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 .
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.
Response to Amendment
In the Amendment dated 12/22/2025, the following has occurred: Claims 1, 3, 11, and 13 have been amended.
Claims 1 – 20 are pending.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) subject matter within a statutory category as a process (claims 1 – 10) and manufacture (claims 11 – 20) which recite the abstract idea steps of
receiving a transcript of a conversation between a first party and a second party;
determining a first sentiment score by evaluating the transcript;
querying an engagement data database with an identification of the first party and receiving engagement data representing an engagement of the first party with assets associated with the second party;
evaluating the first sentiment score and the engagement data to output a value indicative of the likelihood of the first party to take a particular action; and
determining whether the value is above a threshold, and if so, sending a notification specifying shipping a product associated with the particular action to the first party.
These steps of claims 1 – 20, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. The Examiner understands the claimed invention, as a whole, in light of the Specification. Emphasis added.
[0068] 2.3 BENEFITS AND IMPROVEMENTS
[0069] The embodiments of this disclosure offer numerous benefits and improvements over prior approaches. The techniques of this disclosure are highly scalable as compared to custom scripting or other manual programming techniques. Embodiments of the computer systems allow enterprises to create or build unique models to determine the likelihood of a second enterprise or person taking a particular action. That likelihood allows for the enterprise to better manage its resources and direct products with a more intelligent supply chain flow. Moreover, the embodiments described herein improve the computing system and technological environment of the enterprise by allowing the enterprise to create or build unique, trained models in order to transform a large amount of raw data into score data and further into data that is usable by the computing system and administrators to implement new processes or take actions.
[0070] Furthermore, in past practice, human intuition or heuristics based on memory or feelings have been used to determine the propensity of an HCP to prescribe a pharmaceutical composition and/or determine the next best actions to take after each digital meeting. Using embodiments of the disclosure, data-driven sentiment scores based on evidence represented in meeting transcription data and digital engagement behavior of the HCP can be evaluated using machine learning models to more objectively and accurately predict the propensity, whether the propensity is increasing or decreasing, and/or the next best actions to take in relation to a particular digital meeting.
Therefore, understanding the claimed invention as a whole and in light of the Specification, the invention is directed toward providing better data for supplying prescriptions which are part of commercial transactions. The invention applies technology such as machine learning and natural language processing to this abstract idea. That technology provides benefits that are achieved by applying the abstract idea to technology. The invention does not invent the machine learning, the invention does not invent the natural language processing, the invention does not invent the sentiment score, but rather applies these tools to a field of use. There is no technical or technological improvement to a technical problem.
The result of the invention is data. That data has a potential usage but does not have a practical application.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2 – 10 and 12 – 20, reciting particular aspects of how ranking and sorting may be performed but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of cause the one or more processors to execute amounts to invoking computers as a tool to perform the abstract idea)
add insignificant extra-solution activity to the abstract idea (such as recitation of receive a … amounts to mere data gathering, recitation of querying a digital engagement amounts to selecting a particular data source or type of data to be manipulated, recitation of automatically sending … amounts to insignificant application, see MPEP 2106.05(g))
Please see how the Specification describes the invention applied to technology.
[0033] In an embodiment, a computer system 100 comprises components implemented at least partially by hardware at one or more computing devices, such as one or more hardware processors executing stored program instructions stored in one or more memories for performing the functions described herein. In other words, all functions described herein are intended to indicate operations that are performed using programming in a special-purpose computer or general-purpose computer in various embodiments. FIG. 1 illustrates only one of many possible arrangements of components configured to execute the programming described herein. Other arrangements may include fewer or different components, and the division of work between the components may vary depending on the arrangement.
The Specification does not describe the invention as a technical or technological improvement. The improvements, from paragraph 6, “As such, an automated method of analyzing these records to predict events and react accordingly is needed to improve the operation of the enterprise and its supply chain.” This idea of improving supply chain analysis is repeated, as quoted above, in paragraphs 69 and 70.
