,DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on February 26, 2026, for the application with serial number 18/795,211.
Claims 1, 3, 9, 10, 14, and 16 are amended.
Claims 1-20 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the independent claims as being directed to an ineligible abstract idea, contending that the present claims are subject matter eligible because the claims recite steps that cannot be performed in the human mind. See Remarks p. 9. In response, the Examiner points to the rejection, below, which concludes that the claims are directed to a method of organizing human activity. The Applicant’s arguments with respect to the mental processes category of abstract idea are moot. Contrary to the Applicant’s assertions, only generic computer hardware is recited in the claims to implement the abstract idea. Automation of a manual process does not constitute an improvement to the functioning of a computer. See MPEP §2106.05(a)[I]{iii]. Contrary to the Applicant’s assertions, no apparent improvement in natural language processing is recited in the claims. Computing a similarity score is a mathematical process that is also an abstract idea. See MPEP §2106.04(a). Overall, the subject matter of the claims involves a decision regarding whether to recall a product, which is a business decision. The decision could be implemented mentally or on paper by a human being, but a general purpose computer is recited for implementation. Merely reciting the use of generic computer hardware to implement an abstract idea does not provide significantly more than the abstract idea. See MPEP §2106.05(f).
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §102/103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further consideration of the cited prior art. Independent claim 1 now stands rejected as being obvious over Babu in view of Adler. The Applicant’s arguments with respect to the anticipatory rejection of independent claim 1 from the previous Office Action are moot.
Claims 6, 7, 9, 10, 13-15, 18, and 19 now stand rejected as being obvious over Babu in view of Adler and Seelinger. The Applicant’s arguments with respect to the rejection from the previous Office Action are moot.
The Applicant further contends that the Adler reference is deficient because Adler relies on X-ray analysis attributes of components, while the present claims rely on “attributes associated with manufacturing.” See Remarks pp. 17-18. In response, the Examiner submits that the Applicant takes a narrower interpretation of “attributes associated with manufacturing” than the broadest reasonable interpretation applied by the Examiner. Adler involves inspection of integrated circuits that have failed. See abstract. Integrated circuits are manufactured products. In fact, the analysis used by Adler produces feedback that informs the manufacturing process. See at least Adler ¶[0005]. Therefore, the attributes in Adler are “attributes associated with manufacturing of the at least one product.”
The rejection of the remaining claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-20 are all directed to one of the four statutory categories of invention, the claims are directed to assessing risk for determining recall of products (as evidenced by exemplary independent claim 1; “initiate a risk assessment process for the products based on the one or more identified attributes, the risk assessment process comprising execution of the workflow for a process of deciding recall of the other products”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “execute a workflow for a process of deciding recall of products;” “execute a workflow for a process of executing recall of the products;” “receiv[e] . . . a request to recall at least one product;” “initiat[e] . . . execution of the recall of the at least one product;” “retriev[e] . . . attributes associated with manufacturing of the at least one product;” “identify[ ] . . . one or more attributes that contribute to [an] anomaly in the at least one product;” “determin[ ] if the identified attributes affect other products;” “retriev[e] at least one attribute of the other products;” “comput[e] a similarity score ;” and “communicat[e] the one or more identified attributes . . . to initiate a risk assessment process.” The steps are all steps for managing personal behavior related to the abstract idea of assessing risk for determining recall of products that, when considered alone and in combination, are part of the abstract idea of assessing risk for determining recall of products. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of for assessing risk for determining recall of products. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes product quality analysis for determining recall of products in a distribution chain.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a system and sub-system in independent claims 1 and 9; and a computer readable medium in independent claim 14). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims require no more than a generic computer (a system and sub-system in independent claims 1 and 9; and a computer readable medium in independent claim 14) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
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) 1, 3, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20050004811 A1 to Babu (hereinafter ‘BABU’) in view of US 20210010954 A1 to Adler et al. (hereinafter ‘ADLER’).
