DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to a response filed 19 December 2025, on an application filed 27 February 2023, which claims domestic priority to a provisional application filed 31 March 2022.
Claims 1, 5, 8, 10 and 16 have been amended.
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation 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 35 U.S.C. 112 (pre-AIA ), first paragraph:
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 1-20 are rejected under 35 U.S.C. 112, 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(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 10 and 16 recite the limitation … to reduce computational latency relative to rules-based record analysis systems. The patent application does not provide adequate support for this limitation. For example, the specification, in paragraph [0027] states “The trained ML model can then be used to rapidly, and computationally cheaply, during an online inference phase to perform the prediction(s).” The specification does not, however, disclose an adequate support for the claimed limitation. Therefore, one skilled in the art of healthcare intervention, upon reading the specification, would not conclude that the inventor had possession of the claimed inventions on the day the application was filed.
To the extent that other claims rely on claims that are rejected under 35 USC 112 and fail to correct the deficiencies of the claims they rely on, those other claims are rejected for the same reasons as the claims they rely on. Appropriate correction is required.
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.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-9 are drawn to a method, which is within the four statutory categories (i.e. process). Claims 10-15 are drawn to an apparatus, which is within the four statutory categories (i.e. machine). Claims 16-20 are drawn to a non-transitory computer-readable medium, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 1 recites: A method, comprising:
determining, by a first machine learning (ML) model, a plurality of attributes of a textual description of a progress note for a patient medical examination, comprising analyzing the textual description using a trained natural language parsing model configured to output feature vector data representing parsed symptom, diagnosis, and treatment attributes;
predicting, by a second ML model distinct from the first ML model, an error in the progress note and a change to an electronic health record (EHR) for the patient, comprising:
accessing historical medical data for the patient and one or more historical progress notes for the patient; and
providing, to the second ML model, the plurality of attributes of the textual description, the historical medical data, and the one or more historical progress notes, wherein the second ML model is a supervised learning model trained, during an offline training phase, using (i) feature vector data representing parsed textual attributes and (ii) labeled examples of historical updates to patient EHRs and progress notes, to determine correlations between the parsed textual attributes and corresponding structured record changes, wherein the second ML model is executed during an online inference phase to generate, in real time, predicted structured update fields representing (i) a predicted progress note error and (ii) a predicted change to one or more fields of the EHR, to reduce computational latency relative to rules-based record analysis systems;
changing the progress note based on the predicted error, comprising transmitting the predicted error to a first electronic repository that maintains progress notes; and
changing the EHR based on the predicted change, comprising transmitting the predicted change to a second electronic repository that maintains EHRs, wherein changing the EHR alters one or more of the symptom, diagnosis, or treatment attributes of the EHR to affect medical treatment for the patient.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of “mathematical concepts” (herein the training of the model) and/or the abstract idea of a mental process because nothing in the claim element precludes the step from practically being performed in the mind, for example a mental process that a physician would follow when predicting a patient outcome by segmenting historical patient data into various groups of in order to utilize the most relevant patient data (i.e. in this case the system obtains patient data and analyzes it to determine whether there is a needed change to the data), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for claims 10 and 16 are identical as the abstract idea for claims 1, because the only difference between claims 1, 10, and 16 is that claim 1 recites a method, whereas claim 10 recites an apparatus and claim 16 recites a non-transitory computer-readable media.
Dependent claims 2-9, 11-15 and 17-20 include other limitations, for example claims
2-8, 11-14 and 17-19 recite further details regarding predicting the change based on the textual description, and claims 9, 15 and 20 recite details on issuing an alert based on identifying a task, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Hence dependent claims 2-3, 5-7, 9-10, 12-14, and 16-22 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 10, and 16.
Prong 2 of Step 2A
Claims 1, 10 and 16 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the machine learning model and the structural components of the computer, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraph 38 of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language limiting the data to patient medical data, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application, for example the transmission of data (e.g. see MPEP 2106.05(g)).
Additionally, dependent claims 2-9, 11-15 and 17-20 include other limitations, but these limitations also amount to no more than amount to mere instructions to apply the exception (e.g. type of data disclosed in claim 2, and the recitation of the electronic system of claims 5, 8 and transmission of data of claims 9, 15 and 20.), and/or do not include any additional elements beyond those already recited in independent claims 1, 10 and 16, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1, 10 and 16 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the machine learning models and the structural components of the computer), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
Paragraph 38 of the Specification discloses that the additional elements (i.e. the memory and one or more processors) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare).
