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.
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 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;
retrieving one or more second objects associated with the one or more fields of the recall request;
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;
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 facilitates 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Singh (US 20180114228) in view of Maheshwari (US 20250068666) in further view of Alexander (US 20130036061).
Claims 1, 8, and 15
Singh discloses (claim 1 being representative):
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.”}
Singh does not disclose, however, Maheshwari, in a similar field of endeavor directed to search query resolution techniques, teaches:
parsing at least one first object of the one or more first objects through a model {The search query (first object) is processed or parsed through models, e.g., “[0103] In some examples, the expanded query may be generated from a search query using a language model. For example, a language model may be applied to the search query to interpret the search query and generate contextual information to augment the natural language sequence of text of the search query.”}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the return transaction management features of Singh to include the data search analysis and visualization features of Maheshwari, to improve a user interface and/or a user device by optimizing presentation of visual data via the user interface and/or by minimizing a number of user interactions with respect to the user interface, thereby reducing a number of computing resources utilized by the user device. (see paragraph 0006 of Maheshwari).
The combination of Singh and Maheshwari does not teach, however, Alexander, in a similar field of endeavor directed to managing product recall information, 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.”}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Singh and Maheshwari to include the product data management on recall processes technology of Alexander, to improve the approach to determining whether a consumer owns product affected by a recall. (see paragraph 0004 of Alexander).
Claims 2, 9, and 16
The combination of Singh, Maheshwari, and Alexander teaches the limitations set forth above. Maheshwari further teaches (claim 2 being representative):
selecting the at least one first object from the one or more first objects {“[0108] In some examples, the search query 402 may include user input, such as text input and/or text generated from one or more audio, tactile, and/or like inputs. In some examples, the search query 402 may include a natural language sequence of text. (i.e., takes a search query (first objects) from among potential objects)”.}
applying the model to the at least one first object {The search query is processed through models, e.g., “[0114] By way of example, the embedding representation 406 may be generated by processing the search query 402 with a machine learning embedding model, such as the machine learning model described herein”. See also, “[0103] In some examples, the expanded query may be generated from a search query using a language model. For example, a language model may be applied to the search query to interpret the search query and generate contextual information to augment the natural language sequence of text of the search query.”}
The motivation and rationale to include the additional features of Maheshwari are the same as set forth previously.
Claims 3, 10, and 17
The combination of Singh, Maheshwari, and Alexander teaches the limitations set forth above. Maheshwari further teaches (claim 3 being representative):
identifying the first match associated with the one or more first objects based on the parsing of the at least one first object, wherein the first match corresponds to at least one of a synonym match and a semantic match of the one or more first objects with the one or more second objects {“[0139] In this way, keyword matching techniques may be leveraged to capture literal similarities, while matching on embeddings captures semantic and contextual similarities between the search query 402 and the channel-specific feature 426” (i.e., first object is matched by both synonym and semantic comparison).}
determining at least one second object that matches each of the one or more first objects based on the first match {“[0142] The query resolution 422, for example, may identify one or more query result data objects 360 for the search query 402 based on a plurality of source features identified by the intermediate query resolution 420” (i.e., uses the matches to identify corresponding second objects based on the initial match).}
The motivation and rationale to include the additional features of Maheshwari are the same as set forth previously.
Claims 4, 11, and 18
The combination of Singh, Maheshwari, and Alexander teaches the limitations set forth above. Maheshwari further teaches (claim 4 being representative):
identifying a second match associated with the one or more first objects based on the one or more algorithms and the one or more factors, wherein the second match corresponds to at least one of a fuzzy name match, a workflow-based match, a grammar-based synonym match, one or more historical matches, and one or more historical workflow-based matches of the one or more first objects with the one or more second objects {“[0099] In some examples, a keyword search routine may receive a search query (and/or portions thereof), such as a name/address, one or more geo-structured filters, and/or the like, and initiate one or more computing tasks, such as typeahead/term query tasks, spelling correction tasks, and/or the like, based on the search query” (i.e., fuzzy name match). “[0088] In addition, or alternatively, the domain knowledge datastore 302 may include medical claim data including medical codes, such as ICD codes, CPT codes, and/or the like, submitted by a provider in the last N months (default N=12)” (i.e., historical match).}
determining at least one second object that matches each of the one or more first objects based on the second match {“[0142] The query resolution 422, for example, may identify one or more query result data objects 360 for the search query 402 based on a plurality of source features identified by the intermediate query resolution 420” (i.e., determining the second object based on the second match).}
The motivation and rationale to include the additional features of Maheshwari are the same as set forth previously.
