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 . Claims 1-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's RCE submission filed on 7/24/2026 with claims corresponding to 6/24/2026 has been entered.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claims 1 and 2 are directed to a method (i.e., a process), claim 8 is directed to a system (i.e., a machine), and claim 15 is directed to a non-transitory, computer-readable storage medium (i.e., machine or manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 8, and 15 (claim 1 being representative) is:
receiving one or more first objects from a user associated with the facility, wherein the one or more first objects are associated with one or more fields of a recall request;
parsing at least one first object of the one or more first objects through a model to determine a context associated with the one or more first objects and identify a type of the recall request;
retrieving one or more second objects associated with the one or more fields of the recall request, wherein the one or more second objects comprise template objects stored in a structured manner according to one or more requirements of the facility;
determining a first mapping for the one or more first objects with the one or more second objects based on the parsing of the at least one first object;
determining a second mapping for the one or more first objects with the one or more second objects based at least on one or more factors, wherein the one or more factors comprise at least a workflow of a recall process;
providing one or more static tags from the second mapping, wherein the one or more static tags indicate a probability of the one or more first objects and the one or more second objects to the workflow;
filtering out at least one of the one or more first objects and the one or more second objects based on the one or more static tags;
identifying a mapping for each of the one or more first objects with the corresponding one or more second objects based on the first mapping and the second mapping, wherein identifying the mapping comprises:
assigning a first weighted score to the first mapping based on a first match of the one or more first objects with the one or more second objects; and
assigning a second weighted score to the second mapping based on a second match of the one or more first objects with the one or more second objects;
rendering the mapping for each of the one or more first objects using one or more visual representations.
The abstract idea steps italicized above involves managing products’ recall workflow, which constitutes a process that, under its broadest reasonable interpretation (BRI), covers commercial activity. This is further supported by paragraph [0031] of applicant’s specification as filed. If a claim limitation, under its BRI, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps recited above are those which could be performed mentally, including with pen and paper. Applicant has broadly claimed receiving information, parsing, comparing, and assigning scores. Under its BRI, it is covered under Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas, given that they pertain to data gathering and analysis. This is further supported by paragraphs [0053] – [0054] of applicant’s specification as filed. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: wherein the model corresponds to one or more language learning models trained based on one or more requirements of the facility; from a database; one or more algorithms; via a user interface.
Claim 8 recites the following additional elements: A system for managing one or more recall requests in a facility, the system comprising: a processor; a memory communicatively coupled to the processor, wherein the memory comprises one or more instructions which when executed by the processor, cause the processor to; wherein the model corresponds to one or more language learning models trained based on one or more requirements of the facility; from a database; one or more algorithms; via a user interface.
Claim 15 recites the following additional elements: A non-transitory, computer-readable storage medium having stored thereon executable instructions that, when executed by one or more processors, cause the one or more processors to; wherein the model corresponds to one or more language learning models trained based on one or more requirements of the facility; from a database; one or more algorithms; via a user interface.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraphs [0067] – [0069] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system. Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP §2106.05(f).
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Regarding claims 2-3, 5-7, 9-10, 12-14, 16-17, and 19-20, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible. See MPEP §2106.05(f).
