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 .
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims at a high level recite classifying and marching documents.
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 21-40 recite a method and a system and therefore, are directed to the statutory class of machine and a product.
The USPTO Guidance recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Step 2A, Prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (Step 2A, Prong 2). MPEP §§ 2106.04(a), (d).
Only if the claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look in Step 2B to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field; or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. MPEP § 2106.05(d).
Step 2A, Prong One: Is a Judicial Exception Recited?
First, determine whether the claims recite any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes). MPEP § 2106.04(a).
Claim 21 recites -
▪ identify at least a significant category as a function of a preference request (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can mentally determine a significant category);
▪ produce an alimentary instruction set as a function of the at least a significant category (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — based on the category a user can logically identify alimentary instruction set);
▪ analyzing historical user data related to the preference request (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can analyze historical data);
▪ generating an evaluation metric as a function of the analyzed historical user data (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can apply mathematical evaluations on data);
▪ generate a transport request as a function of the alimentary instruction set and the preference request (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — based on alimentary data a user can organize a delivery); and
▪ enact the transport request as a function of a fulfillment network (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can request the delivery to fulfill the request).
These limitations, based on their broadest reasonable interpretation, recite a mental process, i.e. a judicial exception. For these reasons, the independent claim 1, as well as independents claim 31, which include limitations commensurate in scope with claim 1, recite a judicial exception.
A method, like the claimed method, “a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” See Digitech Image Techs, LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) where collecting information, analyzing it, and displaying results from certain results of the collection and analysis was held to be an abstract idea. See In re Meyer, 688 F.2d 789, 795—96 (CCPA 1982), which held that “a mental process that a neurologist should follow” when testing a patient for nervous system malfunctions was not patentable.
Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
Next determine whether the claims recite additional elements that integrate the judicial exception into a practical application (see MPEP §§ 2106.05(a)-(c), (e)-(h)). To integrate the exception into a practical application, the additional claim elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)).
Additional elements:
▪ a computing device (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: the data is received from the decentralized network, thus, using the network profile data amount to merely invoking a computer (to receive the data) component to apply the exception).
The term “additional elements” for claim features, limitations, or steps that the claim recites beyond the identified judicial exception. However, claims do not recite any improvements to these additional elements, nor does the claims recite any particularly programmed or configured computer system, device, or machine learning. Rather, the additional elements in claims 21 and 31 serve merely to automate the abstract idea. See Int’l Bus. Machs. Corp. v. Zillow Group, Inc., 50 F. 4" 1371, 1382 (Fed. Cir. 2022) (“[A] patent that ‘automate[s] “pen and paper methodologies” to conserve human resources and minimize errors’ is a ‘quintessential “do it on a computer” patent’ directed to an abstract idea.”) (quoting Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019)). Therefore, none of these recited additional elements, whether considered individually or in combination, integrates the judicial exception into a practical application.
The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating and processing known data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
For these reasons, independent claims 21 and 31 are directed to an abstract idea.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, determine whether the claims recite an “inventive concept” that “must be significantly more than the abstract idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer.” BASCOM Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016); see MPEP § 2106.05(d). There must be more than “computer functions [that] are “well-understood, routine, conventional activit[ies]’ previously known to the industry.” Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 225 (2014) (second alteration in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 73 (2012)); see MPEP § 2106.05(d).
Step 2B: The additional elements are not sufficient to amount to significantly more than the judicial exception.
Additional elements (see MPEP 2106.05(d)(Il). Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. Using a computer and associated computer network to obtain data, use data to identify other data, and comparing data, are some of the most basic functions of a computer. All of these computer functions are well-understood, routine, conventional activities previously known to the industry. The method claims do not, for example, purport to improve the functioning of the computer itself. Nor do they effect an improvement in any other technology or technical field. Instead, the claims at issue amount to nothing significantly more than an instruction to apply the abstract idea of displaying, processing and storing data using some unspecified, generic computer).
Note, that in similar case, such as Collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), the Courts have identified that the additional elements of displaying and analyzing data, as shown in the independent claims 21, 31 do not amount to significantly more than the judicial exception. Consequently, that is not enough to transform an abstract idea into a patent-eligible invention.