Claim 1 (Currently Amended)
BABU discloses a method for product recall management comprising: in a product recall management system (see abstract; a computerized recall management tool) comprising a recall decision sub-system configured to execute a workflow for a process of deciding recall of products (see again abstract; a computerized recall management tool permits an organization to recognize and proactively manage events that can indicate a need to initiate a product recall) and a recall execution sub-system configured to execute a workflow for a process of executing recall of the products (see again abstract; manage regulatory reporting events and other notification milestones and to manage a recall itself), receiving, by the recall execution sub-system, a request to recall at least one product, wherein the request is based on an anomaly relating to the at least one product (see again abstract; manage events that can indicate a need to initiate a product recall. Recognize patterns of product defects);
upon approval of the request, initiating, by the recall execution sub-system, execution of the recall of the at least one product (see ¶[0015] and [0041] and Fig. 6; recognize patterns of product defects from product performance data, to alert operators when such patterns are detected, to manage regulatory reporting events and other notification milestones and to manage a recall itself. Determine whether the product is subject to recall 620);
retrieving, by the recall execution sub-system during execution of the recall of the at least one product, from a quality events database, attributes associated with manufacturing of the at least one product (see abstract and ¶[0022] & [0024]; product performance data often is made available to an organization through very diverse communication channels, including from customers, distributors, suppliers, governmental or industry agencies in addition to its internal manufacturing and testing sources. The data interface unit 160 may solicit product defect data from various entities in the product's distribution chain. These can include various members from with the manufacturer's company itself. Exemplary internal sources include internal testing systems and quality control or quality management systems The data harvesting agent 210 may collect data from one or more of these sources and populate data structures according to a variety of performance dimensions);
identifying, by the recall execution sub-system, from amongst the retrieved attributes, one or more attributes that contribute to the anomaly in the at least one product (see abstract and ¶[0015]; recognize patterns of product defects from product performance data, to model an extent to which a product defect may proliferate throughout its distributed products); and
determining, by the recall execution sub-system, if the one or more identified attributes affect other products related to the at least one product (see again abstract and ¶[0015]; recognize patterns of product defects from product performance data, to model an extent to which a product defect may proliferate throughout its distributed products).
BABU does not specifically disclose, but ADLER discloses, retrieving at least one attribute of the other products related to the at least one product from the quality event database (see abstract and ¶[0035] and [0094]; identify the subset of components that have same or similar features with failed components to identify products that need to be included in a recall. Use a similarity metrics vector to calculate a similarity score); and
computing a similarity score between the retrieved attributes of the at least one product and the at least one attribute of the other products using a weighted comparison of the corresponding attributes (see abstract and ¶[0035] and [0094]; identify the subset of components that have same or similar features with failed components to identify products that need to be included in a recall. Use a similarity metrics vector to calculate a similarity score. Weight particular attributes).
BABU further discloses communicating, by the recall execution sub-system, the one or more identified attributes to the recall decision sub-system to initiate a risk assessment process for the other products based on the one or more identified attributes, the risk assessment process comprising execution of the workflow for a process of deciding recall of the other products (see ¶[0029], [0034], and [0045]-[0046]; parts manufacturer PM3 likely supplied defective component parts during a three month period. Product diffusion modeling may permit the alert process 240 to estimate the propagation of the defective component parts through its distribution chain. Recall notification to a distributor by contrast may include information identifying which batches are likely to contain defects and which are not).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. It would have been obvious for one of ordinary skill in the art at the time of invention to include the similarity score as taught by ADLER in the system executing the method of BABU to manage recalls.
Claim 3 (Currently Amended)
The combination of BABU and ADLER discloses the method as set forth in claim 1.
BABU does not specifically disclose, but ADLER discloses, wherein determining if the one or more identified attributes affect other products related to the at least one product comprises assessing a degree of similarity between the one or more identified attributes of the at least one product and corresponding attributes of the other products (see abstract and ¶[0035] and [0094]; identify the subset of components that have same or similar features with failed components to identify products that need to be included in a recall. Use a similarity metrics vector to calculate a similarity score), wherein the similarity score is used to quantify the degree of similarity (see again ¶[0094]; calculate an overall similarity score based on the similarity metric vector between features).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. It would have been obvious for one of ordinary skill in the art at the time of invention to include the similarity score as taught by ADLER in the system executing the method of BABU to manage recalls.
