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 Claims
This action is in reply to the Applicant Response filed on 10/30/2025.
Claims 1, 2, 8, 9 and 15 have been amended and are hereby entered.
Claims 5, 12, 16 and 19 have been canceled.
Claims 1-4, 6-11, 13-15, 17, 18 and 20 are currently pending and have been examined.
The current action is Non-FINAL
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 1-4, 6-11, 13-15, 17, 18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under the broadest reasonable interpretation, the following claim terms are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. MPEP § 2111.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Claim 1 recites a process, which is a statutory category of invention (Step 1: YES). Claim 8 recites a system, which is a statutory category of invention (Step 1: YES). Claim 15 recites a product (apparatus), which is a statutory category of invention (Step 1: YES).
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a)). Yes.
The claims are analyzed to determine whether it is directed to a judicial exception. The following claims identify the limitations that recite additional elements in bold and the abstract idea without bold. Underlined claim limitations denote newly added claim limitations:
The claim is analyzed to determine whether it is directed to a judicial exception. Claim 1, 8 and 15 recite a method, comprising: monitoring, by a computer program, a messaging interface for transactions; extracting, by the computer program, data from messaging interface using a named entity recognition model; updating, by the computer program, a heterogeneous graph with data from the transactions, wherein the heterogeneous graph identifies a plurality of assets and a plurality of clients by: mapping, by the computer program, asset features to asset nodes in the heterogeneous graph; removing, by the computer program, nodes for the asset features from the heterogeneous graph; mapping, by the computer program, neighbor nodes that are connected to client nodes to the client node; and removing, by the computer program, edges between the neighbor nodes and the client node; training, by the computer program, a graph model with the heterogeneous graph; querying, by the computer program, the graph model with one of the plurality of assets, wherein the graph model returns a recommendation that identifies a subset of the plurality of clients for the asset; and outputting, by the computer program, the recommendation. These limitations, as drafted, under its broadest reasonable interpretation, covers performance via certain methods of organizing human activity and mental processes, but for the recitation of generic computer components. Under human activity, the limitations are commercial interactions, such as business relations or sales activities. Also, the claim limitations are managing interactions between people, such as following instructions. Lastly, the claims are a fundamental economic activity, such as trading. Accordingly, the claim recites an abstract idea. The mere recitation of generic computer components in the claims do not necessarily preclude that claim from reciting an abstract idea. (Step 2A-Prong 1: Yes. The claims recite an abstract idea).
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d)). No.
The above judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computer program, system, electronic device, computer processor, non-transitory computer readable storage medium with instructions, and messaging interface. The additional elements of a computer program, system, electronic device, computer processor, non-transitory computer readable storage medium with instructions,, are just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)). The additional elements of a messaging interface are generally linking the use of the judicial exception to a particular technological environment or field of use, for the particular technology of Graphical User Interfaces (MPEP 2106.05(h)). The computer components are recited at such a high-level of generality (i.e. as a generic computer components) such that it amounts to no more than mere instructions to apply the exception using generic computer components, and the claims fail to recite technological detail as to how the step of the judicial exception is accomplished. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. (Step 2A-Prong 2: NO. The judicial exception is not integrated into a practical application).
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05). No.
The claims are next analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements of (a computer program, system, electronic device, computer processor, non-transitory computer readable storage medium with instructions, and messaging interface) in the claims amount to no more than mere instructions to apply the exception using a generic computer component and generally linking the use of GUI’s to judicial exception. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component and generally linking the use of GUI’s to judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claims do not amount to significantly more than the recited abstract idea (Step 2B: NO; The claims do not provide significantly more, and are not patent eligible).
Claim 2 recites wherein the messaging interface comprises a chat interface. The additional element of messaging interface and chat interface is also generally linking the use of the judicial exception to a particular technological environment or field of use, for the particular technology of graphical user interface (MPEP 2106.05(h)), and the claim fails to recite technological detail as to how the step of the judicial exception is accomplished. Therefore, this claim is similarly rejected under the same rationale as claim 1, supra.
Claim 3 recites wherein each transaction identifies a type of transaction, a client identifier, an asset, and a currency for the asset. These limitations are also part of the abstract idea identified in claim 1, and is similarly rejected under the same rationale as claim 1, supra.
Claim 4 recites wherein each transaction further identifies a parent company for the asset, a sector for the asset, a country for the asset, a rating for the asset, and/or a maturity for the asset. These limitations are also part of the abstract idea identified in claim 1, and is similarly rejected under the same rationale as claim 1, supra.
