DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The status of the claims as of the response filed 9/8/2025 is as follows: Claims 1-2, 6-8, 11-12, 15-17, and 19 are currently amended. Claims 3-5, 9-10, 13-14, 18, and 20 are original. Claims 120 are currently pending in the application and have been considered below.
Response to Amendment
Double Patenting Rejection
The amendments to the independent claims alter the scope of the invention such that the double patenting rejections are withdrawn.
Objection to Claims
Claim 15 has been sufficiently amended to correct the minor informalities objected to in the previous office action, and thus the corresponding objection is withdrawn.
Rejection Under 35 USC 101
The claims have been amended but the 35 USC 101 rejections for claims 1-20 are upheld.
Rejection Under 35 USC 103
The amendments made to the claims introduce limitations that are not fully addressed in the previous office action (e.g. details about training an ANN), and thus the corresponding 35 USC 103 rejections are withdrawn. However, Examiner will consider the amended claims in light of an updated prior art search and address their patentability with respect to prior art below.
Response to Arguments
Rejection Under 35 USC 101
On pages 12-13 of the response filed 9/8/2025 Applicant argues that the amended claims provide integration into a practical application by “providing an improvement in technology within the field of artificial intelligence (“AI”) simulation and modeling in virtual networks” by including “a multi-stage provision of filtering and transforming data using specifically designed artificial neural network (“ANN”) activities, thereby, and advantageously, enhancing efficiency in processing data and optimizing manipulation of large volumes of data… [which] can desirably streamline complex AI activities resulting in the benefits of reduced downstream computational steps, increased accuracy, and can even potentially mitigate the associated carbon footprint.” Applicant further submits that the newly-introduced limitations “provide a solution to a problem rooted in AI-assisted technology involving simulation and predictive modeling in virtual networks.” Applicant’s arguments are fully considered, but are not persuasive.
Examiner notes that the alleged improvements to technology cited by Applicant are not reflected or discussed in the specification, rendering the arguments that the claims seek to solve a particular technical problem with a particular technical solution unpersuasive. The specification does not outline any specific computational limitations, technological barriers, drawbacks of current machine learning models, etc. that the instant claims seek to solve; instead, the instant claims appear to broadly be directed to the automation of identifying appropriate user support networks in an effort to improve a business method that is currently challenging and can result in misaligned matches between user preferences and selected support networks (see para. [0003] of Applicant’s specification). Such efforts do not reflect an improvement to technical processes themselves, and instead show that known machine learning modeling techniques (such as artificial neural networks) are merely applied to an abstract business practice to improve the abstract idea itself. That is, the instant claims do not appear to be improving technical aspects of specific machine learning techniques or processing capacity of computer hardware, and instead apply known ANN modelling operations to the field of user support network selection and management. These models do not improve the functioning of a computer or other technological field, and instead merely apply the principles of ANNs to improve the efficiency and accuracy of otherwise-abstract support network determinations. Improvements to an abstract idea itself (e.g. selecting and managing user support networks) do not provide integration into a practical application, as indicated above. Further, MPEP 2106.05(f)(2) states that “‘claiming the improved speed or efficiency inherent with applying the abstract idea on a computer’ does not integrate a judicial exception into a practical application or provide an inventive concept;” accordingly, the implementation of an otherwise-abstract business process on a computer via known types of machine learning methods as in the instant claims is not a technical improvement to a computer or other technical field.
On page 13 of the response, Applicant analogizes the instant claims to those found eligible in Example 39 because “both claims are directed to a computer-implemented method reciting training of a neural network and involve transformation of training data, from a first form to a second form, and retraining of the neural network.” Applicant’s arguments are fully considered, but are not persuasive. The claim at issue in Example 39 was directed to a method of training a neural network for facial detection that involved the collection and transformation of digital facial images to create two training sets for the neural network trained in two stages. Though this claim was not found to recite an abstract idea at all, it differs from the claims of the instant application due to the nature of the collected and transformed data. That is, the claim of Example 39 recited the collection and transformation of digital facial images to create training sets, which could not reasonably be accomplished by a human actor mentally or by following instructions for managing personal behavior, whereas the instant claim recites creating support network training data from an expert database correlating expert biological extraction table data to expert support network table data, which a human actor would be capable of achieving by accessing or creating this type of human-understandable data in table form. Further, Example 39 was directed only to a method of training a neural network via the creation and use of training datasets, and did not include further steps that could be characterized as aspects of a certain method of organizing human activity. In contrast, the instant claims do include steps that can be reasonably characterized as “certain methods of organizing human activity” (e.g. receiving a biological extraction and user preference, generating a request for the user to join a support network, identifying a support network for the user, assessing a membership of the support networks, organizing and assigning member participants of the support networks, selecting a leader for each support network, etc.). Accordingly, the claim of Example 39 does not recite an abstract idea, while the independent claims of the instant application do.