No “inventive concept” sufficient to transform the abstract method of organizing human activity into a patent-eligible application. See MPEP § 2106.05. Rather, the additional elements identified above are merely well-understood, conventional computer components, as confirmed by the Specification. See MPEP § 2106.05(d)(1). For example, the Specification refers to the additional elements in generic terms.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate and process known data.
Additionally, the computer components are used for performing insignificant extra-solution activity and well understood, routine, and conventional functions. For example, the claimed processor and machine learning merely communicates and processes known data. Activities such as these are insignificant extra-solution activity and, therefore, well understood, routine, and conventional. See MPEP 2106.05(d); see also, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price); CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (Obtaining information about transactions using the Internet to verify credit card transactions); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d at 715, 112 USPQ2d at 1754 (Consulting and updating an activity log); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display); Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016) (Recording a customer’s order); Return Mail, Inc. v. U.S. Postal Service, -- F.3d --, -- USPQ2d --, slip op. at 32 (Fed. Cir. August 28, 2017) (Identifying undeliverable mail items, decoding data on those mail items, and creating output data); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015) (Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price). Furthermore, limitations such as integrating account details are well-understood, routine, and conventional activity. See Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log).
Independent system claims 21 and 31 contain the identified abstract ideas, with the additional elements of a processor, hardware and the media, which is a generic computer component, and thus not significantly more for the same reasons and rationale above.
Dependent claims further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
With respect to claims 22-23, 30, 32-33, 40:
Step 2A Prong 1: the claims recite a judicial exception (an abstract idea)
▪ claims recite further data evaluation and analysis and receiving such data from experts, and using language process module to parse data (Abstract Idea of a mental process. Under the broadest reasonable interpretation, the obtaining/determining probability distribution and divergence, as drafted, is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion).
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application. Additional elements: language process module (a generic computer functions of receiving and processing that are well-understood, routine, and conventional activities previously known to the industry. Extracting caption data and natural text processing are merely extra-solution activities and does not meaningfully limit the independent claims. Generic computer implementation does not provide significantly more than the abstract idea). Step 2B: the additional element is not sufficient to amount to significantly more than the judicial exception.
With respect to claims 25-28, 35-38:
▪ Dependent claims recite a using an alimentary instruction label learner. The claims disclose assigning weights to profiles, either numerically or by a slider (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using routine computer hardware to merely invoking a computer component to apply the exception).
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application. Additional elements: label learner, machine- learning model (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using routine computer hardware to merely invoking a computer component to apply the exception). Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application.
With respect to claims 29, 29:
Step 2A Prong 1: the claims recite a judicial exception (an abstract idea)
▪ a feedback mechanism (A generic computer functions of data processing, storing, encrypting that are well-understood, routine, and conventional activities previously known to the industry. Extracting caption data and natural text processing are merely extra-solution activities and does not meaningfully limit the independent claims. Generic computer implementation does not provide significantly more than the abstract idea. Amount to no more than mere instructions to apply the abstract idea using a generic computer component- see MPEP 2106.05(f))).
Additional elements: No additional elements. Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application. Step 2B: the additional element is not sufficient to amount to significantly more than the judicial exception.
Therefore, claims 22-30, 32-40 are ineligible for the reasons discussed above.
Claim Objections
Claim 26 and 36 are objected to because of the following informalities: Claims recite “a correlated alimentary process label bas a function of”, which should be corrected to “a correlated alimentary process label as a function of”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
Claim(s) 21-24, 31-34, 29-30, 39-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hujsak (US 20180240359) in view of Mirabile (US 20140236759).
