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 .
DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Status of the Claims
Claims 21-38 are currently pending.
Claims 21 and 31 are independent.
Claims 21 and 31 are amended.
The rejection under 35 USC 101 is maintained.
Response To Applicant Remarks
Applicant’s well-articulated remarks have been considered but are unpersuasive for the reasons below.
Regarding the rejection under 35 USC 101, Applicant argues that the claimed invention is a practical application of an abstract idea. (Applicant’s 1/5/26 remarks, p.11, “Applicant asserts that the Tracker site integrates any alleged judicial exception into a practical application under Step 2A Prong Two by providing a technical improvement to accessing data from multiple locations by showing "a graph of the historical trend of the inventory item count across the plurality of locations," as required by limitation (b).”)”). The examiner respectfully disagrees.
The examiner notes that generally collecting, analyzing, manipulating, and displaying data has not found to be patent eligible. (Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016); see also University of Florida Research Foundation v. GE Company, 916 F. 3D 1363 (Fed. Cir. 2019), The claims at issue covered the conversion of data received from a medical monitoring machines into a "machine independent format," and then doing further analysis on that data. The court viewed the claim as one directed toward collecting, analyzing, manipulating, and displaying data. Since the basic process was previously performed using pen and paper, the court referred to this as a quintessential "do it on a computer" patent.”; See also U.S. Patent 7,062,251, claim 1, “1. A method of integrating physiologic treatment data comprising the steps of:
receiving physiologic treatment data from at least two bedside machines; converting said physiologic treatment data from a machine specific format into a machine independent format within a computing device remotely located from said bedside machines;
performing at least one programmatic action involving said machine-independent data; and
presenting results from said programmatic actions upon a bedside graphical user interface.”) The examiner respectfully suggests that Applicant’s invention bears more similarity to these patent ineligible cases. Although the case law may be silent on the display of a graph of a historical trend, the examiner respectfully suggests that a human analyst could graph and present a trendline of data, if that was pertinent to analyzing the data. The examiner does not consider this to be significantly more than an abstract idea.
Applicant’s amendments are addressed by the newly cited art.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Regarding independent claims 21,38 the claimed invention recites an abstract idea without significantly more. The claims recite the abstract idea of cataloging inventory items which is a mental process. Other than reciting a devices, sensor, model, database nothing in the claims precludes the steps from being performed mentally. But for the devices, sensor, model, database the limitations on collect information, request for cataloging, verify request, generate class, search and identify duplicate data, generate count and trend of item data across locations, update data entry, access updated data is a process that under its broadest reasonable interpretation could be performed by mentally but for the recitation of generic computer elements claim limitations, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, cataloging inventory items could also fall under the organizing human activity (a “fundamental economic practice”) of abstract ideas. Thus, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. The computers are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Simply implementing the abstract idea on a generic computer environment is not a practical application of the abstract idea and does not take the claim out of the mental process or organizing human activity grouping.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of the abstract idea into a practical application, the additional element of a devices, sensor, model, database amounts to no more than mere instructions to apply the exception using a generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Collecting, analyzing and displaying information, and receiving and transmitting over a network are conventional in the computing arts. (MPEP 2106.05h; See also MPEP 2106.05, Alice v. CLS, “. Nearly every computer will include a ‘communications controller’ and ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”).] The claims are not patent eligible.
Regarding the dependent claims, these claims are directed to limitations which serve to limit the item cataloging steps. The subject matter of claims 22 (type of sensor is lidar), 23 (item has label or barcode), 24 (item is a tool), 25 (characteristic is physical), 26 (model is FNN or RNN), 27 (item is an adapter substrate transitory or battery), 28 (verify request using second process), 29 (third processor generates model), 30 (third location has inventory and sensor), 32 (reject unauthorized request), 33 comparing values with different item), 34 (search for item based on characteristic), 35 (update database when no entry class found), 36 (count inventory into class), 37 (creating subclass of inventory), 38 (input as a vector with dynamic weight) appear to add additional steps to the abstract idea, implemented by generic computers. To the extent that machine learning concepts are claimed (eg. Claims 26, 38), these do not appear to be significantly more than applying mathematical concepts using generic computers. These claims do not introduce additional limitations which are significantly more than an abstract idea. They provide descriptive details that offer helpful context, but have no impact on statutory subject matter eligibility.