On pages 14-15 of the response Applicant argues that the newly-added steps of “training an ANN, meaningfully modifying and updating the training data, and then using it to retrain the ANN in the context of generating automated and dynamic results in real-time” amount “to significantly more than any judicial exception” and are not well-understood, routine, and conventional. Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that ANNs comprising layer-based node structures with weighted connections that are iteratively trained using correlations in training data and feedback are well-understood, routine, and conventional, particularly for clinical or health-based applications, as evidenced by at least Gaon et al. (US 20200351234 A1) [0162]-[0166]; Mellem et al. (US 20190341152 A1) [0155] & [0158]-[0162]; and De Vries et al. (US 20210043328 A1) [0019].
For the reasons outlined above, the 35 USC 101 rejections are upheld for claims 1-20.
Rejection Under 35 USC 103
On pages 15-19 of the response Applicant argues various deficiencies of the presently cited art with respect to the newly-introduced claim language. Applicant’s arguments are fully considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 16/863,113, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. For example, the ‘113 application does not provide adequate support for the following limitations:
wherein one or more user disapprovals are communicated to each support network of the plurality of support networks from the member participants in claim 4;
wherein assessing the membership of the plurality of support networks comprises assigning member participants to the plurality of support networks as a function of a support input in claim 5;
wherein selecting the support network leader for each support network of the plurality of support networks comprises generating one or more support blocks as a function of a support input in claim 6;
wherein generating the one or more support blocks comprises receiving the one or more support blocks from a database in claim 7;
wherein each support network comprises a chatroom and wherein the machine-learning module is configured to generate member engagement as a function of engagement with the chatroom in claim 8;
communicating, by the computing device, one or more user disapprovals for each support network of the plurality of support networks from the member participants in claim 14;
wherein assessing, by the computing device, the membership of the plurality of support networks comprises assigning member participants to the plurality of support networks as a function of a support input in claim 15;
wherein selecting, by the computing device, the support network leader for each support network of the plurality of support networks comprises generating one or more support blocks as a function of a support input in claim 16;
wherein generating, by the computing device, the one or more support blocks comprises receiving the one or more support blocks from a database in claim 17; and
wherein each support network comprises a chatroom and wherein the computing device is configured to generate member engagement as a function of engagement with the chatroom in claim 18.
Claims 4-8 and 14-18 are therefore not entitled to the priority date of the previously filed ‘113 application. The effective filing date of claims 4-8 and 14-18 is considered to be the filing date of the instant application: 10/05/2023.
However, claims 1-3, 9-13, and 19-20 recite subject matter that is fully supported by the parent application, and accordingly these claims are entitled to the filing date of the ‘113 application: 4/30/2020.
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
In the instant case, claims 1-10 are directed to a system (i.e. a machine) and claims 11-20 are directed to a method (i.e. a process). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claims 1 and 11 recite functions that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. managing personal behavior or interactions between people. Specifically, claim 1 (as representative) recites:
a support module operating on a computing device, the support module configured to:
receive a biological extraction related to a user, wherein the biological extraction comprises an element of user physiological data;
receive at least a user preference from a remote device;
generate a request for the user to join a support network as a function of the biological extraction;
identify a support network for the user from a plurality of support networks as a function of the biological extraction by:
creating support network training data using data from an expert database correlating expert biological extraction table data to expert support network table data;
identifying the support network for the user as a function of the biological extraction using the retrained ANN; and
display to the user on the computing device, the identified support network; and
a machine-learning module operating on the computing device, the machine-learning module configured to:
assess a membership of the plurality of support networks;
organize member participants of the plurality of support networks utilizing a first machine-learning process generated based on a member enhancement factor, wherein the member enhancement factor comprises similarities between the at last a user preference and members of support groups;
assign member participants to the plurality of support networks as a function of the first machine-learning process; and
select a support network leader for each support network of the plurality of support networks.
