes documentation and recommendations for establishing corporate framework and legal partnerships, infrastructure, and facilities with coordination across the other functionality tracks, wherein establishing corporate framework includes establishing the management team, [7:14-25]).
Lu and Burns are both considered to be analogous to the claimed invention because they are both in the field of risk identification, insight, and mitigation. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the AI risk-based knowledge management of Lu with the course of action analysis of Burns to provide corporate management and structure for commercialization for improved impact and return on investment, Burns [4:41-42]).
Regarding claim 4, the method of claim 3, further comprising: for one or more optimal entity management policies included in the recommended policy set:
determining, by entity improvement circuitry, an implementation cost estimate for the optimal entity management policy, Burns teaches, (at the end of Phase 0, one of the exit criteria is estimating the cost to mature the candidate solution set. The cost estimate includes technology specific costs as well as support ancillary costs. The estimate includes licensing fees for any foundational IP, software development costs, software licenses unique to the solution, hardware development materials, computing hardware or cloud-based hosting, specialty equipment access, source materials, patent costs, costs associated with regulatory compliance, anticipated testing costs and any additional expected costs unique to the maturing solution, [62:24-34]), and,
generating, by the entity improvement circuitry, an implementation service offer, Burns teaches, (TILLER (TCF Integrated Logic Layer and Enterprise Record) logic evaluates the cost data in reference to other components of Phase 0 and produces a pricing model and assesses an overall confidence factor in the feasibility of the TO achieving TCF revenue goals, [62:38-42]), wherein the implementation service offer comprises a financial instrument offer and a value of the financial instrument offer covers at least a portion of the implementation cost estimate, Burns teaches, (The present invention further stands apart from prior art as it includes a capital infusion ladder that provides a TO (Transformative Organization) financial resources sufficient to accomplish the phased goals of a TO, [42:4-7]. The funding ladder affords a TO the opportunity to increase sales and profitability by providing the precisely sized funding increments needed to shorten new product Time-To Market (TTM), [42:8-11], In one embodiment, TCF includes a TCF Commercialization Fund. The TCF Commercialization Fund includes multiple investment opportunities for investors. A remote device communicates via the cloud-based network with the TCF Commercialization Fund to receive access to funding, [42:33-37]), and
providing, by communications hardware, an implementation service offer message, wherein the implementation offer message comprises each generated implemented service offer for the one or more optimal entity management policies, (The present invention provides access to public and private funding to the user via the GUI and cloud-based network. A TO is able to present its market solution and communicate with investors via network communications through the GUI. The GUI enables a TO to see an investor's requirements and/or interests, [42:26-31]).
Lu and Burns are both considered to be analogous to the claimed invention because they are both in the field of risk identification, insight, and mitigation. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the AI risk-based knowledge management of Lu with the financial analysis and vehicles of Burns to allow the TO to find the investor that is the best fit, Burns, [42:31-32]).
Regarding claim 6, The method of claim 1, further comprising: for an entity asset in the entity asset set:
receiving, by the communications hardware, a resource consumption set, wherein the resource consumption set comprises one or more resource consumption metrics for the entity asset collected over a time frame, Lu does not teach, Burns teaches, (wherein each functionality track of the plurality of functionality tracks includes a plurality of goals and a plurality of milestones for each phase, wherein the plurality of goals is designed to limit risk, cost, and time and is designed to keep the startup company and/or the plurality of startup companies on track to meet the plurality of exit thresholds, (0018) and the at least one enterprise tool is configured to collect input data related to the startup company's technology and activity [ ], wherein the input data includes management data, intellectual property data, market data, innovation data, financial data, future sales data, time data, consumer data, and product data, wherein the web crawler is configured to automatically generate the input data, wherein the input data is updated in real-time, wherein the at least one set of parameters and the at least one ruleset include focused and repeatable metrics for the startup company and/or the plurality of startup companies to reduce risk, cost, and time to achieve success, creating a management system via the at least one server platform, wherein the management system is designed to lower risk, improve capital resource efficiency and reduce time to launch, wherein the management system includes a first phase, a second phase, a third phase, and a fourth phase, [86:2-17]),
wherein determining the entity physical nature risk score and the entity transitional nature risk score for the entity asset is based on the resource consumption set associated with the entity asset, (one server platform is configured to track the progress of each phase, [6:11-12], [ ], wherein the at least one server platform is configured to generate at least one confidence score for the input data and for the progress of the startup company and/or the plurality of startup companies, wherein the at least one confidence score indicates the risk of the startup company and/or the plurality of startup companies, [6:17-22], wherein the management system is designed to lower risk, improve capital resource efficiency and reduce time to launch, [10:31-33]).
Lu and Burns are both considered to be analogous to the claimed invention because they are both in the field of risk identification, insight, and mitigation. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the AI risk-based knowledge management of Lu with the metrics of Burns to allow the TO to find the investor that is the best fit, Burns, [42:31-32]).
Regarding claim 9, The method of claim 1, further comprising:
determining, by the risk scoring circuitry and based on the entity physical nature risk score and the entity transitional nature risk score for each entity asset, one or more global risk scores; Lu does not teach, Burns teaches, (Each datastore and database is cross-leveraged to enhance the global offerings and confidence factors in the TCF system. Different types of algorithms and scoring are applied depending on the data of relevance to the management tier (TO or BoD/investor), TILLER-enabled data collection, aggregation and algorithm engines allow the production of critical insights, alerts and auto-nominated
process improvements, [74:30-37], and
generating, by the insight circuitry and based on the one or more global risk scores, one or more global insights for the entity, wherein the each global insight is indicative of an inferred cause for the global risk score associated with the entity, Lu teaches, (the intelligent risk control and knowledge management agent 135 includes notification generator (shown in FIG. 2) to notify the users existing and potential damages to the covered entities, and to provide advices on how to react to the alerts and damages. The agent 135 adopts the proactive approaches rather than reactive to the damages caused by the reasons such as natural disasters, human mistakes, equipment failures, and all kind of chaos situations, which prevents the potential damages and losses by alarming and advising the users before the damages happen and ahead of time, [0045]).
Lu and Burns are both considered to be analogous to the claimed invention because they are both in the field of risk identification, insight, and mitigation. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the AI risk-based knowledge management of Lu with the global span of data and application of Burns to allow the production of critical insights, alerts, and auto-nominated process improvements, Burns, [74:35-37].
Regarding claim 11, The method of claim 1, further comprising:
identifying, by the entity analysis circuitry, one or more comparative entities, wherein the one or more identified comparative entities share at least one common industry category with the entity; Lu teaches, (the extracted entities are processed by entity categorizers or configurations, which are to categorize the entities into corres