Therefore the limitations on the invention, when viewed individually and in ordered combination are directed to in-eligible subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 21-31,33,34 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Hawkins 20210012889 in view of
In view of “How to assign new data to an existing clustering”, 2017, https://stats.stackexchange.com/questions/258711/how-to-assign-new-data-to-an-existing-clustering, hereinafter Stackexchange in view of
Lee US20210304121A1 in view of
Dong 20140114964
Regarding Claim 21,
a facility disposed at a first location, comprising:
Hawkins is directed to a system for tracking inventory. (Hawkins, abstract). Hawkins discloses that inventory sensors could be disposed at different locations. (Hawkins, para 0068, “[0068] FIG. 3 shows an exemplary embodiment, when the sensor is utilized at a location where a medical procedure may take place. In some instances, the sensors may be used for additional functions beyond inventory management. For instance, the same sensor may be used track a surgical procedure or surgical site, and also to keep track of inventory. Alternatively, different sensors may be used for inventory management and for the medical procedure. The sensors may optionally be supported by a medical console, the storage unit, or a portion of the room, such as a wall, ceiling, or furniture.”)
a first inventory item, a first sensor configured to collect information about the first inventory item, and a first computing device in data communication with the first sensor, wherein the first computing device is configured to send a request for cataloging the first inventory item using the collected information about the first inventory item;
Hawkins discloses cataloguing inventory using a sensor. (Hawkins, para 0006, “[0006] In some embodiments, the enterprise resource planning application further comprises a reporting module receiving the practitioner response and updating at least one of the first item stock, the second item stock, the first item utilized quantity, and the second item utilized quantity based on the practitioner response. In some embodiments, at least one of the first item and the second item comprise a tool, an instrument, an implant, a medicine, an ointment, or a practitioner device. In some embodiments, at least one of the first indicator and the second indicator comprise a barcode, a QR code, an image, a label, an icon, an alphanumeric identification, a weight, or any combination thereof. In some embodiments, the sensor comprises a camera, a video camera, an RFID reader, a radar, a sonar, a LIDAR, a thermal imaging camera, a scale, a button, or any combination thereof.”)
a second computing device disposed at a second location in data communication with the first computing device, wherein the second computing device comprises one or more computer processors, and wherein the second computing device is configured to: verify the request for cataloging the first inventory item, …
(Hawkins, para 0006, “In some embodiments, the inventory module further receives a practitioner item confirmation, a practitioner item note, or both, wherein the practitioner item confirmation and the practitioner item note are associated with the first indicator.”)
a third computing device disposed at a third location, wherein the third computing device is configured to access the updated database.
(Hawkins, para 0049, “[0049] The user device may be a tablet, computer (e.g., laptop, desktop), smartphone, personal digital assistant, or any other device that may be capable of network communications. The user device may communicate with one or more other remote devices 125. The communications 130 may occur directly or over a network. A remote user 127 may interact with the remote device. In some instances, data from the user device may be used to update information stored in a remote device (e.g., database) and/or cloud computing infrastructure. The remote user may be able to access the information.”)
Hawkins does not explicitly disclose
generate a catalog model based on the request for cataloging the first inventory item, determine, using an output of the catalog model, a class of the first inventory item,
Stackexchange is a online software developer knowledgebase. (Stackexchange, p.1). Stackexchange discloses that a clustering scheme may be used to classify new data, but that clustering may need to be rerun in the future, because new data may render the existing clustering obsolete. (Id., “Make a classifier where you treat the labels you assigned during clustering as classes. When new points appear, use the classifier you trained using the data you originally clustered, to predict the class the new data have (ie. the cluster they are in). At some-point though you should redo this clustering-classification procedure, the clustering will most probably evolve after you accumulate enough new data so the original class/cluster member will be obsolete.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Hawkins with the model application of Stackexchange with the motivation of classifying new data. Id.