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Dependent claims 2-9, 11-15 and 17-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 10 and 16, and/or the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. type of data disclosed in claim 2, and the recitation of the electronic system of claims 5, 8 and transmission of data of claims 9, 15 and 20.), and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being obvious over Allen et al. (U.S. PG-Pub 2017/0193174 A1), hereinafter Allen, further in view of Lucas et al. (U.S. Patent 10,395,772 B1), hereinafter Lucas, and Beker (U.S. PG-Pub 2015/0356252 A1).
As per claims 1, 10 and 16, Allen discloses a method, a non-transitory computer-readable medium comprising instructions and an apparatus (Allen, Figs. 1 and 4.) comprising:
a memory; and a hardware processor communicatively coupled to the memory, the hardware processor configured to perform operations (See Allen, Figs. 1 and 4 and paragraphs 26 and 27.) comprising:
determining … a plurality of attributes of a textual description of a progress note for a patient medical examination, comprising analyzing the textual description using a … natural language parsing model configured to output … data representing parsed symptom, diagnosis, and treatment attributes (Allen discloses examining a new medical examination note to be added to a patient’s medical record by parsing text using a natural language processing capability in order to identify a plurality of attributes of said text, see paragraphs 62-65. It is old and well known for patient medical records to include records reflecting medical examinations including progress notes.);
predicting, by a second ML model …, an error in the progress note and a change to an electronic health record (EHR) for the patient (Allen determines whether the new information to be added to the existing medical record conflicts with existing data, thereby predicting an error in the progress note that requires a change to the health record, see paragraphs 61 and 70.), comprising:
accessing historical medical data for the patient and one or more historical progress notes for the patient (Allen discloses processing of all of the patient’s previous known medical history, see paragraphs 61, 62, 91-93 and 96-98.); and
providing, to the second ML model, the plurality of attributes of the textual description, the historical medical data, and the one or more historical progress notes, wherein the second ML model is a … learning model trained, during an offline training phase, using (i) feature … data representing parsed textual attributes and (ii) labeled examples of historical updates to patient EHRs and progress notes, to determine correlations between the parsed textual attributes and corresponding structured record changes, wherein the second ML model is executed during an online inference phase to generate, in real time, predicted structured update fields representing (i) a predicted progress note error and (ii) a predicted change to one or more fields of the EHR, to reduce computational latency relative to rules-based record analysis systems (Information gathered by natural language processing is provided to the machine learning model QA pipeline along with historical data to determine whether a change is needed, see Allen, paragraphs 48-53, 70, 88, 89 and 93; and Fig. 3. Model is trained separate from first process, and is therefore offline, model is operative in real time and is therefore operates online.);
changing the progress note based on the predicted error, comprising transmitting the predicted error to a first electronic repository that maintains progress notes (Allen discloses changing progress notes based on predicted errors that require changes, see paragraphs 48-53, 72 and Fig. 1. Allen discloses a first electronic repository, see Fig. 1 #150.); and
changing the EHR based on the predicted change, comprising transmitting the predicted change to a second electronic repository that maintains EHRs, wherein changing the EHR alters one or more of the symptom, diagnosis, or treatment attributes of the EHR to affect medical treatment for the patient (Allen discloses changing progress notes/medical records based on predicted errors that require changes, see paragraphs 48-53, 72 and Figs. 1 and 3-5.).
Allen fails to explicitly disclose:
A trained first machine learning (ML) model configured to output feature vector data;
A second ML model distinct from the first ML model comprising a supervised learning model that processes feature vector data; and
a plurality of electronic repositories that store medical data.
Lucas teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to utilize a first machine learning (ML) model trained to parse patient textual descriptions (See Lucas, C7L24-67 and C9L64-C10L11.), a plurality of distinct ML models (Lucas, C7L8-37 and C28L20-49.) and a plurality of electronic repositories that store medical data (See Lucas, Figs. 11-15 #s 144, 156 and 164.) in order to provide “mechanisms for automatically processing clinical documents in bulk, identifying and extracting key characteristics, and generating machine learning models that are refined and optimized through the use of continuous training data” (See Lucas, C3L10-14.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the medical record error detection system of Allen to include a first machine learning (ML) model trained to parse patient textual descriptions and a plurality of electronic repositories that store medical data, as taught by Lucas, in order to arrive at a medical record error detection system that can provide “mechanisms for automatically processing clinical documents in bulk, identifying and extracting key characteristics, and generating machine learning models that are refined and optimized through the use of continuous training data” (See Lucas, C3L10-14.).