Claim 6 recites further comprising: ranking, the subset of the plurality of clients based on a probability of how likely each of the clients is to trade the asset. These limitations are also part of the abstract idea identified in claim 1, and the additional elements of the computer program are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 1 analysis above.
Claim 7 recites wherein the probability is further based on a trading history of each client. These limitations are also part of the abstract idea identified in claim 1, and the additional elements of the computer program are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 1 analysis above.
Claim 9 recites wherein the messaging interface comprises a chat interface. The additional element of messaging interface and chat interface is also generally linking the use of the judicial exception to a particular technological environment or field of use, for the particular technology of graphical user interface (MPEP 2106.05(h)), and the claim fails to recite technological detail as to how the step of the judicial exception is accomplished. Therefore, this claim is similarly rejected under the same rationale as claim 8, supra.
Claim 10 recites wherein each transaction identifies a type of transaction, a client identifier, an asset, and a currency for the asset. These limitations are also part of the abstract idea identified in claim 8, and is similarly rejected under the same rationale as claim 8, supra.
Claim 11 recites wherein each transaction further identifies a parent company for the asset, a sector for the asset, a country for the asset, a rating for the asset, and/or a maturity for the asset. These limitations are also part of the abstract idea identified in claim 8, and is similarly rejected under the same rationale as claim 8, supra.
Claim 13 recites computer program is further configured to rank the subset of the plurality of clients based on a probability of how likely each of the clients is to trade the asset. These limitations are also part of the abstract idea identified in claim 1, and the additional elements of the computer program are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 1 analysis above.
Claim 14 recites wherein the probability is further based on a trading history of each client. These limitations are also part of the abstract idea identified in claim 8, and is similarly rejected under the same rationale as claim 8, supra.
Claim 17 recites wherein each transaction identifies a type of transaction, a client identifier, an asset, and a currency for the asset. These limitations are also part of the abstract idea identified in claim 15, and the additional elements of the non-transitory computer readable storage medium are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 15 analysis above.
Claim 18 recites wherein each transaction further identifies a parent company for the asset, a sector for the asset, a country for the asset, a rating for the asset, and/or a maturity for the asset. These limitations are also part of the abstract idea identified in claim 15, and the additional elements of the non-transitory computer readable storage medium are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 15 analysis above.
Claim 20 recites further including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising: ranking the subset of the plurality of clients based on a probability of how likely each of the clients is to trade the asset, wherein the probability is further based on a trading history of each client. These limitations are also part of the abstract idea identified in claim 15, and the additional elements of the non-transitory computer readable storage medium are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 15 analysis above.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1, 2, 3, 8, 9, 10, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bharti US 20240211586, in view of Johnson US 20210272040.
Regarding claims 1, 8 and 15, Bharti discloses a method, comprising:
monitoring, by a computer program, a messaging interface for transactions (Social media system 140 is monitored for a communication which is a type of transaction; a dynamic, ongoing process or transaction between two or more people; Para. 40, manager system 110 examines data of the social media system 140 to determine if first and second user are in communication via a messaging system);
extracting, by the computer program, data from messaging interface using a named entity recognition model (System uses natural language processing; Para. 31, “Manager system 110 determining profile data can include manager system 110 subjecting user content to natural language processing. User content can include, e.g., session data conversation content of a user, submitted registration data of a user, social media content of a user, and message data content of a user. Manager system 110 can use profile data of a user to establish and/or adapt relationship graph 200 for presentment to the user”; Para. 37, NLP process 115; Para. 52, subjecting communication between of conversation to NLP process 115)
updating, by the computer program, a heterogeneous graph with data from the transactions, wherein the heterogeneous graph identifies a plurality of assets and a plurality of clients by (Fig. 4, related assets relationships, Claim 8; Abstract, establishing and iteratively updating a relationship graph (a heterogeneous graph is a type of relational graph with nodes and edges) and presenting the iteratively updated relationship graph to one or more user):
mapping, by the computer program, asset features to asset nodes in the heterogeneous graph (Fig. 5, mapped asset features to asset nodes in the graph; Fig. 9, more specific with nodes and edges);
removing, by the computer program, nodes for the asset features from the heterogeneous graph (Para. 106, removed node NA044 from baseline to user A and B; Claim 10);