The Examiner specifically is pointing out this limitation as describing the technology as a tool. The development of the machine learning model is not disclosed but the result is described at a high level using functional terms.
wherein the trained machine learning model has been trained by querying a database of domain data to select domain data comprising data elements based on the natural language transcript, the first party and/or the second party within the natural language transcript,
the domain data comprising labeled conversation data of conversations using terminology and a taxonomy of sentiments,
the domain data comprising a digitally stored data table storing a plurality of words or phrases and one or more associated scores that are unique to a category of emotional data, and fine-tune trained using the domain data to form the trained machine learning model to accept the natural language transcript as an input, predict or classify emotional content of one or more portions of the natural language transcript related to the first party, and output a sentiment score, the sentiment score representing a likelihood of the first party to take an action;
First, the machine learning is described as already been trained “the trained machine learning model has been trained by…” The training of the machine learning model is not claimed.
Second, the training data required is claimed at a high level using non-specific terms as, “the domain data comprising labeled conversation data of conversations using terminology and a taxonomy of sentiments.” What is required in the training data is not disclosed or claimed.
Third, how the training occurs is not claimed or disclosed.
Fourth, the outcome of the model is described at a high level using functional terms, “to select domain data comprising data elements based on the natural language transcript.”
The result is that the claims and the disclosure describe generic tool requirements but do not describe the specific creation of that tool.
As these tools are described passively and not positively being created or not positively used, the Examiner does not believe that a 112(a) rejection is required.
The result of the invention is data.
[0062] At decision block 207, the value is compared to a predetermined threshold. If the value is not greater than the predetermined threshold, then the value may be stored in a profile of the first party within a database at block 209. If the value is greater than the predetermined threshold, then a notification to a user computing device associated with the second party is transmitted at block 208. In some embodiments, the predetermined threshold may be manually set by an administrator or automatically updated based on rules associated with the second party. For example, the threshold may be adjusted based on a number of products associated with the particular action that the second party has in inventory, a price of the particular products, a number of available samples, and so on. The notification may be in the form of a push notification, an alarm, an email, a text message, etc. The notification is configured to notify personnel that the particular action is likely to be taken. In some embodiments, the second computer may automatically submit an order to the second computer if the value is above the threshold, where the order specifies shipping a product associated with the event to the first party.
Note how the claimed invention and the Specification agree that the data is either stored or transmitted. Therefore the Examiner believes that there is no practical application. Even if the data is “a push notification, an alarm, an email, a text message, etc,” that data only has a function if and only if it is acted upon.
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 – 10 and 12 – 20, additional limitations which amount to invoking computers as a tool to perform 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 a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
For example claims 3 and 13 describe the invention having a “model has been selected from a FLARE model, a spaCY model, a BERT model, a RoBERTa model, or a ClinicalBERT model.” The invention does not create these models but uses them as tools. This is further emphasized in MPEP 2106.05(f)(1)
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described “the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’” 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of “collecting, displaying, and manipulating data.” 850 F.3d at 1340; 121 USPQ2d at 1946. In addition to the abstract idea, the claims also recited the additional element of modifying the underlying XML document in response to modifications made in the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the claims purported to modify the underlying XML document in response to modifications made in the dynamic document, nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea in the context of XML documents. The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words “apply it”. 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims “so result focused, so functional, as to effectively cover any solution to an identified problem”)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as 1 – 20; receiving and sending, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); determining a score, evaluating the score, determining if the score is above a threshold, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii))
Additional elements:
Computer – paragraph 33 “In an embodiment, a computer system 100 comprises components implemented at least partially by hardware at one or more computing devices…” paragraph 72 The computing devices may be server computers, workstations, personal computers, portable computer systems, handheld devices, mobile computing devices…
natural language – It should be emphasized that the Specification does not show how the natural language processing occurs. There is an inference in paragraph 48, “performing a machine learning algorithm on the noise in order to transform the noise into natural language text” but the details of the model, the details of the specific training of that model including training data is absent.
