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
In light of Applicant's submission filed June 25, 2025, the Examiner has maintained and updated the 35 USC § 101 and 103 rejections.
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 - 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) the following limitations that are considered to be abstract ideas:
Claims 1
training with historical feature vector inputs to generate a service score output, wherein the historical feature vector inputs include historical service data structures specific to multiple historical entities, and historical structured firmographic data; preprocessing the historical feature vector inputs by grouping the historical feature vector inputs according to predetermined weight of evidence values to create binary dummy variable inputs, performing a bivariate analysis to determine an association between at least one dependent historical feature vector input and at least one independent historical feature vector input, performing a stratified sampling based on the bivariate analysis, and training the machine learning model using the binary dummy variable inputs;
obtaining a set of entities; for each entity in the set of entities: obtaining structured firmographic data associated with the entity from a structured firmographic database;
generating a feature vector input according to the obtained structured firmographic data; processing, by the machine learning model, the feature vector input to generate the service score output, wherein the service score output is indicative of a likelihood that the entity is a service-providing entity; and
selectively including the entity in a subset of entities based on a comparison of the service score output to a threshold value;
identifying a set of targets associated with the entity; and automatically distributing structured campaign data to the identified set of targets
The limitations of independent claim 1, 8, and 15 as detailed above, as drafted, falls within the “Certain Methods of Organizing Human activity because the claims have concepts of managing interactions between people. The applicant’s claims depend upon information derived from people driving the transports or people using other devices to transmit the information needed. Accordingly, the claims recite an abstract idea This judicial exception is not integrated into a practical application. In particular the claims recite the additional elements of using a machine learning model. The aforementioned additional generic computing elements perform the steps of the claims at a high level of generality (i.e. As a generic medium performing generic computer function of training, obtaining, generating, processing, selecting, identifying, distributing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 machine learning model amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Thus, taken individually and in combination, the additional elements do not amount to
significantly more than the above-identified judicial exception (the abstract idea). (i.e. “PEG”
Step 2B=No) The dependent claims 2-9 appear to merely further limit the abstract and as such, the analysis of dependent claims 2-9 results in the claims “reciting” an abstract idea The claims the claims do not recite additional elements that integrate the exception into a practical application the additional elements do not amount to an inventive concept (significantly more) other than the above-identified judicial exception (the abstract idea). Thus, based on the detailed analysis above, claims 1 - 9 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3, 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schwarm et al. (US 2018/0101771) in view of Miranda et al. (US 2018/0357299).
Claim 1: Schwarm discloses a computerized method of automatic distributed communication, the method comprising:
training a machine learning model with binary dummy variable inputs to generate a service score output, by obtaining historical feature vector inputs include historical service data structures specific to multiple historical entities, and historical structured firmographic data; (see for example [0083], the robust firmographic data mapped to the company win data and, if available, loss data. Where the company win loss database 301 includes employee identifiers such as name data and employee title data, the system can be configured to map data from the business entity database to company employee identifiers such as names and/or titles when compiling the training database. In at least one of the various embodiments, if the company name is not identified in the business entity database, the system can be configured to generate an identifier and a firmographic database for the company. Non limiting examples of company entity data mapping, generating firmographic databases and scoring for companies, [0084] identify one or more attractive classes and one or more unattractive classes. The classification engine can be configured to train the classifier on the training database from the data inputs from the company data from the client company win/loss database 301 and the business entity information database 304. The classification modeler can include a machine learning model builder such as, for example, a decision tree, a random forest decision tree, a cluster modeler, a K Means cluster modeler, Neural Nets, Gradient Boosted Trees machine, and Support Vector Machines (SVM)., and [0085] , the classification engine trains a customer profile classifier on the training database 306 using K-means clustering as described herein. The classifier modeler then outputs the profile classifier trained on client “wins” to run on business entity database 304 to generate optimized company lists of targeted companies as described with respect to FIG. 4. For example, at operation 605 the system can be configured to run the company profile classifier on the business entity database 304 to classify companies into customer profiles and output the results to a user.)
