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 .
Priority
Acknowledgment is made of applicant’s claim for domestic benefit to Provisional Application #63/699576, originally filed on 09/26/2024.
Claim Objections
Claims 4 and 5 are objected to because of the following informalities:
Claim 4 recites “The method of claim 1, wherein generating, by the processor, user profiles for each of the users based on the collected user information comprises . . .” (emphasis added) and depended upon Claim 1 recites “. . . generating, by the processor, user profiles for each of the users based on the received user information, . . .” (emphasis added). Although it is clear from the context of the limitation (i.e. “wherein [previously recited limitation] comprises [new limitation]”), that “the collected user information” of Claim 4 is referencing “the received user information” of Claim 1, and therefore satisfies 35 U.S.C. 112(b), the inconsistent language does create an additional burden for the reader that gives rise to a minor informality.
Claim 5 recites “The method of claim 1, wherein generating, by the processor, user profiles for each of the users based on the collected user information comprises using a LLM to generate the user profiles based on the collected user information received from the users as a prompt.” (emphasis added) and depended upon Claim 1 recites “. . . generating, by the processor, user profiles for each of the users based on the received user information, . . .” (emphasis added). Although it is clear from the context of the limitation (i.e. “wherein [previously recited limitation] comprises [new limitation]”), that “the collected user information” of Claim 5 is referencing “the received user information” of Claim 1, and therefore satisfies 35 U.S.C. 112(b), the inconsistent language does create an additional burden for the reader that gives rise to a minor informality.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 10 and 16-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites the limitation "The method of claim 1, further comprising: . . . determining users having the unique characteristics by providing, by the processor, the user profiles and the tag parameters as input to a pretrained machine learning model, and receiving as output, by the processor, from the pretrained machine learning model the user profiles that have profile fields which match the tag parameters; . . .” (emphasis added) at the first and third paragraphs. Depended upon Claim 1 recites “a pretrained machine learning model” at the second to last paragraph. There is insufficient antecedent basis for this limitation in the claim because “a pretrained machine learning model” is introduced twice within the scope of Claim 10 (i.e. once in Claim 10 and once in depended upon Claim 1) before the reference to “the pretrained machine learning model” in Claim 10. Further examination of Claim 10 herein will be based on interpreting Claim 10 as referencing “the pretrained machine learning model” of Claim 1.
Claim 16 recites “A system, the system comprising: a segmentation component that (i) receives instructions from a host of the system, wherein the instructions include parameters for users networks of the system, (ii) receives user information from users of the user networks, (iii) generates user profiles having profile fields for each of the users based on the collected user information, . . .” at the first and second paragraphs. There is insufficient antecedent basis for this limitation in the claim. Further examination of Claim 16 herein will be based on interpreting Claim 16 as if the second paragraph stated “(iii) generates user profiles having profile fields for each of the users based on the received user information.”
Claims 17-20 are rejected based on dependency on Claim 16.
Claim Interpretation
Claims 1-20 recite a “host” throughout the claims. The claimed “host” includes users (e.g. humans) that function as a host, e.g. “host users” of Specification Paragraph 27, see also Paragraph 33 showing “hosts 110.”
Claims 1-20 recite a “user network” throughout the claims. The claimed “user network” includes a social network (i.e. group) of individuals (i.e. users). The term alone is not necessarily tied to computers or computer networks. Specification Paragraph 31 states “The user network can include and/or be integrated into computer networks [(i.e. because the user network “[can] be integrated into a computer network,” the user network itself is not restricted to a computer network)] and/or services. The user network can be referred to as an online user group, a user group [(i.e. a non-online group)], a member network, an online user network, a mighty user network, mighty network, a user account database, a user profile database, among other terms.” (Emphasis added). For example, the information that “John Doe is friends with Jane Doe” could be considered a “user network.”
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a segmentation component that
(i) receives instructions from a host of the system, wherein the instructions include parameters for users networks of the system,
(ii) receives user information from users of the user networks,
(iii) generates user profiles having profile fields for each of the users based on the collected user information, and
(iv) determines which users to assign to the user networks based on the profile fields which match the received parameters for users networks” (emphasis added) in Claim 16.
“an automation component that assigns users to the user networks based on the determined user profiles that have profile fields which match the parameters of the user networks” (emphasis added) in Claim 16.
“a dynamic experiences component that presents the users with their respective assigned user networks” (emphasis added) in Claim 16.
“wherein the segmentation component determines users to assign to the user networks by providing the user profiles and the parameters as input to a pretrained machine learning model, and receives as output from the pretrained machine learning model the user profiles that have profile fields which match the parameters of the user networks” in Claim 18.
“wherein the automation component includes a LLM that assigns users to the user networks based on the determined user profiles that have profile fields which match the parameters of the user networks” (emphasis added) in Claim 19.
“wherein the dynamic experiences component includes a LLM that presents the users with their respective assigned user networks” (emphasis added) in Claim 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-11 recite a method (i.e. a process), Claims 12-15 recite a method (i.e. a process), and Claims 16-20 recite a system (i.e. a machine or manufacture). Therefore, Claims 1-20 all fall within the one of the four statutory categories of invention of 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 1 recites the abstract idea of “A method for automatic assignment of users to custom user networks, the method comprising:”
“receiving, . . . , parameters for the custom user networks from a host . . . , wherein the parameters are used to determine which users to assign to the custom user networks;
generating, . . . , the custom user networks;
receiving, . . . , user information from users . . . ;
generating, . . . , user profiles for each of the users based on the received user information, wherein the user profiles include profile fields that describe properties of the users;
determining users to assign to the custom user networks by providing, . . . , the user profiles and the parameters as input to a . . . model, and receiving as output, . . . , from the . . . model the user profiles that have profile fields which match the parameters of the custom user network; and
assigning, . . . , users to the custom user networks based on the determined user profiles that have profile fields matching the parameters of the custom user networks.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) receiving user group (i.e. network) parameters, (2) generating user network, (3) receiving user information including user profile, and (4) assigning user to a group based on matching profile with the group parameters, all of which are: commercial or legal interactions (i.e. the user networks are at least “business relations”) and managing personal behavior by following rules and interacting between people (i.e. forming groups based on group parameters and user information is at least “social activities” and matching the parameters and profiles to assign user’s to groups is at least “following rules or instructions.”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II, and observations (i.e. receiving profiles and parameters), evaluations (i.e. matching profiles and parameters), and judgments (i.e. assigning), which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III. The mere the recitation of generic computer components (i.e., the “computer system,” “processor, “a memory,” and “pretrained machine learning model”) implementing the identified abstract idea does not prevent the claim from “reciting” certain methods of organizing human activity and mental processes grouping. MPEP 2106.04. Therefore, Claim 1 “recites” an abstract idea in Step 2A Prong One.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) computer system,
(ii) processor,
(iii) memory, and
(iv) pretrained machine learning model.
The additional elements of (i) computer system (Fig. 8 and Paragraph 68 shows “computer system 80.” See also Paragraph 73 defining “system.”), (ii) processor (Fig. 8 and Paragraph 68 shows “processor 802.”), (iii) memory (Fig. 8 and Paragraph 68 shows “memory 804.”), and (iv) pretrained machine learning model (Fig. 1 and Paragraph 39 shows “retrained machine learning (ML) model” of “subsystem 102.”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 1 and 8 and Paragraphs 39 and 68 shows elements in combination.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). Additionally, when viewed with the abstract idea in the claim as a whole, the additional elements do not provide a patent eligible improvement to technology per MPEP 2106.05(a).
The (i) computer system, (ii) processor, (iii) memory, and (iv) pretrained machine learning model, when viewed as whole/ordered combination (Fig. 1 and 8 and Paragraphs 39 and 68 shows elements in combination.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 1 and 8 and Paragraphs 39 and 68 shows elements in combination.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) computer system, (ii) processor, (iii) memory, and (iv) pretrained machine learning model, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 1 and 8 and Paragraphs 39 and 68 shows elements in combination.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 2-11 recite the abstract idea of:
“. . . wherein the profile fields comprise at least one of a user's skills, a user's name, a user's birthday, a user's age, a user's address, a user's occupation, a user's specialization, a user's education, or a user's certifications.” (Claim 2).
“. . . wherein receiving user information from the users comprises receiving, . . . , user information as prompts to a large language model (LLM).” (Claim 3).
“. . . wherein generating, . . . , user profiles for each of the users based on the collected user information comprises generating, . . . , a structured dataset for each of the users that includes the user information associated with each user.” (Claim 4).
“. . . wherein generating, . . . , user profiles for each of the users based on the collected user information comprises using a LLM to generate the user profiles based on the collected user information received from the users as a prompt.” (Claim 5).
“. . . wherein receiving, . . . , parameters for custom user networks from the host comprises receiving, . . . , keywords used to determine which users to assign to the custom user networks.” (Claim 6).
“. . . wherein receiving, . . . , parameters for custom user networks from the host comprises receiving, . . . , parameters as prompts to a LLM.” (Claim 7).
“. . . wherein determining users to assign to the custom user networks comprises the , . . . model clustering and segmenting the user profiles based on their corresponding profile fields and the parameters of the custom user networks.” (Claim 8).
“. . . wherein assigning the users to the custom user networks comprises using a LLM to automatically assign users to the custom user networks based on the determined user profiles that have profile fields matching the parameters of the custom user networks.” (Claim 9).
“. . . receiving, . . . , tag parameters from the host, wherein the tag parameters define unique characteristics of the users; determining users having the unique characteristics by providing, . . . , the user profiles and the tag parameters as input to a , . . . model, and receiving as output, . . . , from the, . . . model the user profiles that have profile fields which match the tag parameters; and assigning, . . . , tag identifiers to the user profiles having profile fields which match the tag parameters, wherein the tag identifiers are only visible to the hosts.” (Claim 10).
