DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non- statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims recite “An artificial intelligence infrastructure…”, but recite no hardware to perform the claimed steps.
The claims lack the necessary physical articles or objects to constitute a machine or manufacture within the meaning of 35 USC 101. One of ordinary skill in the art may conclude that the steps associated with digital content generation may reasonably be implemented as mere software routines since no requisite computer hardware, such as a processor and memory, is recited as elements of the claimed invention The claims lack the necessary physical articles or objects to constitute a machine or manufacture within the meaning of 35 USC 101. They are clearly not a series of steps or acts to be a process nor are they a combination of chemical compounds to be a composition of matter. As such, they fail to fall within a statutory category. They are, at best, functional descriptive material.
Claims 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims’ subject matter eligibility will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
With respect to claim 21.
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? No—claim 21 is not one of a process, machine, manufacture, or composition of matter.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations identified below each, under its broadest reasonable interpretation, covers mental processes abstract idea grouping (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)), see MPEP 2106.04(a)(2), subsection III and the 2019 PEG, but for the recitation of generic computer components:
“generation objective.”: (Mental processes or mathematical- concept of observation and evaluation of collecting persons personal data and generating a topology from the data to generate an objective).
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application.
“a first artificial intelligence” and “a second artificial intelligence”: mere instructions to “apply it” because it only includes high level of generality description to apply the abstract idea. See MPEP § 2106.05(f). Therefore, the additional element(s) do not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—there are no additional limitations beyond the mental processes identified above. The limitation treated above, are directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). It also includes limitations that Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The additional element is insignificant application, which is similar to examples of activities that the courts have found to be insignificant extra-solution activity, in accordance with MPEP 2106.05(g), Insignificant Extra-Solution Activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 22.
Step 1 and Step 2A Prong 1: see claim 21 above.
Step 2A Prong 2, Step 2B: The claim recites that “the artificial intelligence infrastructure is operable to facilitate secure communication via communication circuitry” involves mere instructions to “apply it” because it only includes high level of generality description to apply the abstract idea. See MPEP § 2106.05(f). Therefore, the additional element(s) do not integrate the judicial exception into a practical application. See MPEP 2106.05(f). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 23.
Step 1 and Step 2A Prong 1: see claim 21 above.
Step 2A Prong 2, Step 2B: The claim recites that “wherein: the artificial intelligence infrastructure is operable to enable personalization according to user-specific data using independent software applications” involves mere instructions to “apply it” because it only includes high level of generality description to apply the abstract idea. See MPEP § 2106.05(f). Therefore, the additional element(s) do not integrate the judicial exception into a practical application. See MPEP 2106.05(f). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 24.
Step 1 and Step 2A Prong 1: see claim 21 above.
Step 2A Prong 2, Step 2B: The claim recites that “wherein: the artificial intelligence infrastructure is operable to ensure secure communication through encryption mechanisms” involves mere instructions to “apply it” because it only includes high level of generality description to apply the abstract idea. See MPEP § 2106.05(f). Therefore, the additional element(s) do not integrate the judicial exception into a practical application. See MPEP 2106.05(f). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 25.
Step 1 and Step 2A Prong 1: see claim 21 above.
Step 2A Prong 2, Step 2B: The claim recites that “wherein: the artificial intelligence infrastructure is operable to use master topology for data gathering and analysis” involves mere instructions to “apply it” because it only includes high level of generality description to apply the abstract idea. See MPEP § 2106.05(f). Therefore, the additional element(s) do not integrate the judicial exception into a practical application. See MPEP 2106.05(f). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 26.
Step 1 and Step 2A Prong 1: see claim 21 above.
Step 2A Prong 2, Step 2B: The claim recites that “wherein: the artificial intelligence infrastructure is operable to utilize search nodes to process user input text” involves mere instructions to “apply it” because it only includes high level of generality description to apply the abstract idea. See MPEP § 2106.05(f). Therefore, the additional element(s) do not integrate the judicial exception into a practical application. See MPEP 2106.05(f). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 27.
