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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 8, 2026 has been entered.
Status of the Claims
Claims 1, 4-14, and 17-26 are all the claims pending in the application.
Claims 1, 4-14, 18, and 20 are amended.
Claims 2, 3, 15, and 16 are cancelled.
Claims 21-26 are new.
Claims 1, 4-14, and 17-26 are rejected.
The following is a Non-Final Office Action in response to amendments and remarks filed July 8, 2026
Response to Arguments
Regarding the 101 rejections, the rejections are maintained for the following reasons. First, Applicant asserts the claimed training data improves how the machine learning model operates. Examiner respectfully does not find this assertion persuasive because the recent Board Decision in the present application is binding and that Decision did not find the training data reflected an improvement, see Patent Board Decision 2025-003045, dated Feb. 18, 2026, pgs. 12-13.
Second, Applicant asserts that estimating a company’s number of employees reduces processing, etc. Examiner respectfully does not find this assertion persuasive because an improvement in the abstract idea itself is not an improvement in technology, see MPEP 2106.05(a) (discussing Trading Techs.). That is, ¶[0043] of the Specification as filed states the prediction of employees improves the availability and/or granularity of data. Improving the availability and/or granularity of data is not an improvement in a technology because it is an improvement in the abstract idea (i.e., market research).
Third, Applicant asserts allocating resources based on the prediction reflects an improvement because the limitation uses machine learning to adjust the operation of the computer. Examiner respectfully does not find this assertion persuasive because assigning resources based on estimates is a part of performing market research (e.g., estimating companies’ sizes and revenues in the market to determine appropriate allocations of resources like money, hardware and people).
Fourth, similarly, Applicant assert claims 21-26 further reflect this improvement. Similarly, Examiner respectfully does not find this assertion persuasive because assigning resources based on estimates is a part of performing market research (e.g., estimating companies’ sizes and revenues in the market to determine appropriate allocations of resources like money, hardware and people).
Fifth, Applicant asserts the claimed training data improves data storage and data structures. Examiner respectfully does not find this assertion persuasive because the recent Board Decision in the present application is binding and that Decision did not find the training data reflected an improvement, see Patent Board Decision 2025-003045, dated Feb. 18, 2026, pgs. 12-13.
Sixth, Applicant asserts the present claims are distinct from Recentive because the present claims produce a machine driven adjustment. Examiner respectfully does not find this assertion persuasive because assigning resources based on estimates is a part of performing market research (e.g., estimating companies’ sizes and revenues in the market to determine appropriate allocations of resources like money, hardware and people).
Seventh, Applicant assert the claims are eligible under Step 2B because the claims improves entity attribute prediction and resource allocation. Examiner respectfully does not find this assertion persuasive because estimating company features and assigning resources based on estimates is a part of performing market research (e.g., estimating companies’ sizes and revenues in the market to determine appropriate allocations of resources like money, hardware and people) and an improvement in the abstract idea itself is not an improvement in technology, see MPEP 2106.05(a) (discussing Trading Techs.).
Eighth, Applicant asserts the record does not include evidence the training process in the claims are not well-understood, routine and conventional. Examiner respectfully does not find this assertion persuasive because the recent Board Decision in the present application is binding and that Decision did not find the training data reflected an inventive concept, see Patent Board Decision 2025-003045, dated Feb. 18, 2026, pgs. 12-15.
Ninth, Applicant asserts the allocating resources and new claims 21-26 recite meaningful limitations beyond a general link to technological environment. Again, Examiner respectfully does not find this assertion persuasive because assigning resources based on estimates is a part of performing market research (e.g., estimating companies’ sizes and revenues in the market to determine appropriate allocations of resources like money, hardware and people).
Accordingly the 101 rejections are maintained, please see below for the complete analysis of the claims as amended and the new claims.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-14, and 17-26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The independent claims recite the newly amended limitation (emphasized) “…assigning computing resources for the first entity based on the first prediction of the first number of employees in the first entity…” however there is no discussion, throughout the entirety of the specification and drawings, of assigning resources based on a prediction. For example, ¶[0077] of the Specification as filed discusses assigning network resources based on customer service requests, and ¶[0043] of the Specification as filed discusses the advantages of estimating a company's number of employees like improving the availability and/or granularity of data but the Specification does not discuss assigning resources based on the prediction.
As such, the Examiner asserts this as evidence that the newly amended claims 1, 14, and 20 are new matter. Accordingly claims 1, 14, and 20 are rejected under 35 USC 112(a). Claims 4-13, 17-19 and 21-26 do not overcome this rejection and accordingly are rejected under 112(a) due to their dependencies.
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, 4-14, and 17-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention. Applying Step 1 to the claims it is determined that: claims are directed to a process; and claims 1, 4-13 and 20 are directed to a machine and claim 14, 17-19 and 21-26 are directed to a process.
