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 Application
This office action is in response to the most recent filings filed by applicant on 04/01/26.
No claims are amended
No claims are cancelled
No claims are added
Claims 1-20 are pending
Note:
In light of interview held on 03/27/26 and the remarks submitted by applicants on 04/01/26, the previously made 112 and 103 rejections have been withdrawn in this office action. However, the issues with the breadth of claims and the lack of adequate details in the specification still make the scope of the claims broad and ambiguous as is discussed below:
Regarding independent claims 1, 8 and 14, it appears that the claims are missing steps. It is difficult to understand the scope of the claims in light of the specification based on how they are currently presented. For instance, in claim 1:
The claim term “prospective-based data” is described in the specification in [0004], [0007], and [0008]. Here, the term is not described, but used pretty much like it appears in the claims. Further in the specification, applicants discuss prospective as a second dataset, for instance, in [0030]: second dataset 220 includes previous, current, and/or prospective goods/services…. In particular, the plurality of target variables may be derived from one or more interactions of the party interacting with the prospective including but not limited to an entity acting on the generated recommendation, indicators of whether an opportunity was created, indicators of whether a sale was completed, or any other ascertainable target variables know to those of ordinary skill in the art. So, the question is whether “prospective” in the spec is related to “prospective-based data” in the claim?
Even while keeping, MPEP ¶ 7.37.08 in mind, Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The specification can serve as a way to understand the claim language better. However, in this case, the terms used in the spec and the claims are different and as such, it is unclear what the claim term “prospective-based data” means. Applicants may want to either amend the claim terms to match the spec. or point to parts of the spec. that the claimed terminology is described in so that one of ordinary skill in the art can understand the claim term recited in the claim. For the purposes of this office action, “prospective-based data” is understood as potential customer/ lead based on the description of the term in [0030] of the specification.
Similarly, the claim limitation “detecting, via the computing device, a plurality of target variables associated with the B2B party within the first and second datasets” recites the term “B2B party” the specification also recites this term verbatim without providing an explanation of what a B2B party entails so that one of ordinary skill in the art can understand the scope of the term? Does it mean a business-to-business partnership? Is a “B2B party” a term applicant have coined? A quick google search does not bring up anything for the terms B2B party. Note on interpretation of claim terms - Unless a term is given a “clear definition” in the specification (MPEP § 2111.01), the examiner is obligated to give claims their broadest reasonable interpretation, in light of the specification, and consistent with the interpretation that those skilled in the art would reach (MPEP § 2111). An inventor may define specific terms used to describe invention, but must do so “with reasonable clarity, deliberateness, and precision” (MPEP § 2111.01.III). Applicants may want to either amend the claim terms to match the spec. or point to parts of the spec. that the claimed terminology is described in so that one of ordinary skill in the art can understand the claim term recited in the claim. For the purposes of this office action, “B2B party” is understood as a business-to-business partnership.
The claim term “stacked model” is described at a high level in the PGPub of the current application in [0031]-[0038]. There is very little detail provided as to how the disparate data sources of the two target variables are merged? How the models are stacked? The amended limitations are discussed broadly in the specification, but there is not much detail for one of ordinary skill in the art to understand how this process occurs and why this is different from already existing technologies that perform similar functions.
In the most recent remarks dated 04/01/26 on pages 2-3, applicants point to paragraph [0037] of the specification and then explain: “Applicant notes that a data agnostic stacked model is the result of two machine learning models of disparate data types being combined into one. Reconsideration is requested.” This still does not address the issue raised above, which is that the limitations are discussed at a very high level of generality in the specification and claims. The explanation in [0037] or for that matter in [0031]-[0038] and applicants remarks on pages 2-3 is not enough to explain to one of ordinary skill in the art how this merging or stacking takes place.
For the purposes of this office action, the claim terms are reasonably understood as being separate datasets.
In addition, the claim limitations: “determining, via the computing device, a convergence of at least two target variables of the plurality of target variables ...; inserting the first and second machine learning model outputs into the data-agnostic stacked model ...; generating, via the computing device, a product recommendation derived from the data agnostic stacked model …” is broad and recited at a high level of generality. For the purposes of this office action, the claim terms are reasonably understood as being a stacked or converged model.