Step 1 and Step 2A Prong 1: see claim 21 above.
Step 2A Prong 2, Step 2B: The claim recites that “wherein: the artificial intelligence infrastructure is operable to access data nodes containing human-generated content” involves mere instructions to “apply it” because it only includes high level of generality description to apply the abstract idea. See MPEP § 2106.05(f). Therefore, the additional element(s) do not integrate the judicial exception into a practical application. See MPEP 2106.05(f). This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
With respect to claim 28.
Claim 28 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? No—claim 28 is not one of a process, machine, manufacture, or composition of matter.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations identified below each, under its broadest reasonable interpretation, covers mental processes abstract idea grouping (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)), see MPEP 2106.04(a)(2), subsection III and the 2019 PEG, but for the recitation of generic computer components:
“ (Mental processes or mathematical- concept of observation and evaluation of collecting persons personal data and generating a topology from the data to generate an objective).
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application.
“a first artificial intelligence” and “a second artificial intelligence”: mere instructions to “apply it” because it only includes high level of generality description to apply the abstract idea. See MPEP § 2106.05(f). Therefore, the additional element(s) do not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—there are no additional limitations beyond the mental processes identified above. The limitation treated above, are directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). It also includes limitations that Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The additional element is insignificant application, which is similar to examples of activities that the courts have found to be insignificant extra-solution activity, in accordance with MPEP 2106.05(g), Insignificant Extra-Solution Activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
claims 29-34 are dependent to claim 28 and recites limitations that are similar to the limitations recited in claims 21-27. Therefore, claims 29-34 are rejected with the same rationale applied against claims 21-27 above.
With respect to claim 35.
Claim 34 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? No—claim 35 is not one of a process, machine, manufacture, or composition of matter.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations identified below each, under its broadest reasonable interpretation, covers mental processes abstract idea grouping (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)), see MPEP 2106.04(a)(2), subsection III and the 2019 PEG, but for the recitation of generic computer components:
“ (Mental processes or mathematical- concept of observation and evaluation of collecting persons personal data and generating a topology from the data to generate an objective).
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application.
“a first artificial intelligence based multi-node topology partition configured to select to interact with a second artificial intelligence based multi-node topology partition, the first artificial intelligence based multi-node topology partition being configured to operate on a first system while the second artificial intelligence based multi-node topology partition being configured to operate on a second system”: mere instructions to “apply it” because it only includes high level of generality description to apply the abstract idea. See MPEP § 2106.05(f). Therefore, the additional element(s) do not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—there are no additional limitations beyond the mental processes identified above. The limitation treated above, are directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). It also includes limitations that Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The additional element is insignificant application, which is similar to examples of activities that the courts have found to be insignificant extra-solution activity, in accordance with MPEP 2106.05(g), Insignificant Extra-Solution Activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
claims 36-40 are dependent to claim 35 and recites limitations that are similar to the limitations recited in claims 21-27. Therefore, claims 36-40 are rejected with the same rationale applied against claims 21-27 above.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Myhre et al. (US 20200004890 A1) in view of Gurwicz et al. (US 20190042917 A1).
Regarding claim 21.