“. . . receiving, . . . , badge parameters from the host, wherein the badge parameters define a user activity of the users to track; tracking, . . . , the user activity of the users defined by the badge parameters; determining, . . . , the users that performed the user activity based on the tracked user activity and the badge parameters; and assigning, . . . , badge identifiers to the user profiles of the users that performed the user activity, wherein the badge identifiers are visible to all users and hosts.” (Claim 11).
Dependent Claims 2-11, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 2-11 fail to establish claims that are not directed to an abstract idea because the further limitations include (1) limiting the profile fields to certain information, (2) using prompts and an LLM to receive information and generate profiles, (3) generating profiles and structured data based on certain information, (4) receiving keywords as network parameters, (5) using profiles, clustering, and segmenting, and an LLM to assign users to networks, and (6) using tag and badge parameters to match and assign users to certain networks, which are a part of the abstract idea. The further elements of Claims 2-11 (i.e. “computer system” of Claims 3, 6-7, and 10-11, “processor” of Claims 4-5 and 10-11, and “pretrained machine learning model” of Claims 8 and 10) fails to establish claims that are not directed to an abstract idea because the elements merely recite generic computer components similar to the generic computer components of Claim 1 and generally link the abstract idea to a particular technology or field of use (i.e. computer environment) just as in Claim 1. The organization of the further limitations of Claims 2-11 fail to integrate an abstract idea into a practical application just as discussed above for Claim 1. Additionally, performing the abstract idea of Claim 1 as recited in each of the further limitations of Claims 2-11, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 1. Therefore, Claims 2-11 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 2-11 fail to establish that the claims provide an inventive concept, just as in Claim 1. Therefore, Claims 2-11 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 12 recites the abstract idea of “A . . . method, the method comprising:
collecting user information from users of a plurality of user networks;
generating user profiles for each of the users based on the collected user information;
receiving user network parameters from hosts, wherein the user network parameters include attributes that describe at least one user network of the plurality of user networks;
selecting at least one user profile that has profile fields that match the attributes of the user network parameters; and
assigning at least one user to the at least one user network based on the selected user profile that matches the user network parameters.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) collecting user information (2) generating user profile, (3) receiving user group (i.e. network) parameters, and (4) assigning user to a group based on selecting matched profiles with the group parameters, all of which are: commercial or legal interactions (i.e. the user networks are at least “business relations”) and managing personal behavior by following rules and interacting between people (i.e. forming groups based on group parameters and user information is at least “social activities” and matching the parameters and profiles to assign user’s to groups is at least “following rules or instructions.”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II, and observations (i.e. collecting and receiving user information and parameters), evaluations (i.e. matching profiles and parameters), and judgments (i.e. generating profiles and assigning users), which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III. The mere the recitation of generic computer components (i.e., the “computer-implemented method”) implementing the identified abstract idea does not prevent the claim from “reciting” certain methods of organizing human activity and mental processes grouping. MPEP 2106.04. Therefore, Claim 1 “recites” an abstract idea in Step 2A Prong One.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) “computer-implemented method.”
The additional elements of (i) computer (Fig. 8 and Paragraph 68 shows “computer system 80.” See also Paragraph 73 defining “system.”), amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). Additionally, when viewed with the abstract idea in the claim as a whole, the additional elements do not provide a patent eligible improvement to technology per MPEP 2106.05(a).
The (i) computer, when viewed as whole/ordered combination (Fig. 1 and 8 and Paragraphs 39 and 68 shows elements in combination.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 1 and 8 and Paragraphs 39 and 68 shows elements in combination.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) computer system, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 1 and 8 and Paragraphs 39 and 68 shows elements in combination.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 13-15 recite the abstract idea of:
“. . . wherein collecting user information comprises collecting at least one of a user's skills, a user's name, a user's birthday, a user's age, a user's address, a user's occupation, a user's specialization, a user's education, or a user's certifications.” (Claim 13).
“. . . wherein collecting user information from users comprises receiving user information as prompts to a LLM.” (Claim 14).
“. . . wherein generating user profiles for each of the users based on the collected user information comprises generating a structured dataset for each of the users that includes the user information associated with each user.” (Claim 15).
Dependent Claims 13-15, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 13-15 fail to establish claims that are not directed to an abstract idea because the further limitations include (1) limiting the profile fields to certain information, (2) using prompts and an LLM to receive information and generate profiles, and (3) generating profiles and structured data based on certain information, which are a part of the abstract idea. The further elements of Claims 2-11 (i.e. “computer” implementing the method of Claims 13-15) fails to establish claims that are not directed to an abstract idea because the elements merely recite generic computer components similar to the generic computer components of Claim 12 and generally link the abstract idea to a particular technology or field of use (i.e. computer environment) just as in Claim 12. The organization of the further limitations of Claims 13-15 fail to integrate an abstract idea into a practical application just as discussed above for Claim 12. Additionally, performing the abstract idea of Claim 12 as recited in each of the further limitations of Claims 13-15, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 12. Therefore, Claims 13-15 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 13-15 fail to establish that the claims provide an inventive concept, just as in Claim 12. Therefore, Claims 13-15 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 16 recites the abstract idea of:
“. . . (i) receives instructions from a host of the system, wherein the instructions include parameters for users networks of the system,
(ii) receives user information from users of the user networks,
(iii) generates user profiles having profile fields for each of the users based on the collected user information, and
(iv) determines which users to assign to the user networks based on the profile fields which match the received parameters for users networks;
. . . assigns users to the user networks based on the determined user profiles that have profile fields which match the parameters of the user networks; and
. . .presents the users with their respective assigned user networks.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) receiving user group (i.e. network) parameters, (2) receiving user information included in a generated user profile, (3) assigning user to a group based on matching profile with the group parameters, and (4) presenting the users with their respective group assignment, all of which are: commercial or legal interactions (i.e. the user networks, and the presentation thereof, are at least “business relations”) and managing personal behavior by following rules and interacting between people (i.e. forming groups based on group parameters and user information is at least “social activities” and matching the parameters and profiles to assign user’s to groups is at least “following rules or instructions.”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II, and observations (i.e. receiving profiles and parameters), evaluations (i.e. matching profiles and parameters), and judgments (i.e. assigning), which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III. The mere the recitation of generic computer components (i.e., the “system,” “segmentation component,” “automation component,” and “dynamic experiences component”) implementing the identified abstract idea does not prevent the claim from “reciting” certain methods of organizing human activity and mental processes grouping. MPEP 2106.04. Therefore, Claim 16 “recites” an abstract idea in Step 2A Prong One.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) system comprising
(ii) segmentation component,
(iii) automation component, and
(iv) dynamic experiences component.
The additional elements of (i) system comprising (ii) segmentation component, (iii) automation component, and (iv) dynamic experiences component (Fig. 8 and Paragraph 68 shows “computer system 80.” See also Paragraph 73 defining “system.” Fig. 1 and Paragraph 37 shows “subsystem 102” includes “a segmentation component 104, an automation component 106, [and] a dynamic experiences component 108.”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 1 and 8 and Paragraphs 37 and 68 shows elements in combination.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). Additionally, when viewed with the abstract idea in the claim as a whole, the additional elements do not provide a patent eligible improvement to technology per MPEP 2106.05(a).
The (i) system comprising (ii) segmentation component, (iii) automation component, and (iv) dynamic experiences component, when viewed as whole/ordered combination (Fig. 1 and 8 and Paragraphs 37 and 68 shows elements in combination.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 1 and 8 and Paragraphs 37 and 68 shows elements in combination.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) system comprising (ii) segmentation component, (iii) automation component, and (iv) dynamic experiences component, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 1 and 8 and Paragraphs 37 and 68 shows elements in combination.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 17-20 recite the abstract idea of:
“. . . wherein the user information comprises at least one of a user's skills, a user's preferences, a user's name, a user's birthday, a user's age, a user's address, a user's occupation, a user's specialization, a user's education, or a user's certifications.” (Claim 17).
“. . . determines users to assign to the user networks by providing the user profiles and the parameters as input to a . . . model, and receives as output from the . . . model the user profiles that have profile fields which match the parameters of the user networks.” (Claim 18).
“. . . a LLM that assigns users to the user networks based on the determined user profiles that have profile fields which match the parameters of the user networks.” (Claim 19).
“. . . a LLM that presents the users with their respective assigned user networks.” (Claim 20).
Dependent Claims 17-20, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 17-20 fail to establish claims that are not directed to an abstract idea because the further limitations include (1) limiting the profile fields to certain information, (2) using an LLM to assign users to networks based on matching profiles and parameters, and (3) using an LLM to present users information, which are a part of the abstract idea. The further elements of Claims 17-20 (i.e. “segmentation component” of Claim 18, “pretrained machine learning model” of Claim 18, “automation component” of Claim 19, and “dynamic experiences component” of Claim 20) fails to establish claims that are not directed to an abstract idea because the elements merely recite generic computer components similar to the generic computer components of Claim 16 and generally link the abstract idea to a particular technology or field of use (i.e. computer environment) just as in Claim 16. The organization of the further limitations of Claims 17-20 fail to integrate an abstract idea into a practical application just as discussed above for Claim 16. Additionally, performing the abstract idea of Claim 16 as recited in each of the further limitations of Claims 17-20, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 16. Therefore, Claims 17-20 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 17-20 fail to establish that the claims provide an inventive concept, just as in Claim 16. Therefore, Claims 17-20 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 12-13 and 16-17 are rejected under 35 U.S.C. 102(a)(1) and (2) as being anticipated by US-20150095428-A1 (“Isidore”).