Neither Allen nor Luc disclose the generation of feature vector data, and a ML model comprising a supervised learning model that processes feature vector data.
Beker teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to utilize the generation of feature vector data, and a ML model comprising a supervised learning model that processes feature vector data (Beker, paragraphs 100, 156 and Fig. 2.) in order to fill “a need for a system which can identify medically-relevant information, such as drug prescription compatibility, in a medical file of a subject and thus prevent potential prescription errors in cases where such potential errors would otherwise go undetected by present day systems” (Beker, paragraph 4.).
Allen, Lucas and Beker are all directed to the electronic processing of patient healthcare data and specifically to the analysis of unstructured patient data. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
A recitation directed to the manner in which a claimed apparatus is intended to be used does not distinguish the claimed apparatus from the prior art, if the prior art has the capability to so perform, see MPEP 2114 (II) and Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987). “Language that suggest or makes optional but does not require steps to be performed or does not limit a claim to a particular structure does not limit the scope of a claim or claim limitation”, see MPEP 2111.04.
The following limitation is interpreted as an intended use of the claimed invention:
… to reduce computational latency relative to rules-based record analysis systems.
The prior art is capable of performing the intended use recitation, therefore the prior art meets the limitations.
As per claims 2, 3, 7-9, 11, 12, 14, 15, 17, 19 and 20, Allen/Lucas/Beker discloses claims 1, 10 and 16, discussed above. Allen also discloses:
2,11. wherein the predicting the error comprises predicting a change to the textual description of the patient medical examination (Allen, Fig. 4 and corresponding text. Allen determines whether the new information to be added to the existing medical record conflicts with existing data, thereby predicting an error in the progress note that requires a change to the health record, see paragraphs 61 and 70.);
3,12,17. wherein the predicting the change to the textual description comprises identifying an error for the textual description, based on the patient medical data (Allen, Fig. 4 and corresponding text.);
7,14,19. wherein the predicting the change to the electronic health record comprises predicting a change to at least one of a: (i) symptom, (ii) diagnosis, or (iii) treatment for the patient recorded in the electronic health record for the patient (Allen, Fig. 4 and corresponding text.);
8. updating the electronic health record to reflect the change the at least one of the: (i) symptom, (ii) diagnosis, or (iii) treatment for the patient (Allen, Fig. 4 and corresponding text.); and
9,15,20. identifying a prophylactic treatment task for the patient based on the plurality of attributes of the textual description; and transmitting an electronic alert relating to the treatment task (Allen, Fig. 4 and corresponding text. System is operative to identify errors, concluding errors in the suggested treatment, accordingly Allen would find a treatment error and providing an indication related to whether it needed to be changed.).
As per claims 4-6, 13 and 18, Allen/Lucas/Beker discloses claims 2, 12 and 17, discussed above. Allen also discloses:
4,13,18. wherein the error for the textual description comprises the textual description being associated with an error (Allen, Fig. 4 and corresponding text.);
5. removing the association of the textual description with the error wherein removing the association comprises applying the predicted structured update field generated during the online inference phase (Allen, Fig. 4 and corresponding text.);
6. determining that the textual description is associated with an error based on comparing a number of inconsistencies between the textual description and electronic health record to a threshold value (Allen, paragraphs 89-90 disclose comparing inconsistency rating to a threshold.)
Allen fails to explicitly disclose that the system determines that the input is associated with the wrong patient.
Lucas teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to determines that the input is associated with the wrong patient (See Lucas, C18L61-C49L9, wherein a user indicates that there is a discrepancy with the input information and the existing medical record.) in order to provide “mechanisms for automatically processing clinical documents in bulk, identifying and extracting key characteristics, and generating machine learning models that are refined and optimized through the use of continuous training data” (See Lucas, C3L10-14.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the medical record error detection system of Allen to include wherein the system determines that the input is associated with the wrong patient, as taught by Lucas, in order to arrive at a medical record error detection system that can provide “mechanisms for automatically processing clinical documents in bulk, identifying and extracting key characteristics, and generating machine learning models that are refined and optimized through the use of continuous training data” (See Lucas, C3L10-14.).
Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Response to Arguments
Applicant’s arguments filed 19 December 2025 concerning the rejection of all claims under 35 U.S.C. 112(a) have been fully considered and are deemed persuasive in view of the cancellation of the contested limitation. However, a new rejection has been provided in view of the amendments to the claims.