Since the claims are recited at a high level of generality and there is inadequate support in the specification to show how this is done, the limitations are recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
In light of these notes, the amended claims, do not overcome previously presented rejections under 101.
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 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-7 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 8-13 is/are directed to a system which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 14-20 is/are directed to a computer program product which is a statutory category.
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
With respect to the Step 2A, Prong One, the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Claim 1 is directed to an abstract idea, as evidenced by claim limitations “receiving, a first dataset pertaining to a territory plan associated with a user and a second dataset pertaining to prospective-based data; detecting, a plurality of target variables associated with the B2B party within the first and second datasets, wherein the first dataset and the second dataset are two inconsistent types for input; determining, a convergence of at least two target variables of the plurality of target variables; generating, a product recommendation comprising a detailed explanation relating to a relationship between the at least two target variables associated with the B2B party based on the convergence.”
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to helping prospect clients both from a digital standpoint and a field standpoint in a business-to-business industry (see applicants’ specification [0002]. Managing prospect client relationships for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claims 8 and 14 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2A for similar reasons to claim 1 above.
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “A computer-implemented method for automatically generating product recommendations, the method comprising:, A computer system for automatically generating product recommendations, the computer system comprising: one or more processors, one or more computer-readable memories, and program instructions stored on at least one of the one or more computer-readable memories for execution by at least one of the one or more processors to cause the computer system to: program instructions, A computer program product using a computing device for automatically generating product recommendations, the computer program product comprising: one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media, the program instructions, when executed by the computing device, cause the computing device to perform a method comprising: via the computing device; wherein detecting comprises generating a first machine learning model output based on the first dataset via a first machine learning model and a second machine learning model output based on the second dataset via a second machine learning model; wherein the determining comprises merging the first machine learned model and the second machine learned model into a data-agnostic stacked model and wherein the merging is based on a mapping of the at least two target variables from disparate data sources; inserting the first and second machine learning model outputs into the data-agnostic stacked model resulting in the at least two target variables being mapped at a data layer and an output layer; and derived from the data agnostic stacked model” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
The additional elements of a “machine learning model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer.
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 8 and 14 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
Similarly dependent claims 2-7, 9-13 and 15-20 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recite “wherein detecting the plurality of target variables comprises: generating, a first machine learning model output pertaining to a demand associated with the B2B party; and generating, based on the second dataset, output pertaining to a product or service associated with the demand” and dependent claims 4 recite “wherein determining the convergence further comprises: mapping, the two target variables of the plurality of target variables based on the merger; and generating, the recommendation wherein the recommendation is an output of the stacked model.”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 3 and 4 recites “stacked model” in the claim limitations. In this claim, “stacked model” is an additional element, but it is still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-7, 9-13 and 15-20 are also directed to the abstract idea identified above.
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A computer-implemented method for automatically generating product recommendations, the method comprising:, A computer system for automatically generating product recommendations, the computer system comprising: one or more processors, one or more computer-readable memories, and program instructions stored on at least one of the one or more computer-readable memories for execution by at least one of the one or more processors to cause the computer system to: program instructions, A computer program product using a computing device for automatically generating product recommendations, the computer program product comprising: one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media, the program instructions, when executed by the computing device, cause the computing device to perform a method comprising: via the computing device; wherein detecting comprises generating a first machine learning model output based on the first dataset via a first machine learning model and a second machine learning model output based on the second dataset via a second machine learning model; wherein the determining comprises merging the first machine learned model and the second machine learned model into a data-agnostic stacked model and wherein the merging is based on a mapping of the at least two target variables from disparate data sources; inserting the first and second machine learning model outputs into the data-agnostic stacked model resulting in the at least two target variables being mapped at a data layer and an output layer; and derived from the data agnostic stacked model” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0007], [0023], [0026]-[0027], [0040]-[0045]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 8 and 14 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2B for similar reasons to claim 1 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 1 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-7, 9-13 and 15-20 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 8 and 14. As a result, Examiner asserts that dependent claims, such as dependent claims 2-7, 9-13 and 15-20 are also directed to the abstract idea identified above.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103 - withdrawn
In the most recent filings and in light of the discussion held during the interview on 03/27/26, the previously made rejection under 35 U.S.C. 103 has been withdrawn in this office action. Please see the Remarks dated 04/01/26, especially on pages 5-10.