Claim 8 (Original)
The combination of BABU and ADLER discloses the method as set forth in claim 1.
BABU further discloses wherein the request to recall further comprises a compilation of reports corresponding to instances of detection of the anomaly associated with the at least one product (see ¶[0036]-[0037]; firms are subject to specific requirements regarding the reporting of defective products).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20050004811 A1 to BABU and US 20210010954 A1 to ADLER et al. as applied to claim 1 above, and further in view of US 20180285810 A1 to Ramachandran et al. (hereinafter ‘RAMACHANDRAN’).
Claim 2 (Original)
The combination of BABU and ADLER discloses the method as set forth in claim 1.
The combination of BABU and ADLER does not specifically disclose, but RAMACHANRDAN discloses, wherein the request to recall the at least one product received by the recall execution sub-system is independent of a risk assessment process carried out by the recall decision sub-system for the at least one product (see ¶[0044]; in the case of a recall, if the root cause was identified, specific shipments downstream of the incidents could also be retrieved in a pinpointed recall, saving a company millions compared to a blanket recall).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). RAMACHANRDAN discloses blockchain records in a supply chain to identify root causes of a recall to make a pinpointed recall of products, rather, than a blanket recall. It would have been obvious to include the pinpointed recall of products as taught by RAMACHANDRAN in the system executing the method of BABU with the motivation to save money (see RAMACHANRAN ¶[0044]).
Claim(s) 6, 7, 9, 10, 13-15, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20050004811 A1 to BABU and US 20210010954 A1 to ADLER et al. as applied to claim 1 above, and further in view of in view of US 20020077857 A1 to Seelinger (hereinafter ‘SEELINGER’).
Claim 6 (Original)
The combination of BABU and ADLER discloses the method as set forth in claim 1.
The combination of BABU and ADLER does not specifically disclose, but SEELINGER discloses, further comprising: completion of an attestation process to update predefined information pertaining to the at least one product in the recall decision sub-system prior to the execution of the recall of the at least one product by the recall execution sub-system (see ¶[0023]-[0024] and [0130]; review and authorize recalls before the information is disseminated to the end user. See also abstract; provide timely medication data updates).
BABU discloses a recall management tool that permits an organization to manage events that indicate a need to initiate a product recall. SEELINGER discloses medication data management that includes recall information, where recalls are reviewed and authorized before the information regarding the recall is disseminated. It would have been obvious to include the authorization of recalls as taught by SEELINGER in the system executing the method of BABU with the motivation to process recalls.
Claim 7 (Original)
The combination of BABU, ADLER, and SEELINGER discloses the method as set forth in claim 6.
BABU does not specifically disclose, but SEELINGER discloses, wherein the completion of the attestation process is verified by a user authenticated to access the recall decision sub-system of the product recall management system (see ¶[0078]; security features include firewall security, and user logon name and password management employing a high level of physical security).
BABU discloses a recall management tool that permits an organization to manage events that indicate a need to initiate a product recall. SEELINGER discloses medication data management that includes recall information, where users login with a password. It would have been obvious to include the user authorization with a password as taught by SEELINGER in the system executing the method of BABU with the motivation to process recalls.
Claim 9 (Currently Amended)
BABU discloses a product recall management system (see abstract; a computerized recall management tool), comprising: a recall execution sub-system (see again abstract; a computerized recall management tool permits an organization to recognize and proactively manage events that can indicate a need to initiate a product recall) configured to: receive an approval to initiate a recall of at least one product based on an anomaly relating to the at least one product (see again abstract; manage events that can indicate a need to initiate a product recall. Recognize patterns of product defects).