Hawkins does not explicitly disclose
search, using an output of the catalog model, a database for a data entry belonging to the class of the first inventory item,
identify a duplicate data entry of the first inventory item, generate a data entry of the first inventory item based on the class of the first inventory item, and update the database with the data entry of the first inventory item; and
Lee is directed to a system for deduplicating products. (Lee, abstract). Lee discloses that the system may use machine learning classify products to find and eliminate duplicates. (Lee, para 0027, “In some implementations, the online matching system may use a machine learning model to determine a match score between the first product and each of the second products. The match score may be calculated using the tagged keywords associated with the first product and the second products. The match score may be calculated using any combination of methods (e.g., Elasticsearch, Jaccard, naïve Bayes, W-CODE, ISBN, etc.). For example, the match score may be calculated by measuring the spelling similarities between the keywords of the first product and the keywords of the second product. In some embodiments, the match score may be calculated based on the number of shared keywords between the first product and the second product. The machine learning model of the online matching system may determine that the first product is identical to one of the second products when the match score is above a predetermined threshold (e.g., the second product with the highest match score and a minimum number of matching attributes, the second product associated with the highest match score, the second product with the highest match score and a price within a certain price range, etc.). The machine learning model may then modify the database to include data indicating that the first product is identical to the second product, thereby merging the products into a single listing and preventing product duplication. The machine learning model may determine that the first product is not any of the second products when the match scores do not meet a predetermined threshold. The machine learning model may then modify database to include data indicating that the first product is not any of the second products, thereby listing the first product as a distinct new listing.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Hawkins and Stackexchange with the deduplication of Lee with the motivation of improving product search. (Lee, background)
Hawkins does not explicitly disclose
wherein the data entry comprises a current count of the inventory item and a historical trend of the inventory item count across a plurality of locations,
and
Dong is directed to a system for searching data in a supply chain. (Dong, abstract). Dong discloses that the system may aggregate inventory information from multiple locations and display past/trend information for the inventory. (Dong, para 0105, “[0105] FIG. 12 is an exemplary illustration of displaying search results based on a material in an exemplary supply chain management workspace in accordance with an embodiment of the invention. As shown in FIG. 12, an exemplary supply chain management workspace 1200, e.g. PW Pro, can display a supply chain graph 1201 and a trend chart 1202 for a search term including "Arab Heavy" 1210. Here, the supply chain graph displays supply chain information associated with Arab heavy crude (e.g. the supply, consume, produce, trade, demand, exchange, and transfer information) at all locations, and the trend chart displays inventory projection for the Arab heavy crude at all locations.”; para 0049, “[0049] Furthermore, the supply chain management workspace can include built-in reports and editors that can display information on various aspects of a supply chain, such as Supply & Demand Balance, Inventory Projection, Stock Transfers, Trades, Inventory Projection, Inventory Constraint Violations, Actual Inventory, Demand Forecast, Exchanges, Inventory Limits, Prices, Plant Production, and Recipes.”).
a Tracker site, wherein the Tracker site is configured to show a graph of the historical trend of the inventory item count across the plurality of locations on the third computing device.
The system could be web based. (Dong, para 0029, “[0029] In accordance with an embodiment of the invention, the supply chain management workspace can be either a supply chain management software application, e.g. SIMTO Planning Workspace Pro (PW Pro), or a web service that is based on the supply chain management software.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Hawkins, Stackexchange and Lee with the inventory data of Dong with the motivation of making assisting supply chain decisions. (Dong, summary).
Regarding Claim 22, Hawkins, Stackexchange, Lee and Dong disclose the system of claim 21.
wherein the first sensor comprises a Lidar sensor.
See prior art rejection of claim 1 regarding Hawkins.
Regarding Claim 23, Hawkins, Stackexchange, Lee and Dong disclose the system of claim 21.
wherein the first inventory item comprises at least one selected from the group consisting of a label and a barcode.
See prior art rejection of claim 1 regarding Hawkins.
Regarding Claim 24, Hawkins, Stackexchange, Lee and Dong disclose the system of claim 21.
wherein the first inventory item is a mechanical tool.
See prior art rejection of claim 1 regarding Hawkins.
Regarding Claim 25, Hawkins, Stackexchange, Lee and Dong disclose the system of claim 21.
Hawkins does not explicitly disclose
wherein the output of the catalog model comprises at least one characteristic value corresponding to a physical characteristic of the first inventory item.
