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 .
Response to Amendment
The following Office Action is responsive to the amendments and remarks received on March 10, 2026.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1, claim 1 is directed to a system, claim 9 is directed to a method, and claim 17 is directed to a non-transitory computer readable medium. Thus, claims 1, 9, and 17 are directed to statutory categories of patentable subject matter.
At step 2A, prong 1, claim 1 recites, " A sales management system comprising: CRM data comprising: structured CRM data received from a structured CRM data source and indicative of attributes and stages of sales cycles; and unstructured CRM data received from an unstructured CRM data source, and comprising documents comprising textual data corresponding to electronic communications between clients and providers of sales cycles; and program instructions stored thereon that are executable by a processor to perform the following operations for conducting sales management: generating, based on the structured CRM data, a historical structured CRM dataset corresponding to a set of historical sales cycles, the historical structured CRM dataset indicative of attributes and stages of the set of historical sales cycles; training, using the historical structured CRM dataset, a stage identification model configured to identify a stage of a sales cycle based on structured CRM data; generating, based on the unstructured CRM data, a historical unstructured CRM dataset corresponding to the set of historical sales cycles, the historical unstructured CRM dataset comprising a set of documents comprising textual data corresponding to electronic communications between one or more clients and one or more providers associated with the set of historical sales cycles; determining, for each document of the set of documents: a stage of a sales cycle associated with that document; and a numerical representation of that document, wherein the numerical representation is included in a set of numerical representations corresponding to the set of documents and wherein the numerical representation comprises a document vector included in a set of document vectors; training, using the set of documents, a document-to-stage model configured to determine a stage of a sales cycle based on textual data of documents associated with the sales cycle; determining, for each document of the set of documents, a sentiment for that document; determining, based on the set of numerical representations, document clusters; determining, for each document cluster of the document clusters, a cluster word set for that document cluster by extracting words from documents of that document cluster; determining, for each document cluster of the document clusters based on the cluster word set for that document cluster, cluster attributes; training, using (a) the document-to-stage model, (b) the cluster attributes, and (c) the sentiments for the documents, a document model configured to determine current document attributes based on numerical representations of a set of current documents; training, using (a) the stage identification model and (b) the sentiments for the documents, a stage prediction model configured to determine attributes of a current sales cycle based on (i) a current sales cycle stage and (ii) current document attributes; obtaining a current CRM sales cycle dataset comprising: current sales cycle structured data indicative of attributes of a current stage of a current sales cycles; current sales cycle unstructured data comprising a current set of documents comprising textual data corresponding to current electronic communications between one or more clients and one or more providers of the current sales cycle; determining, based on application of the current sales cycle structured data to the stage identification model, a current stage of the current sales cycle; determining, based on the current sales cycle unstructured data, a current set of numerical representations of the current set of documents; determining, based on application of the current set of numerical representations of the current set of documents to the document model, a current set of attributes for the current sales cycle; and determining, based on application of (a) the current stage of the current sales cycle and (b) the current set of attributes for the current sales cycle to the stage prediction model, a predicted outcome for the current sales cycle, the predicted outcome indicative of a stage that the current sales cycle is predicted to reach; determining, based on the predicted outcome, a CRM plan configured to generate an improved outcome; and providing the predicted outcome and the CRM plan for display on a management dashboard.”
These limitations, under their broadest reasonable interpretations, recite Certain Methods of Organizing Human Activity. The claimed invention obtains data, generates dataset, determines a stage identification model, generates a dataset, determines a stage of a sales cycle, determines a document-to
stage model, determines a sentiment, determines document clusters, determines a cluster word set, determines cluster attributes, determines a stage prediction model, obtains a dataset, determines a current state, determines a current set of numerical representations, determines a current set of attributes, determines a predicted outcome, and determines and outputs a plan, which are sales activities and behaviors since communications between clients and providers are used to determine the stage of a sales cycle and to predict a plan. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all
of the claim elements individually, as a whole, and in ordered combination.
