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 .
Status of Claims
This action is in reply to the amendment/response filed on 23 Oct. 2025.
Claim 1 and 8 amended.
Claim 1-20 currently pending and have been examined.
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 U.S.C. 101 because the claimed invention is directed to a judicial exception 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, claim 8-14 is to a system (i.e., a machine), claim 1-7 to a method (i.e., a process), and claim 15-20 to a non-transitory computer-readable medium (i.e., a manufacture or machine). 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 claim 1 is:
obtaining an input that identifies a device type;
obtaining, based on the input, information indicating a configuration associated with the device type;
determining, …., compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type, …
determining a recommendation of one or more memory types, of the plurality of memory types, for the device type based on the compatibilities between the plurality of memory types and the device type; and
transmitting an indication of the recommendation of the one or more memory types.
The abstract idea of claim 8 is:
obtain review data indicating a review associated with a historical interaction relating to a memory type; ….
to identify that the review relates to compatibility of the memory type;
process the review data to identify one or more keywords indicative of a device type associated with the review;
obtain information indicating a hardware configuration and a software configuration associated
The Abstract idea of claim 15 is:
…
obtain information indicating a configuration associated with a device type; and
determine, …., a compatibility between the memory type and the device type based on the configuration associated with the device type,
…to determine a compatibility of the memory type with a given configuration based on review data indicating reviews associated with historical interactions relating to the memory type.
The abstract idea steps above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, organizing compatibility information for devices. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim is directed to organizing compatibility information for devices., which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. If a claim limitation, under its broadest reasonable interpretation, 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.
Step 2A Prong Two: 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). Further, in MPEP 2106.05(f) it is noted that "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology.
Claims 1, 8 and 15 recite the following additional elements:
using a plurality of machine learning models respectively associated with a plurality of memory types
wherein each machine learning model of the plurality of machine learning models is trained to determine a compatibility of a respective memory type, of the plurality of memory types, with a given configuration;
one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:
perform natural language processing of the review data
A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to:
using a machine learning model associated with a memory type
wherein the machine learning model is trained
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 para 66 of applicant’s specification as filed.
Examiner interprets machine learning described in para 66 of applicant’s specification as filed as additional elements. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, machine learning are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). 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.
Therefore, per Step 2A Prong Two, the 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 pertaining to 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).
Further, the combination of these elements is nothing more than a generic computing system with machine learning models. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Dependent claims 2-7, 9-14 and 16-20 further limit the abstract idea. The recitation of parsing and natural language processing is nothing more than a generic computer system.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory 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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim(s) 1, 3-4 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bikumala et al. (US 2022/0351066) further in view of Bikumala et al. (US2021/0233129).
Claim 1: Bikumala et al. (‘066) teach a method, comprising:
obtaining an input that identifies a device type; (see at least [21] (electronic assets (e.g. servers, displays, mobile devices, laptop computers, desktop computers…); [24] (IHS may be a desktop or laptop computer, mobile phone, mobile table…); [41] (database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset) of Bikumala et al. (‘066))
obtaining, based on the input, information indicating a configuration associated with the device type; (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset); [32]; [41] (telemetry data…inventory of parts… service records…database of electronic assets…telemetry data includes data …assess current health of electronic assets and/or part deployed… purchase orders and invoices include data relating to parts and/or electronic assets that have been purchased… global parts catalog includes part numbers and part specifications for all part types used in the electronic asset) of Bikumala et al. (‘066))
determining, using a plurality of machine learning models respectively associated with a plurality of memory types, compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type, (see at least Fig. 2 (exploded laptop w parts which includes RAM); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors) of Bikumala et al. (‘066))
determining a recommendation of one or more memory types, of the plurality of memory types, for the device type based on the compatibilities between the plurality of memory types and the device type; (see at least Fig. 2 (exploded laptop w parts which includes RAM); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [41] (database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors) of Bikumala et al. (‘066)) and
transmitting an indication of the recommendation of the one or more memory types. (see at least Fig. 4 (404, 406, 408), Fig. 9 (908, 910, 904) of Bikumala et al. (‘066))
Bikumala et al. (‘066) does not explicitly disclose:
wherein each machine learning model of the plurality of machine learning models is trained to determine a compatibility of a respective memory type, of the plurality of memory types, with a given configuration;
As noted in the Non-Final office action Bikumala et al. (‘066) teaches:
[23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models);
[41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…);
[40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [41] (database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset);