mapping, by the computer program, neighbor nodes that are connected to client nodes to the client node (Fig. 5, Maps baseline with User A and User B; Para. 47, “Manager system 110 running graph generating process 112 can generate relationship graph 200. Manager system 110 in one use case running graph generating process 113, can include manager system 110 generating relationship graph 200 s (mind maps) defining prompting data in the background for subsequent presentment to users. In one example, manager system 110 can be iteratively generating a plurality of baseline relationship graph 200, the background for a plurality of topics for which prompting data is expected to be regularly invoked”);
training, by the computer program, a graph model with the heterogeneous graph (Bharti discloses throughout the use of machine learning to train the graph model with the graph; Claim 9; Fig. 3a-3b; Para. 75, “Manager system 110 performing profile updating at block 1104 can include manager system 110 training linguistic complexity predictive model 4101. In one use case, manager system 110 performing profile updating at block 1104 can include manager system 110 updating predictive model 4101 as set forth in FIG. 3A. Linguistic complexity predictive model 4101 can be a predictive model for predicting linguistic complexity of user content based on topic of the content. The complexity metric can be used as a measure of knowledge level of a user as set forth herein. Linguistic complexity predictive model 4101 can be trained with iterations of training data and, once trained, linguistic complexity predictive model 4101 is able to respond to query data. Iterations of training data for training linguistic complexity predictive model 4101 can include data tags associated asset data of a user. Asset data of a user can include, e.g., social media posts including text and/or photographs, documents, e.g., published papers, and session data, e.g., text-based conversation content (e.g., original text based content or converted from voice) during a prompting data session in which the user is presented with prompting data defined by a relationship graph 200 as set forth herein”);
querying, by the computer program, the graph model with one of the plurality of assets, wherein the graph model returns a recommendation that identifies a subset of the plurality of clients for the asset (Para. 78, vectors of different assets as a definitional asset; Para. 81, 110 can select asset for inclusion in relationship model 200 with N nearest neighbors; Fig. 6A and 7A, 6014 and 7014, display final mind map to user with machine learning feedback 6012 to 6016, and ability to keep refining further 6018 if necessary based on threshold);
and outputting, by the computer program, the recommendation (Para. 158, provide recommendation to end user; Para. 81, displayed on UE device; Para. 100, adapted differently for personalized presentment).
Bharti fails to disclose removing, by the computer program, edges between the neighbor nodes and the client node. However, Johnson discloses a language speech processing system with artificial intelligence that can remove edges in a node capacity (Fig. 3, Para. 336, “Graph constructor 2624 can add/remove nodes and edges from the graph as new text is process. In this way, the graph can be dynamically updated to account for new information” Para. “centrality identifier 2626 removes edges which have centrality scores equal to 0.0 and sorts edges based on their centrality scores”; Para. 339, “If present in sentiment top scores, centrality identifier 2626 may leave the centrality nodes as is. Otherwise, centrality identifier 2626 may remove the sentiment top scores from the centrality nodes”).
It would have been considered obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Bharti with removing the edges from Johnson. Doing so allows the graph to be dynamically updated to account for new information as new test is processed.
Regarding claims 2 and 9, modified Bharti discloses wherein the messaging interface comprises a chat interface (Para. 40, user can enter details on user interface displayed to user on display 120A-120B; Para. 144, promoting data iteratively presented to user).
Regarding claims 3, 10 and 17, modified Bharti discloses wherein each transaction identifies a type of transaction (Para. 134, Fig. 7a/7b, Communication about plants and the processor of making food), a client identifier (Para. 31, “On registration of a user in the system 100, manager system 110 can associate to each user a universal unique identifier (UUID)”, an asset (Fig. 7a/b, Mind map of plants), and a currency for the asset (Fig. 7a/b, sunlight as currency for the plants).
Claim(s) 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bharti US 20240211586, in view of Johnson, as applied to claims 1, 8 and 15 above, further in view of Reitz GB 2375409
Regarding claim 4, 11, and 18, modified Bharti fails to disclose wherein each transaction further identifies a parent a sector for the asset (Applicant used “and/or” and denoting one of the list).
However, Reitz discloses a messaging interface in a transaction context where the asset managers interface between each other, with the possibility of trading sector related information (Reitz, pg. 5, discussing stocks, bonds, money market, currency, gold, silver, oil and gas sectors, transmitted throughout the day).
It would have been considered obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Bharti with the asset manager communication example with different sectors. Doing so allows the managers to interact regarding a trade, and for the system to understand the discussion regarding a particular sector trade and make changes accordingly to benefit the user trading.
Claim(s) 6, 7, 13, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bharti US 20240211586, in view of Johnson, as applied to claims 1, 8 and 15 above, further in view of Faraday “Predicting a customer’s propensity to buy using machine learning” and Priess US 20150026027.
Regarding claim 6 and 13, modified Bhurti fails to disclose [determining], by the computer program, the subset of the plurality of clients based on a probability of how likely each of the clients is to trade the asset. However, Faraday discloses the likelihood of a consumer to transact using machine learning (random decision forest; transacting is a form of trading).