Independent Claims
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
The independent claims recite an abstract idea. Specifically, independent claim 1 recites an abstract idea in the limitations (emphasized):
…training a first machine learning model to predict a number of employees in a target entity at least by:
obtaining a training data set of historical data, each data point of the training data set comprising:
attributes of a particular entity, including: a particular industry and a number of employees;
a presence score associated with the particular entity; and
information about interactions in one or more forums referencing the particular entity;
training the first machine learning model based on a first subset of the training data set;
iteratively applying the first machine learning model to additional subsets of the training data set; and
updating the first machine learning model based on results generated by iteratively applying the first machine learning model to additional subsets of the training data set;
collecting features comprising a set of attributes of a first entity, wherein the set of attributes comprises an industry of the first entity and the presence score corresponding to a detected presence of the first entity in each of a set of forums, wherein the detected presence of the first entity is determined based on (a) a first number of interactions generated by, or referencing, at least one employee of the first entity in a first forum, the first number greater than one, and (b) a second number of interactions generated by, or referencing, the at least one employee of the first entity in a second forum, the second number greater than one;
applying the first machine learning model to the features to generate a first prediction of a first number of employees in the first entity; and
assigning computing resources for the first entity based on the first prediction of the first number of employees in the first entity.
These limitations recite an abstract idea because these limitations encompass managing personal behavior or relationships or interactions between people. These limitations managing personal behavior or relationships or interactions between people because these limitations essentially encompass performing market research. That is, gathering data on companies to assess their features and assigning resources based on the assessment is a part of performing market research (e.g., estimating companies’ sizes and revenues in the market to determine appropriate allocations of resources like money, hardware and people). Claims that recite commercial interactions (i.e., advertising, marketing or sales activities or behaviors) fall within the “Certain Methods Of Organizing Human Activity. Claims 1, 14, and 20 recite an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. Claim 1 recites the additional elements (emphasized):
…training a first machine learning model to predict a number of employees in a target entity at least by:
obtaining a training data set of historical data, each data point of the training data set comprising:
attributes of a particular entity, including: a particular industry and a number of employees;
a presence score associated with the particular entity; and
information about interactions in one or more forums referencing the particular entity;
training the first machine learning model based on a first subset of the training data set;
iteratively applying the first machine learning model to additional subsets of the training data set; and
updating the first machine learning model based on results generated by iteratively applying the first machine learning model to additional subsets of the training data set;
collecting features comprising a set of attributes of a first entity, wherein the set of attributes comprises an industry of the first entity and the presence score corresponding to a detected presence of the first entity in each of a set of forums, wherein the detected presence of the first entity is determined based on (a) a first number of interactions generated by, or referencing, at least one employee of the first entity in a first forum, the first number greater than one, and (b) a second number of interactions generated by, or referencing, the at least one employee of the first entity in a second forum, the second number greater than one;
applying the first machine learning model to the features to generate a first prediction of a first number of employees in the first entity; and
assigning computing resources for the first entity based on the first prediction of the first number of employees in the first entity.
These additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of training the machine learning by obtaining the claimed training data and applying the machine learning model, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited sufficiently broadly and generally (i.e., as generic machine learning) such that it amounts to no more than mere instructions to apply the exception.
Second, the additional elements of iteratively applying and updating the machine learning model, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because iterative training and dynamic adjustments are incident to machine learning, see Patent Board Decision 2025-003045, dated Feb. 18, 2026, pgs. 12-13 (discussing Recentive Analytics).
Third, claims 1 and 20 further recite the additional elements a “non-transitory computer readable medium comprising instructions” and “one or more processors; and memory storing instructions”, respectively. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Claims 1, 14, and 20 are directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent 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 amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claims 1, 14 and 20 are not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract idea for the following reasons.
Claims 4, 9-12 and 19 recite the additional elements of collecting and labeling training data or identifying the collected data. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (e.g., supervised machine learning techniques) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claim 5 recites the additional elements of updating a user interface based on configuration parameters. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (e.g., user interface design) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claims 7 and 17 recite the same abstract idea as the independent claims because scoring aspects of companies is a part of performing market research.
Claims 8 and 18 recites the additional elements of collecting and extracting data from a website. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (e.g., web-crawlers) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claim 13 essentially recites the additional elements of combining weight features when applying the machine learning model. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (i.e., common machine learning techniques like (linear regression, logistic regression, and neural network) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claims 21 and 22 recite the same abstract idea as the independent claims because assigning amounts of resources and types of resources based on predictions is a part of market research (i.e., determining appropriate allocations of resources like money, hardware and people).
Claim 23 recites assigning resources reduces inefficiencies. Examiner does not find this limitation substantially further limits the scope of the claim because it is only the intended use of the assigning, see MPEP 2103.I.C.
Claims 24 and 25 recites the same abstract ideas the independent claims because assigning resources based on requested resources (i.e., queries) is a part of managing personal behavior or relationships or interactions between people (i.e., allocating resources to customers based on customer activity).
Claim 25 further recites the scaling reduces processing, etc. Examiner does not find this limitation substantially further limits the scope of the claim because it is only the intended use of the scaling, see MPEP 2103.I.C.
Claim 26 recites the same abstract idea as the independent claims because monitoring data for verified services is a part of market research (e.g., determining the reliability of collected data).
Claim 26 further recites the additional elements of applying the machine learning model. These additional elements of, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited sufficiently broadly and generally (i.e., as generic machine learning) such that it amounts to no more than mere instructions to apply the exception.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626
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