Claims 5, 12, and 19
The combination of Singh, Maheshwari, and Alexander teaches the limitations set forth above. Singh further discloses (claim 5 being representative):
determining, based at least on the first weighted score and the second weighted score, at least one second object that matches each of the one or more first objects {The system uses the combined scores to determine a final match, e.g., [0071] [T]he 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.}
Claims 6, 13, and 20
The combination of Singh, Maheshwari, and Alexander teaches the limitations set forth above. Singh further discloses (claim 6 being representative):
determining the one or more visual representations based on the mapping for each of the one or more first objects, wherein the one or more visual representations comprise at least one of: visually highlighting a first object and a corresponding second object to which the first object is to be mapped, showing a link between the first object and the corresponding second object, and highlighting the first object and one or more top second objects that map onto the first object with a color {The system produces visual identification results that directly associate the returned object with a database object, e.g., [0071] the item identifying module 224 determines the match score 626 between item 308 and item G is ninety-five (95); and [0057] Upon determining that item 308 is eligible for return, controller 201 provides a notification to the customer service device 504 that item 308 is eligible for return.}
Claims 7 and 14
The combination of Singh, Maheshwari, and Alexander teaches the limitations set forth above. Alexander further teaches (claim 7 being representative):
creating one or more tasks to address the recall request based on the mapping {[0037] The product recall apparatus 132 may do so by aggregating product recall information from a plurality of Internet 155 sources (such as the recall sites 150a-c), gathering product information for products 120 in the consumer's possession, comparing the product identification for the products 120 with the product recall.} information, and notifying the consumer of the product recall information that is associated with one or more products 132 owned by the consumer.
The motivation and rationale to include the additional features of Alexander are the same as set forth previously.
Response to Arguments
Applicant’s arguments filed on 12/24/2025 have been carefully considered.
Rejections under 35 USC §101
Under Step 2A, Prong One, claim 1 recites limitations which amount to collecting, analyzing, and presenting information in order to manage recall requests within a facility. Such activity falls within the abstract idea groupings of Certain Methods of Organizing Human Activity, namely administrative management of recall workflows, and Mental Processes, such as evaluating information, comparing terminology, and selecting the best match, as established in the analysis above. The claim therefore recites a judicial exception.
Applicant’s argument that the recited steps cannot be performed mentally because they involve language models, databases, and algorithmic processing is not persuasive. The claim does not recite any specific technical implementation of the model or algorithms, but instead recites using a “model”, “algorithms”, and “factors” at a high level to analyze and match information. These elements merely serve as a tool to perform the abstract data analysis and do not change the character of the claim.
Under Step 2A, Prong Two, the claim does not integrate the abstract idea into a practical application. The additional elements represent generic computer components performing routine data processing functions. The claim uses these generic computing elements only to implement the abstract idea of matching recall requests information with internal templates. The claim does not recite an improvement computer functionality, machine learning technology, or database technology, but instead uses generic computing components as tools to perform the abstract data analysis.
Under Step 2B, the claim does not recite significantly more than the abstract idea. The additional limitations amount to apply the abstract idea using generic computer technology. The claim does not recite any specific improvement to computer functionality, machine learning technology, or database technology, but instead uses generic computing components as tools to perform the abstract data analysis, that is, the conclusions from Step 2A, Prong Two are carried over in view of the MPEP 2106.05(f) application.
Independent claims 8 and 15 recite implementations that perform substantially the same steps as claim 1. Accordingly, this claims, as well as their dependent claims, fall under the same analysis above.
Therefore, the rejection under 35 U.S.C. §101 is maintained.
Rejections under 35 USC §103
The rejection does not rely on Singh for “recall management” specifically but rather for the claimed data processing steps of identifying mappings and assigning weighted scores based on matches between objects. A reference does not need to disclose the same field of use as the claimed invention to be applicable prior art. (See MPEP §2141.01(a)).
Singh discloses determining mappings between an unknown object and stored objects by comparing parsed attributes and generating scores that represent that degree of match. For example, Singh discloses that an “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” and evaluates parameters (e.g., dimensions, shape, color) to compute a match score for the item (¶ [0063]). Singh further discloses that the match score is determined using weighted scoring logic, explaining that the score “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” (¶ [0070]). Based on these weighted comparisons, Singh determines that the unidentified item corresponds to a stored item, e.g., “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” (¶ [0071]). That is, identifying a mapping between a first object (the unidentified item) and a second object (the stored item) based on multiple scored matches derived from parsed attributes.
Applicant argues that Singh only identifies a single item and does not perform mapping between object sets. Singh explicitly compares an unknown object to stored objects in a database (item information data 238) and evaluates multiple matching factors to determine the most likely corresponding object (¶¶ [0063] – [0071]). Such comparison constitutes determining mappings between a first object and candidate second objects based on scored matches. Moreover, Singh expressly teaches assigning weighted contributions from different matching parameters to determine the final match score (¶ [0070]), i.e., assigning weighted scores to mappings based on different matches as recited in the claim.
Accordingly, the rejection under 35 U.S.C. §103 is maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629