Claims 4, 11, and 18 include further additional elements with additional description: the one or more algorithms. There are no further additional elements to consider beyond those highlighted above. This does not integrate the abstract idea into practical application and is not significantly more.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
No Prior Art Applied to Claims 1-20
Claims 1, 8, and 15
There is no prior art applied to claims 1, 8, and 15 because the cited prior art fails to disclose or suggest the complete feature set recited in the claims. Singh (US 20180114228), considered the closest prior art, discloses:
receiving one or more first objects from a user associated with the facility, wherein the one or more first objects are associated with one or more fields of a recall request {“[0053] After the customer 302 has entered the store and has been identified, as explained above with regards to FIG. 3, the controller 201 (FIG. 2) may determine customer 302 is holding item 308. Generally, the controller 201 may infer that a customer entering the environment 100 with an item intends to return said item.” The item is subsequently processed using stored transaction data fields (e.g., item identification, purchase date), which are associated with data fields analogous to fields of a recall request. [0046] – [0051]}
retrieving from a database, one or more second objects associated with the one or more fields of the recall request; {The system retrieves item data from databases for comparison, e.g., “[0046] The transaction manager module 230 (FIG. 2) may keep track of virtual transactions 440 that have been previously completed for customers. The previously completed transactions 452 contains transactions for two previously purchased items G and H. While two items are used for simplicity, any number of items may be tracked in the previously completed transactions 452. Additionally, the previously completed transactions 452 may store entire transactions, or receipts, instead of the individual items”; and “[0064] After controller 201 determines the dimensions of an unknown item, the item identifying module 224 may compare the determined dimensions of the unknown item to the item information data 238 (FIG. 2) to determine the dimensions match 610.”}
determining a first mapping for the one or more first objects with the one or more second objects based on the parsing of the at least one first object {A first mapping is executed where a known item is compared to known database entries, e.g., “[0063] For example, the item identifying module 224 may compare the unknown item to all of the known items stored within item information data 238 to determine a match.”}
determining a second mapping for the one or more first objects with the one or more second objects based at least on one or more algorithms and one or more factors {The mapping is further refined using algorithms and weighted scoring, e.g., “[0070] The match score 626 may be determined using any suitable arithmetic and/or logical functions, such as a weighted sum, comparing the parameter values with thresholds to produce a binary decision for each, fuzzy logic, and so forth. The item identifying module 224 may compare the match score 626 with a threshold value to determine a match exists between the unknown item and the known item.”}
identifying a mapping for each of the one or more first objects with the corresponding one or more second objects based on the first mapping and the second mapping {“[0071] That is, the item identifying module 224 determines the match score 626 between item 308 and item G is ninety-five (95), which indicates a very high likelihood that item 308 is in fact item G. In this manner, the controller 201 is able to accurately identify an item within environment 100.” (i.e., a final mapping).}
wherein identifying the mapping comprises: assigning a first weighted score to the first mapping based on a first match of the one or more first objects with the one or more second objects {“[0064] After controller 201 determines the dimensions of an unknown item, the item identifying module 224 may compare the determined dimensions of the unknown item to the item information data 238 (FIG. 2) to determine the dimensions match 610. In the example depicted in identification of item 602, the dimensions match 610 has a maximum possible score of one hundred (100)”.}
assigning a second weighted score to the second mapping based on a second match of the one or more first objects with the one or more second objects {“[0070] The item identifying module 224 (FIG. 2) may… determine a match score 626. The match score 626 may be determined using any suitable arithmetic and/or logical functions, such as a weighted sum, comparing the parameter values with thresholds to produce a binary decision for each, fuzzy logic, and so forth.” Multiple matching factors (e.g., dimensions, shape, color) are evaluated and contribute to scoring. [0066] – [0067]}
rendering, via a user interface, the mapping for each of the one or more first objects using one or more visual representations. {The system renders mapping results via interfaces, e.g., “[0055] In one exemplary embodiment, upon retrieving the customer information 430 (FIG. 4B), controller 201 (FIG. 2) sends the previously completed transactions 452 of the customer (FIG. 4B) in real-time to the customer service device 504 via the network 206 (FIG. 2). Customer service device 504 may be any type of electronic device such as a computer, a cash register, a handheld electronic device such as a smartphone or tablet, and so forth.” Also, “[0061] In another exemplary embodiment, within return zone 112 there may be a kiosk (not shown) having a display, a communication device (e.g., a speaker, a display, etc.), a scale, and a visual sensor 102. The kiosk may have information designating the kiosk as the return location for environment 100. The customer 302 may then approach the kiosk to facilitate the return of item 308. The controller 201 may communicate with customer 302 via the kiosk in order to provide customer 302 with instructions on how to return item 308.”}
Maheshwari (US 20250068666) teaches:
parsing at least one first object of the one or more first objects through a model, wherein the model corresponds to one or more language learning models trained based on one or more requirements of the facility to determine a context associated with the one or more first objects and identify a type of the recall request; {The system supports parsing a search query through an LLM “configured to generate contextual information for a search query that is grounded by a particular search domain” and “trained using text data… for a search domain” [0067], [0094]}
Alexander (US 20130036061) teaches:
(claim 1) A method for managing one or more recall requests in a facility, the method comprising {“[0007] The method may also involve comparing the product identification for the products and the product recall information, and notifying the consumer of the product recall information that is associated with one or more of the products.”}
(claim 8) A system for managing one or more recall requests in a facility, the system comprising {“[0006] The invention may also be realized as a system that includes a first computing device and a second computing device. [A] computing device may compare the product identification for the products with product recall information and notify the consumer of the product recall information that is associated with one or more of the products.”}
(claim 8) a processor {“[0020] Modules may also be implemented in software for execution by various types of processors.”}
(claim 8) a memory communicatively coupled to the processor, wherein the memory comprises one or more instructions which when executed by the processor, cause the processor to {“[0025] In one embodiment, the computer readable medium may comprise a combination of one or more computer readable storage mediums and one or more computer readable signal mediums. For example, computer readable program code may be both propagated as an electro-magnetic signal through a fibre optic cable for execution by a processor and stored on RAM storage device for execution by the processor.”}
(claim 15) A non-transitory, computer-readable storage medium having stored thereon executable instructions that, when executed by one or more processors, cause the one or more processors to {“[0025] In one embodiment, the computer readable medium may comprise a combination of one or more computer readable storage mediums and one or more computer readable signal mediums. For example, computer readable program code may be both propagated as an electro-magnetic signal through a fibre optic cable for execution by a processor and stored on RAM storage device for execution by the processor.”}
However, neither Singh, Maheshwari, nor Alexander disclose or suggest “wherein the one or more second objects comprise template objects stored in a structured manner in the database according to one or more requirements of the facility; wherein the one or more factors comprise at least a workflow of a recall process; providing one or more static tags from the second mapping, wherein the one or more static tags indicate a probability of the one or more first objects and the one or more second objects to the workflow; filtering out at least one of the one or more first objects and the one or more second objects based on the one or more static tags”.
