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 .
DETAILED ACTION
1. This Office Action is in response to the communication filed on April 1, 2025, which paper has been placed of record in the file.
2. Claims 1-20 are pending in this application.
Information Disclosure Statement
3. The information disclosure statements (IDS) submitted on April 18, 2025, August 21, 2025, December 17, 2025 and April 30, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
4. 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.
5. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,900,294 (“the reference patent”). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are anticipated, respectively, by claims 1-20 of the reference patent. Every limitation in the instant application claims is recited in the conflicting reference patent claims, and the differences between the claims are highlighted below by bolding all limitations that differ and italicizing additional limitations, that will be addressed below.
Instant Application 19/097,358
US Patent No. 11,900,294
1. A method in which one or more processing devices of a server system perform operations comprising:
receiving, from a user device, a request for a recommendation for modifying a current value of a risk assessment score computed from input attribute values of an entity associated with the user device to a target value of the risk assessment score, the risk assessment score used to control access by a computing device associated with the entity to an interactive computing environment hosted by a host system, wherein the host system is configured to present a first type of interactive user experience to a first type of user group associated with the current value of the risk assessment score and present to a second type of interactive user experience to a second type of user group associated with the target value of the risk assessment score;
computing an attribute-change vector indicating a path from (a) a first point that is located in an m-dimensional space and that corresponds to the current value of the risk assessment score to (b) a second point in the m-dimensional space and that corresponds to the target value of the risk assessment score, wherein applying a risk assessment function to a sum of an input attribute vector having the input attribute values of the entity and the attribute-change vector outputs the target value of the risk assessment score, wherein computing the attribute- change vector comprises:
determining a requirement on movement from the first point to the second point, and
selecting the attribute-change vector that complies with the requirement;
generating, using the computed attribute-change vector, the recommendation; and
transmitting, to the user device, the recommendation in response to the request for the recommendation for use in improving the current value of the risk assessment score to the target value of the risk assessment score which causes the host system to modify a functionality of an online interface by rearranging a layout of the online interface to switch from presenting the first type of interactive user experience to presenting the second type of interactive user experience.
1. A method in which one or more processing devices of a server system perform operations comprising:
receiving, from a user device, a request for a recommendation for modifying a current value of a risk assessment score computed from input attribute values of an entity associated with the user device to a target value of the risk assessment score, the risk assessment score used to control access by a computing device associated with the entity to an interactive computing environment hosted by a host system, wherein the host system is configured to present a first type of interactive user experience to a first type of user group associated with the current value of the risk assessment score and present to a second type of interactive user experience to a second type of user group associated with the target value of the risk assessment score;
accessing (a) an input attribute vector having the input attribute values of the entity and (b) clusters of points in an n-dimensional space, wherein the clusters of points are respectively defined by historical attribute vectors;
assigning the input attribute vector to a particular cluster based on a similarity measure between the particular cluster and an input point defined by the input attribute vector;
computing an attribute-change vector indicating a path from (a) a first point that is located in an m-dimensional space and that corresponds to the current value of the risk assessment score to (b) a second point in the m-dimensional space and that corresponds to the target value of the risk assessment score, wherein applying a risk assessment function to a sum of an input attribute vector having the input attribute values of the entity and the attribute-change vector outputs the target value of the risk assessment score, wherein computing the attribute- change vector comprises:
determining, based on statistics computed from an associated user group to which the input attribute vector is assigned, a requirement on movement from the first point to the second point, and
selecting the attribute-change vector that complies with the requirement;
generating, using the computed attribute-change vector, the recommendation for modifying the current value of the risk assessment score to the target value of the risk assessment score; and
transmitting, to the user device, the recommendation in response to the request for the recommendation for use in improving the current value of the risk assessment score to the target value of the risk assessment score which causes the host system to modify a functionality of an online interface by rearranging a layout of the online interface to switch from presenting the first type of interactive user experience to presenting the second type of interactive user experience.
2. The method of claim 1, wherein:
the path complying with the requirement comprises the path being a shortest path along the surface between the first point and the second point in m-dimensional space.
2. The method of claim 1, wherein:
the risk assessment function defines a surface within the m-dimensional space, and
the path complying with the requirement comprises the path being a shortest path along the surface between the first point and the second point.