Regarding Claim 12, Isidore discloses “A computer-implemented method” (Fig. 1 and Paragraph 18 shows “DPS 100 [(i.e. computer system)] comprises at least one processor or central processing unit (CPU) 101 connected to system memory 106 via system interconnect/bus 102.” Fig. 1 and Paragraph 21 shows “In addition to the above described hardware components of DPS 100, various features of the invention are completed/supported via software (or firmware) code or logic stored within memory 106 or other storage and executed by Processor 101. . . Thus, for example, illustrated within memory 106 are a number of software/firmware/logic components, including . . . network requests 113 and Dynamic Network Creation (DNC) logic/utility 110.” See also Fig. 2 and Paragraph 25 showing “dynamic network definitions 113.”), “the method comprising:”
“collecting user information from users of a plurality of user networks” (Fig. 1 and Paragraph 21 shows “members account data 112 are stored in DBase 109.” Fig. 2 and Paragraphs 25-29 shows that “profile information 220” and “reported/authenticated activities information 216” are “imported” from commercial systems, and are used to determine if a user’s information satisfies the DCN requirements. Paragraph 30 shows “In one embodiment, an individual is able to trigger authenticated self-reporting based on the use of electronic receipts. In one embodiment, DNC utility 110 processes a suitably formatted electronic receipt that provides proof of an activity/transaction to verify/confirm that an individual did participate in an activity/transaction that is indicated by the electronic receipt. Thus, DNC utility 110 authenticates the individual's participation in the activity and allows the individual apply this authentication in order to access selected dynamic communication networks. In one embodiment, DNC utility 110 is configured to provide manual authentication. For example, DNC utility 110 may allow individuals to join a dynamic communication network configured as a millionaire's club in a particular region based on visual inspection of bank documentation and/or other supporting documents [(i.e. user information)] of an individual interested in accessing the network.” Thus, Paragraph 30 teaches that the “user information” is received from the “users.” Additionally, Paragraph 32 shows that a user can provide supplemental information that cures the deficiencies of the imported information. See also Paragraphs 26, 31, 36, and 39-40 further discussing receiving user information.);
“generating user profiles for each of the users based on the collected user information” (Fig. 1 and Paragraph 21 shows “members account data 112 are stored in DBase 109.” Paragraph 36 shows “Using the network contact profile, DNC utility 110 enables the individual to select the type of the personal data that is used to determine whether the individual fulfills the access requirements/definition/characteristics of a particular network/sub-network. The personal data includes transaction/activity reports/information, particular experience, credentials, gender, etc.” (Emphasis added). Therefore, Paragraph 36 teaches that the personal data of the users can have different “types,” i.e. profile fields. Fig. 2 and Paragraph 26 shows “Commercial application 136 on third server 135 generates authenticated activity/transaction report(s) 216 [(i.e. generating user profiles)] which is sent to DPS 100.”);
“receiving user network parameters from hosts, wherein the user network parameters include attributes that describe at least one user network of the plurality of user networks” (Fig. 3 and Paragraph 37 shows “At block 306, DNC utility 110 receives from the host/individual [(i.e. hosts)] information pertaining to the (access) definition/requirements of the dynamic communication sub-network [(i.e. network parameters for the custom user networks)]. In particular, DNC utility 110 enables the individual to define the sub-network by one or more of: (a) the characteristics of individual members; (b) location of members; (c) affiliation; (d) credentials; (e) activities; and (f) interests [(i.e. attributes that describe at least one user network)]. For example, the individual may be an authenticated registrant/participant at a convention who wishes to communicate with any other authenticated convention participants who may be open to communication. DNC utility 110 may confirm that individuals are authenticated participants of the convention based upon an activity/transaction report received from the convention management via an external application.” See also Fig. 1-2 and Paragraph 27 showing “DNC utility 110 enables a business entity or individual 207 to specify the definition/requirements of the dynamic communication network according to the members who are intended to receive access to the dynamic communication networks. For example, DNC utility 110 may enable individual 207 on the Las Vegas Strip to create a sub-network that restricts the sub-network membership to registered guests who are staying within the same resort as the individual.”);
“selecting at least one user profile that has profile fields that match the attributes of the user network parameters” (Fig. 3 and Paragraph 39 shows “At block 308, DNC utility 110 automatically registers individuals with the particular sub-network, according to the network definition.” Paragraph 28 shows “In one embodiment, DNC utility 110 determines whether the authenticated report (i.e., the received information) indicates that a corresponding guest has fulfilled registration/membership requirements and/or whether the received information matches DCN requirements within a network contact profile and, as a result, can be automatically provided access to the relevant sub-network(s).” The determinization of whether the report satisfies the requirement in Paragraph 28 shows the claimed selecting of a user profile that matches. Paragraph 30 shows “In one embodiment, an individual is able to trigger authenticated self-reporting based on the use of electronic receipts. In one embodiment, DNC utility 110 processes a suitably formatted electronic receipt that provides proof of an activity/transaction to verify/confirm that an individual did participate in an activity/transaction that is indicated by the electronic receipt. Thus, DNC utility 110 authenticates the individual's participation in the activity and allows the individual apply this authentication in order to access selected dynamic communication networks. In one embodiment, DNC utility 110 is configured to provide manual authentication. For example, DNC utility 110 may allow individuals to join a dynamic communication network configured as a millionaire's club in a particular region based on visual inspection of bank documentation and/or other supporting documents [(i.e. received user information)] of an individual interested in accessing the network.” Thus, regarding the example of Paragraph 30, the system receives bank documentation (i.e. collected user information) and generates an authentication report (i.e. user profile) that has the “profile field” of total assets, which is used to determine if the user qualifies for the millionaire’s club.); and
“assigning at least one user to the at least one user network based on the selected user profile that matches the user network parameters” (Fig. 3 and Paragraph 39 shows “At block 308, DNC utility 110 automatically registers [(i.e. assigns)] individuals with the particular sub-network, according to the network definition.” Paragraph 28 shows “In one embodiment, DNC utility 110 determines whether the authenticated report (i.e., the received information) indicates that a corresponding guest has fulfilled registration/membership requirements and/or whether the received information matches DCN requirements within a network contact profile [(i.e. matches the user network parameters)] and, as a result, can be automatically provided access to the relevant sub-network(s).”).
Regarding Claim 13, Isidore discloses “The method of claim 12,” as discussed above.
Isidore further discloses “wherein collecting user information comprises collecting at least one of a user's skills, a user's name, a user's birthday, a user's age, a user's address, a user's occupation, a user's specialization, a user's education, or a user's certifications” (Paragraph 36 shows “DNC utility 110 enables the individual to select the type of the personal data that is used to determine whether the individual fulfills the access requirements/definition/characteristics of a particular network/sub-network. The personal data includes transaction/activity reports/information, particular experience, credentials [(i.e. user’s specialization and user’s certification)], gender, etc.” Paragraph 37 shows “DNC utility 110 enables the individual to define the sub-network by one or more of: (a) the characteristics of individual members; (b) location of members; (c) affiliation; (d) credentials [(i.e. user’s specialization and user’s certification)]; (e) activities; and (f) interests. For example, the individual may be an authenticated registrant/participant at a convention who wishes to communicate with any other authenticated convention participants who may be open to communication. DNC utility 110 may confirm that individuals are authenticated participants of the convention based upon an activity/transaction report received from the convention management via an external application.” Paragraph 27 shows “For example, DNC utility 110 may enable individual 207 on the Las Vegas Strip to create a sub-network that restricts the sub-network membership to registered guests who are staying within the same resort as the individual. Thus, for example, the individual confirms credentials and/or location as a guest in Caesar's Palace and communicates with others that are (a) currently located in Caesar's Palace and/or (b) a current guest of Caesar's Palace [(i.e. user’s address)].” See also Paragraph 38 further showing requirements of a network.).