obtaining a set of entities; [0083-0085] for each entity in the set of entities: obtaining structured firmographic data associated with the entity from a structured firmographic database;([0083], the robust firmographic data mapped to the company win data and, if available, loss data. Where the company win loss database 301 includes employee identifiers such as name data and employee title data, the system can be configured to map data from the business entity database to company employee identifiers such as names and/or titles when compiling the training database. In at least one of the various embodiments, if the company name is not identified in the business entity database, the system can be configured to generate an identifier and a firmographic database for the company. Non limiting examples of company entity data mapping, generating firmographic databases and scoring for companies)
generating a feature vector input according to the obtained structured firmographic data; processing, by the machine learning model, the feature vector input to generate the service score output, wherein the service score output is indicative of a likelihood that the entity is a service-providing entity;(see for example[0120], AI predictive analytics for targeted classification and scoring the likelihood of companies' and/or company employees responding to marketing messages in accordance with at least one of the various embodiments. [0126], probability score for a message response can be determined and weighted by firmographic data and messaging classes tied to classifiers trained on client provided response wins and losses and non-customer/other company firmographics and messaging. The AI machine learning is configured to train the classifier to identify and assign different classes, for example for different channels and message types their own class, and generate scores as described herein. (also see [0115])) and
selectively including the entity in a subset of entities based on a comparison of the service score output to a threshold value;(see for example [0132], configured to allow a user to initiate a search of scored prospect companies or employees. The search can be performed categories, for example firmographic categories such as industry or region. The interface 800 also includes a Profile search object 802 where the user can employ drop down menus to select a sub-category 803 (e.g. Site, Domestic, Global) and select search operators 804 (e.g., “is Primarily” “Includes,” “Excludes”) as well as a sub-category field 806 to select and define one or more sub-categories (e.g. industry sectors, sales range, employee count, legal status, employee title) for the company or company personnel profile). The Profile Search object 802 also includes an Include Unknown object 805 for including companies in the results for the search parameters and filters are unknown. The interface 800 comprises a Prospect Score area 808 configured to allow the user to receive and prioritize predictive scores. The Prospect Score area 808 includes drop down filters for Filter by 809, Filter 810 (e.g. Product, email response rate, marketing response), and Result 811 (e.g. % of Highest Scored Records, companies meeting or exceeding a probability score threshold, a count threshold). The Prospect Score area 808 includes a graph 812, shown as a bar graph, displaying the scoring results for the companies. The interface 800 is configured to allow the client user to save the Profile of prioritized prospects via a Save Profile object 814. The interface also includes a Clear Search 815 object configured to allow the user to clear the search results of scored prospects.) and for each entity in the subset of entities:
identifying a set of targets associated with the entity; ([0127], quality assured firmographic data from a business entity information database 304 as described herein. Once the new customer database is enriched with firmographic data, at operation 506 the prediction engine is configured to employ the trained classifier to calculate a probability score for companies likely to respond to a message and by what channels. Also see[0128]) and automatically distributing structured campaign data to the identified set of targets.([0129], discloses s configured to allow a client user to provide a list of companies and addresses and win/loss data as described above on which the system trains a company profile classifier, for example using a K-Means clustering component. The system then runs the classifier on data from a company entity database of robust firmographic data to produce a plurality of profiles 701a, 701b, 701c, 701d produced from “win” classes. Each profile 701a, 701b, 701c, 701d includes profile classifications 702 for: market opportunity, number of prospect companies, average sale/deal, and the client user's market share. The interface 700 thus allows the client user to identify the win based profiles trained on the client user's own customers, weighted for highest probability of success, with the best market opportunity, highest average deal per customer, and the client users market share within each profile.) but does not explicitly disclose preprocessing the historical feature vector inputs by grouping the historical feature vector inputs according to predetermined weight of evidence values to create binary dummy variable inputs, performing a bivariate analysis to determine an association between at least one dependent historical feature vector input and at least one independent historical feature vector input, performing a stratified sampling based on the bivariate analysis, and training the machine learning model using the binary dummy variable inputs; However Miranda discloses preprocessing the historical feature vector inputs by grouping the historical feature vector inputs according to predetermined weight of evidence values to create binary dummy variable inputs, performing a bivariate analysis to determine an association between at least one dependent historical