But for the recitation of generic computer components like a computing device and software modules operating on the computing device, each of these functions, when considered as a whole, describe management and organization of members of social networks, which falls within the management of personal behavior and/or interactions between people because interpersonal relationships are being organized and managed. As an example, a social networking administrator could receive data related to a new user’s physiological data and preferences, generate a formal request to join a support network, identify an appropriate network based on the physiological data (e.g. a diabetes-based support network if the physiological data is related to a user’s diabetes), and visually communicate the identified support network to the user (e.g. via a picture, report, etc.). The administrator could create support network training data from correlated expert databases for the purpose of generating a classifier, e.g. by obtaining and correlating data from an expert database (e.g. physically stored files or other resources). The administrator could perform other network management tasks such as assessing membership of various support networks, reorganizing or rebalancing support networks based on similarities and preferences among users, assigning new or existing members to different support network groups, and selecting leaders for each group. Accordingly, claim 1 recites an abstract idea in the form of a certain method of organizing human activity. Independent claim 11 recites substantially similar limitations as claim 1, and thus also recites an abstract idea under the above analysis.
Independent claim 1 and 11 also recite steps that, under their broadest reasonable interpretations, cover mathematical concepts: Specifically, the claims recite:
training a support network classifier as a function of the support network training data, wherein:
the support network classifier comprises an artificial neural network (ANN);
the ANN comprises an input layer of nodes, one or more intermediate layers of nodes and an output layer of nodes; and
training the support network classifier comprises training the ANN by:
applying elements of the support network training data to the input layer of nodes of the ANN;
adjusting connections and weights between nodes in adjacent layers of the ANN to produce outputs of support networks at the output layer of nodes;
receiving feedback on one or more support networks of the plurality of support networks;
modifying the support network training data in response to the feedback to create modified support network training data; and
retraining the ANN as a function of the modified support network training data.
Each of these steps, when considered as a whole, describe mathematical operations for training and retraining an ANN such as applying training data to node layers, adjusting connections and weights between the layers, modifying training data based on feedback, and retraining the ANN as a function of the modified training data. Thus, claims 1 and 11 also recite an abstract idea in the form of mathematical concepts. “Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the first enumerated list of steps fall within the certain methods of organizing human activity grouping of abstract ideas, and the second enumerated list of steps fall within the mathematical concepts grouping of abstract ideas. All of the above-identified limitations are considered together as a single abstract idea for further analysis.
Dependent claims 2-10 and 12-20 inherit the limitations that recite an abstract idea from their dependence on claims 1 and 11, respectively, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 2-10 and 12-20 recite further limitations that, under their broadest reasonable interpretations, amount to additional steps/functions in the method of organizing human activity.
Specifically, claims 2 and 12 describe using a support network preference classifier to match user preferences with support network preferences while considering a diagnosed medical condition of the user and identify the support network based on the matching, which an administrator could accomplish by using a simple classifier (e.g. a regression equation, decision tree, etc.) to find a support network appropriate for the user’s preferences and diagnosis.
Claims 3 and 13 specify that the user preference comprises a cultural preference, which is a type of preference that an administrator would be capable of evaluating when identifying appropriate support network groups for a user.
Claims 4 and 14 specify that one or more user disapprovals are communicated to each support network from the member participants, which further describes the communication or sharing of information socially between human actors.
Claims 5 and 15 recite assigning member participants to support networks as a function of a support input, which an administrator could accomplish by further considering a support input (e.g. “general information associated with activities, exercises and the like” per [0084] of Applicant’s specification) when making appropriate support network group recommendations.
Claims 6-7 and 16-17 recite selecting the support leader by generating support blocks as a function of a support input, e.g. as received from a database. An administrator could achieve this function by looking up or otherwise retrieving support block information (e.g. “a set of information containing an activity, exercise, educational material and the like associated with a disease state” per [0084] of Applicant’s specification) and basing leadership decisions on the retrieved support block information.
Claims 8 and 18 recite that each support network comprises a chatroom and member engagement is generated as a function of engagement with the chatroom. An administrator could accomplish these functions by facilitating a group meeting or other space in which participants may communicate, and making note of how engaged each participant is in the facilitated group discussion.
Claims 9 and 19 describe identifying the support network for the user based on the biological extraction and their engagement, which an administrator could achieve by considering both of these types of metrics about a user when selecting an appropriate support group for a user.