Regarding Claim 16, Isidore discloses “A system” (Fig. 1 and Paragraph 18 shows “DPS 100 [(i.e. system)] comprises at least one processor or central processing unit (CPU) 101 connected to system memory 106 via system interconnect/bus 102.” Fig. 1 and Paragraph 21 shows “In addition to the above described hardware components of DPS 100, various features of the invention are completed/supported via software (or firmware) code or logic stored within memory 106 or other storage and executed by Processor 101. . . Thus, for example, illustrated within memory 106 are a number of software/firmware/logic components, including . . . network requests 113 and Dynamic Network Creation (DNC) logic/utility 110.” Paragraph 23 shows “Among the software code/instructions/logic provided by DNC logic 110, and which are specific to the invention, are: (a) logic for creating dynamic communication networks; (b) logic for developing a network contact profile; (c) logic for utilizing the network contact profile to automatically provide individuals with access to dynamic communication networks; . . .” See also Fig. 2 and Paragraph 25 showing “dynamic network definitions 113.” Thus, the “DPS 100” Isidore shows the system and its components.), “the system comprising:”
“a segmentation component that”
“(i) receives instructions from a host of the system, wherein the instructions include parameters for users networks of the system” (Fig. 3 and Paragraph 37 shows “At block 306, DNC utility 110 [(i.e. system)] receives from the host/individual [(i.e. host of the system)] information pertaining to the (access) definition/requirements of the dynamic communication sub-network [(i.e. instructions including parameters for users networks)]. In particular, DNC utility 110 [(i.e. system)] enables the individual to define the sub-network by one or more of: (a) the characteristics of individual members; (b) location of members; (c) affiliation; (d) credentials; (e) activities; and (f) interests. For example, the individual may be an authenticated registrant/participant at a convention who wishes to communicate with any other authenticated convention participants who may be open to communication. DNC utility 110 may confirm that individuals are authenticated participants of the convention based upon an activity/transaction report received from the convention management via an external application.” See also Fig. 1-2 and Paragraph 27 showing “DNC utility 110 enables a business entity or individual 207 to specify the definition/requirements of the dynamic communication network according to the members who are intended to receive access to the dynamic communication networks. For example, DNC utility 110 may enable individual 207 on the Las Vegas Strip to create a sub-network that restricts the sub-network membership to registered guests who are staying within the same resort as the individual.”),
“(ii) receives user information from users of the user networks” (Fig. 1 and Paragraph 21 shows “members account data 112 are stored in DBase 109.” Fig. 2 and Paragraphs 25-29 shows that “profile information 220” and “reported/authenticated activities information 216” are “imported” from commercial systems, and are used to determine if a user’s information satisfies the DCN requirements. Paragraph 30 shows “In one embodiment, an individual is able to trigger authenticated self-reporting based on the use of electronic receipts. In one embodiment, DNC utility 110 processes a suitably formatted electronic receipt that provides proof of an activity/transaction to verify/confirm that an individual did participate in an activity/transaction that is indicated by the electronic receipt. Thus, DNC utility 110 authenticates the individual's participation in the activity and allows the individual apply this authentication in order to access selected dynamic communication networks. In one embodiment, DNC utility 110 is configured to provide manual authentication. For example, DNC utility 110 may allow individuals to join a dynamic communication network configured as a millionaire's club in a particular region based on visual inspection of bank documentation and/or other supporting documents [(i.e. user information)] of an individual interested in accessing the network.” Thus, Paragraph 30 teaches that the “user information” is received from the “users.” Additionally, Paragraph 32 shows that a user can provide supplemental information that cures the deficiencies of the imported information. See also Paragraphs 26, 31, 36, and 39-40 further discussing receiving user information.),
“(iii) generates user profiles having profile fields for each of the users based on the collected user information” (Fig. 1 and Paragraph 21 shows “members account data 112 are stored in DBase 109.” Paragraph 36 shows “Using the network contact profile, DNC utility 110 enables the individual to select the type of the personal data that is used to determine whether the individual fulfills the access requirements/definition/characteristics of a particular network/sub-network. The personal data includes transaction/activity reports/information, particular experience, credentials, gender, etc.” (Emphasis added). Therefore, Paragraph 36 shows that the personal data of the users can have different “types,” i.e. profile fields. Fig. 2 and Paragraph 26 shows “Commercial application 136 on third server 135 generates authenticated activity/transaction report(s) 216 [(i.e. generating user profiles)] which is sent to DPS 100.” Paragraph 30 shows “In one embodiment, an individual is able to trigger authenticated self-reporting based on the use of electronic receipts. In one embodiment, DNC utility 110 processes a suitably formatted electronic receipt that provides proof of an activity/transaction to verify/confirm that an individual did participate in an activity/transaction that is indicated by the electronic receipt. Thus, DNC utility 110 authenticates the individual's participation in the activity and allows the individual apply this authentication in order to access selected dynamic communication networks. In one embodiment, DNC utility 110 is configured to provide manual authentication. For example, DNC utility 110 may allow individuals to join a dynamic communication network configured as a millionaire's club in a particular region based on visual inspection of bank documentation and/or other supporting documents [(i.e. received user information)] of an individual interested in accessing the network.” Thus, regarding the example of Paragraph 30, the system receives bank documentation (i.e. received user information) and generates an authentication report (i.e. user profile) that has the field of total assets, which is used to determine if the user qualifies for the millionaire’s club.), and
“(iv) determines which users to assign to the user networks based on the profile fields which match the received parameters for users networks” (Fig. 3 and Paragraph 39 shows “At block 308, DNC utility 110 automatically registers individuals with the particular sub-network, according to the network definition.” Paragraph 28 shows “In one embodiment, DNC utility 110 determines whether the authenticated report (i.e., the received information) indicates that a corresponding guest has fulfilled registration/membership requirements and/or whether the received information [(i.e. profile fields)] matches DCN requirements within a network contact profile [(i.e. received parameters for users networks)] and, as a result, can be automatically provided access to the relevant sub-network(s).”);
“an automation component that assigns users to the user networks based on the determined user profiles that have profile fields which match the parameters of the user networks” (Fig. 3 and Paragraph 39 shows “At block 308, DNC utility 110 automatically registers [(i.e. assigns)] individuals with the particular sub-network, according to the network definition.” Paragraph 28 shows “In one embodiment, DNC utility 110 determines whether the authenticated report (i.e., the received information) indicates that a corresponding guest has fulfilled registration/membership requirements and/or whether the received information matches DCN requirements within a network contact profile and, as a result, can be automatically provided access to the relevant sub-network(s).”); and
“a dynamic experiences component that presents the users with their respective assigned user networks” (Paragraph 35 shows “In one embodiment, DNC utility 110 automatically sends notifications [(i.e. presents users with their respective assigned user networks)] to participants/members in the first sub-network and the second sub-network and select whether to allow them to be affiliated with the other network.” Paragraph 36 shows “DNC utility 110 enables individuals to define/determine (via the network contact profile) locations/time/schedule for which the individual accepts (automatic) access to certain dynamic communication networks. DNC utility 110 uses data within the network contact profile to determine whether automatic or semi-automatic network access occurs. In one embodiment, a potential member (i.e., that satisfies sub-network requirements) may choose to receive a notification before permitting other members full sub-network access to the potential member. The potential member is able to inspect current members and/or previous messages before accepting full sub-network membership [(i.e. after being initially assigned)]. In one embodiment, DNC utility 110 enables a member to accept inclusion within a group/sub-network to communicate with specific members, according to preset configurations pertaining to member and peer characteristics.” (Emphasis added).).
Regarding Claim 17, Isidore discloses “The system of claim 16,” as discussed above.
Isidore further discloses “wherein the user information comprises at least one of a user's skills, a user's preferences, a user's name, a user's birthday, a user's age, a user's address, a user's occupation, a user's specialization, a user's education, or a user's certifications” (Paragraph 36 shows “DNC utility 110 enables the individual to select the type of the personal data that is used to determine whether the individual fulfills the access requirements/definition/characteristics of a particular network/sub-network. The personal data includes transaction/activity reports/information, particular experience, credentials [(i.e. user’s specialization and user’s certification)], gender, etc.” Paragraph 37 shows “DNC utility 110 enables the individual to define the sub-network by one or more of: (a) the characteristics of individual members; (b) location of members; (c) affiliation; (d) credentials [(i.e. user’s specialization and user’s certification)]; (e) activities; and (f) interests. For example, the individual may be an authenticated registrant/participant at a convention who wishes to communicate with any other authenticated convention participants who may be open to communication. DNC utility 110 may confirm that individuals are authenticated participants of the convention based upon an activity/transaction report received from the convention management via an external application.” Paragraph 27 shows “For example, DNC utility 110 may enable individual 207 on the Las Vegas Strip to create a sub-network that restricts the sub-network membership to registered guests who are staying within the same resort as the individual. Thus, for example, the individual confirms credentials and/or location as a guest in Caesar's Palace and communicates with others that are (a) currently located in Caesar's Palace and/or (b) a current guest of Caesar's Palace [(i.e. user’s address)].” See also Paragraph 38 further showing requirements of a network.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-11 14-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over US-20150095428-A1 (“Isidore”) in view of US-20220198298-A1 (“Chow”).