3. The method of claim 1, wherein:
the requirement is based on a precision matrix for an associated user group and a mean vector; and
the requirement comprises minimizing an objective function subject to a score change constraint that corresponds to the target value of the risk assessment score, the objective function is computed from the precision matrix, the mean vector, and the attribute-change vector.
3. The method of claim 1, wherein:
the statistics computed from the associated user group comprise a precision matrix for the associated user group and a mean vector, and
the requirement comprises minimizing an objective function subject to a score change constraint that corresponds to the target value of the risk assessment score, the objective function is computed from the precision matrix, the mean vector, and the attribute-change vector.
4. The method of claim 1, wherein
the requirement is based on a precision matrix for an associated user group and a mean vector; and
the requirement comprises maximizing a score change objective function subject to a constraint that is computed from the precision matrix, the mean vector, and the attribute-change vector.
4. The method of claim 1, wherein
the statistics computed from the associated user group comprise a precision matrix for the associated user group and a mean vector; and
the requirement comprises maximizing a score change objective function subject to a constraint that is computed from the precision matrix, the mean vector, and the attribute-change vector.
5. The method of claim 1, further comprising determining the target value of the risk assessment score by applying a risk assessment function to a sum of an input attribute vector having the input attribute values of the entity, and
wherein computing the attribute-change vector further comprises determining that the attribute-change vector further complies with at least one constraint, the at least one constraint comprising:
an integer constraint requiring a first element of the attribute-change vector to have an integer value,
an auto-increment constraint requiring a second element of the attribute-change vector to increase over a time period, the time period defined by a first time value associated with the first point and a second time value associated with the second point,
a time constraint preventing a third element of the attribute-change vector from changing over the time period, or
an autoencoder constraint applied to the sum of the input attribute vector and the attribute-change vector.
5. The method of claim 1,
wherein computing the attribute-change vector further comprises determining that the attribute-change vector further complies with at least one constraint, the at least one constraint comprising:
an integer constraint requiring a first element of the attribute-change vector to have an integer value,
an auto-increment constraint requiring a second element of the attribute-change vector to increase over a time period, the time period defined by a first time value associated with the first point and a second time value associated with the second point,
a time constraint preventing a third element of the attribute-change vector from changing over the time period, or
an autoencoder constraint applied to the sum of the input attribute vector and the attribute-change vector.
6. The method of claim 1, wherein generating the recommendation comprises:
generating explanatory data indicating an impact of each value in the attribute-change vector on modifying the current value of the risk assessment score to the target value of the risk assessment score.
6. The method of claim 1, wherein generating the recommendation comprises:
generating explanatory data indicating an impact of each value in the attribute-change vector on modifying the current value of the risk assessment score to the target value of the risk assessment score.
7. A non-transitory computer-readable storage medium having program code that is executable by a processor device to cause a computing device to perform operations, the operations comprising:
receiving, from a user device, a request for a recommendation for modifying a current value of a risk assessment score computed from input attribute values of an entity associated with the user device to a target value of the risk assessment score, the risk assessment score used to control access by a computing device associated with the entity to an interactive computing environment hosted by a host system, wherein the host system is configured to present a first type of interactive user experience to a first type of user group associated with the current value of the risk assessment score and present to a second type of interactive user experience to a second type of user group associated with the target value of the risk assessment score;
computing an attribute-change vector indicating a path from (a) a first point that is located in an m-dimensional space and that corresponds to a first value of a risk assessment score computed from input attribute values of an entity to (b) a second point in the m-dimensional space and that corresponds to a second value of the risk assessment score, wherein applying a risk assessment function to a sum of an input attribute vector having the input attribute values of the entity and the attribute-change vector outputs the second value of the risk assessment score, wherein computing the attribute-change vector comprises:
determining a requirement on movement from the first point to the second point, and
selecting the attribute-change vector that complies with the requirement;
generating, using the computed attribute-change vector, the recommendation; and
transmitting, to the user device, the recommendation in response to a request for the recommendation for use in improving the first value of the risk assessment score to the second value of the risk assessment score which causes the host system to modify a functionality of an online interface by rearranging a layout of the online interface to switch from presenting the first type of interactive user experience to presenting the second type of interactive user experience.