Claims 10 and 20 recite selecting the support leader as a function of user input, which an administrator could achieve by basing leadership decisions on any kind of user input from a user.
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claims 1 and 11 do not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes the additional elements of a support module operating on a computing device and configured to perform the receiving, generating, identifying, and displaying functions of the invention; receiving the user preference from a remote device; a machine-learning module operating on the computing device and configured to perform the assessing, organizing, assigning, and selecting functions of the invention; and specifying that the first process used to organize and assign member participants is a first machine learning process. Claim 11 recites substantially similar additional elements as claim 1.
These additional elements, when considered in the context of each claim as a whole, merely serve to automate interactions that could occur as a certain method of managing human activity (as described above) or as mathematical operations, and thus amount to instructions to apply the abstract idea (see MPEP 2106.05(f)). For example, a support group administrator may interact with prospective participants and current members of support groups to obtain and share information relevant to the support groups (e.g. member preferences, diagnoses, engagement metrics, group and leadership assignments, group reorganizations, etc.) as well as make administrative determinations/decisions about and otherwise manage the support groups, and use of a computing device executing software modules and a high-level “machine learning” process and interacting with a remote device to achieve these functions merely digitizes and/or automates these otherwise-abstract functions such that they occur in a computerized environment rather than by and among human actors.
The use of the retrained ANN to identify the support network for the user also amounts to instructions to “apply it” with a computer. Many aspects of the training and retraining itself fall into the mathematical concepts grouping of abstract idea (as explained above), while the actual use/execution of the ANN is recited at a high level of generality; that is, the claims merely recite that the retrained ANN is used to provide the outcome of an identified support network for the user based on their biological extraction, without placing any limits on how the ANN functions to achieve this output. Examiner notes that the level of detail in the claims as currently drafted does not expand beyond what artificial neural networks actually are, which are node-based layered data representations that may iteratively learn in some fashion by adjusting weights and connections in the networks based on training data and subsequent retraining using feedback. Accordingly, the use of a trained or retrained ANN amounts to the words “apply it” with computer components because they serve to merely automate and/or digitize steps/functions that are otherwise abstract (e.g. identifying a support network for a user based on user data). Accordingly, claims 1 and 11 as a whole are each directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 2-10 and 12-20 is also not integrated into a practical application under a similar analysis as above. The functions of claims 2-7, 9-10, 12-17, and 19-20 are performed with the same additional elements introduced in claims 1 and 11, without introducing any new additional elements of their own, and accordingly also amount to mere instructions to apply the abstract idea on these same additional elements. Claims 8 and 18 recite that each support network comprises a chatroom, which is defined as “a virtual space in which member participants may communicate with each other” in [0064] of Applicant’s specification; accordingly, the “virtual” aspect of the chatroom is considered to be an additional element for the purpose of eligibility analysis. The use of a virtual chatroom rather than a group discussion facilitated in person again amounts to instructions to apply the abstract idea with computing components, because the otherwise-abstract function of facilitating a group discussion and observing member engagement with the group discussion are merely being digitized in a high-level computing environment.
Accordingly, the additional elements of claims 1-20 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1-20 are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing device operating software modules, a high-level machine learning process, and a retrained ANN and interacting with a remote device to perform the receiving, generating, identifying, displaying, assessing, organizing, assigning, selecting, etc. functions of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes that Applicant’s specification is silent to the particulars of the computing device beyond providing generic examples of computing devices in [0009]-[0010] and [0104]-[0113]. The support module and machine-learning module are each described as being “implemented as any hardware and/or software module” in [0011] and [0067], respectively. Various exemplary, known machine learning techniques are disclosed as possible implementations of the machine learning processes in [0072]-[0078]. From these disclosures, one of ordinary skill in the art would understand that any generic computing device with a memory and processor for executing software modules could be utilized to implement the functions of the invention.
Regarding the use, training, and retraining of an artificial neural network to identify the support network, Examiner notes at least paras. [0074] & [0077] of Applicant’s specification, disclosing how artificial neural networks are typically structurally arranged and trained/retrained. Further, Examiner notes that ANNs comprising layer-based node structures with weighted connections that are iteratively trained using correlations in training data and feedback are well-understood, routine, and conventional, particularly for clinical or health-based applications, as evidenced by at least Gaon et al. (US 20200351234 A1) [0162]-[0166]; Mellem et al. (US 20190341152 A1) [0155] & [0158]-[0162]; and De Vries et al. (US 20210043328 A1) [0019].