Regarding Claim 1, Isidore teaches “A method for automatic assignment of users to custom user networks” (Paragraph 23 shows “Among the software code/instructions/logic provided by DNC logic 110, and which are specific to the invention, are: (a) logic for creating dynamic communication networks; (b) logic for developing a network contact profile; (c) logic for utilizing the network contact profile to automatically provide individuals with access to dynamic communication networks; . . .” See also Fig. 1-4), “the method comprising:”
“receiving, at a computer system comprising a processor and a memory storing instructions executable by the processor, parameters for the custom user networks from a host of the computer system, wherein the parameters are used to determine which users to assign to the custom user networks” (Fig. 1 and Paragraph 18 shows “DPS 100 [(i.e. computer system)] comprises at least one processor or central processing unit (CPU) 101 connected to system memory 106 via system interconnect/bus 102.” Fig. 1 and Paragraph 21 shows “In addition to the above described hardware components of DPS 100, various features of the invention are completed/supported via software (or firmware) code or logic stored within memory 106 or other storage and executed by Processor 101. . . Thus, for example, illustrated within memory 106 are a number of software/firmware/logic components, including . . . network requests 113 and Dynamic Network Creation (DNC) logic/utility 110.” See also Fig. 2 and Paragraph 25 showing “dynamic network definitions 113.” Fig. 3 and Paragraph 37 shows “At block 306, DNC utility 110 [(i.e. computer system)] receives from the host/individual [(i.e. host of the computer system)] information pertaining to the (access) definition/requirements of the dynamic communication sub-network [(i.e. parameters for the custom user networks)]. In particular, DNC utility 110 [(i.e. computer system)] enables the individual to define the sub-network by one or more of: (a) the characteristics of individual members; (b) location of members; (c) affiliation; (d) credentials; (e) activities; and (f) interests. For example, the individual may be an authenticated registrant/participant at a convention who wishes to communicate with any other authenticated convention participants who may be open to communication. DNC utility 110 may confirm that individuals are authenticated participants of the convention based upon an activity/transaction report received from the convention management via an external application.” See also Fig. 1-2 and Paragraph 27 showing “DNC utility 110 enables a business entity or individual 207 to specify the definition/requirements of the dynamic communication network according to the members who are intended to receive access to the dynamic communication networks. For example, DNC utility 110 may enable individual 207 on the Las Vegas Strip to create a sub-network that restricts the sub-network membership to registered guests who are staying within the same resort as the individual.”),
“generating, by the processor, the custom user networks” (Fig. 3 and Paragraph 36 shows that the process of Fig. 3 is “of creating [(i.e. generating)] dynamic communication networks, developing a network contact profile and utilizing the network contact profile [or imported/pseudo network contact profile] to automatically provide individuals with access to dynamic communication networks, according to one embodiment.” Fig. 3 and Paragraph 36 shows “block 304 . . . enable[s] an individual to create [(i.e. generate)] a dynamic communication network. . . In addition, DNC utility 110 provides the capability for individuals/registered users to develop and update [(i.e. generate)] a network contact profile. Using the network contact profile, DNC utility 110 enables the individual to select the type of the personal data that is used to determine whether the individual fulfills the access requirements/definition/characteristics of a particular network/sub-network.” See also Paragraphs 6, 14, 27, and 29 showing creation of the network and network profile.);
“receiving, at the computer system, user information from users of the computer system” (Fig. 1 and Paragraph 21 shows “members account data 112 are stored in DBase 109.” Fig. 2 and Paragraphs 25-29 shows that “profile information 220” and “reported/authenticated activities information 216” are “imported” from commercial systems, and are used to determine if a user’s information satisfies the DCN requirements. Paragraph 30 shows “In one embodiment, an individual is able to trigger authenticated self-reporting based on the use of electronic receipts. In one embodiment, DNC utility 110 processes a suitably formatted electronic receipt that provides proof of an activity/transaction to verify/confirm that an individual did participate in an activity/transaction that is indicated by the electronic receipt. Thus, DNC utility 110 authenticates the individual's participation in the activity and allows the individual apply this authentication in order to access selected dynamic communication networks. In one embodiment, DNC utility 110 is configured to provide manual authentication. For example, DNC utility 110 may allow individuals to join a dynamic communication network configured as a millionaire's club in a particular region based on visual inspection of bank documentation and/or other supporting documents [(i.e. user information)] of an individual interested in accessing the network.” Thus, Paragraph 30 teaches that the “user information” is received from the “users.” Additionally, Paragraph 32 shows that a user can provide supplemental information that cures the deficiencies of the imported information. See also Paragraphs 26, 31, 36, and 39-40 further discussing receiving user information.);
“generating, by the processor, user profiles for each of the users based on the received user information, wherein the user profiles include profile fields that describe properties of the users” (Fig. 1 and Paragraph 21 shows “members account data 112 are stored in DBase 109.” Paragraph 36 shows “Using the network contact profile, DNC utility 110 enables the individual to select the type of the personal data that is used to determine whether the individual fulfills the access requirements/definition/characteristics of a particular network/sub-network. The personal data includes transaction/activity reports/information, particular experience, credentials, gender, etc.” (Emphasis added). Therefore, Paragraph 36 teaches that the personal data of the users can have different “types,” i.e. profile fields. Fig. 2 and Paragraph 26 shows “Commercial application 136 on third server 135 generates authenticated activity/transaction report(s) 216 [(i.e. generating user profiles)] which is sent to DPS 100.” Paragraph 30 shows “In one embodiment, an individual is able to trigger authenticated self-reporting based on the use of electronic receipts. In one embodiment, DNC utility 110 processes a suitably formatted electronic receipt that provides proof of an activity/transaction to verify/confirm that an individual did participate in an activity/transaction that is indicated by the electronic receipt. Thus, DNC utility 110 authenticates the individual's participation in the activity and allows the individual apply this authentication in order to access selected dynamic communication networks. In one embodiment, DNC utility 110 is configured to provide manual authentication. For example, DNC utility 110 may allow individuals to join a dynamic communication network configured as a millionaire's club in a particular region based on visual inspection of bank documentation and/or other supporting documents [(i.e. received user information)] of an individual interested in accessing the network.” Thus, regarding the example of Paragraph 30, the system receives bank documentation (i.e. received user information) and generates an authentication report (i.e. user profile) that has the field of total assets, which is used to determine if the user qualifies for the millionaire’s club.);
“determining users to assign to the custom user networks by providing, by the processor, the user profiles and the parameters as input to a . . . model, and receiving as output, by the processor, from the . . . model the user profiles that have profile fields which match the parameters of the custom user network” (Fig. 3 and Paragraph 39 shows “At block 308, DNC utility 110 automatically registers individuals with the particular sub-network, according to the network definition.” Paragraph 28 shows “In one embodiment, DNC utility 110 [(i.e. model)] determines whether the authenticated report (i.e., the received information) [(i.e. input)] indicates that a corresponding guest has fulfilled registration/membership requirements and/or whether the received information matches DCN requirements within a network contact profile [(i.e. output user profiles that have profile fields which match the parameters of the custom user network)] and, as a result, can be automatically provided access to the relevant sub-network(s) [(i.e. determining users to assign to the custom user networks)].”); and
“assigning, by the processor, users to the custom user networks based on the determined user profiles that have profile fields matching the parameters of the custom user networks” (Fig. 3 and Paragraph 39 shows “At block 308, DNC utility 110 automatically registers individuals with the particular sub-network, according to the network definition.” Paragraph 28 shows “In one embodiment, DNC utility 110 determines whether the authenticated report (i.e., the received information) indicates that a corresponding guest has fulfilled registration/membership requirements and/or whether the received information matches DCN requirements within a network contact profile and, as a result, can be automatically provided access to the relevant sub-network(s) [(i.e. determining users to assign to the custom user networks)].”).
Isidore does not explicitly teach, but Chow teaches “determining users to assign to the custom user networks by providing, by the processor, the user profiles and the parameters as input to a pretrained machine learning model, and receiving as output, by the processor, from the pretrained machine learning model the user profiles that have profile fields which match the parameters of the custom user network” (Fig. 1 and Paragraph 28 shows “The machine learning (ML) application 104 [(i.e. pretrained machine learning model)] analyzes a “target” data set (e.g., a collection of data that has not yet been analyzed) [(i.e. input)] to identify [(i.e. output)], in one embodiment, a set of recommended attributes and/or attribute values to generate data item clusters of at least a threshold size. In another embodiment, the machine learning model may analyze target data to suggest a list of actions for a particular user. In both of these embodiments, the ML application 104 is trained with a corresponding training data set in preparation for analyzing the target data.” (Emphasis added). Paragraph 30 shows “In one example, data items may include user profiles that include one or more attributes (e.g., location, office name, experience level, job function).” Therefore, the generated “data item clusters” in Paragraph 28 teaches outputting “the user profiles that have profile fields [(i.e. attributes)] which match the parameters of the custom user network.” Fig. 1 and Paragraphs 39-40 shows “[0039] The attribute analyzer 112 may analyze user data to identify one or more attributes shared by a set of data items. The attribute analyzer 112 may identify the one or more attributes by generating feature vectors that concisely represent one or more attributes associated with data items of the set. The feature vector representations may then be processed and analyzed by the various aspects of the ML application 104 [(i.e. pretrained machine learning model)], as described below. [0040] In some examples, the attribute analyzer 112 may identify these attributes by extracting the attributes and other data from data items profiles [(i.e. input user profiles)]. In one example, the data item profiles may include user profiles that are used to identify clusters of users that include at least a threshold number of users [(i.e. parameters)]. The attribute analyzer 112 may be configured to identify attributes and corresponding values in data sets and generate corresponding feature vectors. For example, the attribute analyzer 112 may identify entity attributes within . . . “target” data that a trained ML model is directed to analyze. Once identified, the attribute analyzer 112 may extract attribute values from [the] target data.” See also Paragraphs 42-46 further discussing “clustering logic 116” and “analysis logic 118.” Fig. 3A-3B and Paragraphs 80-85 shows a user defining a segment based on providing an “attribute” and “attribute value,” i.e. teaching the claimed parameters as inputs.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore further teaches “wherein the profile fields comprise at least one of a user's skills, a user's name, a user's birthday, a user's age, a user's address, a user's occupation, a user's specialization, a user's education, or a user's certifications” (Paragraph 36 shows “DNC utility 110 enables the individual to select the type of the personal data that is used to determine whether the individual fulfills the access requirements/definition/characteristics of a particular network/sub-network. The personal data includes transaction/activity reports/information, particular experience, credentials [(i.e. user’s specialization and user’s certification)], gender, etc.” Paragraph 37 shows “DNC utility 110 enables the individual to define the sub-network by one or more of: (a) the characteristics of individual members; (b) location of members; (c) affiliation; (d) credentials [(i.e. user’s specialization and user’s certification)]; (e) activities; and (f) interests. For example, the individual may be an authenticated registrant/participant at a convention who wishes to communicate with any other authenticated convention participants who may be open to communication. DNC utility 110 may confirm that individuals are authenticated participants of the convention based upon an activity/transaction report received from the convention management via an external application.” Paragraph 27 shows “For example, DNC utility 110 may enable individual 207 on the Las Vegas Strip to create a sub-network that restricts the sub-network membership to registered guests who are staying within the same resort as the individual. Thus, for example, the individual confirms credentials and/or location as a guest in Caesar's Palace and communicates with others that are (a) currently located in Caesar's Palace and/or (b) a current guest of Caesar's Palace [(i.e. user’s address)].” See also Paragraph 38 further showing requirements of a network.).