In the remarks on pages 5-10, applicants discuss the following underlined claims as not being shown by prior art, in method claim 1:
“A computer-implemented method for automatically generating product recommendations, the method comprising:
receiving, via a computing device, a first dataset pertaining to a territory plan associated with a user and a second dataset pertaining to prospective-based data;
detecting, via the computing device, a plurality of target variables associated with the B2B party within the first and second datasets, wherein detecting comprises generating a first machine learning model output based on the first dataset via a first machine learning model and a second machine learning model output based on the second dataset via a second machine learning model;
wherein the first dataset and the second dataset are two inconsistent types for input;
determining, via the computing device, a convergence of at least two target variables of the plurality of target variables, wherein the determining comprises merging the first machine learned model and the second machine learned model into a data-agnostic stacked model and wherein the merging is based on a mapping of the at least two target variables from disparate data sources;
inserting the first and second machine learning model outputs into the data-agnostic stacked model resulting in the at least two target variables being mapped at a data layer and an output layer; and
generating, via the computing device, a product recommendation derived from the data agnostic stacked model comprising a detailed explanation relating to a relationship between the at least two target variables associated with the B2B party based on the convergence.”
Applicants’ remarks are persuasive and as such, the previously made rejection is withdrawn.
None of the references cited - Gupta et al. (US 2015/0379602), Evans et al. (US 2021/0142345) and Cohen (WO 2021096564 A1) show the claim limitations discussed above in light of the specification.
Reference Gupta et al. (US 2015/0379602) shows in [0017] The present disclosure describes methods, systems and computer program products for using existing member profile and activity data to determine sales leads to recommend to members of a network. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of the various aspects of different implementations. It will be evident, however, to one skilled in the art, that any particular implementation may be practiced without all of the specific details and/or with variations, permutations and combinations of the various features and elements described herein. [0024]: memory and processor. [0047]: a non-transitory computer readable storage medium, [0088]. [0084]-[0085]: In some example embodiments the ranking 614 is based on member preference data 612. Member preference data 612 includes specific preferences received from a respective member, including a specific geographic location, a specific industry, a particular company or group of companies, a particular role, a particular level of seniority, or other information stored in a member profile. For example, a respective member of the server system (e.g., system 120 in FIG. 1) is assigned to sell in a particular geographic location (e.g., a city or a state) and notifies the server system (e.g., system 120 in FIG. 1) that sales lead recommendations need to be within the specified geographic location. [0093] The server system (e.g., server system 120 in FIG. 1) determines that the member has a requested a web page for a specific organization. In response, the server system (e.g., server system 120 in FIG. 1) determines whether the member has any associated geographic preferences and role (e.g., the job title or role that a particular potential lead has) preferences. Gupta shows: [0082]: the server system (e.g., system 120 in FIG. 1) analyzes the previous actions and customer relationships of a respective member when generating sales leads for that respective member. The server system (e.g., system 120 in FIG. 1) identifies attributes of members that the respective member has previously sold to or whose profile the respective member has saved. The server system (e.g., system 120 in FIG. 1) can then identify members with similar attributes. This reads on the “target variables” or attributes of the “second dataset”. [0084]-[0085]: a respective member of the server system (e.g., system 120 in FIG. 1) is assigned to sell in a particular geographic location (e.g., a city or a state) and notifies the server system (e.g., system 120 in FIG. 1) that sales lead recommendations need to be within the specified geographic location. [0093] the server system (e.g., server system 120 in FIG. 1) determines whether the member has any associated geographic preferences and role (e.g., the job title or role that a particular potential lead has) preferences. This reads on the “target variables” or attributes of the “first dataset”.). Gupta shows: [0017] The present disclosure describes methods, systems and computer program products for using existing member profile and activity data to determine sales leads to recommend to members of a network. [0018] The server system (e.g., system 120 in FIG. 1) generates sales leads without receiving specific search criteria (or a search query). For example, a large networked system receives a request for a webpage associated with a particular organization (e.g., the account page for a corporation) without a specific search query, and the large networked system generates and returns one or more sales lead recommendations for members associated with the particular organization based on the information that is stored about the organization, its employees, and the requesting member. [0019] Recommendations can be generated by analyzing the member profiles and activity of at least some of the members of the system. The server system analyzes each member profile to identify one or more signals (e.g., data related to the member) that indicate the associated member is likely to have product purchase potential Examples of information that is important in determining whether a particular member is likely to have product purchasing potential include, but are not limited to, a member's employer, title, job function, skills, work history, member interactions, and member profile. In some example embodiments the system identifies one or more potential sales leads based on an analysis of the data stored in the member profiles and ranks them based on suitability. [0020] The system then selects a number of sales leads to transmit to a client device to display