7. A non-transitory computer-readable storage medium having program code that is executable by a processor device to cause a computing device to perform operations, the operations comprising:
accessing (a) an input attribute vector having the input attribute values of the entity and (b) clusters of points in an n-dimensional space, wherein the clusters of points are respectively defined by historical attribute vectors;
assigning the input attribute vector to a particular cluster based on a similarity measure between the particular cluster and an input point defined by the input attribute vector;
computing an attribute-change vector indicating a path from (a) a first point that is located in an m-dimensional space and that corresponds to a first value of a risk assessment score computed from input attribute values of an entity to (b) a second point in the m-dimensional space and that corresponds to a second value of the risk assessment score, wherein applying a risk assessment function to a sum of an input attribute vector having the input attribute values of the entity and the attribute-change vector outputs the second value of the risk assessment score, wherein computing the attribute-change vector comprises:
determining, based on statistics computed from an associated user group to which the input attribute vector is assigned, a requirement on movement from the first point to the second point, and
selecting the attribute-change vector that complies with the requirement;
generating, using the computed attribute-change vector, a recommendation for modifying the first value of the risk assessment score to the second value of the risk assessment score, the risk assessment score used to control access by a computing device associated with the entity to an interactive computing environment hosted by a host system, wherein the host system is configured to present a first type of interactive user experience to a first type of user group associated with the first value of the risk assessment score and present to a second type of interactive user experience to a second type of user group associated with the second value of the risk assessment score; and
causing the recommendation to be transmitted in response to a request for the recommendation for use in improving the first value of the risk assessment score to the second value of the risk assessment score which causes the host system to modify a functionality of an online interface by rearranging a layout of the online interface to switch from presenting the first type of interactive user experience to presenting the second type of interactive user experience.
8. The non-transitory computer-readable storage medium of claim 7, wherein the operations further comprise:
determining the target value of the risk assessment score by applying a risk assessment function to a sum of an input attribute vector having the input attribute values of the entity;
updating the input attribute vector by adding the attribute-change vector to the input attribute vector;
assigning the updated input attribute vector to a second associated user group based on a similarity measure between the second associated user group and a second input point defined by the updated input attribute vector; and
computing a second attribute-change vector indicating a second path from (a) the second point to (b) a third point in the m-dimensional space and that corresponds to a third value of the risk assessment score, wherein applying the risk assessment function to a sum of the updated input attribute vector and the second attribute-change vector outputs the third value of the risk assessment score, wherein computing the second attribute-change vector comprises:
determining, based on statistics computed from the second associated user group to which the updated input attribute vector is assigned, a second requirement on movement from the second point to the third point, and
selecting the second attribute-change vector that complies with the second requirement,
wherein the recommendation is generated further using the second attribute-change vector.
8. The non-transitory computer-readable storage medium of claim 7, wherein the operations further comprise:
updating the input attribute vector by adding the attribute-change vector to the input attribute vector;
assigning the updated input attribute vector to a second associated user group based on a similarity measure between the second associated user group and a second input point defined by the updated input attribute vector; and
computing a second attribute-change vector indicating a second path from (a) the second point to (b) a third point in the m-dimensional space and that corresponds to a third value of the risk assessment score, wherein applying the risk assessment function to a sum of the updated input attribute vector and the second attribute-change vector outputs the third value of the risk assessment score, wherein computing the second attribute-change vector comprises:
determining, based on statistics computed from the second associated user group to which the updated input attribute vector is assigned, a second requirement on movement from the second point to the third point, and
selecting the second attribute-change vector that complies with the second requirement,
wherein the recommendation is generated further using the second attribute-change vector.
9. The non-transitory computer-readable storage medium of claim 7, wherein
the path complying with the requirement comprises the path being a shortest path along the surface between the first point and the second point in m-dimensional space.
9. The non-transitory computer-readable storage medium of claim 7, wherein the risk assessment function defines a surface within the m-dimensional space, and
the path complying with the requirement comprises the path being a shortest path along the surface between the first point and the second point.
10. The non-transitory computer-readable storage medium of claim 7, wherein:
the requirement is based on a precision matrix for the associated user group and a mean vector, and
the requirement comprises minimizing an objective function subject to a score change constraint that corresponds to the second value of the risk assessment score, the objective function is computed from the precision matrix, the mean vector, and the attribute-change vector.