Regarding Claim 3, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore does not explicitly teach, but Chow further teaches “wherein receiving user information from the users comprises receiving, at the computer system, user information as prompts to a large language model (LLM)” (Paragraph 31 shows “For example (and as described below), various aspects of the system 100 (e.g., the machine learning engine 108) may determine the clusters, tasks, and/or workflows by applying topic modeling, natural language processing, and/or other techniques to the data within the training data sets. These techniques may extract the information used to train the ML application 104 (e.g., workflows, job function, attributes). Similarly, these techniques [(i.e. natural language processing)] may be applied to target data in preparation for analysis of the target data by the ML application 104.” (Emphasis added). Paragraphs 50 shows “In some embodiments, frontend interface 122 is a presentation tier in a multitier application. Frontend interface 122 may process requests received from clients and translate results from other application tiers into a format that may be understood or processed by the clients.” Paragraphs 40-41 shows “[0040] . . . For example, the attribute analyzer 112 may identify entity attributes within . . . ‘target’ data [(i.e. user information)] that a trained ML model is directed to analyze. Once identified, the attribute analyzer 112 may extract attribute values from [the] target data. [0041] The attribute analyzer 112 may tokenize attributes (e.g., user attributes, attributes associated with other types of data items) into tokens. The attribute analyzer 112 may then generate feature vectors that include a sequence of values, with each value representing a different attribute token. The attribute analyzer 112 may use a document-to-vector (colloquially described as “doc-to-vec”) model [(i.e. large language model)] to tokenize attributes and generate feature vectors corresponding to [the] target data.” See also Paragraph 30 discussing the electronic documents. Thus, Chow teaches that the user information received prompts the “document-to-vector model” to tokenize attributes and generate feature vectors.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 4, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore does not explicitly teach, but Chow further teaches “wherein generating, by the processor, user profiles for each of the users based on the collected user information comprises generating, by the processor, a structured dataset for each of the users that includes the user information associated with each user” (Paragraph 75 shows “In some examples of the techniques described above, data corresponding to a set of data items (e.g., user identifiers and their corresponding user attributes) may be stored in a table [(i.e. structured data set)], a set of tables, a database, or other data structure that is convenient for the storage and subsequent analysis of data item attributes.” Paragraph 41 shows “The attribute analyzer 112 may tokenize attributes (e.g., user attributes, attributes associated with other types of data items) into tokens. The attribute analyzer 112 may then generate feature vectors that include a sequence of values, with each value representing a different attribute token. The attribute analyzer 112 may use a document-to-vector (colloquially described as “doc-to-vec”) model to tokenize attributes and generate feature vectors corresponding to one or both of training data and target data.” See also Paragraphs 33 and 45 further discussing tokenizing data to create user profiles.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 5, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore does not explicitly teach, but Chow further teaches “wherein generating, by the processor, user profiles for each of the users based on the collected user information comprises using a LLM to generate the user profiles based on the collected user information received from the users as a prompt” (Paragraph 31 shows “For example (and as described below), various aspects of the system 100 (e.g., the machine learning engine 108) may determine the clusters, tasks, and/or workflows by applying topic modeling, natural language processing, and/or other techniques to the data within the training data sets. These techniques may extract the information used to train the ML application 104 (e.g., workflows, job function, attributes). Similarly, these techniques [(i.e. natural language processing)] may be applied to target data in preparation for analysis of the target data by the ML application 104.” (Emphasis added). Paragraphs 50 shows “In some embodiments, frontend interface 122 is a presentation tier in a multitier application. Frontend interface 122 may process requests received from clients and translate results from other application tiers into a format that may be understood or processed by the clients.” Paragraphs 40-41 shows “[0040] . . . For example, the attribute analyzer 112 may identify entity attributes within . . . ‘target’ data [(i.e. collected user information)] that a trained ML model is directed to analyze. Once identified, the attribute analyzer 112 may extract attribute values from [the] target data. [0041] The attribute analyzer 112 may tokenize attributes (e.g., user attributes, attributes associated with other types of data items) into tokens. The attribute analyzer 112 may then generate feature vectors [(i.e. user profiles)] that include a sequence of values, with each value representing a different attribute token. The attribute analyzer 112 may use a document-to-vector (colloquially described as “doc-to-vec”) model [(i.e. large language model)] to tokenize attributes and generate feature vectors corresponding to [the] target data.” (Emphasis added). See also Paragraph 30 discussing the electronic documents. Thus, Chow teaches that the user information received prompts the “document-to-vector model” to tokenize attributes and generate feature vectors. See also Paragraph 78 showing “Word-to-Vec” feature vector generation algorithm to determine categorical attribute values that are clustered.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 6, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore does not explicitly teach, but Chow further teaches “wherein receiving, by the computer system, parameters for custom user networks from the host comprises receiving, at the computer system, keywords used to determine which users to assign to the custom user networks” (Fig. 2 and Paragraph 64 shows “The system may begin the method 200 by optionally recommending one or more data item attributes to use when executing a selected clustering algorithm on a set of data items (operation 204). In one example, this recommendation improves the efficiency by which relevant clusters may be generated using subsequent operations of the process. For example, because a possible number of data items attributes and the various combinations of attributes may be vast (e.g., 2N, where N is the number of attributes), a user may find it difficult to select meaningful and useful combinations of attributes for even a small number of attributes. In light of this challenge, the system may identify candidate attributes according to the techniques described below and receive user selections of attributes [(i.e. parameters for custom user networks from the host)] to be used for clustering.” (Emphasis added). Fig. 2 and Paragraphs 70-71 shows “[0070] An administrator, in response to receiving the recommendation may select at least one value from the recommended set of values and the system may receive this administrator selection (operation 236). [0071] The system may generate clusters based on the received selection of one or more values from recommended first set value selections (operation 236[,i.e. 240]).” Fig. 3A-3B and Paragraphs 80-85 shows a user defining a segment based on providing an “attribute” and “attribute value,” i.e. teaching the claimed parameters as inputs. Paragraphs 20, 65, and 82 show an example attribute of a “City,” and example attribute values of “New York,” De Moines,” and “San Francisco,” i.e. a keyword.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 7, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore does not explicitly teach, but Chow further teaches “wherein receiving, by the computer system, parameters for custom user networks from the host comprises receiving, by the computer system, parameters as prompts to a LLM” (Fig. 2 and Paragraphs 70-71 shows “[0070] An administrator, in response to receiving the recommendation may select at least one value from the recommended set of values and the system may receive this administrator selection (operation 236). [0071] The system may generate clusters based on the received selection of one or more values from recommended first set value selections (operation 236[, i.e. 240]).” Fig. 3A-3B and Paragraphs 80-85 shows a user defining a segment based on providing an “attribute” and “attribute value,” i.e. teaching the claimed parameters as inputs. Paragraphs 20, 65, and 82 show an example attribute of a “City,” and example attribute values of “New York,” De Moines,” and “San Francisco,” i.e. a keyword. Paragraph 31 shows “For example (and as described below), various aspects of the system 100 (e.g., the machine learning engine 108) may determine the clusters, tasks, and/or workflows by applying topic modeling, natural language processing, and/or other techniques to the data within the training data sets. These techniques may extract the information used to train the ML application 104 (e.g., workflows, job function, attributes). Similarly, these techniques [(i.e. natural language processing)] may be applied to target data in preparation for analysis of the target data by the ML application 104.” (Emphasis added). Therefore, Chow teaches that the inputs, e.g. “New York,” are used as a prompt to an LLM used to cluster the users. See also Paragraphs 106-07 discussing a ML model to recommend actions and receive administrator input.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 8, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore does not explicitly teach, but Chow further teaches “wherein determining users to assign to the custom user networks comprises the pretrained machine learning model clustering and segmenting the user profiles based on their corresponding profile fields and the parameters of the custom user networks” (Fig. 2 and Paragraphs 70-71 shows “[0070] An administrator, in response to receiving the recommendation may select at least one value from the recommended set of values and the system may receive this administrator selection (operation 236). [0071] The system may generate clusters based on the received selection of one or more values from recommended first set value selections (operation 236[, i.e. 240]).” Fig. 1 and Paragraphs 37-38 shows “machine learning engine 108” (i.e. pretrained machine learning model) includes “clustering logic 116,” that “identif[ies] clusters of data items, based on a set of one or more attributes and/or attribute values.” Fig. 3A-3D and Paragraphs 80-89 shows a user defining a “segment” based on providing an “attribute” (i.e. profile field) and “attribute value.” Thus, Chow teaches “clustering and segmenting the user profiles based on their corresponding profile fields and the parameters of the custom user networks.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 9, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore does not explicitly teach, but Chow further teaches “wherein assigning the users to the custom user networks comprises using a LLM to automatically assign users to the custom user networks based on the determined user profiles that have profile fields matching the parameters of the custom user networks” (Paragraph 31 shows “For example (and as described below), various aspects of the system 100 (e.g., the machine learning engine 108) may determine the clusters, tasks, and/or workflows by applying topic modeling, natural language processing, and/or other techniques to the data within the training data sets. These techniques may extract the information used to train the ML application 104 (e.g., workflows, job function, attributes). Similarly, these techniques [(i.e. natural language processing)] may be applied to target data in preparation for analysis of the target data by the ML application 104.” (Emphasis added). Fig. 1 and Paragraph 28 shows “The machine learning (ML) application 104 [(i.e. LLM)] analyzes a “target” data set (e.g., a collection of data that has not yet been analyzed) to identify, in one embodiment, a set of recommended attributes and/or attribute values to generate data item clusters of at least a threshold size.” (Emphasis added). Paragraph 30 shows “In one example, data items may include user profiles that include one or more attributes (e.g., location, office name, experience level, job function).” Therefore, the generated “data item clusters” in Paragraph 28 teaches assigned users based on matching parameters. See also Fig. 2 and Paragraphs 70-71 showing “[0070] An administrator, in response to receiving the recommendation may select at least one value from the recommended set of values and the system may receive this administrator selection (operation 236). [0071] The system may generate clusters based on the received selection of one or more values from recommended first set value selections (operation 236[,i.e. 240]),” and Fig. 3A-3B and Paragraphs 80-85 showing a user defining a segment based on providing an “attribute” and “attribute value” (i.e. parameters).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 10, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore further teaches:
“receiving, at the computer system, tag parameters from the host, wherein the tag parameters define unique characteristics of the users” (Fig. 3 and Paragraph 37 shows “At block 306, DNC utility 110 [(i.e. computer system)] receives from the host/individual [(i.e. host of the computer system)] information pertaining to the (access) definition/requirements of the dynamic communication sub-network [(i.e. tag parameters define unique characteristics of the users)]. In particular, DNC utility 110 [(i.e. computer system)] enables the individual to define the sub-network by one or more of: (a) the characteristics of individual members; (b) location of members; (c) affiliation; (d) credentials; (e) activities; and (f) interests. For example, the individual may be an authenticated registrant/participant at a convention who wishes to communicate with any other authenticated convention participants who may be open to communication. DNC utility 110 may confirm that individuals are authenticated participants of the convention based upon an activity/transaction report received from the convention management via an external application.” (Emphasis added).);
“determining users having the unique characteristics by providing, by the processor, the user profiles and the tag parameters as input to a . . . model, and receiving as output, by the processor, from the . . . model the user profiles that have profile fields which match the tag parameters” (Fig. 3 and Paragraph 39 shows “At block 308, DNC utility 110 automatically registers individuals with the particular sub-network, according to the network definition.” Paragraph 28 shows “In one embodiment, DNC utility 110 [(i.e. model)] determines whether the authenticated report (i.e., the user profile) [(i.e. input)] indicates that a corresponding guest has fulfilled registration/membership requirements [(i.e. tag parameters)] and/or whether the received information matches DCN requirements within a network contact profile [(i.e. tag parameters)] and, as a result, can be automatically provided access to the relevant sub-network(s) [(i.e. output user profiles that have profile fields which match the tag parameters)].”); and
“assigning, by the processor, tag identifiers to the user profiles having profile fields which match the tag parameters” (Fig. 1 and Paragraph 21 shows “members account data 112 are stored in DBase 109.” Fig. 2 and Paragraphs 25-29 shows that “profile information 220” and “reported/authenticated activities information 216” are “imported” from commercial systems, and are used to determine if a user’s information satisfies the DCN requirements. Paragraph 30 shows “In one embodiment, an individual is able to trigger authenticated self-reporting based on the use of electronic receipts. In one embodiment, DNC utility 110 processes a suitably formatted electronic receipt that provides proof of an activity/transaction to verify/confirm that an individual did participate in an activity/transaction that is indicated by the electronic receipt. Thus, DNC utility 110 authenticates the individual's participation in the activity [(i.e. assigning tag identifier to the user profiles having profile fields which match the tag parameters)] and allows the individual apply this authentication in order to access selected dynamic communication networks. In one embodiment, DNC utility 110 is configured to provide manual authentication. For example, DNC utility 110 may allow individuals to join a dynamic communication network configured as a millionaire's club in a particular region based on visual inspection of bank documentation and/or other supporting documents of an individual interested in accessing the network.”), . . .