Myhre teaches an artificial intelligence infrastructure, comprising: a first artificial intelligence based topology that is configured to collect a person's personal data and generates from the personal data a plurality of personal topology elements (see ¶ 64, “The AI model 114 can utilize various technologies to identify topics associated with an activity based upon the user content 116. For example, and without limitation, the AI model 114 can utilize unsupervised clustering, Bayesian networks, representation learning, similarity and metric learning, rule-based machine learning, learning classifier systems, support vector machines (“SVMs”), deep learning, artificial neural networks, associated rule learning, decision tree learning, or other machine learning techniques.”, also see ¶ 65, “When analyzing and processing the user content 116, the AI engine 112 can generate one or more activity graphs 120. Generally described, an activity graph 120 can define a hierarchy of relationships between user-specified activities, topics related to the activities, and the user content 116 that resulted in the association of the topics with the activities.”, also see ¶ 67-71);
and see ¶ 134, “the AI engine 112 utilizes the AI model 114, user content 116 from one or more data sources 118, and other data to select an activity schema 1102 for a particular activity. The activity schema 1102 identifies one or more data sources 118 for obtaining the activity-specific content 504 based on topics associated with the activity. The activity schema 1102 can also include other types of data used to construct interactive activity-specific UIs 802, some of which will be described in detail below. In a similar manner, the AI engine 112 can also select a view definition 1104, which contains data for use in presenting the interactive activity-specific UI 802 for the activity. In particular, the view definition 1104 defines a visual arrangement for UI elements of an interactive activity-specific UI 804 containing relevant activity-specific content 504 obtained from the plurality of data sources 118 that are identified by the activity schema 1102.”, also see ¶ 149, “the AI engine 112 can analyze a datastore having one or more activity schemas 1102 and select at least one schema 1102 based on the activity and/or topic. The selected schemas 1102 can include definitions for the activity-specific UI elements 804 and layout properties for each activity-specific UI element 804.”, also see ¶ 168, “the activity management application 104 provides a schema discovery UI 1310. The schema discovery UI 1310 provides functionality for enabling a user 102 to discover default activity schema 1102 and custom activity schema 1102B that have been customized by other users 102. For example, and without limitation, a schema discovery UI 1310 might provide functionality for enabling users 102 to browse and search the schema 1102 stored in the schema repository 1114.”, also see ¶ 56-59).
Myhre do not specifically teach a second artificial intelligence based topology.
Gurwicz teaches a second artificial intelligence based topology (see ¶ 18, “graphs determined based on the graphical model tree may be converted into topologies for neural networks. Several embodiments may produce and/or utilize an ensemble of different neural network topologies, leading to improved classification capabilities. In these and other ways, the neural network topology system may enable reliable and efficient optimization of neural networks to achieve improved performance and increased accuracy of the neural networks, resulting in several technical effects and advantages.”, also see ¶ 34, “the ensemble of graphs in graph configuration collection 108 may enable a corresponding ensemble of K neural network topologies, or neural networks, to be created for neural network topology (NNT) collection 110, such as with neural network converter 109.”).
Both Myhre and Gurwicz pertain to the problem of neural network data relationship activities, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Myhre and Gurwicz to teach the above limitations. The motivation for doing so would be “Several embodiments may produce and/or utilize an ensemble of different neural network topologies, leading to improved classification capabilities. In these and other ways, the neural network topology system may enable reliable and efficient optimization of neural networks to achieve improved performance and increased accuracy of the neural networks, resulting in several technical effects and advantages.” (see Gurwicz ¶ 18).
Regarding claim 22.
Myhre and Gurwicz teaches the artificial intelligence infrastructure of claim 21,
Myhre further teaches wherein: the artificial intelligence infrastructure is operable to facilitate secure communication via communication circuitry (see ¶ 221, “the server computer 2100A can be a dedicated server computer operable to process and communicate data to and from the client computing devices 2100B-2100G via any of a number of known protocols, such as, hypertext transfer protocol (“HTTP”), file transfer protocol (“FTP”), or simple object access protocol (“SOAP”). Additionally, the networked computing environment 2100 can utilize various data security protocols such as secured socket layer (“SSL”) or pretty good privacy (“PGP”). Each of the client computing devices 2100B-2100G can be equipped with an operating system operable to support one or more computing applications or terminal sessions such as a web browser (not shown in FIG. 21), or other graphical UI (also not shown in FIG. 21), or a mobile desktop environment (also not shown in FIG. 21) to gain access to the server computer 2100A.”, also see ¶ 216-217, also see ¶ 224, “The server computer 2100A can host computing applications, processes and applets for the generation, authentication, encryption, and communication of data and applications, and may cooperate with other server computing environments (not shown in FIG. 21), third party service providers (not shown in FIG. 21), network attached storage (“NAS”) and storage area networks (“SAN”) to realize application/data transactions.”).