[42] (service records may assist in training one or more AI/ML models to identify compatible parts);
[51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…);
[65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors) of Bikumala et al. (‘066))
Bikumala (‘129) et al. wherein each machine learning model of the plurality of machine learning models is trained to determine a compatibility of a respective memory type, of the plurality of memory types, with a given configuration (see at least [23] (the part number 114 may include multiple attributes such as an attribute 120(1) to an attribute 120(N)… attributes 120 may include taxonomy data, and other attributes of the part identified by the part number 114… the attributes 120 of the memory part may include a capacity (e.g. 4 gigabytes (GB), 8 GB, 16 GB…) of the memory, a speed (e.g. DDR3, DDR4..) of the memory a form factor (e.g. dual inline memory module (DIMM), small outline DIMM (SO-DIMM), M.2, non-volatile memory express (NVME)) of the memory, a taxonomy associated with the memory and other attributes related to the memory… attributes 120 may include historical data …quality data… sub-assemblies that include the part, compatibility (e.g. behavioral characteristics) similarity (e.g. physical and electrical characteristics), version compatibility.); [39] 500 illustrating an exemplary taxonomy… taxonomy data 210… an intended user 402 may be either an enterprise customer 504 or a consumer 506. Device form factor 508 may be a rackmount device 510, desktop 512, laptop 514, table 516. Storage type 518 of a storage device maybe a hard disk 520, solid-state drive (SSD) 522, non-volatile memory express (NVME) 524. Storage size 528 of a storage device may include 256 GB 530, 512 GB 532 and 1 TB 534. Memory form factor 536 of a memory device may include a SO-DIMM 538 DDR3 RAM 540 DDR4 RAM 542. Memory size of a memory device may be one of 4 GB 544, 8 GB 546, 16 GB 548. Type of data that may be included in the taxonomy data 210);[40] 600 in which data associated with a new part is added to an intelligent parts catalog… taxonomy data 210 may be processed using the machine learning similarity score associated with each part in the taxonomy data 210 to create an updated taxonomy data 602. Updated taxonomy data 602 may use the similarity scores to group parts with a similar taxonomy… if a first part and a second part have the same taxonomy, the both the first and second part may have the same (or similar e.g. within a predetermined threshold) similar score) [Wingdings font/0xE0] note each part is run through the similarly scoring model separately and given its own similarly score based on taxonomy associated with the memory and other attributes related to the memory… compatibility (e.g. behavioral characteristics) similarity (e.g. physical and electrical characteristics), version compatibility.);[43] (flowchart process 700 that includes part numbers of similar parts ordered based on a similarity score of each similar part to a particular part); [44] (702 ML quality model, ML similarity scoring model, ML taxonomy model to create the intelligent parts catalog 116); [48] (ML quality model… parts reliability data 302 may be processed using the ML clustering 304 to cluster parts having a same or similar reliability score 322. ML classifier 306 used to determine, based on the reliability score 322 whether a part is considered sufficiently reliable to be included in a product of Bikumala et al. (‘129)). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) to wherein each machine learning model of the plurality of machine learning models is trained to determine a compatibility of a respective memory type, of the plurality of memory types, with a given configuration of Bikumala et al. (‘129) to since current parts catalogs are manually maintained and have no -built in intelligence to identify alternate parts if a particular part becomes unavailable, additionally current parts catalogs do not quantify how similar the alternate parts are to a particular part (see at least [4] of Bikumala (‘129)).
Claim 3: Bikumala et al. (‘066) in view of Bikumala et al. (‘129) teach the method of claim 1 above, Bikumala et al. (‘066) further disclose:
wherein the configuration is at least one of a hardware configuration associated with the device type or a software configuration associated with the device type. (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset) of Bikumala et al. (‘066)).
Claim 4: Bikumala et al. (‘066) in view of Bikumala et al. (‘129) teach the method of claim 3 above, Bikumala et al. (‘066) further disclose:
wherein the hardware configuration identifies at least one of one or more processors of the device type, a motherboard of the device type, one or more expansion cards of the device type, or one or more memory devices of the device type. (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset (#29 (HDD); #30 (optical drive); #23 (CPU); #16 (modem board); #26 (VGA board); #27 (Bluetooth board); #28 (infrared board)); [43]-[46] (HDD); [51]-[54] (GPU cards) of Bikumala et al. (‘066)).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Bikumala et al. (‘129) further in view of Burton et al. (US 9552429).
Claim 2: Bikumala et al. (‘066) further in view of Bikumala et al. (‘129) teach the method of claim 1 above, Bikumala et al. (‘066) in view of Bikumala et al. (‘129) do not explicitly disclose:
wherein obtaining the information indicating the configuration associated with the device type comprises: parsing a document relating to the device type to identify the configuration associated with the device type.
Bikumala et al. (‘066) in Fig. 2 teaches an exploded view of parts used in one example of an electronic asset. Bikumala et al. (‘066) further teaches a database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset [41].
Burton et al. teaches wherein obtaining the information indicating the configuration associated with the device type comprises: parsing a document relating to the device type to identify the configuration associated with the device type (see at least Fig. 1 (38 (Spec sheet)); col. 3, ll. 41-42 (structured data 38 may for example include spec sheets associated with located products); col. 4, ll. 36-37 (the attribute data 58 may be derived from any source, e.g. a spec sheet…) of Burton et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) to include product spec sheets of Burton et al. to allows for collection of structured data associated with electronic assets of Bikumala et al. (‘066). As stated above, Bikumala et al. (‘066) teaches the electronic assets 515 data source may include granular information such as, for example, the principal part used in the electronic assets, while Burton teaches this information can be extracted from product specification sheets and such information helps users with evaluating competing products that may have overlapping or non-overlapping features (see at least col. 1, l. 18, 21, 27-28 Burton et al.).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Bikumala et al. (‘129) further in view of Zomaya (US 2007/0180052).