At step 2A, prong 2, the judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of "a customer relationship management (CRM) database", "a CRM engine comprising non-transitory computer readable storage medium", "a non-transitory computer readable storage medium," and “a graphical user interface.” These additional elements are generic computing elements performing generic computer functions such that it amounts to no more than mere instructions to apply the exception using a computer. The claims further recite “vectorizing textual data of that document,” “performing an unsupervised clustering operation on the set of document vectors,” and using trained machine learning models. These are generic computing functions recited at a high level of generality such that it amounts to no more than the mere instructions to apply the exception using a computer. Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
At 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 customer relationship management (CRM) database", "a CRM engine comprising non-transitory computer readable storage medium", and "a non-transitory computer readable storage medium" are generic computing elements as supported by at least paragraphs 103-105 of the specification. “Vectorizing textual data of that document,” is a known, generic computing function as supported by paragraph 65 of the specification, and “performing an unsupervised clustering operation on the set of document vectors” is a known, generic computing function as supported by paragraph 73 of the specification. Using trained machine learning models is equivalent to using the words “apply it” and the machine learning models as disclosed in the specification are known models rather than demonstrating any innovation within the models themselves. These additional elements are generic computing elements performing generic computer functions such that it amounts to no more than mere instructions to apply the exception using a computer. Therefore, the independent claims are not patent eligible.
Independent claims 9 and 17 recite essentially the same limitations as claim 1, and are so rejected using the same rationale.
Dependent claims 2-8 and 10-16, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1 and 9 without significantly more.
Claims 2 and 10 recite, "wherein the CRM plan defines a sales cycle action to be taken, the operations further comprising: executing, based on the CRM plan, the sales cycle action." The executing step is an additional element that is generically recite and does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea because it is generically recited.
Claims 3 and 11 recite, "wherein the stage of the sales cycle associated with a document is determined based on a supervised learning process comprising labeling of the document with the stage of the sales cycle." This is further limiting stage of the sales cycle of the independent claims and is part of the same abstract idea as the independent claims. The supervised learning process is an additional element that is generically recite and does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea because it is generically recited.
Claims 4 and 12 recite, "wherein the numerical representation of the document comprises a vector determined based on vectorization of the textual data of the document." This is further limiting the numerical representation of the independent claims and is part of the same abstract idea as the independent claims.
Claims 5 and 13 recite, "wherein the document-to-stage model comprises one or more of: a document-to-vector model configured to determine vectors based on textual data of one or more documents; and a vector-to-stage model configured to determine a stage based on vectors comprising numerical representations of documents." This is further limiting the document-to-stage model and the vector-to-stage model of the independent claims and is part of the same abstract idea as the independent claims.
Claims 6 and 14 recite, "wherein the cluster attributes for a document cluster comprises a subset of words of the cluster word set for the document cluster." This is further limiting the cluster attributes of the independent claims and is part of the same abstract idea as the independent claims.
Claims 7 and 15 recite, "wherein the numerical representations of the current set of documents comprise vectors representing the current set of documents." This is further limiting the numerical representation of the independent claims and is part of the same abstract idea as the independent claims.
Claims 8 and 16 recite, "wherein the numerical representations of the current set of documents comprise vectors determined by way of vectorization of the textual data of the current set of documents." This is further limiting the numerical representations of the current set of documents of the independent claims and is part of the same abstract idea as the independent claims.
Therefore, claims 1-17 are not patent eligible.
Response to Arguments
Applicant's arguments filed March 10, 2026 have been fully considered but they are not persuasive. Applicant argues that the abstract idea is integrated into a practical application through the limitations of vectorizing textual data, performing unsupervised clustering, extracting words, and using trained models. However, as addressed above, these additional elements are each shown to be known and generic computing functions. Converting data to a vector format and providing unsupervised clustering are both ways of using a computer to implement an abstract idea ‘better’ or ‘faster’ but do not show any improvement of the tool beyond generic use of a computer. Extracting words, as stated in the claims, is not actually recited as a technical element, and could be done by a human as part of the abstract idea. Using trained models is directly equivalent to using the words ‘apply it’ as the claims and specification only discuss use of models which are already known in the art, rather than demonstrating any improvement to the machine or model itself. While the claims as amended are incorporating more technology, they do so in a generic way that does not amount to more than applying the abstract idea using a computer, and therefore cannot be considered a practical application. Applicant’s argument regarding the Mental Process abstract idea is moot, because the only identified abstract idea above is Certain Methods of Organizing Human Activity.
Applicant further argues that the reasoning of Ex parte Carmody and Ex parte Desjardins is applicable here as well. Carmody is not precedential, so this argument is moot. Desjardins specifically focuses on improvements that were disclosed in the specification and reflected in the claims. Applicant has not pointed to any sections of the specification which may disclose improvements in line with the type shown in Desjardins, so this argument is not persuasive. Further, examiner has been unable to identify any teachings in the specification of improvements to the computer, or any language in the claims that refers to supposed improvements in the specification. Therefore, these arguments are not persuasive.
Conclusion
THIS ACTION IS MADE FINAL. 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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/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
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