Regarding claim 23.
Myhre and Gurwicz teaches the artificial intelligence infrastructure of claim 21,
Myhre further teaches wherein: the artificial intelligence infrastructure is operable to enable personalization according to user-specific data using independent software applications (see ¶ 115, “the activity management application 104 can identify and display select portions of the user content 116 that is relevant to a user 102 in customized views that are configured and arranged based on an analysis of the user content 116, the activity graph 120, and/or the AI model 114. The customized views are referred to herein as “activity-specific views,” each of which displays activity-specific content 504A-504N in a layout having display properties that are easy to use and contextually relevant to a user's current situation. The activity-specific views showing the activity-specific content 504A-504N can be presented in a “dashboard UI” 502, which includes activity-specific views for a multitude of activities.”, also see ¶ 118, “The activity-specific views might also be modified based on a context of the user 102 or the user's current situation. For example, when a user-specified activity is related to a number of calendar events, people, and files, a customized view of the activity may change and show different types of user content 116 based on the day or time that the activity is viewed.”, also see ¶ 56-59, customization based on user content, see ¶ 134, “the AI engine 112 utilizes the AI model 114, user content 116 from one or more data sources 118, and other data to select an activity schema 1102 for a particular activity. The activity schema 1102 identifies one or more data sources 118 for obtaining the activity-specific content 504 based on topics associated with the activity.”, also see ¶ 135-151).
Regarding claim 24.
Myhre and Gurwicz teaches the artificial intelligence infrastructure of claim 21,
Myhre further teaches wherein: the artificial intelligence infrastructure is operable to ensure secure communication through encryption mechanisms (see ¶ 221, “the server computer 2100A can be a dedicated server computer operable to process and communicate data to and from the client computing devices 2100B-2100G via any of a number of known protocols, such as, hypertext transfer protocol (“HTTP”), file transfer protocol (“FTP”), or simple object access protocol (“SOAP”). Additionally, the networked computing environment 2100 can utilize various data security protocols such as secured socket layer (“SSL”) or pretty good privacy (“PGP”). Each of the client computing devices 2100B-2100G can be equipped with an operating system operable to support one or more computing applications or terminal sessions such as a web browser (not shown in FIG. 21), or other graphical UI (also not shown in FIG. 21), or a mobile desktop environment (also not shown in FIG. 21) to gain access to the server computer 2100A.”, also see ¶ 216-217, also see ¶ 224, “The server computer 2100A can host computing applications, processes and applets for the generation, authentication, encryption, and communication of data and applications, and may cooperate with other server computing environments (not shown in FIG. 21), third party service providers (not shown in FIG. 21), network attached storage (“NAS”) and storage area networks (“SAN”) to realize application/data transactions.”).
Regarding claim 25.
Myhre and Gurwicz teaches the artificial intelligence infrastructure of claim 21,
Myhre further teaches wherein: the artificial intelligence infrastructure is operable to use master topology for data gathering and analysis (see ¶ 64, “The AI model 114 can utilize various technologies to identify topics associated with an activity based upon the user content 116. For example, and without limitation, the AI model 114 can utilize unsupervised clustering, Bayesian networks, representation learning, similarity and metric learning, rule-based machine learning, learning classifier systems, support vector machines (“SVMs”), deep learning, artificial neural networks, associated rule learning, decision tree learning, or other machine learning techniques.”, also see ¶ 65, “When analyzing and processing the user content 116, the AI engine 112 can generate one or more activity graphs 120. Generally described, an activity graph 120 can define a hierarchy of relationships between user-specified activities, topics related to the activities, and the user content 116 that resulted in the association of the topics with the activities.”, also see ¶ 67-71).
Regarding claim 26.