BABU does not specifically disclose, but SEELINGER discloses, verify the approval to be provided by a user authorized to attest completion of predefined steps of a workflow implemented by a recall decision sub-system of the product recall management system (see ¶[0023]-[0024] and [0130]; review and authorize recalls before the information is disseminated to the end user).
BABU further discloses the workflow implementing a process of determining recall of products (see again abstract; manage regulatory reporting events and other notification milestones and to manage a recall itself);
initiate execution of recall of the at least one product based on the verification (see ¶[0015] and [0041] and Fig. 6; recognize patterns of product defects from product performance data, to alert operators when such patterns are detected, to manage regulatory reporting events and other notification milestones and to manage a recall itself. Determine whether the product is subject to recall 620);
access a quality events database to retrieve attributes of the at least one product, the attributes being associated with manufacturing of the at least one product (see abstract and ¶[0022] & [0024]; product performance data often is made available to an organization through very diverse communication channels, including from customers, distributors, suppliers, governmental or industry agencies in addition to its internal manufacturing and testing sources. The data interface unit 160 may solicit product defect data from various entities in the product's distribution chain. These can include various members from with the manufacturer's company itself. Exemplary internal sources include internal testing systems and quality control or quality management systems The data harvesting agent 210 may collect data from one or more of these sources and populate data structures according to a variety of performance dimensions);
identify an attribute from the retrieved attributes that contributes to the anomaly in the at least one product (see abstract and ¶[0015]; recognize patterns of product defects from product performance data, to model an extent to which a product defect may proliferate throughout its distributed products); and
determine an impact of the identified attribute on other products that are related to the at least one product (see again abstract and ¶[0015]; recognize patterns of product defects from product performance data, to model an extent to which a product defect may proliferate throughout its distributed products).
BABU does not specifically disclose, but ADLER discloses, retrieve at least one attribute of the other products related to the at least one product from the quality event database (see abstract and ¶[0035] and [0094]; identify the subset of components that have same or similar features with failed components to identify products that need to be included in a recall. Use a similarity metrics vector to calculate a similarity score);
compute a similarity score between the retrieved attributes of the at least one product and the at least one attribute of the other products using a weighted comparison of the corresponding attributes (see abstract and ¶[0035] and [0094]; identify the subset of components that have same or similar features with failed components to identify products that need to be included in a recall. Use a similarity metrics vector to calculate a similarity score. Weight particular attributes).
BABU further discloses communicate, to the recall decision sub-system, the identified attribute to initiate a risk assessment for the other products related to the at least one product (see ¶[0029], [0034], and [0045]-[0046]; parts manufacturer PM3 likely supplied defective component parts during a three month period. Product diffusion modeling may permit the alert process 240 to estimate the propagation of the defective component parts through its distribution chain. Recall notification to a distributor by contrast may include information identifying which batches are likely to contain defects and which are not).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. It would have been obvious for one of ordinary skill in the art at the time of invention to include the similarity score as taught by ADLER in the system executing the method of BABU to manage recalls.
BABU discloses a recall management tool that permits an organization to manage events that indicate a need to initiate a product recall. SEELINGER discloses medication data management that includes recall information, where recalls are reviewed and authorized before the information regarding the recall is disseminated. It would have been obvious to include the authorization of recalls as taught by SEELINGER in the system executing the method of BABU with the motivation to process recalls.
Claim 10 (Currently Amended)
The combination of BABU and SEELINGER discloses the system as set forth in claim 9.
The combination of BABU and SEELINGER does not specifically disclose, but ADLER discloses, wherein the recall execution sub-system is to: determine a degree of similarity between the identified attribute of the at least one product and a corresponding attribute of the other products (see abstract and ¶[0035] and [0094]; identify the subset of components that have same or similar features with failed components to identify products that need to be included in a recall. Use a similarity metrics vector to calculate a similarity score), wherein the similarity score is used to quantify the degree of similarity (see again ¶[0094]; calculate an overall similarity score based on the similarity metric vector between features).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. It would have been obvious for one of ordinary skill in the art at the time of invention to include the similarity score as taught by ADLER in the system executing the method of BABU to manage recalls.