10. The non-transitory computer-readable storage medium of claim 7, wherein:
the statistics computed from the associated user group comprise a precision matrix for the associated user group and a mean vector, and
the requirement comprises minimizing an objective function subject to a score change constraint that corresponds to the second value of the risk assessment score, the objective function is computed from the precision matrix, the mean vector, and the attribute-change vector.
11. The non-transitory computer-readable storage medium of claim 7, wherein
the requirement is based on a precision matrix for the associated user group and a mean vector; and
the requirement comprises maximizing a score change objective function subject to a constraint that is computed from the precision matrix, the mean vector, and the attribute-change vector.
11. The non-transitory computer-readable storage medium of claim 7, wherein
the statistics computed from the associated user group comprise a precision matrix for the associated user group and a mean vector; and
the requirement comprises maximizing a score change objective function subject to a constraint that is computed from the precision matrix, the mean vector, and the attribute-change vector.
12. The non-transitory computer-readable storage medium of claim 7, wherein computing the attribute-change vector further comprises determining that the attribute-change vector further complies with at least one constraint, the at least one constraint comprising:
an integer constraint requiring a first element of the attribute-change vector to have an integer value,
an auto-increment constraint requiring a second element of the attribute-change vector to increase over a time period, the time period defined by a first time value associated with the first point and a second time value associated with the second point,
a time constraint preventing a third element of the attribute-change vector from changing over the time period, or
an autoencoder constraint applied to the sum of the input attribute vector and the attribute-change vector.
12. The non-transitory computer-readable storage medium of claim 7, wherein computing the attribute-change vector further comprises determining that the attribute-change vector further complies with at least one constraint, the at least one constraint comprising:
an integer constraint requiring a first element of the attribute-change vector to have an integer value,
an auto-increment constraint requiring a second element of the attribute-change vector to increase over a time period, the time period defined by a first time value associated with the first point and a second time value associated with the second point,
a time constraint preventing a third element of the attribute-change vector from changing over the time period, or
an autoencoder constraint applied to the sum of the input attribute vector and the attribute-change vector.
13. The non-transitory computer-readable storage medium of claim 7, wherein generating the recommendation comprises:
generating explanatory data indicating impact of each value in the attribute-change vector on modifying a current value of the risk assessment score to a target value of the risk assessment score.
13. The non-transitory computer-readable storage medium of claim 7, wherein generating the recommendation comprises:
generating explanatory data indicating impact of each value in the attribute-change vector on modifying a current value of the risk assessment score to a target value of the risk assessment score.
14. A system comprising:
a processing device; and
a memory device in which instructions executable by the processing device are stored for causing the processing device to perform operations comprising:
receiving, from a user device, a request for a recommendation for modifying a current value of a risk assessment score computed from input attribute values of an entity associated with the user device to a target value of the risk assessment score, the risk assessment score used to control access by a computing device associated with the entity to an interactive computing environment hosted by a host system, wherein the host system is configured to present a first type of interactive user experience to a first type of user group associated with the current value of the risk assessment score and present to a second type of interactive user experience to a second type of user group associated with the target value of the risk assessment score;
computing an attribute-change vector indicating a path from (a) a first point that is located in an m-dimensional space and that corresponds to a first value of a risk assessment score computed from input attribute values of an entity to (b) a second point in the m-dimensional space and that corresponds to a second value of the risk assessment score, wherein applying a risk assessment function to a sum of an input attribute vector having the input attribute values of the entity and the attribute-change vector outputs the second value of the risk assessment score, wherein computing the attribute-change vector comprises:
determining a requirement on movement from the first point to the second point, and
selecting the attribute-change vector that complies with the requirement;
generating, using the computed attribute-change vector, a recommendation; and
transmitting, to the user device, the recommendation in response to a request for the recommendation for use in improving the first value of the risk assessment score to the second value of the risk assessment score which causes the host system to modify a functionality of an online interface by rearranging a layout of the online interface to switch from presenting the first type of interactive user experience to presenting the second type of interactive user experience.