Myhre and Gurwicz teaches the artificial intelligence infrastructure of claim 21,
Myhre further teaches wherein: the artificial intelligence infrastructure is operable to utilize search nodes to process user input text (see ¶ 67-71, “Referring now to FIG. 2, aspects of an illustrative activity graph 120 will be described. As shown in FIG. 2, an activity graph can include a number of nodes 202A-202J, including leaf nodes 204. The node 202A at the highest level of the activity graph 120 (i.e. the root node) corresponds to a user's life. The nodes 202B, 202D, 202G and 202H below the root node correspond to categories of activities currently taking place in the user's life. For example, and without limitation, the node 202B corresponds to family-related activities, the node 202D corresponds to other non-family personal activities, the node 202G corresponds to corporate board activities, and the node 202H corresponds to work-related activities”, also see ¶ 61, 75, 103-105, “A user 102 can interact with the configuration UI 106 to provide a query term 110 to the activity management application 104 that identifies an activity. For example, and without limitation, the user 102 might supply a query term 110 that identifies an activity that the user 102 is currently engaged in such as, but not limited to, a personal activity like “Marathon” training, a work-related activity like a project that the user 102 is working on, or another type of activity in the user's life. The query term 110 can be provided by any type of input mechanism such as, but not limited to, a UI capturing a text input, a microphone capturing a voice input, or a camera capturing a gesture.”).
Regarding claim 27.
Myhre and Gurwicz teaches the artificial intelligence infrastructure of claim 21,
Myhre further teaches wherein: the artificial intelligence infrastructure is operable to access data nodes containing human-generated content (see ¶ 67-71, “Referring now to FIG. 2, aspects of an illustrative activity graph 120 will be described. As shown in FIG. 2, an activity graph can include a number of nodes 202A-202J, including leaf nodes 204. The node 202A at the highest level of the activity graph 120 (i.e. the root node) corresponds to a user's life. The nodes 202B, 202D, 202G and 202H below the root node correspond to categories of activities currently taking place in the user's life. For example, and without limitation, the node 202B corresponds to family-related activities, the node 202D corresponds to other non-family personal activities, the node 202G corresponds to corporate board activities, and the node 202H corresponds to work-related activities”, also see ¶ 61, 75, 103-105, “A user 102 can interact with the configuration UI 106 to provide a query term 110 to the activity management application 104 that identifies an activity. For example, and without limitation, the user 102 might supply a query term 110 that identifies an activity that the user 102 is currently engaged in such as, but not limited to, a personal activity like “Marathon” training, a work-related activity like a project that the user 102 is working on, or another type of activity in the user's life. The query term 110 can be provided by any type of input mechanism such as, but not limited to, a UI capturing a text input, a microphone capturing a voice input, or a camera capturing a gesture.”, also see ¶ 65, “an activity graph 120 can define a hierarchy of relationships between user-specified activities, topics related to the activities, and the user content 116 that resulted in the association of the topics with the activities. Additional details regarding an illustrative activity graph 120 will be provided below with regard to FIG. 2.”, 101, “the user content 116 can be any type of data in any suitable format. For instance, user content 116 can include, but is not limited to, images, emails, messages, documents, spreadsheets, contact lists, individual contacts, social networking data, or any other type of data.”).
Regarding claim 28.