feature vector input and at least one independent historical feature vector input, performing a stratified sampling based on the bivariate analysis, and training the machine learning model using the binary dummy variable inputs;( [0015], An identification and management system for transactional activities may be used to process large volumes of transactional data in the form of log entries. The system may minimize the computational complexity of statistical learning by reduction in the volume of log entries. This reduction is performed by the system using a systematic and repeatable approach for filtering raw transaction historical data to generate closed log entries which are stored in a reference dataset in a modeling database. The filtering is performed in order to reduce computational complexity in determining, based on the reference dataset, groupings and categorization of open log entries received by the system. The system may also statistically sample and perform efficient vector-representation of the closed log entries generated from the historical data. Using these techniques, the system may automatically perform choice-based stratified sampling of the open log entries. The choice-based stratified sampling may be leveraged to correct for any bias towards selection of dominant categories within which the open log entries are grouped. Correction may be based on adjusting the groupings of the open log entries. For example, the categorization of open log entries may include supervised learning based on a frequency of occurrence of a predetermined target variable or label in the open log entries. Open log entries which are grouped and categorized by the system may be further refined using supervised learning. A confidence level for the categorization assigned to the respective open log entries may also be generated for each of the open log entries. The system may perform further operations to maximize average grouping confidence
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to modify, Schwarm to include preprocessing the historical feature vector inputs by grouping the historical feature vector inputs according to predetermined weight of evidence values to create binary dummy variable inputs, performing a bivariate analysis to determine an association between at least one dependent historical feature vector input and at least one independent historical feature vector input, performing a stratified sampling based on the bivariate analysis, and training the machine learning model using the binary dummy variable inputs, in order to analyze for accuracy and the results of the analysis may be used to train the statistical model (abstract Miranda) Claim 3: Schwarm discloses the method of claim 2 wherein the training includes removing each historical entity having a number of employees below an employee count threshold. [0101 and 0132]
Claim 7: Schwarm discloses the method of claim 5 wherein the training includes at least one of: but does not explicitly disclose
preprocessing the historical structured firmographic data to transform one or more variables associated with the historical structured firmographic data into binary dummy variables; performing a bivariate analysis to determine an association between at least one dependent variable and at least one independent firmographic variable; and performing a stratified sampling of a subset of historical entities that have been classified as non-service-providing entities.
However Miranda discloses preprocessing the historical structured firmographic data to transform one or more variables associated with the historical structured firmographic data into binary dummy variables; [0015]
by grouping the historical feature vector inputs according to predetermined weight of evidence values and marginal information values [0015]
performing a bivariate analysis to determine an association between at least one dependent variable and at least one independent firmographic variable; [0015] and
performing a stratified sampling of a subset of historical entities that have been classified as non-service-providing entities.[0015]
Furthermore, because each individual element and its function are shown in the prior art, albeit in different references or embodiments, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself––that is in the substitution of a binary dummy variables disclosed by Miranda for the data to be used by the applicant. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim 8: Schwarm discloses the method of claim 1 wherein the machine learning model includes a random forest machine learning model. [0009]
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schwarm et al. (US 2018/0101771) in view of Miranda et al. (US 2018/0357299) and in further view of Subramanian et al. (US 2020/0226503)
Claim 2: Schwarm discloses the method of claim 1 wherein the training includes:but does not explicitly disclose classifying each one of the multiple historical entities as a service- providing entity or a non-service-providing entity according to the historical service data structures; and training the machine learning model using the classifications for supervised learning. However Subramanian disclose classifying each one of the multiple historical entities as a service- providing entity or a non-service-providing entity according to the historical service data structures; and training the machine learning model using the classifications for supervised learning.([0002-0004, 0019, and 0020]) Therefore it would have been obvious to one of ordinary skill in the art at the time of the invention, for Schwarm to have included classifying each one of the multiple historical entities as a service- providing entity or a non-service-providing entity according to the historical service data structures; and training the machine learning model using the classifications for supervised learning, since doing so could be performed readily and easily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results.