Machine learning model - paragraph 39 states that the model exists, “The machine learning model 170 is a trained machine learning model that has been built, for example, using the build instructions machine learning model …” Paragraph 39 goes on to state, “For example, the machine learning model 170 may be” … “Or, the machine learning model can be any of the transformer-based machine learning models such as…”
single-shot detection or singleshot detection or SSD – This is only described in paragraph 57 as, “The trained machine learning model may evaluate all the transcript data, for example, using singleshot detection (SSD), and automatically output the first sentiment score representing the likelihood of the first party to perform a particular action.”
database – paragraph 37 a commercially available …
program / instructions – paragraph 78 the instructions may comprise…
networking – paragraphs 36, 72 “The computing devices may be server computers, workstations, personal computers, portable computer systems, handheld devices, mobile computing devices, wearable devices, body-mounted or implantable devices, smartphones, smart appliances, internetworking devices… paragraph 88 Computer system 800 also includes a communication interface 818 coupled to I/O subsystem 802. Communication interface 818 provides a two-way data communication coupling to network link(s) 820 that are directly or indirectly connected to at least one communication networks, such as a network 822 or a public or private cloud on the Internet…
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 – 10 and 12 – 20, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, trained machine learning model, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii)). 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 a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Response to Arguments
Applicant's arguments filed 12/22/2025 have been fully considered but they are not persuasive.
B. All Claims Recite Eligible Subject Matter
The Applicant states, “While the Office action references selected parts of the specification, the Office conspicuously omits all the parts of the specification that mandate a technical interpretation that the PHOSITA would adopt.” The Examiner’s point is that the abstract idea is applied to technology to achieve all the benefits of applying that abstract idea to technology.
The Applicant further states, “Applicant expressly disclaims any claim interpretation that could cover an abstract idea or other judicial exception, confining the claim interpretation to the technical embodiments of the disclosure.” The Examiner understands that the Applicant applied the abstract idea to technology. An automated abstract idea is still an abstract idea.
Step 2A, including Prong l(a) and Prong l(b) - "Directed To" Assessment, Identification of Limitations, Subject Matter Groupings
The Applicant states, “The underlined limitations direct the step away from any abstract interpretation to a technical interpretation.” The Examiner understands that a technical interpretation is an abstract idea applied to technology.
The Applicant states, “The approach of the Office action is not to quote and consider the pending claim language, but to selectively delete claim limitations and reject the written claim clauses.” It is the Examiner’s point to show the Abstract idea separate from the technology applied.
The Applicant states, “The claimed attributes of the training data represent data science discoveries, specifically new feature engineering, that contribute to inventiveness for purposes of 35 U.S.C. § 101 and further remove any chance of monopolizing an abstract idea.” The Applicant’s opinion is noted. The Examiner is bound by what is included in the originally filed Specification.
The Applicant states, “In that case, the Office had no problem determining that the invention provided technical improvements to the technical problem of automated natural language transcript analysis, rather than merely providing better data.” The Examiner takes each application on its own give the current guidance.
The Applicant further states, “CardioNet, LLC v. InfoBionic, Inc. , 955 F.3d 1358 (Fed. Cir. 2020). The Court of Appeals for the Federal Circuit held that claims for a cardiac monitoring device were patent-eligible because they did more than just collect and analyze data; they utilized a specific method to…” The Examiner notes several things from the CardioNet decision. First, the invention regards the transformation of a cardiac signal, “an event generator to generate an event when the variability in the beat-to-beat timing is identified as relevant to the at least one of atrial fibrillation…” Second the decision states, “The written description confirms our conclusion. It explains that, by identifying “variability in the beat-to-beat timing . . . as relevant to the at least one of atrial fibrillation and atrial flutter in light of the variability in the beat-to-beat timing caused by ventricular beats identified by the ventricular beat detector,” the claimed invention achieves multiple technological improvements” Third, and even more important, the Court states, “. The court need not consult the prior art to see if, in fact, the assertions of improvement in the patent’s written description are true.” The Court repeatably looks toward the Written Description for guidance and here also does the Examiner. It is the written description that shows that this invention is directed towards
[0070] Furthermore, in past practice, human intuition or heuristics based on memory or feelings have been used to determine the propensity of an HCP to prescribe a pharmaceutical composition and/or determine the next best actions to take after each digital meeting. Using embodiments of the disclosure, data-driven sentiment scores based on evidence represented in meeting transcription data and digital engagement behavior of the HCP can be evaluated using machine learning models to more objectively and accurately predict the propensity, whether the propensity is increasing or decreasing, and/or the next best actions to take in relation to a particular digital meeting.