Myhre teaches an artificial intelligence infrastructure, comprising: a first artificial intelligence based topology configured to generate from a person's personal data a personal topology element (see ¶ 64, “The AI model 114 can utilize various technologies to identify topics associated with an activity based upon the user content 116. For example, and without limitation, the AI model 114 can utilize unsupervised clustering, Bayesian networks, representation learning, similarity and metric learning, rule-based machine learning, learning classifier systems, support vector machines (“SVMs”), deep learning, artificial neural networks, associated rule learning, decision tree learning, or other machine learning techniques.”, also see ¶ 65, “When analyzing and processing the user content 116, the AI engine 112 can generate one or more activity graphs 120. Generally described, an activity graph 120 can define a hierarchy of relationships between user-specified activities, topics related to the activities, and the user content 116 that resulted in the association of the topics with the activities.”, also see ¶ 67-71);
a plurality see ¶ 134, “the AI engine 112 utilizes the AI model 114, user content 116 from one or more data sources 118, and other data to select an activity schema 1102 for a particular activity. The activity schema 1102 identifies one or more data sources 118 for obtaining the activity-specific content 504 based on topics associated with the activity. The activity schema 1102 can also include other types of data used to construct interactive activity-specific UIs 802, some of which will be described in detail below. In a similar manner, the AI engine 112 can also select a view definition 1104, which contains data for use in presenting the interactive activity-specific UI 802 for the activity. In particular, the view definition 1104 defines a visual arrangement for UI elements of an interactive activity-specific UI 804 containing relevant activity-specific content 504 obtained from the plurality of data sources 118 that are identified by the activity schema 1102.”, also see ¶ 149, “the AI engine 112 can analyze a datastore having one or more activity schemas 1102 and select at least one schema 1102 based on the activity and/or topic. The selected schemas 1102 can include definitions for the activity-specific UI elements 804 and layout properties for each activity-specific UI element 804.”, also see ¶ 168, “the activity management application 104 provides a schema discovery UI 1310. The schema discovery UI 1310 provides functionality for enabling a user 102 to discover default activity schema 1102 and custom activity schema 1102B that have been customized by other users 102. For example, and without limitation, a schema discovery UI 1310 might provide functionality for enabling users 102 to browse and search the schema 1102 stored in the schema repository 1114.”, also see ¶ 56-59).
Myhre do not specifically teach a second artificial intelligence based topology and the plurality of second artificial intelligence based topologies each being associated with a compensation requirement for the inclusion.
Gurwicz teaches a second artificial intelligence based topology and the plurality of second artificial intelligence based topologies each being associated with a compensation requirement for the inclusion (see ¶ 18, “graphs determined based on the graphical model tree may be converted into topologies for neural networks. Several embodiments may produce and/or utilize an ensemble of different neural network topologies, leading to improved classification capabilities. In these and other ways, the neural network topology system may enable reliable and efficient optimization of neural networks to achieve improved performance and increased accuracy of the neural networks, resulting in several technical effects and advantages.”, also see ¶ 34, “the ensemble of graphs in graph configuration collection 108 may enable a corresponding ensemble of K neural network topologies, or neural networks, to be created for neural network topology (NNT) collection 110, such as with neural network converter 109.”).
Both Myhre and Gurwicz pertain to the problem of neural network data relationship activities, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Myhre and Gurwicz to teach the above limitations. The motivation for doing so would be “Several embodiments may produce and/or utilize an ensemble of different neural network topologies, leading to improved classification capabilities. In these and other ways, the neural network topology system may enable reliable and efficient optimization of neural networks to achieve improved performance and increased accuracy of the neural networks, resulting in several technical effects and advantages.” (see Gurwicz ¶ 18).
claims 29-34 are dependent to claim 28 and recites limitations that are similar to the limitations recited in claims 21-27. Therefore, claims 29-34 are rejected with the same rationale applied against claims 21-27 above.
Regarding claim 35.