Claim 13 (Original)
The combination of BABU, ADLER, and SEELINGER discloses the system as set forth in claim 9.
BABU does not specifically disclose, but SEELINGER discloses, further comprising: completion of an attestation process to update predefined information pertaining to the at least one product in the recall decision sub-system prior to the initiation of the execution of the recall of the at least one product by the recall execution sub-system (see ¶[0023]-[0024] and [0130]; review and authorize recalls before the information is disseminated to the end user. See also abstract; provide timely medication data updates).
BABU discloses a recall management tool that permits an organization to manage events that indicate a need to initiate a product recall. SEELINGER discloses medication data management that includes recall information, where recalls are reviewed and authorized before the information regarding the recall is disseminated. It would have been obvious to include the authorization of recalls as taught by SEELINGER in the system executing the method of BABU with the motivation to process recalls.
Claim 14 (Currently Amended)
BABU discloses a non-transitory computer-readable medium comprising instructions executable by a processing resource (see claim 17; computer readable medium executable by a processing device) to: receive a request to recall a batch of products identified as non-compliant based on a detected anomaly (see abstract; a computerized recall management tool permits an organization to recognize and proactively manage events that can indicate a need to initiate a product recall. Recognize patterns of product defects).
BABU does not specifically disclose, but SEELINGER discloses, determine the request to be approved by an authorized user (see ¶[0023]-[0024] and [0130]; review and authorize recalls before the information is disseminated to the end user).
BABU further discloses verify completion of predefined steps of a workflow implemented for determining recall of the batch of products (see ¶[0043] and Fig. 7; authorize remediation, indicate that remediation has been performed, engage verification procedures, and process compensation);
based on the verification, initiate the recall of the batch of products (see again abstract; manage regulatory reporting events and other notification milestones and to manage a recall itself);
access a quality events database to retrieve attributes of the batch of products, the quality events database comprising attributes associated with manufacturing of each product of the batch of the products (see abstract and ¶[0022] & [0024]; product performance data often is made available to an organization through very diverse communication channels, including from customers, distributors, suppliers, governmental or industry agencies in addition to its internal manufacturing and testing sources. The data interface unit 160 may solicit product defect data from various entities in the product's distribution chain. These can include various members from with the manufacturer's company itself. Exemplary internal sources include internal testing systems and quality control or quality management systems The data harvesting agent 210 may collect data from one or more of these sources and populate data structures according to a variety of performance dimensions);
identify one or more attributes from the retrieved attributes of the batch of products to be associated with the detected anomaly (see abstract and ¶[0015]; recognize patterns of product defects from product performance data, to model an extent to which a product defect may proliferate throughout its distributed products); and
determine if the one or more identified attributes affect other products related to the batch of products (see again abstract and ¶[0015]; recognize patterns of product defects from product performance data, to model an extent to which a product defect may proliferate throughout its distributed products).
BABU does not specifically disclose, but ADLER discloses, retrieve at least one attribute of the other products related to the batch of products from the quality events database (see abstract and ¶[0035] and [0094]; identify the subset of components that have same or similar features with failed components to identify products that need to be included in a recall. Use a similarity metrics vector to calculate a similarity score);
compute a similarity score between the retrieved attributes of the batch of products and the at least one attribute of the other products using a weighted comparison of corresponding attributes (see abstract and ¶[0035] and [0094]; identify the subset of components that have same or similar features with failed components to identify products that need to be included in a recall. Use a similarity metrics vector to calculate a similarity score. Weight particular attributes).
BABU further discloses cause the one or more identified attributes to be available for use in a workflow implemented for determining recall of other products related to the batch of products (see again abstract and ¶[0015]; recognize patterns of product defects from product performance data, to model an extent to which a product defect may proliferate throughout its distributed products).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. It would have been obvious for one of ordinary skill in the art at the time of invention to include the similarity score as taught by ADLER in the system executing the method of BABU to manage recalls.