to a member. The number of sales leads is determined based on the amount of display area that is allocated to display sales lead recommendations. For example, when a member requests a page of a particular company, the system allocates a small section of the displayed interface to contain sales lead recommendations. The system then selects the number of sales leads that will fit into the allocated space (e.g., one or two recommendations).). [0041]: the potential target members that have significant numbers of contacts in common, have similar biographical details (e.g., education, past work history, membership in organizations such as fraternities, home addresses, or interaction with similar non-profits), and any other commonality in the information stored in the social graph or the profiles of the members are ranked higher than potential target members that have no commonality with the first member. In some example embodiments the more commonality between two members, the higher the ranking, such that the potential target member with the most in common with the first member or the first member's current customers are ranked the highest. [0042]: the match score is time weighted, such that activities or commonalities that occurred more recently are weighted more heavily. For example, the messages and profile views that are more recent are more important than old messages and/or profile views. In some example embodiments, popular members (e.g., members who have been highly rated, whose profiles are frequently view, or whose profile is frequently saved) are more highly rated than members without as many profile views or high ratings (e.g., who are less popular). [0092] In some example embodiments, potential sales leads are identified based solely on the preferences of the member and an influencer score associated with the potential sales leads. This is especially true when the server system (e.g., server system 120 in FIG. 1) does not have previously established sales leads for a member and thus cannot yet perform a commonalities analysis for additional sales lead recommendations.
Even though Gupta discusses business relationships in [0026], [0031], and [0057], Gupta does not explicitly show a business-to-business relationship since a business relationship can still be between a business and a customer. Gupta does not explicitly show “stacked” model. Gupta does not explicitly show “and wherein the merging is based on a mapping of the at least two target variables from disparate data sources”. However, the reference does not show the above underlined claim limitations.
Reference Evans shows a business-to-business relationship at least in [0010]-[0014], [0054]: A business, while generally a single physical location, can have administratively distinct operations at a single location, and be considered as multiple distinct businesses. In another aspect, the information associated with the first business may include one or more images of the first business. Embodiments, the system may be configured to retrieve one or more images associated with the one or more structures of the one or more business names from the real estate database. [0024] As used herein, a “user” may be an individual attempting to determine the value of the business. In some embodiments, a user may be any individual, business or system who has an existing relationship with the business. In some other embodiments, a user may be an individual, business or system who does not have an existing relationship with the business. Evan shows the above limitations at least in [0058]: a self-organizing map method, a learning vector quantization method, etc.), a deep learning algorithm (e.g., a restricted Boltzmann machine, a deep belief network method, a convolution network method, a stacked auto-encoder method, etc.), a dimensionality reduction method (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, etc.), an ensemble method (e.g., boosting, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosting machine method, random forest method, etc.), and any suitable form of machine learning algorithm. Each processing portion of the system 100 can additionally or alternatively leverage: a probabilistic module, heuristic module, deterministic module, or any other suitable module leveraging any other suitable computation method, machine learning method or combination thereof. However, any suitable machine learning approach can otherwise be incorporated in the system. Further, any suitable model (e.g., machine learning, non-machine learning, etc.) can be used in generating data relevant to the system. Evans shows: [0058]: Each module of the plurality can implement any one or more of: a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.), a regularization method (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, etc.), a decision tree learning method (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, chi-squared automatic interaction detection, decision stump, random forest, multivariate adaptive regression splines, gradient boosting machines, etc.), a Bayesian method (e.g., naïve Bayes, averaged one-dependence estimators, Bayesian belief network, etc.), a kernel method (e.g., a support vector machine, a radial basis function, a linear discriminate analysis, etc.), a clustering method (e.g., k-means clustering, expectation maximization, etc.), an associated rule learning algorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), an artificial neural network model (e.g., a Perceptron method, a back-propagation method, a Hopfield network method, a self-organizing map method, a learning vector quantization method, etc.), a deep learning algorithm (e.g., a restricted Boltzmann machine, a deep belief network method, a convolution network method, a stacked auto-encoder method, etc.), a dimensionality reduction method (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, etc.), an ensemble method (e.g., boosting, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosting machine method, random forest method, etc.), and any suitable form of machine learning algorithm. Each processing portion of the system 100 can additionally or alternatively leverage: a probabilistic module, heuristic module, deterministic module, or any other suitable module leveraging any other suitable computation method, machine learning method or combination thereof. However, any suitable machine learning approach can otherwise be incorporated in the system. Further, any suitable model (e.g., machine learning, non-machine learning, etc.) can be used in generating data relevant to the system.