14. A system comprising:
a processing device; and
a memory device in which instructions executable by the processing device are stored for causing the processing device to perform operations comprising:
accessing (a) an input attribute vector having the input attribute values of the entity and (b) clusters of points in an n-dimensional space, wherein the clusters of points are respectively defined by historical attribute vectors;
assigning the input attribute vector to a particular cluster based on a similarity measure between the particular cluster and an input point defined by the input attribute vector;
computing an attribute-change vector indicating a path from (a) a first point that is located in an m-dimensional space and that corresponds to a first value of a risk assessment score computed from input attribute values of an entity to (b) a second point in the m-dimensional space and that corresponds to a second value of the risk assessment score, wherein applying a risk assessment function to a sum of an input attribute vector having the input attribute values of the entity and the attribute-change vector outputs the second value of the risk assessment score, wherein computing the attribute-change vector comprises:
determining, based on statistics computed from an associated user group to which the input attribute vector is assigned, a requirement on movement from the first point to the second point, and
selecting the attribute-change vector that complies with the requirement;
generating, using the computed attribute-change vector, a recommendation for modifying the first value of the risk assessment score to the second value of the risk assessment score, the risk assessment score used to control access by a computing device associated with the entity to an interactive computing environment hosted by a host system, wherein the host system is configured to present a first type of interactive user experience to a first type of user group associated with the first value of the risk assessment score and present to a second type of interactive user experience to a second type of user group associated with the second value of the risk assessment score; and
causing the recommendation to be transmitted in response to a request for the recommendation for use in improving the first value of the risk assessment score to the second value of the risk assessment score which causes the host system to modify a functionality of an online interface by rearranging a layout of the online interface to switch from presenting the first type of interactive user experience to presenting the second type of interactive user experience.
15. The system of claim 14, wherein the operations further comprise:
updating the input attribute vector by adding the attribute-change vector to the input attribute vector;
assigning the updated input attribute vector to a second associated user group based on a similarity measure between the second associated user group and a second input point defined by the updated input attribute vector; and
computing a second attribute-change vector indicating a second path from (a) the second point to (b) a third point in the m-dimensional space and that corresponds to a third value of the risk assessment score, wherein applying the risk assessment function to a sum of the updated input attribute vector and the second attribute-change vector outputs the third value of the risk assessment score, wherein computing the second attribute-change vector comprises:
determining, based on statistics computed from the second associated user group to which the updated input attribute vector is assigned, a second requirement on movement from the second point to the third point, and
selecting the second attribute-change vector that complies with the second requirement,
wherein the recommendation is generated further using the second attribute-change vector.
15. The system of claim 14, wherein the operations further comprise:
updating the input attribute vector by adding the attribute-change vector to the input attribute vector;
assigning the updated input attribute vector to a second associated user group based on a similarity measure between the second associated user group and a second input point defined by the updated input attribute vector; and
computing a second attribute-change vector indicating a second path from (a) the second point to (b) a third point in the m-dimensional space and that corresponds to a third value of the risk assessment score, wherein applying the risk assessment function to a sum of the updated input attribute vector and the second attribute-change vector outputs the third value of the risk assessment score, wherein computing the second attribute-change vector comprises:
determining, based on statistics computed from the second associated user group to which the updated input attribute vector is assigned, a second requirement on movement from the second point to the third point, and
selecting the second attribute-change vector that complies with the second requirement,
wherein the recommendation is generated further using the second attribute-change vector.
16. The system of claim 14, wherein
the path complying with the requirement comprises the path being a shortest path along the surface between the first point and the second point in the m-dimensional space.
16. The system of claim 14, wherein the risk assessment function defines a surface within the m-dimensional space, and
the path complying with the requirement comprises the path being a shortest path along the surface between the first point and the second point.
17. The system of claim 14, wherein:
the requirement is based on a precision matrix for the associated user group and a mean vector, and
the requirement comprises minimizing an objective function subject to a score change constraint that corresponds to the second value of the risk assessment score, the objective function is computed from the precision matrix, the mean vector, and the attribute-change vector.
17. The system of claim 14, wherein:
the statistics computed from the associated user group comprise a precision matrix for the associated user group and a mean vector, and
the requirement comprises minimizing an objective function subject to a score change constraint that corresponds to the second value of the risk assessment score, the objective function is computed from the precision matrix, the mean vector, and the attribute-change vector.
18. The system of claim 14, wherein generating the recommendation comprises:
generating explanatory data indicating impact of each value in the attribute-change vector on modifying a current value of the risk assessment score to a target value of the risk assessment score.