BABU discloses a recall management tool that permits an organization to manage events that indicate a need to initiate a product recall. SEELINGER discloses medication data management that includes recall information, where recalls are reviewed and authorized before the information regarding the recall is disseminated. It would have been obvious to include the authorization of recalls as taught by SEELINGER in the system executing the method of BABU with the motivation to process recalls.
Claim 15 (Original)
The combination of BABU and SEELINGER discloses the non-transitory computer-readable medium as claimed in claim 14.
The combination of BABU and SEELINGER does not specifically disclose, but ADLER discloses, further comprising instructions executable by the processing resource to assess a degree of similarity between the one or more identified attributes of the batch of products and corresponding attributes of the other products to determine if the one or more identified attributes affect the other products (see abstract and ¶[0035] and [0094]; identify the subset of components that have same or similar features with failed components to identify products that need to be included in a recall. Use a similarity metrics vector to calculate a similarity score).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. It would have been obvious for one of ordinary skill in the art at the time of invention to include the similarity score as taught by ADLER in the system executing the method of BABU to manage recalls.
Claim 18 (Original)
The combination of BABU, ADLER and SEELINGER discloses the non-transitory computer-readable medium as claimed in claim 14.
BABU additionally discloses further comprising instructions executable by the processing resource to present a compilation of reports corresponding to instances of detection of the anomaly associated with the batch of products (see ¶[0036]-[0037]; firms are subject to specific requirements regarding the reporting of defective products).
Claim 19 (Original)
The combination of BABU, ADLER and SEELINGER discloses the non-transitory computer-readable medium as claimed in claim 14.
BABU further discloses further comprising instructions executable by the processing resource to verify the completion of the predefined steps prior to initiation of the recall of the batch of products (see ¶[0043] and Fig. 7; authorize remediation, indicate that remediation has been performed, engage verification procedures, and process compensation).
Claim(s) 4 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20050004811 A1 to BABU in view of US 20210010954 A1 to ADLER et al. as applied to claims 1 and 3 above, and further in view of US 11776024 B2 to Toren (hereinafter ‘TOREN’).
Claim 4 (Original)
The combination of BABU and ADLER discloses the method as set forth in claim 3.
The combination of BABU and ADLER does not specifically disclose, but TOREN discloses, wherein the degree of similarity is based on a weightage assigned to each of the one or more identified attributes that affect the other products (see col 30, ln 51-col 31, ln 12; score similarity between products by weighting particular attributes).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. TOREN discloses determining similarity between products based on a similarity score calculated using weighted attributes. It would have been obvious for one of ordinary skill in the art at the time of invention to weight attributes when determining similarity scores as taught by TOREN in the system executing the method of BABU and ADLER with the motivation to manage recalls.
Claim 5 (Original)
The combination of BABU, ADLER, and TOREN discloses the method as set forth in claim 4.
BABU does not specifically disclose, but TOREN discloses, wherein the weightage assigned to each of the one or more identified attributes that affect the other products is configurable based on a user input (see col 27, ln 4-33 and col 28, ln 4-12; merchant data may include data entered by the merchant through the user interface. Attributes are viewed on a user interface).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. TOREN discloses determining similarity between products based on a similarity score calculated using weighted attributes, where data is entered through a user interface. It would have been obvious for one of ordinary skill in the art at the time of invention to include the user interface as taught by TOREN in the system executing the method of BABU and ADLER with the motivation to manage recalls.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20050004811 A1 to BABU in view of US 20020077857 A1 to SEELINGER and US 20210010954 A1 to ADLER et al. as applied to claim 14 above, and further in view of US 20180285810 A1 to RAMACHANDRAN et al.
Claim 20 (Original)
The combination of BABU, ADLER, and SEELINGER discloses the non-transitory computer-readable medium as claimed in claim 14.