Regarding claim 21, Hujsak teaches a system for arranging transport of an alimentary component, the system comprising: a computing device, wherein the computing device is configured to:
identify at least a significant category ([0052] “a variety of objectives such as increasing physical activity, improving sleep, or enhancing cognitive performance”, [0058], [0128], F11K) as a function of a preference request ([0057] “user … request nutritional guidance for a food consumption plan of the user”, [0078] “users may be consumers seeking to meet essential nutrient requirements, prevent or treat disorders, signs or symptoms, or alternatively, achieve particular physiological enhancements … users may be consumers seeking to avoid allergens”; [0075] “a consumer planning a recipe to alleviate a specific condition”, also see [0091]-[0092] “the query contains a request for nutritional information relevant to the subject of the query”, “receive a query for which foods are beneficial for preventing fatty liver disease”, [0148]);
produce an alimentary instruction set as a function of the at least a significant category ([0075] “consumer planning a recipe to alleviate a specific condition"; “provide beneficial options for recipe design that are targeted specifically at a disease, sign, symptom, injury, or physiological/psychological enhancement”, [0094], [0150]),
wherein producing the alimentary instruction set further comprises:
analyzing historical user data related to the preference request ([0053], [0120] “receives the actual choices made by the user”, [0126], [0154]);
generating an evaluation metric as a function of the analyzed historical user data ([0115] “determine various effectiveness metrics”, [0116] “Based on the consumer information, the behavioral planning module can generate a dietary plan”, [0117] );
generate a transport request as a function of the alimentary instruction set and the preference request (see Figure 3, [0039], [0045], [0118]); and enact the transport request as a function of a fulfillment network ([0137]-[0138], [0142], [0145], F12F see Locations -Grocery Delivery, -Food delivery service)(see NOTE).
NOTE - Hujsak teaches a plurality of options for ordering a takeout [0145], which includes a plurality of business for a food delivery (aka “plurality of physical performance entity networks”) in accordance with nutritional recommendations determined for a user [0150]. The businesses include Food Retailers (F12A:1210), Grocery Delivery (including “Automated grocery delivery”), Order-in Food Delivery services (F12F) and ordering a takeout, “food business” ([0138], [0145]), which are available for the user selection. Such plurality of businesses capable of ordering and delivering food in accordance with user’s nutritional need are construed to be analogous to the limitation “generate a transport request as a function of the alimentary instruction set and the preference request; and enact the transport request as a function of a fulfillment network.”
However, to fully obviate such reasoning, Mirabile teaches - generate a transport request as a function of the alimentary instruction set and the preference request; and enact the transport request as a function of a fulfillment network ([0022]-[0029], [0032], [0041]-[0042], [0138], [0140]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Hujsak to generate a transport request as a function of user preference data as disclosed by Mirabile. Doing so would provide real-time physical data in concert with a person's expressed preferences and wellness goals to guide the selection of a person's food (Mirabile [0011]).
Regarding claim 31, Hujsak teaches a method for arranging transport of an alimentary component, the method comprising: identifying, by at least a computing device, at least a significant category as a function of a preference request; producing, by the at least a computing device, an alimentary instruction set as a function of the at least a significant category, wherein producing the alimentary instruction set further comprises: analyzing historical user data related to the preference request; generating an evaluation metric as a function of the analyzed historical user data; generating, by the at least a computing device, a transport request as a function of the alimentary instruction set and the preference request; and enacting, by the at least a computing device, the transport request as a function of a fulfillment network.
Claim 31 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claims 22 and 32, Hujsak as modified teaches the system and the method, wherein the preference request comprises one or more of a dietary preference, cuisine preference, religious presence, flavor-profile preference and an ingredient-quality preference (Hujsak F11K, [0148]).
Regarding claims 23 and 33, Hujsak as modified teaches the system and the method, wherein the computing device is configured to receive a list of significant categories from at least an expert, wherein the at least a significant category is selected from the list of significant categories (Hujsak [0107], Mirabile [0052], wherein Hujsak teaches that categories, representing candidate knowledge entities, are extracted from peer review articles, books, reports, or published on Internet Web sites and are reviewed “by human subject matter experts following the extraction process which in some cases may become interactive and iterative” [0107]. Such candidate knowledge entities (categories) are stored in “the ontology data structures and other databased that span knowledge domains including medicine, biochemistry, nutrition, botany, physiology, pharmacology, cellular biology and others” [0160], such ontology of categories is analogous to a list of significant categories from at least an expert . Thus, it is obvious and reasonable to conclude that receiving data from medical sources, such as medicine, biochemistry, nutrition, botany, physiology, pharmacology, reviewed by experts is obviously analogous to receive … significant categories from at least an expert.” Mirabile further discloses that such information is assembled in a list [0052]).