The Specification repeats that the technology improves human output.
The Applicant states, “All the benefits and improvements of this paragraph are technical and unrelated to "better data" or non-technical advantages because a process of selecting the right trained ML model, extracting transcript data in a better way, and accomplishing a result with the data, which otherwise would have been available, provides improved computer system operation.” It is the Applicant’s opinion that these are technical improvements or technological improvements to a technical problem. However, the Specification does not match the Applicant’s opinion. The Examiner believes that paragraph 69 does not show a technical improvement because it only shows “allowing the enterprise to create or build unique, trained models in order to transform a large amount of raw data into score data and further into data that is usable by the computing system and administrators to implement new processes or take actions.” Allowing the potential creation of models is different from the Specification disclosing the creation.
Step 2A, Prong 2 and Step 2B - The Additional Elements Integrate any Judicial Exception into a Practical Application; the Additional Elements Amount to Significantly More Than any Exception
The Applicant states, “These limitations place the claimed process within the context of distributed computing, utilizing specific technical means for machine communication. Furthermore, they embody the practical application of selecting a trained ML model, among many, unique to the parties identified in a natural language transcript after executing record analysis instructions.” The Examiner believes that the Applicant is describing a mathematical concept applied to technology.
The Applicant is using “distributed computing” as a tool to apply technology. The Specification only uses the phrase “distributed computing” and does not describe the specific network architecture of the computers.
The Applicant states, “The Applicant respectfully submits that all these limitations provide the practical application of machine learning and natural language transcript analysis to determine specific insights from a meeting or other event that otherwise would be impossible. The Examiner and his SPE disagree.
The Applicant states, “The Office's critique of the specification and claims, in paragraphs 10-14 of the action, as excessively functional (in the Office's opinion), stands in tension with the cases of the Court of Appeals for the Federal Circuit that have identified eligible subject matter when the functioning of the computer is improved.” The Examiner does not see a specific quotation from the Specification that describes that the “functioning of the computer is improved.” The Examiner notes that the invention is applied to existing technology.
The Applicant further states, “A new process by which the computer operates to accomplish a useful result in a technical context is sufficient.” The Examiner notes that “a new process by which the computer operates” does not improve the functioning of the computer. The computer functions the same whether this application is operating or if the application stops operating.
The Applicant states, “The Office does not appear to accept that a new process of deriving useful data can be eligible. But nothing in the law, rules, or case decisions requires the "practical application" or "significantly more" to involve using the data developed in a process. Instead, the accomplishments of the process, of whatever kind, can be the practical application if they improve the usefulness of the inputs in some way-and that occurs in these claims.” The Examiner knows that Statestreet allowed for the patenting of processes and that Alice restricted the patentability of those processes. The Examiner notes that it was Diehr that explained what a practical application could be and multiple other cases have described significantly more. In the instant case, the Examiner and his SPE disagree that something significantly more is claimed.
The Applicant states, “Finally, this Office action does not reject the claims under 35 U.S.C. § § 102 or 103, since…” The Examiner that novel and non-obvious abstract idea is still an abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Moudy et al Pub. No.: US 2016/0300135 Applications of text mining and sentiment analysis systems can be applied in various fields including market research, customer experience management, and social media monitoring applications.
Gupta et al Pub. No.: US 2020/0065848 A device may obtain customer data, associated with a customer identifier, that includes an indication of a recency of a past purchase, a frequency of past purchases, and/or a monetary value associated with past purchases by a customer associated with the customer identifier.
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 Neal R Sereboff whose telephone number is (571)270-1373. The examiner can normally be reached M - T, M - F 8AM - 6PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571)272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NEAL SEREBOFF/
Primary Examiner
Art Unit 3626