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the computing device implementing software modules, an ANN, and a machine learning process and communicating with a remote device in combination is to digitize and/or automate a social network management and organization operation that could otherwise be achieved as a certain method of organizing human activity and mathematical operations. Thus, when considered as a whole and in combination, claims 1-20 are not patent eligible.
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 1-3, 5-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gunnarsson et al. (US 20150281384 A1) in view of Gaon et al. (US 20200351234 A1).
Claims 1 and 11
Gunnarsson teaches a system for a physiologically informed virtual support network, the system comprising:
a support module operating on a computing device (Gunnarsson [0016]-[0017], noting a computing system such as a server implementing various software modules to perform the functions of the invention), the support module configured to:
receive a biological extraction related to a user, wherein the biological extraction comprises an element of user physiological data (Gunnarsson Fig. 15, [0080], noting a user can provide data related to their current weight, starting weight, goal weight, etc., i.e. elements of user physiological data in line with Applicant’s definitions in at least paras. [0018] and [0022] of the specification);
receive at least a user preference from a remote device (Gunnarsson [0028], noting a user can input preferences for a support group at a user interface module, i.e. a remote device);
generate a request for the user to join a support network as a function of the biological extraction (Gunnarsson [0028], [0035], noting a request to join an online support group can be generated based on input received from the user, i.e. including preferences for a group similar to the user in regard to age, location, gender, weight goal, and other preferences; generating a request for joining a support network of users with similar weight preferences is considered equivalent to generating a request “as a function of the biological extraction”);
identify a support network for the user from a plurality of support networks as a function of the biological extraction (Gunnarsson [0030], [0037], noting a support group is selected by the server based on the user preferences included in the request, i.e. as a function of the biological extraction such as weight) by:
display to the user on the computing device, the identified support network (Gunnarsson [0031], [0082], noting information about the selected group can be displayed to a user at a computing device); and
a (Gunnarsson [0016]-[0017], noting a computing system such as a server implementing various software modules to perform the functions of the invention), the
assess a membership of the plurality of support networks; organize member participants of the plurality of support networks utilizing criteria based on a member enhancement factor, wherein the member enhancement factor comprises similarities between the at last a user preference and members of support groups; assign member participants to the plurality of support networks as a function of the criteria (Gunnarsson [0038]-[0039], [0042]-[0045], noting the system assesses and reorganizes group membership by assigning users to groups based on a variety of optimization criteria, including user similarities, preferences, participation levels, etc. (i.e. member enhancement factors as defined in this claim)); and
select a support network leader for each support network of the plurality of support networks (Gunnarsson [0061], [0065]-[0067], noting the system can select a facilitator (i.e. support network leader) for a group; see also [0071], noting management of facilitators selected by the system, indicating that there may be multiple facilitators across multiple groups such that the system is considered to be capable of selecting a support network leader for each of the plurality of support networks).
In summary, Gunnarsson teaches a system that allows a user to join a wellness-oriented support group based on user characteristics, where the system selects appropriate support networks for the user based on user preferences, progress towards wellness goals, and other matching criteria correlated to particular groups similar to the user’s characteristics, as described in paras. [0030] & [0037] of Gunnarsson. However, this reference fails to explicitly disclose creating support network training data, training and retraining an artificial neural network support network classifier, and using the retrained ANN support network classifier to identify the appropriate support network in the specific manner recited in the claim. Gunnarsson further teaches that the system can manage and organize membership in the various groups (including creating and deleting groups, assigning members to different groups, selecting group facilitators, etc.) according to various criteria as disclosed in [0038]-[0039] & [0042]-[0045]. Group organization and management functions are judged based on success criteria like user satisfaction, participation levels, success of users achieving their goals, etc. as described in [0039], allowing for the dynamic optimization of user groups as new information is received or collected by the system. Thus, the organization and assignment of users to groups is contemplated as occurring dynamically in response to collected feedback metrics, but the reference fails to explicitly disclose the use of a first machine learning process for this group management process as in the instant claim.