18. The system of claim 14, wherein generating the recommendation comprises:
generating explanatory data indicating impact of each value in the attribute-change vector on modifying a current value of the risk assessment score to a target value of the risk assessment score.
19. The system of claim 14, wherein
the requirement is based on a precision matrix for the associated user group and a mean vector; and
the requirement comprises maximizing a score change objective function subject to a constraint that is computed from the precision matrix, the mean vector, and the attribute-change vector.
19. The system of claim 14, wherein
the statistics computed from the associated user group comprise a precision matrix for the associated user group and a mean vector; and
the requirement comprises maximizing a score change objective function subject to a constraint that is computed from the precision matrix, the mean vector, and the attribute-change vector.
20. The system of claim 14, wherein computing the attribute-change vector further comprises determining that the attribute-change vector further complies with at least one constraint, the at least one constraint comprising:
an integer constraint requiring a first element of the attribute-change vector to have an integer value,
an auto-increment constraint requiring a second element of the attribute-change vector to increase over a time period, the time period defined by a first time value associated with the first point and a second time value associated with the second point, a time constraint preventing a third element of the attribute-change vector from changing over the time period, or an autoencoder constraint applied to the sum of the input attribute vector and the attribute-change vector.
20. The system of claim 14, wherein computing the attribute-change vector further comprises determining that the attribute-change vector further complies with at least one constraint, the at least one constraint comprising:
an integer constraint requiring a first element of the attribute-change vector to have an integer value,
an auto-increment constraint requiring a second element of the attribute-change vector to increase over a time period, the time period defined by a first time value associated with the first point and a second time value associated with the second point, a time constraint preventing a third element of the attribute-change vector from changing over the time period, or an autoencoder constraint applied to the sum of the input attribute vector and the attribute-change vector.
As shown in the mapping above, claims 1-20 of the reference patent include all the limitations of claims 1-20 of the instant application, while also reciting further limitations.
Examiner Note:
Claims 1-20 are patent eligible because the claims recite significantly more than the abstract idea and integrate the abstract idea into a practical application, specially the limitations “…transmitting, to the user device, the recommendation in response to the request for the recommendation for use in improving the current value of the risk assessment score to the target value of the risk assessment score which causes the host system to modify a functionality of an online interface by rearranging a layout of the online interface to switch from presenting the first type of interactive user experience to presenting the second type of interactive user experience”, provide a technical improvement to the technical area of controlling access to online interactive computing environment by providing improved security. For instance, the claimed features involve modifying a functionality of an online interface by rearranging a layout of the online interface based on improved risk assessment scores. This modification allows the host system to present, in the online interactive computing environment, features or content associated with certain risk levels more prominently and hide features or content associated with different risk levels, and to switch from a first type of interactive user experience presented to a first type of user group associated with the certain risk levels to a second type of interactive user experience presented to a second type of user group associated with the different risk levels. As a result, the online interactive computing environment can prevent certain features and content from being accessed by high-risk users while allowing low-risk users to access them. The claimed technique thus provides an improvement to the security of the online interactive computing environment by modifying the functionality of an online interface by rearranging a layout of the online interface based on improved risk assessment scores.
Therefore, the claims are patent eligible.
Conclusion
6. Claims 1-20 are rejected.
7. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
1) Yamashita et al. (US 2018/0101907) disclose methods and systems for cluster-based historical data are disclosed. In one embodiment, a method includes accessing historical data associated with a plurality of users and, based on the historical data, constructing a plurality of clusters.
2) Rajnayak et al. (US 2019/0325354) disclose an Artificial Intelligence (AI) based performance prediction system predicts the performance and behavior of an entity via a complex structure made of iterative and parallel machine learning (ML) model rebuilds with real time data collection.
3) Scheidler et al. (US 10,681,060) disclose a computer-implemented method for determining computer system security threats, the computer system including user accounts established on the computer system.
4) Godfrey et al. (US 2020/0279192) disclose the subject technology receives assessment values determined by a first machine learning model deployed on a client electronic device, the assessment values being indicative of classifications of input data and the assessment values being associated with constraint data that comprises a probability distribution of the assessment values with respect to the classifications of the input data.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 July 23, 2026
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