Lee discloses the model may account for characteristics of the inventory items. (Lee, para 0016-17, “System 800 may receive candidates 801 from candidate search system 650 and construct product clusters 802. The products in each cluster 802 may be similar (e.g., share at least one product image). System 800 may the tokenize product clusters 802 in a manner similar to tokenization described above.
System 800 may then calculate token vectors 804. Each feature may represent a dimension for a token vector 804. Features may include characters (e.g., “a”, “b”, “c”, etc.), contexts (e.g., foreign, group score of token in product cluster, position score, percentage of existing products that contain a token, character placement, number of different vendors involved in an alphanumeric namespace, alphanumeric namespace confidence score), format (e.g., banned product, age range, gender, clothing size, floating number, digit, alphanumeric digit, English words, Korean words, word length, weight, length, volume, quantity, etc.), statistics (e.g., is token from exposed attributes for requested products, number of times token is used in exposed attributes, number of vendors who have this token, number of products that have this token, number of categories that have this token, where token appears most, percentage of tokens in exposed attributes, etc.), location (e.g., how often is token in brand field, model number field, search tags, manufacturing field, SKU field, barcode field, CQI brand field, color field, etc.), statistics rate (e.g., increase velocity of global exposed count, increase velocity of average full position score, etc.), statistics relative rate (e.g., average global token count of all tokens of a product, minimum global token count of all tokens of a product, etc.), or general product pair level feature (e.g., normalized product identification gap, sales price difference, total product count of product cluster, percentage of shared Korean text, etc.).”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Hawkins and Stackexchange with the features of Lee with the motivation of improving product search. (Lee, background)
Regarding Claim 26, Hawkins, Stackexchange, Lee and Dong disclose the system of claim 21.
wherein the catalog model comprises at least one selected from the group consisting of a feedforward neural network (FNN) and a recurrent neural network (RNN).
(Hawkins, para 0028, “The item may be recognized based on image recognition techniques. In some instances, machine learning techniques may be used to aid in identification of the item. In some embodiments, the machine learning may utilize deep convolution neural networks/Faster R-CNN Nast NasNet (COCO). The machine learning may utilize any type of convolutional neural network (CNN) and/or recurrent neural network (RNN). Shift invariant or space invariant neural networks (SIANN) may also be utilized. Image classification, object detection and object localization may be utilized. Any machine learning technique known or later developed in the art may be used. For instance, different types of neural networks may be used, such as Artificial Neural Net(ANN), Convolution Neural Net (CNN), Recurrent Neural Net (RNN), and/or their variants.”)
Regarding Claim 27, Hawkins, Stackexchange, Lee and Dong disclose the system of claim 21.
wherein a class of the first inventory item comprises at least one selected from the group consisting of an adapter, a substrate, a transistor, and a battery.
See prior art rejection of claim 1 regarding Hawkins. The examiner interprets a medical implant to be a type of adapter.
Regarding Claim 28, Hawkins, Stackexchange, Lee and Dong disclose the system of claim 21.
wherein a first processor of the one or more computer processors is configured to command a second process of the one or more computer processors …
Hawkins discloses that the system may employ parallel processing to perform tasks. (Hawkins, para 0083, “[0083] Referring to FIG. 5, in a particular embodiment, a digital processing device 501 is programmed or otherwise configured to create an enterprise resource planning application. The device 501 is programmed or otherwise configured to create an enterprise resource planning application. In this embodiment, the digital processing device 501 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 505, which is optionally a single core, a multi core processor, or a plurality of processors for parallel processing.”)
to verify the request for cataloging the first inventory item.
(Hawkins, para 0006, “In some embodiments, the inventory module further receives a practitioner item confirmation, a practitioner item note, or both, wherein the practitioner item confirmation and the practitioner item note are associated with the first indicator.”)
Regarding Claim 29, Hawkins, Stackexchange, Lee and Dong disclose the system of claim 28.
wherein the first processor, in response to verification, is configured to command a third processor to generate the catalog model.
See prior art rejection of claim 1 regarding Stackexchange.