However, Gaon teaches a system for matching and recommending a user to other similar users for support based on user characteristics like receiving a diagnosis or other life experiences (Gaon [0009], [0075]-[0077]) that utilizes a machine-learning-based matching engine/algorithm to identify/assign the appropriate matches (Gaon [0121], [0160]-[0161]). The matching engine may comprise an artificial neural network with an input layer of nodes, one or more intermediate layers of nodes, and an output layer of nodes that is trained by applying training data correlating inputs (e.g. user characteristics) to outputs (e.g. matching users) to the node layers such that connections and weights between nodes in adjacent layers of the ANN are learned and adjusted (Gaon [0161]-[0164]). The matching algorithm may also be trained based upon user’s responses to suggested matches and used to match users going forward (Gaon [0121]), considered equivalent to receiving feedback, modifying the training data in response to the feedback, retraining the ANN as a function of the modified training data, and using the retrained ANN to identify a support network for a user. Further, user matches/groups may be managed and assigned by the matching engine/algorithm based on engagement and other user metrics (Gaon [0115]-[0121]), considered equivalent to organizing and assigning members to groups as a function of a first machine-learning process. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the support network selection and organization methods of Gunnarsson to include use of a machine-learning-based ANN classifier trained and retrained on correlations of user characteristics, user matches, and user responses to matches as in Gaon in order to allow the system to self-learn the relationships between inputs (i.e. user characteristics) and outputs (i.e. user matches / support networks) so that the most appropriate/optimized user group matches may be automatically identified and assigned for the user (as suggested by Gaon [0121] & [0162]-[0164]).
Claim 11 recite substantially similar limitations as claim 1, and is also rejected as above.
Claims 2 and 12
Gunnarsson in view of Gaon teaches the system of claim 1, and the combination further teaches wherein identifying the support network further comprises:
matching, using a support network preference classifier, the at least a user preference and at least a support network preference, wherein the matching is performed as a function of a diagnosed medical condition of the user (Gunnarsson [0013], [0037], noting a support group may be identified for a user (i.e. by a trained ANN classifier when considered in the context of the combination with Gaon) based on how closely attributes or preferences of the user match the attributes and preferences of other members of the support group, including health conditions like weight; see also Gaon [0009], [0160], noting the matching engine (i.e. support network classifier) matches a user based on many types of characteristics and preferences, including diagnosed medical conditions); and
identifying the support network for the user using the support network classifier and the biological extraction in combination with the matching between the at least a user preference and the at least a support network preference (Gunnarsson [0013], [0030], [0037], noting the support group is selected by the server (i.e. by a trained ANN classifier when considered in the context of the combination with Gaon) based on the user preferences and attributes included in the request (i.e. as a function of the biological extraction such as weight, other preferences, and medical diagnosis (as in Gaon [0009])) matching preferences and attributes of other users in the group).
Claim 12 recite substantially similar limitations as claim 2, and is also rejected as above.
Claims 3 and 13
Gunnarsson in view of Gaon teaches the system of claim 1, and the combination further teaches wherein the at least a user preference comprises a cultural preference (Gunnarsson [0028], [0037], noting the user preference can indicate that a user would like to join a support group with members of similar age, location, gender, etc., considered to be “cultural” preferences in accordance with Applicant’s explanation in para. [0065] of the specification).
Claim 13 recite substantially similar limitations as claim 3, and is also rejected as above.
Claims 5 and 15
Gunnarsson in view of Gaon teaches the system of claim 1, and the combination further teaches wherein assessing the membership of the plurality of support networks comprises assigning member participants to the plurality of support networks as a function of a support input (Gunnarsson [0067], noting the facilitator (i.e. leader) of a support group may have certain special powers such as kicking people out of the group or inviting people into the group, considered equivalent to assigning member participants to the plurality of support networks as a function of a “support input” as defined in para. [0100] of Applicant’s specification as “an input made by support network leader 172 in association to their support network 120”).
Claim 15 recite substantially similar limitations as claim 5, and is also rejected as above.