Claim(s) 4 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schwarm et al. (US 2018/0101771) in view of Miranda et al. (US 2018/0357299) in view of Subramanian et al. (US 2020/0226503) and in further view of Hummer et al. (US 2011/0295614)
Claim 4: Schwarm discloses the method of claim 2 but does not explicitly disclose wherein the classifying includes classifying a historical entity as a service-providing entity in response to determining that the historical entity is enrolled in an employer-sponsored health insurance database. However Hummer discloses wherein the classifying includes classifying a historical entity as a service-providing entity in response to determining that the historical entity is enrolled in an employer-sponsored health insurance database. [0026]
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to modify, Schwarm Miranda, and Subramanian to include wherein the classifying includes classifying a historical entity as a service-providing entity in response to determining that the historical entity is enrolled in an employer-sponsored health insurance database, in order to verify the identity of the member or the dependent to prevent fraud ([0026], Hummer)
Claim 5: Schwarm discloses the method of claim 2 wherein the classifying includes: Hummer discloses identifying a consumer enrolled in an individual family plan (IFP) database;[0026] determining one of the multiple historical entities that employs the identified consumer; [0026] Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to modify, Schwarm Miranda, and Subramanian to include identifying a consumer enrolled in an individual family plan (IFP) database, in order to verify the if a person is part of a family plan or an individual plan. ([0026], Hummer) Schwarm and Hummer do not explicitly disclose classifying the determined historical entity as a non-service-providing entity.
However Subramanian discloses classifying the determined historical entity as a non-service-providing entity. ([0002-0004, 0019, and 0020]), Therefore it would have been obvious to one of ordinary skill in the art at the time of the invention, for Schwarm, Miranda and Hummer to have classifying the determined historical entity as a non-service-providing entity, since doing so could be performed readily and easily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schwarm et al. (US 2018/0101771) in view of Miranda et al. (US 2018/0357299) in view of Subramanian et al. (US 2020/0226503) in further view of Hummer et al. (US 2011/029514) in further view of Mun et al. (US 2013/0246086)
Claim 6: Schwarm discloses the method of claim 5 wherein the determining includes: but does not explicitly disclose determining whether the identified consumer is a full-time employee; and only determining the one of the historical entities that employs the identified consumer and classifying the determined historical entity in response to the identified consumer being a full-time employee.
However Mun discloses determining whether the identified consumer is a full-time employee;[0010-0011] and only determining the one of the historical entities that employs the identified consumer and classifying the determined historical entity in response to the identified consumer being a full-time employee.[0010-0011]
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to modify, Schwarm Miranda, and Subramanian, and Hummer to include determining whether the identified consumer is a full-time employee; and only determining the one of the historical entities that employs the identified consumer and classifying the determined historical entity in response to the identified consumer being a full-time employee, in order to verify that the employer is classified as a large employer and thereby required to provide insurance to the employee([0010], Mun)
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schwarm et al. (US 2018/0101771) in view of Miranda et al. (US 2018/0357299) and in view of Taghavi et al. (US 2017/0237792)
Claim 9: Schwarm discloses the method of claim 1 but does not explicitly disclose further comprising training a second machine learning model to identify consumers employed by entities according to predictor variables associated with the consumers, wherein the identifying includes processing a plurality of consumers with the second machine learning model to identify the set of targets associated with the entity. However Taghavi discloses training a second machine learning model to identify consumers employed by entities according to predictor variables associated with the consumers, wherein the identifying includes processing a plurality of consumers with the second machine learning model to identify the set of targets associated with the entity. [0032, 0061, 0082]
Furthermore, because each individual element and its function are shown in the prior art, albeit in different references or embodiments, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself––that is in the substitution of a data derived from a second machine learning model as disclosed by Taghavi for the data to be used by the applicant. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Response to Arguments
Applicant's arguments filed June 25, 2025 have been fully considered but they are not persuasive.