Regarding Claim 30, Hawkins, Stackexchange, Lee and Dong disclose the system of claim 21.
wherein the third location comprises:
a second inventory item; and
a second sensor in data communication with the third computing device, wherein the second sensor is configured to collect information about the second inventory item, and
wherein the third computing device is configured to send, to the second computing device, a request for cataloging the second inventory item using the collected information about the second inventory item.
See prior art rejection of claim 1 regarding Hawkins. The examiner interprets that the system of Hawkins is capable of sensing inventory items using multiple devices at multiple locations.
Regarding Claim 31, See prior art rejection of claim 21.
Regarding Claim 33, Hawkins, Stackexchange, Lee and Dong disclose the method of claim 31.
wherein determining, using an output of the catalog model, a class of the first inventory item comprises comparing at least one characteristic value of the first inventory item with a characteristic value of a different inventory item in the database.
See prior art rejection of claim 31 regarding Lee.
Regarding Claim 34, Hawkins, Stackexchange, Lee and Dong disclose the method of claim 31.
wherein the searching, using the output of the catalog model, the database for the data entry belonging to the class of the first inventory item comprises:
searching within the database for at least one characteristic value of the first inventory item,
wherein a selection of the at least one characteristic value of the first inventory item is based on the output of the catalog model.
See prior art rejection of claim 31 regarding Lee.
Claim(s) 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Hawkins 20210012889 in view of
In view of “How to assign new data to an existing clustering”, 2017, https://stats.stackexchange.com/questions/258711/how-to-assign-new-data-to-an-existing-clustering, hereinafter Stackexchange in view of
Lee US20210304121A1 in view of Dong
In view of AAPA
Regarding Claim 32, Hawkins, Stackexchange, Lee and Dong disclose the method of claim 31.
Hawkins does not explicitly disclose
wherein processing further comprises rejecting the request for cataloging the first inventory item upon determining that the first computing device does not have authority to make a cataloging request.
The examiner notes that at least Stackexchange discloses a ML system may be requested to catalog new data (see above), but does not disclose the request could be requested based on requester authority. AAPA teaches that a computing system may assign roles and permissions to users as to what functions they may execute and prevent execution of unapproved acts. For example, a user might have full administrative access and be able to execute all functions and modify data, or conversely a low level user might have read only access and not be able to execute functions that may alter data, or a user might fall anywhere in-between. Applying the concept of permissions to a particular function (cataloging) is understood to be an obvious modification. Presumably any computer function could be restricted based on permissions assigned to an end user. The claim would have been obvious because a particular known technique (user permission) was recognized as part of the ordinary capabilities of one skilled in the art, and combining it with cataloging would have yielded a predictable result.
Claim(s) 35,36 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Hawkins 20210012889 in view of
In view of “How to assign new data to an existing clustering”, 2017, https://stats.stackexchange.com/questions/258711/how-to-assign-new-data-to-an-existing-clustering, hereinafter Stackexchange in view of
Lee US20210304121A1 in view of Dong
In view of “Defining Item Group in SAP Business One – Defining Group Overview”, 2017, https://www.vinasystem.com/en/blogs/customers/defining-item-group-in-sap-business-one-defining-item-group-overview, hereinafter “Vinasystem”
Regarding Claim 35 Hawkins, Stackexchange, Lee and Dong disclose the method of claim 31.
Hawkins does not explicitly disclose
wherein, upon finding no data entry belonging to the class of the first inventory item,
See prior art rejection of claim 21 regarding Stackexchange. The examiner notes that a new data point may very well not fit an existing scheme in a trained classification model, because data evolves. This may necessitate retraining the model to determine new clustering. (Stackexchange, p.2, “As mentioned in the comments, at some point though we should redo this clustering-classification procedure because the clustering will most probably evolve after we accumulate enough new data. This "evolution" will be first noticed on points that lay close to the border of two clusters. To that extent, a single new point might "pull" the centre of a cluster away from that border-point enough to lead into a change of cluster membership, ie. render our original cluster/class assignment obsolete. When we should retrain is again not well defined; I would suggest as soon as we have computational time or we believe that the underlying structure of our data should have changed substantially (ie. we have concept-drift).”)
updating the database with the data entry of the first inventory item comprises retrieving the class of the first inventory item and restructuring the database for the data entry.