It would have been considered obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Bharti with the random forest predicting of likelihood from Faraday. Doing so helps ensure greater prediction capability of the model and increases the likelihood of trades occurring.
Modified Bhuarti fails to disclose ranking clients based on trading behavior. However, Priess discloses an AI system that is able to rank consumer behavior against others consumers (Green, yellow, red designations based on behavior for risk).
It would have been considered obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Bharti with the ranking of Priess. Doing so allows the system to isolate for the best consumers likely to trade.
Regarding claim 7 and 14, The method of claim 6, wherein the probability is further based on a trading history of each client (Faraday, Using customer historical patterns for predictions; “Propensity models use machine learning algorithms to pore over your customer data to find historical patterns in it”).
Regarding claim 20, the non-transitory computer readable storage medium of claim 15, further including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:
Modified Bhurti fails to disclose [determining] the subset of the plurality of clients based on a probability of how likely each of the clients is to trade the asset. However, Faraday discloses the likelihood of a consumer to transact using machine learning (random decision forest; transacting is a form of trading).
It would have been considered obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Bharti with the random forest predicting of likelihood from Faraday. Doing so helps ensure greater prediction capability of the model and increases the likelihood of trades occurring.
Modified Bhuarti fails to disclose ranking clients based on trading behavior. However, Priess discloses an AI system that is able to rank consumer behavior against others consumers (Green, yellow, red designations based on behavior for risk).
It would have been considered obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Bharti with the ranking of Priess. Doing so allows the system to isolate for the best consumers likely to trade.
Modified Bhurti also fails to disclose wherein the probability is further based on a trading history of each client. However, Farady discloses using customer historical patterns for predictions (Faraday, Using customer historical patterns for predictions; “Propensity models use machine learning algorithms to pore over your customer data to find historical patterns in it”)..
It would have been considered obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Bharti with the customer history patterns for Farady. Doing so creates a more accurate model for predictions based on the customer past history.
Response to Arguments
Applicant's arguments filed 10/30/2025 have been fully considered but they are not persuasive.
Applicant argues that the currently recited claim limitations integrate the alleged judicial exception into a practical application (Applicant arguments, pg. 11). Applicant further notes that the claims “meaningfully limit” information by an “updated heterogeneous graph” by “querying the heterogeneous graph and returning a recommendation.” Examiner disagrees. Ex Parte Smith was eligible because they added a timer to transactions in order to handicap electronic transactions and be fair with physical transactions. Simply changing trading decisions is not an improvement to computer functionality, such as specific UI improvements and improved data management, or a solution to a technical problem. Technical details must be in the claim and the specification. For a trading method to be patent-eligible under USPTO Section 101, merely "improving computational resources" is not enough to overcome the abstract idea exception established in Alice Corp. v. CLS Bank International. The claim must specify a concrete, technological improvement to the computer's functionality, rather than simply implementing a conventional trading practice on a generic computer.
In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. In contrast, the current claims are not directed to an improvement to computer functionality and instead merely recite the computer program and messaging interface elements at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
In DDR Holdings LLC v. Hotels.com, LP, the claims were found eligible as they reflected improvements to the functioning of a computer, i.e. a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage. In contrast, the current claims do not contain limitations reflective of an improvement to computer functionality and instead merely recite the computer program and messaging interface elements at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
In Finjan, the claims to a “behavior-based virus scan” were found to provide greater computer security and were thus directed to a patent-eligible improvement in computer functionality. In contrast, the current claims do not contain limitations reflective of an improvement to computer functionality and instead merely recite the computer program and messaging interface elements at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
Regarding the “training” of the model using machine learning, The currently recited claims recite how a typical machine learning model works, using specific attributes and parameters. However, the claims do not describe any particular improvement in the manner of computer functions. Although a machine learning model is used for the purposes of determining a graph model with a heterogenous graph and querying with asset for a recommendation, such uses is both generic and conventional. The object of the claims is to determine a graph model with a heterogenous graph and querying with asset for a recommendation, which is not to produce technology enabling a machine learning model to operate. The claims call for generic use of such a machine learning model in the manner such models conventionally operate. Simply reciting a particular technological module or piece of equipment in a claim does not confer eligibility. The MPEP notes this distinction.
The MPEP notes this distinction (For example, in MPEP 2106.05(f)(I), it states: Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743). In the instant application, the currently recited claims use machine learning as generic data processing.
Conclusion
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/BRANDON M DUCK/Examiner, Art Unit 3693