Isidore does not explicitly teach, but Chow further teaches:
“determining users having the unique characteristics by providing, by the processor, the user profiles and the tag parameters as input to a pretrained machine learning model, and receiving as output, by the processor, from the pretrained machine learning model the user profiles that have profile fields which match the tag parameters” (Fig. 1 and Paragraph 28 shows “The machine learning (ML) application 104 [(i.e. pretrained machine learning model)] analyzes a “target” data set (e.g., a collection of data that has not yet been analyzed) [(i.e. input)] to identify [(i.e. output)], in one embodiment, a set of recommended attributes and/or attribute values to generate data item clusters of at least a threshold size. In another embodiment, the machine learning model may analyze target data to suggest a list of actions for a particular user. In both of these embodiments, the ML application 104 is trained with a corresponding training data set in preparation for analyzing the target data.” (Emphasis added). Paragraph 30 shows “In one example, data items may include user profiles that include one or more attributes (e.g., location, office name, experience level, job function).” Therefore, the generated “data item clusters” in Paragraph 28 teaches outputting “the user profiles that have profile fields [(i.e. attributes)] which match the tag parameters.” Fig. 1 and Paragraphs 39-40 shows “[0039] The attribute analyzer 112 may analyze user data to identify one or more attributes shared by a set of data items. The attribute analyzer 112 may identify the one or more attributes by generating feature vectors that concisely represent one or more attributes associated with data items of the set. The feature vector representations may then be processed and analyzed by the various aspects of the ML application 104 [(i.e. pretrained machine learning model)], as described below. [0040] In some examples, the attribute analyzer 112 may identify these attributes by extracting the attributes and other data from data items profiles [(i.e. input user profiles)]. In one example, the data item profiles may include user profiles that are used to identify clusters of users that include at least a threshold number of users [(i.e. tag parameters)]. The attribute analyzer 112 may be configured to identify attributes and corresponding values in data sets and generate corresponding feature vectors. For example, the attribute analyzer 112 may identify entity attributes within . . . “target” data that a trained ML model is directed to analyze. Once identified, the attribute analyzer 112 may extract attribute values from [the] target data.” See also Paragraphs 42-46 further discussing “clustering logic 116” and “analysis logic 118.” Fig. 3A-3B and Paragraphs 80-85 shows a user defining a segment based on providing an “attribute” and “attribute value,” i.e. teaching the claimed tag parameters as inputs.) and
“assigning, by the processor, tag identifiers to the user profiles having profile fields which match the tag parameters, wherein the tag identifiers are only visible to the hosts” (Paragraph 44 shows “The clustering logic 116 may also receive feedback (e.g., via a client 102A, 102B) from an administrator that selects an attribute and/or attribute value as part of the clustering process and/or adds and/or removes one or more attributes from the attributes used to generate the one or more clusters. In this way, an administrator may exert supervisory control over the attributes selected by the system to generate clusters having a threshold size.” Thus, Paragraph 44 teaches that the “administrator” (i.e. host) has different privileges, e.g. “supervisory control,” than other users. Fig. 4 and Paragraphs 103-08 shows that an “action” (i.e. tag identifier) may be recommended to the administrator for a cluster of users. Specifically, Paragraph 106-08 shows that an administrator may “pin” certain actions to a group, i.e. making them mandatory. Thus, Chow teaches that “tag identifiers” (i.e. actions) are only visible to the hosts (i.e. administrator) and are assigned to user profiles that match the parameters (i.e. in the cluster).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 11, Isidore and Chow teach “The method of claim 1,” as discussed above.
Isidore further teaches:
“receiving, at the computer system, badge parameters from the host, wherein the badge parameters define a user activity of the users to track” (Fig. 3 and Paragraph 37 shows “At block 306, DNC utility 110 [(i.e. computer system)] receives from the host/individual [(i.e. host of the computer system)] information pertaining to the (access) definition/requirements of the dynamic communication sub-network [(i.e. badge parameters define unique characteristics of the users)]. In particular, DNC utility 110 [(i.e. computer system)] enables the individual to define the sub-network by one or more of: (a) the characteristics of individual members; (b) location of members; (c) affiliation; (d) credentials; (e) activities; and (f) interests. For example, the individual may be an authenticated registrant/participant at a convention who wishes to communicate with any other authenticated convention participants who may be open to communication. DNC utility 110 may confirm that individuals are authenticated participants of the convention based upon an activity/transaction report received from the convention management via an external application.” (Emphasis added).);
“tracking, by the processor, the user activity of the users defined by the badge parameters” (Paragraph 38 shows “In one embodiment, DNC utility 110 enables individuals to satisfy authentication requirements based on a preset combination of two or more particular sets of information. For example, a person may also be authenticated as an authorized guest in a hotel by a GPS/LPS reading/report via a cell phone confirming a person's location/presence in a hotel room.” Thus, the example of Paragraph 38 teaches that each user’s location is tracked. See also Paragraphs 25-31 showing other reported activities that are tracked to automatically add a user to a network.);
“determining, by the processor, the users that performed the user activity based on the tracked user activity and the badge parameters” (Paragraph 36 shows “Using the network contact profile, DNC utility 110 enables the individual to select the type of the personal data [(i.e. tracked user activity)] that is used to determine whether the individual fulfills the access requirements/definition/characteristics of a particular network/sub-network [(i.e. badge parameters)]. The personal data [(i.e. tracked user activity)] includes transaction/activity reports/information, particular experience, credentials, gender, etc. DNC utility 110 enables the individual to map accounts/IDs from other applications/platforms to the individual via the network contact profile [(i.e. determining the users that performed the user activity)]. DNC utility 110 enables individuals to define/determine (via the network contact profile) locations/time/schedule for which the individual accepts (automatic) access to certain dynamic communication networks. DNC utility 110 uses data within the network contact profile to determine whether automatic or semi-automatic network access occurs.”); and
“assigning, by the processor, badge identifiers to the user profiles of the users that performed the user activity” (Paragraph 36 shows “Using the network contact profile, DNC utility 110 enables the individual to select the type of the personal data [(i.e. tracked user activity)] that is used to determine whether the individual fulfills the access requirements/definition/characteristics of a particular network/sub-network [(i.e. badge parameters)]. The personal data [(i.e. tracked user activity)] includes transaction/activity reports/information, particular experience, credentials, gender, etc. DNC utility 110 enables the individual to map [(i.e. assign)] accounts/IDs from other applications/platforms to the individual via the network contact profile [(i.e. user profile)]. DNC utility 110 enables individuals to define/determine (via the network contact profile) locations/time/schedule for which the individual accepts (automatic) access to certain dynamic communication networks. DNC utility 110 uses data within the network contact profile [(i.e. badge identifiers)] to determine whether automatic or semi-automatic network access occurs.” See also Fig. 3 and Paragraphs 39-40 showing automatic registration to a network.),
wherein the badge identifiers are visible to all users and hosts” (Fig. 3 and Paragraph 40 shows “. . . DNC utility 110 displays [(i.e. visible)] a list of active/registered sub-network members [(i.e. all users and hosts)], as shown at block 310. . . At block 314, DNC utility 110 receives a unanimous [(i.e. all users and hosts)] consent/agreement to initiate the exchange of directions between individuals. . .” Paragraph 40 further shows “At block 316, DNC utility 110 provides directions from a first individual to a second individual. In one embodiment, DNC utility 110 provides directions from a first individual to a second individual via GPS and/or LPS technology (which may be facilitated by GPS/LPS application 218). In one embodiment, DNC utility 110 provides a seamless combination of GPS and LPS technology to produce a set of directions between individuals (or to another selected destination). In addition, DNC utility 110 provides information about separation distance/time based on walking/driving. In one embodiment, DNC utility 110 dynamically updates user directions and time/distance/separation, according to a dynamic GPS/LPS technology which provides directions between targets which may both be moving. In another embodiment, a target may send GPS/LPS information to the individual to set an appointment for a subsequent meeting at a particular location.” Thus, Paragraph 40 further provides an example of user’s sharing their location (i.e. badge identifier) with other user of the network, i.e. the location (i.e. badge identifier) is able to be viewed (i.e. visible) to all.).