Evans does not explicitly show “and wherein the merging is based on a mapping of the at least two target variables from disparate data sources”. However, the reference does not show the above underlined claim limitations.
Reference Cohen shows in in [0054] FIG.2 schematically illustrates a system 200 that can generate a decision model and an explainability model of the decision model. The decision model may be a model that makes recommendations to a person or entity (e.g., a business). The recommendations may be actions that minimize, maximize, or otherwise optimize target variables of interest to the person or entity. For example, a decision model for a sales organization may recommend that a sales representative initiate a customer contact that maximizes the likelihood that the customer purchases a product. The recommendation may include the substance, time, and mode (e.g., in-person, telephone call, or email) of the customer contact. [0055] The decision model may be so complex that its behavior is opaque and requires explanation. The system 200 can generate an explainability model of the decision model that, for each recommendation, generates an explanation that demonstrates why the decision model made the particular recommendation that it did. For example, with continued reference to the decision model for the sales organization described above, the explainability model can generate an explanation that demonstrates why the decision model recommended a particular mode of customer contact. [0087] FIG.4 shows two graphs of the number of observations in the above-mentioned data. The graphs show the distribution of observations across the facility decile and the number of visits to the facility. Prediction and Decision Models [0088] The company used a random forest model as the predictive model, with the target variable Y being the deviation in sales to a facility from the mean sales of facilities in the same decile of sales as the facility. Random forest models are ensemble machine learning models that can perform both regression and classification. Random forest models may merge predictions from multiple decision trees to achieve a more accurate and stable prediction than a single decision tree. Each decision tree in a random forest may learn from a random sample of training data. By training each tree on different samples, the random forest model may achieve low variance. However, the reference does not show the above underlined claim limitations.
*Additionally, the prior art made of record and not relied upon is considered pertinent to applicant's disclosure; however, the reference does not show the above claim limitations:
NPL Reference:
Reference R. Puravankara and C. Narendra Babu, "Lead Forecasting using LSTM based Deep Learning Architecture for Sentiment Analysis," 2020 3rd International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 2020, pp. 159-164, doi: 10.1109/ICOIACT50329.2020.9332092. Lead Forecasting using LSTM based Deep Learning Architecture for Sentiment Analysis | IEEE Conference Publication | IEEE Xplore
This reference discloses marketers adopt various strategies to improve the efficiency of lead conversion by understanding more about their prospective customers. Advancements in data mining and machine leaning techniques helped industry to adopt these techniques to the business benefit. Use of modern deep learning technology for marketing industry is limited. This paper proposes a methodology for identifying the probability of conversion of a lead by employing a structured and unstructured data analysis and modelling. A binary classification model is built using feed forward neural network for structured data classification. The unstructured user comments data is represented using a long short-term memory (LSTM) network which has the ability to capture context information effectively. This method provide a comprehensive approach to consider both structured and unstructured data so as to utilize the data effectively to know the customer more and there by improve the probability of predicting the lead conversion. This methodology provide a strong basis for using LSTM based sentiment analysis forecasting of unstructured data along with the classification performed using structured data (abstract). However, the reference does not show the claim limitations underlined above.