Examiner also considered the following additional references:
Gasperecz (US 20200279271), which teaches extracting requirements from regulatory content data.
Katzin (US 20240378377), which teaches: The Controversy Resolution Assistant Engine Machine Learning Apparatuses, Processes and Systems (“CRAEML”) transforms matter milestone interaction input datastructure/inputs via CRAEML components into matter milestone interaction output outputs. A matter milestone interaction request datastructure is obtained. Milestone document details are determined via an NLP engine. The milestone document details are evaluated via a first ML prediction logic datastructure to determine relevant matter data. Similar prior matters are determined by executing a search query. The milestone document details, the relevant matter data and similar prior matters data are evaluated via a second ML prediction logic datastructure to determine a best matching resolution document template. Template placeholder values for a template placeholder of the best matching resolution document template are generated via an LLM. User selection of a template placeholder value is obtained. Content of the best matching resolution document template and the selected template placeholder value are composited generating a resolution document.
Balasubramanian (US 20200311349), which teaches: An embodiment of the present invention is directed to combining natural language processing with constrained grammar defined pattern matching to advantageously allow a system to translate natural language questions into any number of underlying technologies. By using a unique intermediate parse tree representation, the disclosed embodiments are able to instantiate a corresponding data store adapter for each given query, which may be for a relational database or a no-SQL database, for example. The ability to abstract underlying storage technology advantageously allows the management of disparate database systems, which is not possible using existing methods and technology.
However, neither reference discloses nor suggests the aforementioned claim limitations.
Accordingly, there is no prior art applied to claims 1, 8, and 15. The rest of the claims, by virtue of their dependency, also have no prior art applied.
Response to Arguments
Applicant’s arguments filed on 06/24/2026 have been carefully considered but are not persuasive.
Rejections under 35 USC §101
The claims recite an abstract idea. The claimed steps include in part analyzing recall request information, determining context and request type, and filtering information. These are evaluations and judgements that fall within the Mental Processes grouping. Using a language learning model or other computer components to perform these steps does not change the nature of the abstract idea.
The claims do not integrate the abstract idea into a practical application. The claimed model, database, mapping, tagging, and filtering may improve the handling of recall information, but they do not improve the operation of the computer, database, or model itself. Likewise, storing template objects in a structured manner organizes information for a latter use, but does not recite an improvement to database technology. Thus, the computer components are used as tools to perform the abstract idea.
Applicant’s reliance on Berkheimer is not persuasive because the rejection did not rely on a finding that the additional elements are well understood, routine, or conventional. Instead, the Office Action relied on 2106.05(f), finding that the additional elements amount to instructions to implement the abstract idea on a computer. Accordingly, the additional elements, alone or in combination, do not amount to significantly more than the abstract idea.
Therefore, the rejection under 35 USC §101 is maintained.
Rejections under 35 U.S.C. §103
Arguments are moot under 35 USC §103 because there is no prior art applied to claims 1-20. Accordingly, Examiner directs Applicant’s attention to the analysis above.
In summary, examiner has responded to all arguments and found them unpersuasive.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS F MONTALVO whose telephone number is (703)756-5863. The examiner can normally be reached Monday - Friday 8:00AM - 5:30PM; First Fridays OOO.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
/C.F.M./Examiner, Art Unit 3629
Prosecution Insights