Regarding Claim 14, Isidore teaches “The method of claim 12,” as discussed above.
Isidore does not explicitly teach, but Chow teaches “wherein collecting user information from users comprises receiving user information as prompts to a LLM” (Paragraph 31 shows “For example (and as described below), various aspects of the system 100 (e.g., the machine learning engine 108) may determine the clusters, tasks, and/or workflows by applying topic modeling, natural language processing, and/or other techniques to the data within the training data sets. These techniques may extract the information used to train the ML application 104 (e.g., workflows, job function, attributes). Similarly, these techniques [(i.e. natural language processing)] may be applied to target data in preparation for analysis of the target data by the ML application 104.” (Emphasis added). Paragraphs 50 shows “In some embodiments, frontend interface 122 is a presentation tier in a multitier application. Frontend interface 122 may process requests received from clients and translate results from other application tiers into a format that may be understood or processed by the clients.” Paragraphs 40-41 shows “[0040] . . . For example, the attribute analyzer 112 may identify entity attributes within . . . ‘target’ data [(i.e. user information)] that a trained ML model is directed to analyze. Once identified, the attribute analyzer 112 may extract attribute values from [the] target data. [0041] The attribute analyzer 112 may tokenize attributes (e.g., user attributes, attributes associated with other types of data items) into tokens. The attribute analyzer 112 may then generate feature vectors that include a sequence of values, with each value representing a different attribute token. The attribute analyzer 112 may use a document-to-vector (colloquially described as “doc-to-vec”) model [(i.e. large language model)] to tokenize attributes and generate feature vectors corresponding to [the] target data.” See also Paragraph 30 discussing the electronic documents. Thus, Chow teaches that the user information received prompts the “document-to-vector model” to tokenize attributes and generate feature vectors.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 15, Isidore teaches “The method of claim 12,” as discussed above.
Isidore does not explicitly teach, but Chow teaches “wherein generating user profiles for each of the users based on the collected user information comprises generating a structured dataset for each of the users that includes the user information associated with each user” (Paragraph 75 shows “In some examples of the techniques described above, data corresponding to a set of data items (e.g., user identifiers and their corresponding user attributes) may be stored in a table [(i.e. structured data set)], a set of tables, a database, or other data structure that is convenient for the storage and subsequent analysis of data item attributes.” Paragraph 41 shows “The attribute analyzer 112 may tokenize attributes (e.g., user attributes, attributes associated with other types of data items) into tokens. The attribute analyzer 112 may then generate feature vectors that include a sequence of values, with each value representing a different attribute token. The attribute analyzer 112 may use a document-to-vector (colloquially described as “doc-to-vec”) model to tokenize attributes and generate feature vectors corresponding to one or both of training data and target data.” See also Paragraphs 33 and 45 further discussing tokenizing data to create user profiles.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 18, Isidore teaches “The system of claim 16,” as discussed above.
Isidore does not explicitly teach, but Chow teaches “wherein the segmentation component determines users to assign to the user networks by providing the user profiles and the parameters as input to a pretrained machine learning model, and receives as output from the pretrained machine learning model the user profiles that have profile fields which match the parameters of the user networks” (Fig. 1 and Paragraph 28 shows “The machine learning (ML) application 104 [(i.e. pretrained machine learning model)] analyzes a “target” data set (e.g., a collection of data that has not yet been analyzed) [(i.e. input)] to identify [(i.e. output)], in one embodiment, a set of recommended attributes and/or attribute values to generate data item clusters of at least a threshold size. In another embodiment, the machine learning model may analyze target data to suggest a list of actions for a particular user. In both of these embodiments, the ML application 104 is trained with a corresponding training data set in preparation for analyzing the target data.” (Emphasis added). Paragraph 30 shows “In one example, data items may include user profiles that include one or more attributes (e.g., location, office name, experience level, job function).” Therefore, the generated “data item clusters” in Paragraph 28 teaches outputting “the user profiles that have profile fields [(i.e. attributes)] which match the parameters of the custom user network.” Fig. 1 and Paragraphs 39-40 shows “[0039] The attribute analyzer 112 may analyze user data to identify one or more attributes shared by a set of data items. The attribute analyzer 112 may identify the one or more attributes by generating feature vectors that concisely represent one or more attributes associated with data items of the set. The feature vector representations may then be processed and analyzed by the various aspects of the ML application 104 [(i.e. pretrained machine learning model)], as described below. [0040] In some examples, the attribute analyzer 112 may identify these attributes by extracting the attributes and other data from data items profiles [(i.e. input user profiles)]. In one example, the data item profiles may include user profiles that are used to identify clusters of users that include at least a threshold number of users [(i.e. parameters)]. The attribute analyzer 112 may be configured to identify attributes and corresponding values in data sets and generate corresponding feature vectors. For example, the attribute analyzer 112 may identify entity attributes within . . . “target” data that a trained ML model is directed to analyze. Once identified, the attribute analyzer 112 may extract attribute values from [the] target data.” See also Paragraphs 42-46 further discussing “clustering logic 116” and “analysis logic 118.” Fig. 3A-3B and Paragraphs 80-85 shows a user defining a segment based on providing an “attribute” and “attribute value,” i.e. teaching the claimed parameters as inputs.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 19, Isidore teaches “The system of claim 16,” as discussed above.
Isidore does not explicitly teach, but Chow further teaches “wherein the automation component includes a LLM that assigns users to the user networks based on the determined user profiles that have profile fields which match the parameters of the user networks” (Paragraph 31 shows “For example (and as described below), various aspects of the system 100 (e.g., the machine learning engine 108) may determine the clusters, tasks, and/or workflows by applying topic modeling, natural language processing, and/or other techniques to the data within the training data sets. These techniques may extract the information used to train the ML application 104 (e.g., workflows, job function, attributes). Similarly, these techniques [(i.e. natural language processing)] may be applied to target data in preparation for analysis of the target data by the ML application 104.” (Emphasis added). Fig. 1 and Paragraph 28 shows “The machine learning (ML) application 104 [(i.e. LLM)] analyzes a “target” data set (e.g., a collection of data that has not yet been analyzed) to identify, in one embodiment, a set of recommended attributes and/or attribute values to generate data item clusters of at least a threshold size.” (Emphasis added). Paragraph 30 shows “In one example, data items may include user profiles that include one or more attributes (e.g., location, office name, experience level, job function).” Therefore, the generated “data item clusters” in Paragraph 28 teaches assigned users based on matching parameters. See also Fig. 2 and Paragraphs 70-71 showing “[0070] An administrator, in response to receiving the recommendation may select at least one value from the recommended set of values and the system may receive this administrator selection (operation 236). [0071] The system may generate clusters based on the received selection of one or more values from recommended first set value selections (operation 236[,i.e. 240]),” and Fig. 3A-3B and Paragraphs 80-85 showing a user defining a segment based on providing an “attribute” and “attribute value” (i.e. parameters).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 20, Isidore teaches “The system of claim 16,” as discussed above.
Isidore does not explicitly teach, but Chow further teaches “wherein the dynamic experiences component includes a LLM that presents the users with their respective assigned user networks” (Paragraph 31 shows “For example (and as described below), various aspects of the system 100 (e.g., the machine learning engine 108) may determine the clusters, tasks, and/or workflows by applying topic modeling, natural language processing, and/or other techniques to the data within the training data sets. These techniques may extract the information used to train the ML application 104 (e.g., workflows, job function, attributes). Similarly, these techniques [(i.e. natural language processing)] may be applied to target data in preparation for analysis of the target data by the ML application 104.” (Emphasis added). Paragraphs 50 shows “In some embodiments, frontend interface 122 is a presentation tier in a multitier application. Frontend interface 122 may process requests received from clients and translate results from other application tiers into a format that may be understood or processed by the clients.” (Emphasis added). Therefore, Chow teaches outputting results via an LLM.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chow with Isidore because Chow teaches that using machine learning to group users based on attributes is useful when dealing with a large user base (Paragraphs 62-63). Thus, combining Chow with Isidore furthers the interest taught in Chow, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is as follows:
WO-2025072802-A2 (“Starratt”) shows using natural language inputs to analyze users and recommend certain actions.
US-10237256-B1 (“Pena”) shows generating and updating user profiles dynamically and storing the profiles in a database.
US-20210020182-A1 (“Sarikaya”) shows using natural language processing to identify user characteristics and categorize users.
US-12033050-B1 (“Mancuso”) shows using prompts to large language models and other machine learning to automate communication to certain users or user groups.
US-20130262573-A1 (“McMaster”) shows a system similar to Isidore.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW PARKER GOODMAN whose telephone number is (571) 272-5698. The examiner can normally be reached on Monday-Thursday from 9:30 AM ET to 6:00 PM ET. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Zimmerman, can be reached at telephone number (571) 272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW PARKER GOODMAN/Examiner, Art Unit 3628
/JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628
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