Myhre teaches an artificial intelligence infrastructure, comprising: a first artificial intelligence based multi-node topology partition configured to select to interact with a see ¶ 64, “The AI model 114 can utilize various technologies to identify topics associated with an activity based upon the user content 116. For example, and without limitation, the AI model 114 can utilize unsupervised clustering, Bayesian networks, representation learning, similarity and metric learning, rule-based machine learning, learning classifier systems, support vector machines (“SVMs”), deep learning, artificial neural networks, associated rule learning, decision tree learning, or other machine learning techniques.”, also see ¶ 65, “When analyzing and processing the user content 116, the AI engine 112 can generate one or more activity graphs 120. Generally described, an activity graph 120 can define a hierarchy of relationships between user-specified activities, topics related to the activities, and the user content 116 that resulted in the association of the topics with the activities.”, also see ¶ 67-71);
and the first artificial intelligence based multi-node topology partition configured to perform functionality based on private data collected from the first system, wherein the first artificial intelligence based multi-node topology partition selects to interact based on an outcome of the performed functionality (see ¶ 134, “the AI engine 112 utilizes the AI model 114, user content 116 from one or more data sources 118, and other data to select an activity schema 1102 for a particular activity. The activity schema 1102 identifies one or more data sources 118 for obtaining the activity-specific content 504 based on topics associated with the activity. The activity schema 1102 can also include other types of data used to construct interactive activity-specific UIs 802, some of which will be described in detail below. In a similar manner, the AI engine 112 can also select a view definition 1104, which contains data for use in presenting the interactive activity-specific UI 802 for the activity. In particular, the view definition 1104 defines a visual arrangement for UI elements of an interactive activity-specific UI 804 containing relevant activity-specific content 504 obtained from the plurality of data sources 118 that are identified by the activity schema 1102.”, also see ¶ 149, “the AI engine 112 can analyze a datastore having one or more activity schemas 1102 and select at least one schema 1102 based on the activity and/or topic. The selected schemas 1102 can include definitions for the activity-specific UI elements 804 and layout properties for each activity-specific UI element 804.”, also see ¶ 168, “the activity management application 104 provides a schema discovery UI 1310. The schema discovery UI 1310 provides functionality for enabling a user 102 to discover default activity schema 1102 and custom activity schema 1102B that have been customized by other users 102. For example, and without limitation, a schema discovery UI 1310 might provide functionality for enabling users 102 to browse and search the schema 1102 stored in the schema repository 1114.”, also see ¶ 56-59, 61-66).
Myhre do not specifically teach a second artificial intelligence based topology.
Gurwicz teaches a second artificial intelligence based topology (see ¶ 18, “graphs determined based on the graphical model tree may be converted into topologies for neural networks. Several embodiments may produce and/or utilize an ensemble of different neural network topologies, leading to improved classification capabilities. In these and other ways, the neural network topology system may enable reliable and efficient optimization of neural networks to achieve improved performance and increased accuracy of the neural networks, resulting in several technical effects and advantages.”, also see ¶ 34, “the ensemble of graphs in graph configuration collection 108 may enable a corresponding ensemble of K neural network topologies, or neural networks, to be created for neural network topology (NNT) collection 110, such as with neural network converter 109.”).
Both Myhre and Gurwicz pertain to the problem of neural network data relationship activities, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Myhre and Gurwicz to teach the above limitations. The motivation for doing so would be “Several embodiments may produce and/or utilize an ensemble of different neural network topologies, leading to improved classification capabilities. In these and other ways, the neural network topology system may enable reliable and efficient optimization of neural networks to achieve improved performance and increased accuracy of the neural networks, resulting in several technical effects and advantages.” (see Gurwicz ¶ 18).
claims 36-40 are dependent to claim 35 and recites limitations that are similar to the limitations recited in claims 21-27. Therefore, claims 36-40 are rejected with the same rationale applied against claims 21-27 above.
Related prior arts:
Cummings (US 2021/0264520 A1) teaches receiving, from a client device, a request for advice regarding a hierarchical portfolio of assets owned by an investor. The method may further comprise generating, based on the output of a neural network machine learning model, artificial intelligence suggestions for changing the hierarchical portfolio, assembling the AI suggestions and suggestion locations into an actionable artificial intelligence view of the hierarchical portfolio, and transmitting the AI view to the client device. The method may further comprise making a cryptocurrency payment related to service fees associated with the AI view via a blockchain network.
Rouhani et al. (US 2021/0019605 A1) teaches first digital watermark may correspond to input samples altering the low probabilistic regions of an activation map associated with the hidden layer of the first machine learning model. Alternatively, the first digital watermark may correspond to input samples rarely encountered by the first machine learning model. The first digital watermark may be embedded in the first machine learning model by at least training, based on training data including the input samples, the first machine learning model. A second machine learning model may be determined to be a duplicate of the first machine learning model based on a comparison of the first digital watermark embedded in the first machine learning model and a second digital watermark extracted from the second machine learning model.
Conclusion
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/IMAD KASSIM/Primary Examiner, Art Unit 2129
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