The applicant argues the 35 USC 101 in regards to Step 2A Prong one, that the claims do not have concepts that fall with in Certain Methods of Organizing human activity. The Examiner respectfully disagrees the claims use machine learning to organize/manage how service entities are evaluated. Specifically managing commercial interactions through scoring service entities based on historical and firmographic data. The abstract idea appears to be implemented using generic computer components (machine learning model) performing data manipulation (weight of evidence, dummy variable creation, bivariate analysis, and stratified sampling, training) which are themselves mathematical concepts(see updated rejection above). Thus the applicant’s claims are directed to Mathematical Concepts and Certain Methods of Organizing Human Activity. See MPEP 2106.04(a)(2)(I) - The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The Supreme Court has identified a number of concepts falling within this grouping as abstract ideas including: a procedure for converting binary-coded decimal numerals into pure binary form, Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972); a mathematical formula for calculating an alarm limit, Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ2d 193, 195 (1978); the Arrhenius equation, Diamond v. Diehr, 450 U.S. 175, 191, 209 USPQ 1, 15 (1981); and a mathematical formula for hedging, Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ 2d 1001, 1004 (2010). And • a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); The applicant further argues the 101 rejection that the claims are directed to integration into a practical application, due to specific improvements to machine learning model. While the applicant has cited [0248] as an improvement. The Examiner respectfully disagrees, [0248] merely explains mathematical concepts. [0248], does not teach or suggest that an improvement is present to a person of ordinary skill in the art. Furthermore the machine learning model is part of the abstract idea. The abstract idea can not provide the improvement. Per MPEP 2106.05(a)(2) - However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Also, MPEP 2105.05(a) - If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016). The applicant has not provided a citation that identifies a technical problem and explains the details of an unconventional solution. The applicant argues that the claims provide meaningful limits beyond generally linking the judicial exception, the Examiner respectfully disagrees per MPEP 2106.05(e) - When evaluating whether additional elements meaningfully limit the judicial exception, it is particularly critical that examiners consider the additional elements both individually and as a combination. When an additional element is considered individually by an examiner, the additional element may be enough to qualify as "significantly more" if it meaningfully limits the judicial exception, and may also add a meaningful limitation by integrating the judicial exception into a practical application. However, even in the situation where the individually-viewed elements do not add significantly more or integrate the exception, those additional elements when viewed in combination may render the claim eligible. See Diamond v. Diehr, 450 U.S. 175, 188, 209 USPQ2d 1, 9 (1981) ("a new combination of steps in a process may be patentable even though all the constituents of the combination were well known and in common use before the combination was made"); BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341, 1349, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016). It is important to note that, when appropriate, an examiner may explain on the record why the additional elements meaningfully limit the judicial exception. The applicant’s claims as stated above when considering the additional elements individually and as a combination do not have indications of integration into a practical application. The limitation the applicant has cited “..by processing feature vector inputs using the training machine learning model”, do not indicate improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a) The applicant argues that it provides new functionality and therefore the abstract idea is practically applied to the specific and unique implementation by the claims, the Examiner respectfully disagrees per MPEP 2106.05 - Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016) ("The inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art. . . . [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces."). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. The distinction between eligibility (under 35 U.S.C. 101 ) and patentability over the art (under 35 U.S.C. 102 and/or 103 ) is further discussed in MPEP § 2106.05(d)
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The applicant’s claims do not appear to have limitations that are indicative of integration into a practical application. Thus the 35 USC 101 rejection is maintained.
Applicant’s arguments with respect to claim(s) 1-9 have been considered but are moot due to the updated rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARNELL A POUNCIL whose telephone number is (571)270-3509. The examiner can normally be reached Monday - Friday 10:00 - 6:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.A.P/Examiner, Art Unit 3622
/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
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