Foreign Reference:
Reference (DE 112022001848 T5) Kanjilal et al. Methods, Systems, Manufacturing Objects And Devices For Determining Product Similarity Values. This reference discloses methods, systems, articles of manufacture and devices for determining product similarity scores are disclosed. An example device includes circuitry for generating a calculation set for identifying a set of candidate comparison objects based on primary features corresponding to a focal object and a calculation set of objects from the set of candidate comparison objects based on secondary features, which correspond to the market performance, and a weight calculation circuit for calculating primary feature values corresponding to the focal object, the primary feature values being based on a uniqueness between the primary features corresponding to the focal object and the primary features corresponding to the Calculation set of objects correspond, based (abstract). However, the reference does not show the claim limitations underlined above.
Reference (CA 3148847 A1) Adoni et al. This reference discloses a method of generating a neural network includes iteratively performing operations including generating, for each neural network of a population, a matrix representation. The matrix representation of a particular neural network includes rows of values, where each row corresponds to a set of layers of the particular neural network and each value specifies a hyperparameter of the set of layers. The operations also include providing the matrix representations as input to a relative fitness estimator that is trained to generate estimated fitness data for neural networks of the population. The estimated fitness data are based on expected fitness of neural networks predicted by the relative fitness estimator. The operations further include generating, based on the estimated fitness data, a subsequent population of neural networks. The method also includes, when a termination condition is satisfied, outputting data identifying a neural network as a candidate neural network (abstract). However, the reference does not show the above underlined claim limitations.
None of the prior art of record, taken individually or in combination, teach, interalia, the claimed invention as detailed in independent claims 1, 8 and 14, wherein the novelty of the claimed invention is in the combination of limitations and not in any single limitation.
Response to Arguments
Applicant’s Argument #1
Applicants argue on page(s) 12-15 of applicants remarks that the claims overcome previously made rejection under 101: “Applicant respectfully contends that the implementation of the instantly claimed invention is an apparent improvement. In particular, disparate (i.e., not the same) data sources applied in learning systems result in an inability to combine multiple machine learned models due to their inconsistent data types, inherent target variables, and inherent algorithms. 3 The claimed invention determines a convergence of target variables by merging two machine learning models trained on data from disparate sources (e.g., inconsistent algorithms, data types, etc.) into a data agnostic stacked model based on a mapping of the target variables. The mapping of relations among the target variables of disparate data sources at both the data layer and the output layer of a machine learning model is something that cannot possible be performed in the human mind, much less be considered a human activity to be organized. Thus, the applicant firmly asserts that the claims utilize a set of rules or procedures geared towards novelly creating a data agnostic stacked machine learning model derived from convergence of disparate data sources. As discussed throughout, the concept of a data agnostic stacked machine learning model is an unconventional concept that is ascertained in an unconventional way furthermore to address a technical problem.” (See applicants remarks for more details).
Response to Argument #1
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
Please see Note and 101 rejection above for details.
The terms “data-agnostic stacked model” is ambiguous as is discussed above. Further, the additional elements of a “machine learning model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer.
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 8 and 14 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
Further, the specification in discloses: [0002] Common issues within the business-to-business (B2B) industry include the extensive amount of time needed to prospect clients both from a digital standpoint and a field standpoint. For example, developing territory plans based on data pertaining to business entities within the territory for parties in the digital realm requires the most amount of time during digital prospecting, and interpreting historical pipeline activity, firmographics, and other applicable data pertaining to clients in the field requires an extensive amount of time and resources. In addition, the needs/demands of a client and the products/services configured to fulfill said needs/demands are not correlated because the aforementioned data for each respective demand and product are two separate data types.
Since the specification lacks details as to how the steps in the claims are carried out (please see Note and 101 rejection above), it is unclear how the claims are improving the technology or technological environment. In fact, as is discussed in [0002], the claimed invention simply seems to take two machine learning models, combine them and in an “apply it” manner to address the business problem for a business-to-business industry by managing prospect client relationships for one or more human entities. This involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts.