The combination of BABU, ADLER, and SEELINGER does not specifically disclose, but RAMACHANRDAN discloses, wherein the request to recall the batch of products received by the recall execution sub-system is independent of a risk assessment process carried out by the recall decision sub-system for the batch of products (see ¶[0044]; in the case of a recall, if the root cause was identified, specific shipments downstream of the incidents could also be retrieved in a pinpointed recall, saving a company millions compared to a blanket recall).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). RAMACHANRDAN discloses blockchain records in a supply chain to identify root causes of a recall to make a pinpointed recall of products, rather, than a blanket recall. It would have been obvious to include the pinpointed recall of products as taught by RAMACHANDRAN in the system executing the method of BABU with the motivation to save money (see RAMACHANRAN ¶[0044]).
Claim(s) 11, 12, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20050004811 A1 to BABU in view of US 20020077857 A1 to SEELINGER, US 20210010954 A1 to ADLER et al. as applied to claims 9 and 10 above, and further in view of US 11776024 B2 to TOREN.
Claim 11 (Original)
The combination of BABU, SEELINGER, and ADLER discloses the system as set forth in claim 10.
The combination of BABU, SEELINGER, and ADLER does not specifically disclose, but TOREN discloses, wherein the degree of similarity is based on a weightage assigned to the identified attribute that impacts the other products (see col 30, ln 51-col 31, ln 12; score similarity between products by weighting particular attributes).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. TOREN discloses determining similarity between products based on a similarity score calculated using weighted attributes. It would have been obvious for one of ordinary skill in the art at the time of invention to weight attributes when determining similarity scores as taught by TOREN in the system executing the method of BABU and ADLER with the motivation to manage recalls.
Claim 12 (Original)
The combination of BABU, SEELINGER, ADLER, and TOREN discloses the system as set forth in claim 11.
BABU does not specifically disclose, but TOREN discloses wherein the weightage assigned to the identified attribute that impacts the other products is configurable based on a user input (see col 27, ln 4-33 and col 28, ln 4-12; merchant data may include data entered by the merchant through the user interface. Attributes are viewed on a user interface).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. TOREN discloses determining similarity between products based on a similarity score calculated using weighted attributes, where data is entered through a user interface. It would have been obvious for one of ordinary skill in the art at the time of invention to include the user interface as taught by TOREN in the system executing the method of BABU and ADLER with the motivation to manage recalls.
Claim 16 (Currently Amended)
The combination of BABU, SEELINGER, and ADLER discloses the non-transitory computer-readable medium as claimed in claim 15.
The combination of BABU, SEELINGER, and ADLER does not specifically disclose, but TOREN discloses, wherein the degree of similarity is based on a weightage assigned to each of the one or more identified attributes that affect the other batches of products (see col 30, ln 51-col 31, ln 12; score similarity between products by weighting particular attributes) wherein the similarity score is used to quantify the degree of similarity (see again col 30, ln 51-col 31, ln 12; score similarity between candidate products based on attributes).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. TOREN discloses determining similarity between products based on a similarity score calculated using weighted attributes. It would have been obvious for one of ordinary skill in the art at the time of invention to weight attributes when determining similarity scores as taught by TOREN in the system executing the method of BABU and ADLER with the motivation to manage recalls.
Claim 17 (Original)
The combination of BABU, SEELINGER, ADLER, and TOREN discloses the non-transitory computer-readable medium as claimed in claim 16.
BABU does not specifically disclose, but TOREN discloses wherein the weightage assigned to each of the one or more identified attributes that affect the other batches of products is configurable based on a user input (see col 27, ln 4-33 and col 28, ln 4-12; merchant data may include data entered by the merchant through the user interface. Attributes are viewed on a user interface).
BABU discloses recall management that includes tracking defects in a distribution chain (see abstract and ¶[0018]). ADLER discloses x-ray image examination using similarity scores of products to products with failed components to determine products that need to be included in a recall. TOREN discloses determining similarity between products based on a similarity score calculated using weighted attributes, where data is entered through a user interface. It would have been obvious for one of ordinary skill in the art at the time of invention to include the user interface as taught by TOREN in the system executing the method of BABU and ADLER with the motivation to manage recalls.
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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/RICHARD N SCHEUNEMANN/ Primary Examiner, Art Unit 3624
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