Vinasystem discloses that a user of SAP may add new item groupings and change the item group an item is assigned to. (Vinasystem, section 2/Item Groups in the Item Master) As Stackexchange indicates a machine learning classification of inventory may very well evolve, the examiner submits that it would be common sense to update an ERP system to achieve consistency with the identified classification determined through machine learning. The claim would have been obvious because “a person of ordinary skill has good reason to pursue the known options within his or her technical grasp (implementing a classification as determined through ML). If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.”
Regarding Claim 36, Hawkins, Stackexchange, Lee and Dong disclose the method of claim 31.
wherein, upon finding a data entry belonging to the class of the first inventory item, updating the database with the data entry of the first inventory item comprises counting the first inventory item into the class of the first inventory item.
See prior art rejection of claim 35 regarding Vinasystem. The examiner understands that a user of SAP could assign an item to a previously defined item group in SAP should it be a compatible classification.
Claim(s) 37,38 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Hawkins 20210012889 in view of
In view of “How to assign new data to an existing clustering”, 2017, https://stats.stackexchange.com/questions/258711/how-to-assign-new-data-to-an-existing-clustering, hereinafter Stackexchange in view of
Lee US20210304121A1 in view of Dong in view of
Raja, “A Clustering Classification of Spare Parts for Improving Inventory Policies”, 2015, https://iopscience.iop.org/article/10.1088/1757-899X/114/1/012075/pdf
Regarding Claim 37, Hawkins, Stackexchange, Lee and Dong disclose the method of claim 31.
Hawkins does not explicitly disclose
wherein processing further comprises:
identifying that a first group of data entries within the class share a common characteristic value corresponding to a common characteristic that is not shared with a second group of data entries within the class; and
creating a subclass with the first group of data entries.
Raja discloses that different clusters (some characteristics are shared and some are not) could be treated as subclasses for the purpose of inventory policy). (Raja, table 6) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Hawkins, Stackexchange and Lee with the subclasses of Raja with the motivation of implementing an efficient inventory policy. (Raja, abstract).
Regarding Claim 38, Hawkins, Stackexchange, Lee and Dong disclose the method of claim 31.
Hawkins does not explicitly disclose
wherein generating a catalog model based on the request for cataloging the first inventory item comprises: generating a set of inputs from the collected information about the first inventory item, and transforming the set of inputs into a vector by calculating a dynamic weight of an individual key from the set of inputs, wherein the dynamic weight represents a relative importance of the individual key to a sequenced element in the output of the catalog model.
However is limitation is obvious in view of the Introduction of Raja. Raja discloses that researchers in inventory classification have considered weighting inventory data criteria to perform classification. (Raja, p.2, “Therefore, many researchers conducted also a research on inventory classification based on multi
criteria. Bacchaetti et al. [6] did inventory classification with spare parts and held in Italian household
appliance manufacturing company using four criteria. There are life-cycle phase of the related final
product, volume, critically, and competition. Kabir & Sumi [7] held the research at Energypac
Engineering Limited (EEL), a large power engineering company in Bangladesh using multi criteria
inventory classifications through integrating Fuzzy Delphi Method (FDM) with Fuzzy Analytical
Hierarchy Process (FAHP). The criteria used in this research are unit price, annual demand, critically,
last used date, and durability. Hadi-Vencheh and Mohamadghasemi [8] used Fuzzy Analytical
Hierarchy Process (FAHP) to determine the weights of criteria, linguistic terms such as Very High,
High, Medium, Low and Very Low to assess each item under each criterion. The Data Envelopment
Analysis (DEA) method was used to determine the values of the linguistic terms, and the Simple
Additive Weighting (SAW) method to aggregate item scores under different criteria into an overall
score for each item. “) Although Raja does not appear to employ weighting of data in its classifier, the examiner respectfully suggests that the technique was well known and understood and could have been used had a particular data feature been deemed particularly important. The claim would have been obvious because “a person of ordinary skill has good reason to pursue the known options within his or her technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN C CHEIN whose telephone number is (571)270-7985. The examiner can normally be reached Monday-Friday 8am -5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian Zeender can be reached at (571) 272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLEN C CHEIN/Primary Examiner, Art Unit 3627
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