Applicant’s Argument #2
Applicants argue on page(s) 16-21 of applicants remarks that the claims overcome previously made rejection under 101: “Example 48 specifically notes that claims 2 & 3 do not recite any of the judicial exceptions enumerated in the 2024 PEG due to the fact that the claims as a whole improve speech separation technology and integrates the exception into a practical application of separating speech. Such a functionality does not recite any mathematical relationships, formula, … The limitations preclude the training of both neural networks and machine learning models from being performed in the mind are similar in that both essentially require the surrender of control to the machine itself so that the logic may be derived from the input and outputs. Indeed, the only controls which may be exerted by humans are, fundamentally, what is input and what outputs are considered proper and thus appropriate for inclusion. The actual derivation of the operative/controlling logic occurs entirely within the confines of the machine itself, unaltered by the mind. The claimed functionality of executing such machine learning logic, and iteratively updating the logic based on outcomes, therefore cannot be performed within the mind given the inherent process of how machines learn and how they are trained to do so. Therefore, the human mind alone cannot practically perform the features of Applicant's amended claim 1. See M.P.E.P. 2106.04(a)(2)IIIA.” (See applicants remarks for more details).
Response to Argument #2
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
In Example 48, the claim 2, step f is considered to be eligible under 101 step 2A prong 2 because:
Step (f) recites “synthesizing speech waveforms from the masked clusters, wherein each speech waveform corresponds to a different source sn,” and step (g) recites “combining the speech waveforms to generate a mixed speech signal x' by stitching together the speech waveforms corresponding to the different sources sn, excluding the speech waveform from a target source ss such that the mixed speech signal x' includes speech signals from the different sources sn, where n ∈ {1, . . . N}, and excludes the speech signal from the target source ss.” Steps (f) and (g) integrate the abstract idea into a practical application. The disclosure explains that devices that capture audio cannot properly distinguish different speech sources belonging to the same class and that the current available solutions do not adequately address this problem because they require a target user, whose speech is to be recognized, to explicitly interact with the device to provide training data. The disclosure states that this invention offers an improvement over existing speech-separation methods by providing a particular speech-separation technique that solves the problem of separating speech from different speech sources belonging to the same class, while not requiring prior knowledge of the number of speakers or speaker-specific training. The claim reflects the improvement discussed in the disclosure by reciting details of how the DNN aids in the cluster assignments to correspond to the sources identified in the mixed speech signal, which are then synthesized into separate speech waveforms in the time domain and converted into a mixed speech signal, excluding audio from the undesired source. See MPEP 2106.05(a). While on their own steps (b)-(e) recite judicial exceptions, steps (f) and (g) are directed to creating a new speech signal that no longer contains extraneous speech signals from unwanted sources. The claimed invention reflects this technical improvement by including these features. Further, converting clusters into separate speech waveforms and generating a mixed speech signal from the separate speech waveforms are not insignificant extra-solution activity, mere instructions to apply the exception, or mere field of use limitations. Rather, these steps reflect the improvement described in the disclosure. Accordingly, the claim is directed to an improvement to existing computer technology or to the technology of speech separation, and the
claim integrates the abstract idea into a practical application. (Step 2A, Prong Two: YES). The claim is eligible. (Step 2A: NO).
The type of details that are recited in the specification and claims is what allows the claim to be eligible. This is also true of claim 3 for Example 48. Unlike the claims discussed in Example 48 and the support for the claims in the specification, the current application is lacking many details that will help one of ordinary skill in the art reasonably understand the metes and bounds of the claim. The claims are recited at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
R. Puravankara and C. Narendra Babu, "Lead Forecasting using LSTM based Deep Learning Architecture for Sentiment Analysis," 2020 3rd International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 2020, pp. 159-164, doi: 10.1109/ICOIACT50329.2020.9332092. Lead Forecasting using LSTM based Deep Learning Architecture for Sentiment Analysis | IEEE Conference Publication | IEEE Xplore
Foreign Reference:
(DE 112022001848 T5) Kanjilal et al. Methods, Systems, Manufacturing Objects And Devices For Determining Product Similarity Values.
Methods, systems, articles of manufacture and devices for determining product similarity scores are disclosed. An example device includes circuitry for generating a calculation set for identifying a set of candidate comparison objects based on primary features corresponding to a focal object and a calculation set of objects from the set of candidate comparison objects based on secondary features, which correspond to the market performance, and a weight calculation circuit for calculating primary feature values corresponding to the focal object, the primary feature values being based on a uniqueness between the primary features corresponding to the focal object and the primary features corresponding to the Calculation set of objects correspond, based.
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/N.N.P/Examiner, Art Unit 3624 /HAMZEH OBAID/Primary Examiner, Art Unit 3624
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