Regarding claims 24 and 34, Hujsak as modified teaches the system and the method, wherein the list of significant categories is processed using at least a language process module (Hujsak [0107], see OCR tools to extract diagram annotations and terminology i.e. semantics).
Regarding claims 29 and 39, Hujsak as modified teaches the system and the method, wherein the at least a computing device further comprises a feedback mechanism (Hujsak [0159], Mirabile [0042]).
Regarding claims 30 and 40, Hujsak as modified teaches the system and the method, wherein the alimentary instruction set is further generated as a function of a received constitutional restriction (Hujsak [0051], [0058], [0148], Mirabile [0039], [0137]).
NOTE in analogous art Grimmer et al. (US 2018/0144820) likewise teaches claims 4 and 14 in [0067],[0086] and further obviates the teachings of Hujsak.
Claim(s) 22-24, 33-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hujsak (US 20180240359) in view of FITZPATRICK et al. (US 20190000382).
Regarding claims 23 and 33, if Hujsak as modified does not explicitly teach FITZPATRICK discloses the system and the method, wherein the computing device is configured to receive a list of significant categories from at least an expert, wherein the at least a significant category is selected from the list of significant categories ([0073], [0086], [0100], [0114]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Hujsak as modified to include a list of significant categories from at least an expert as disclosed by FITZPATRICK. Doing so would provide trusted sources of information.
Regarding claims 24 and 34, Hujsak as modified teaches the system and the method, wherein the list of significant categories is processed using at least a language process module (Hujsak [0107], see OCR tools to extract diagram annotations and terminology i.e. semantics, FITZPATRICK [0207]).
Claim(s) 25-28, 35-38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hujsak (US 20180240359) in view of Abou Shousha et al. (US 10468142).
Regarding claims 25 and 35, Hujsak as modified teaches the system and the method, wherein the at least an alimentary instruction set is generated using an alimentary instruction label learner (Hujsak [0105], [0118]).
Hujsak as modified does not explicitly teach, however Abou Shousha discloses a label learner (C16L29-31, C22l5-35, C29L9-26, C43L30-35, C44L4-14).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Hujsak as modified to include a label learner as disclosed by Abou Shousha. Doing so would provide predictions utilizing class probability values or scores having the highest correlation to a prediction accuracy to be included in a diagnosis (Abou Shousha C7L29, C22L4-14).
Regarding claims 26 and 36, Hujsak as modified teaches the system and the method, wherein the alimentary instruction label learner is configured to generate a correlated alimentary process label bas a function of an alimentary data output (Hujsak see Figure 3, [0039], [0045], [0118]-[0120], Abou Shousha C18L11-18, C34L45-48, C38L50-52).
Regarding claims 27 and 37, Hujsak as modified teaches the system and the method, wherein generating the at least an alimentary instruction set using the alimentary instruction label learner comprises creating at least a first machine- learning model configured to relate the preference request to an alimentary label (Hujsak [0156], [0166] “receives a request for mitigating a biological condition”; “platform identifies a set of nodes related to the biological condition of the user by traversing through semantic links associated with the biological condition of the user in the knowledge database. The set of nodes indicate nutritional information associated with the biological condition. The application platform provides nutritional guidance based on the set of identified nodes and the semantic links associated with the set of nodes”).
Regarding claims 28 and 38, Hujsak as modified teaches the system and the method, wherein the alimentary instruction label learner is configured to uses a scoring function to determine an optimal relationship between the preference request and the alimentary label (Hujsak [0139] “components are ranked according to the user's specific needs and wellness goals”, [0156] “artificial intelligence (AI) recommender included in the concussion application searches for the best match and optimizes recipes that match the preferences of the user,” [0161], F11A-B, Abou Shousha C18L11-65, C45L15-16).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
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/POLINA G PEACH/ Primary Examiner, Art Unit 2165 June 26, 2026
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