Applicant’s arguments filed 19 December 2025 concerning the rejection of all claims under 35 U.S.C. 101 and 103(a) have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 101, Applicant argues on pages 11-12 that the claims are incorrectly identified as being nonstatutory:
Because various elements cannot be “performed mentally by humans”;
“Further, the claims provide a technical solution (e.g., offline training and online inference) to solve latency issues of rules-based EHR systems, and thus improve the ‘functioning of the computer itself’;
“Applicant submits that the claims integrate any alleged exception into a practical application. In the claims, the output predictions directly alter medical records in two distinct electronic repositories, and the update to the EHR alters symptom, diagnosis, or treatment attributes and therefore affects clinical workflows”; “Further, the latency reduction directly improves the functioning of the computer system in clinical settings”; and
“Applicant further submits that the claims include ‘significantly more’. The claims describe two distinct ML models having specific architectures and distinct training, supervised training using historical EHR update labels, feature vector NLP parsing, operation in offline training and online inference phases, and structured update fields that are transmitted to electronic repositories.”
The Office respectfully disagrees. please see the rejection of the claims above, wherein the claims are shown to be directed to a judicial exception without significantly more.
Regarding A., the mere fact that the claims contain other elements that are not part of the abstract idea does not make the claims statutory. As shown above, these additional elements do not amount to significantly more.
Regarding B. and C., there is no clear nexus indicating that the claims provide any latency improvement.
Regarding C., MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer. See also MPEP 2106.05(a)(I). The technological environment of Applicant’s claim is a general-purpose computer (see Specification paragraph 38.). Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the computer that results from the implementation of Applicant’s claim. The claims generically recite the use and training of various machine learning models, but there is no nexus between the actual claim language and the advantages claimed by the Applicant, as stated in the specification or otherwise. There is no indication that the computer is made to run faster, more efficiently, or utilize less power. In fact, the computer may be caused to operate slower and less efficiently through the implementation of Applicant’s claimed invention; we do not know. Because there is no improvement to the function of the computer, a practical application is not present.
Regarding D., the claim elements do not amount to significantly more than the abstract idea.
With regard to the rejection of the claims under 35 USC 103, Applicant argues on pages 12-13 that:
A. The cited references fail to disclose the claims as amended. In support the Applicant cites various language from the claims; and
B. There is no motivation to combine Allen with Lucas.
The Office respectfully disagrees. Please see the rejection of the claims above wherein all limitations are mapped to the cited references.
Merely repeating the claim language, and the teachings relied upon by the Office in the rejection are not tantamount to a responsive argument. Such a response to the Office’s findings is insufficient to persuade us of Examiner error, as mere attorney arguments and conclusory statements that are unsupported by factual evidence are entitled to little probative value. In re Geisler, 116 F.3d 1465, 1470 (Fed. Cir. 1997); see also In re De Blauwe, 736 F.2d 699, 705 (Fed. Cir. 1984); Ex parte Belinne, No. 2009-004693, slip op. at 7-8 (BPAI Aug. 10, 2009) (informative); see also In re Lovin, 652 F.3d 1349, 1357 (Fed. Cir. 2011) (“[W]e hold that the Board reasonably interpreted Rule 41.37 to require more substantive arguments in an appeal brief than a mere recitation of the claim elements and a naked assertion that the corresponding elements were not found in the prior art.”); cf. In re Baxter Travenol Labs., 952 F.2d 388, 391 (Fed. Cir. 1991) (“It is not the function of this court to examine the claims in greater detail than argued by an appellant, looking for [patentable] distinctions over the prior art.”). Our rules require that an Appeal Brief include “arguments” that “shall explain why the examiner erred.” 37 C.F.R. § 41.37(c)(l)(iv). “[M]ere statements of disagreement... do not amount to a developed argument.” SmithKline Beecham Corp. v. Apotex Corp., 439 F.3d 1312, 1320 (Fed. Cir. 2006).
Regarding B., in response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the motiviation is located within the references themselves, as shown above.
In conclusion, all of the limitations which Applicant disputes as missing in the applied references, including the features newly added by amendment, have been fully addressed by the Office as either being fully disclosed or obvious in view of the collective teachings of Allen, Lucas and Beker, based on the logic and sound scientific reasoning of one ordinarily skilled in the art at the time of the invention, as detailed in the remarks and explanations given in the preceding sections of the present Office Action and in the prior Office Actions (26 August 2025, 5 May 2025 and 28 October 2024), and incorporated herein.
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 extension fee 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 date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
2 April 2025
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