Claims 6 and 16
Gunnarsson in view of Gaon teaches the system of claim 1, and the combination further teaches wherein selecting the support network leader for each support network of the plurality of support networks comprises generating one or more support blocks as a function of a support input (Gunnarsson [0047]-[0048], [0053]-[0059], noting the system selects and adjusts sub-goals (e.g. performing tasks like walking for a set amount of time, lifting weights, logging meals, etc., which is considered equivalent to one or more support blocks in accordance with Applicant’s definition in para. [0100] of the specification as “a set of information containing an activity, exercise, educational material and the like associated with a disease state”) for users of a group. Completion of sub-goals and other incentives/activities of a group contribute to the success rate of a given group which is a factor in selecting or adjusting facilitators of the groups per [0060]-[0063] such that the generation of one or more support blocks is considered to be part of the step of selecting a support network leader. Further, selected facilitators have the ability to provide support inputs like managing membership of their group as well as bestowing rewards for achievements, broadcasting messages to all members of a group, posting content, and reposting content automatically suggested by the system (as in [0067]-[0068]); the sub-goals, incentives, activities, etc. (i.e. the one or more support blocks) selected for a group are thus considered to be generated “as a function of” the support input because the facilitator’s support inputs such as who is or is not a member of the group would impact the selected sub-goals, incentives, activities, etc. for the group).
Claim 16 recite substantially similar limitations as claim 6, and is also rejected as above.
Claims 7 and 17
Gunnarsson in view of Gaon teaches the system of claim 6, and the combination further teaches wherein generating the one or more support blocks comprises receiving the one or more support blocks from a database (Gunnarsson [0019]-[0020], [0051], noting the system includes a database that stores information determined about users of the system such as goals, patterns/behaviors/sub-goals, etc.; selecting or adjusting specific sub-goals, incentives, or other activities as in [0047]-[0048], [0053]-[0059] would thus rely on the system obtaining sub-goal/incentive/activity data it has previously stored about users in the database such that the system is considered to generate the one or more support blocks by at least receiving the one or more support blocks from a database).
Claim 17 recite substantially similar limitations as claim 7, and is also rejected as above.
Claims 8 and 18
Gunnarsson in view of Gaon teaches the system of claim 1 and the combination further teaches wherein each support network comprises a chatroom and wherein the machine-learning module is configured to generate member engagement as a function of engagement with the chatroom (Gunnarsson [0026], [0050], noting an interactions module that manages interactions between users of the system, e.g. in a chatroom-style format with freeform text comments as in Fig. 8; see also [0044], [0071], noting the system tracks member engagement/participation with the group via various metrics, considered to include tracking participation in chatroom-style unstructured communications. See also Gaon [0116]-[0117], noting 1-to-1 and 1-to-many user chats and engagement metrics are tracked).
Claim 18 recite substantially similar limitations as claim 8, and is also rejected as above.
Claims 9 and 19
Gunnarsson in view of Gaon teaches the system of claim 8, and the combination further teaches wherein identifying the support network for the user from the plurality of support networks, as a function of the biological extraction further comprises identifying the support network for the user from the plurality of support networks, as a function of the biological extraction and as a function of the member engagement (Gunnarsson [0037], noting a support group is selected by the server based on the user preferences included in the request (i.e. as a function of the biological extraction such as weight) as well as group participation levels (i.e. as a function of the member engagement). See also Gaon [0116]-[0117], noting 1-to-1 and 1-to-many user chats and engagement metrics are tracked for user matching purposes).
Claim 19 recite substantially similar limitations as claim 9, and is also rejected as above.
Claims 10 and 20
Gunnarsson in view of Gaon teaches the system of claim 1, and the combination further teaches wherein selecting the support network leader for each support network of the plurality of support networks comprises selecting the support network leader for each support network of the plurality of support networks as a function of user input (Gunnarsson [0063], [0066], noting the facilitators (i.e. support network leaders) may be selected via members of the group voting, i.e. based on user input).
Claim 20 recite substantially similar limitations as claim 10, and is also rejected as above.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gunnarsson and Gaon as applied to claims 1 or 11 above, and further in view of Garbowicz et al. (US 20170103134 A1).
Claims 4 and 14
Gunnarsson in view of Gaon teaches the system of claim 1, but the present combination fails to explicitly disclose wherein one or more user disapprovals are communicated to each support network of the plurality of support networks from the member participants. However, Garbowicz teaches that users may communicate one or more user disapprovals of content or topics they do not wish to engage with in a social networking context (Garbowicz [0033], noting the user may set up content filters with keywords that block unwanted content topics such as politics). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the support group networks of the combination such that users may communicate/set up user disapprovals for certain topics or content as in Garbowicz in order to allow each user to avoid content they do not wish to see or engage with, thereby enhancing the user experience of the system (as suggested by Garbowicz [0033]).
Claim 14 recite substantially similar limitations as claim 4, and is also rejected as above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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