Claim 5: Bikumala et al. (‘066) in view of Bikumala et al. (‘129) teach the method of claim 3 above, Bikumala et al. (‘066) in view of Bikumala et al. (‘129) do not explicitly disclose:
wherein the software configuration identifies at least one of a basic input/output system (BIOS) of the device type, an operating system of the device type, firmware of the device type, or application software of the device type.
Zomaya teaches wherein the software configuration identifies at least one of a basic input/output system (BIOS) of the device type, an operating system of the device type, firmware of the device type, or application software of the device type (see at least [29] (BIOS determines whether the computer's components are operational, and then loads OS files from the computer's hard drive or disk drive into the computer's RAM. BIOS takes an inventory of equipment and resources, and loads SMBIOS data such as configuration information and drivers into SMBIOS area 65. For example, the SMBIOS data contains data from chipset 20 regarding the memory module capacity of chipset 20. Additional SMBIOS data includes motherboard parameters 75 such as the manufacturer ID and a product ID of motherboard 10) of Zomaya). One of ordinary skill in the art would have been motivated to modify Bikumala et al. (‘066) to include identifying BIOS and/or OS of a device since optimizing the accuracy of an upgrade recommendation results in enhanced product reliability and component compatibility, an accurate upgrade recommendation should be based on the most reliable data in determining the currently system configuration (see at least [8] of Zomaya).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Bikumala et al. (‘129) further in view of Byron et al. (US 2019/0095973).
Claim 6: Bikumala et al. in view of Bikumala et al. (‘129) teach the method of claim 3 above, Bikumala et al. in view of Bikumala et al. (‘129) does not explicitly disclose:
wherein the plurality of machine learning models are trained based on review data indicating reviews associated with historical interactions relating to the plurality of memory types.
As noted above, Bikumala et al. (‘066) teaches identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models and identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models [23].
Byron et al. teach wherein the plurality of machine learning models are trained based on review data indicating reviews associated with historical interactions relating to the plurality of memory types. (see at least Abstract; [38] (machine learning of previous user queries, determine user would be dissatisfied with a particular feature or attribute’s omission from or unfavorable reviews in a product…). One of ordinary skill in the art would have been motivated to modify Bikumala et al. (‘066) to include wherein the plurality of machine learning models are trained based on review data indicating reviews associated with historical interactions relating to the plurality of memory types of Byron et al. since cohort (cohort product reviews extracted from social media sites, online retailers, online review sites, data analytics repositories) generation may allow a user with specific interests to receive information from a conversational system that more accurately predicts product attributes and features the user may deem favorable (see at least [44] of Byron et al.).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Bikumala et al. (‘129) further in view of Vincent et al. (US 7885862).
Claim 7: Bikumala et al. (‘066) in view of Bikumala et al. (‘129) teach the method of claim 1 above, Bikumala et al. (‘066) further disclose:
wherein the input indicates a [[model]] identifier that identifies the device type.
Bikumala et al. (‘066) in view of Bikumala et al. (‘129) do not explicitly disclose:
model identifier
Vincent et al. teaches model identifier (see at least col. 11, ll. 35-44 (memory card example, information in the databases include information detailing the types of memory cards that are compatible with a particular make/model of digital camera as well as identifying information for the selected type of memory card… the identifying information is of sufficient detail as to determine whether the memory card is compatible with the selected consumer electronics device, in this case a particular make/model of digital camera) of Vincent et al.). One of ordinary skill in the art would have been motivated to modify Bikumala et al. (‘066) to include model identifier as taught by Vincent since it important to keep compatibility databases updated including positive and negative user reviews for each item (see at least col. 7, ll. 15-19 of Vincent et al.).
Claim(s) 8-9, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vincent et al. in view of Byron et al. further in view of Bikumala et al. (‘066) further in view of Buchheit (US 2008/0228503).
Claim 8: A system, comprising: (see at least Fig. 1 & 2 of Vincent et al.)
one or more memories; (see at least Fig. 1 & 2 of Vincent et al.) and
one or more processors, communicatively coupled to the one or more memories, configured to: (see at least Fig. 1 & 2 of Vincent et al.)
obtain review data indicating a review associated with a historical interaction relating to a memory type; (see at least col. 5, ll. 36-40 (information describing a particular brand of removable memory card of the type commonly used in consumer electronic devices, such as digital cameras, smart phones and media players); col. 5, ll. 44-57 (information is obtained from databases located on one or more server…when an item is the subject of the request, the information can detail features and specifications of the item… included within both types of information are reviews or editorial comments from other users about the particular item… along with prices…); col. 7, ll. 15-22 (databases or other memory areas updated periodically… in addition to positively identifying compatibility between items, contrary indicators for compatibility include, for example a negative review by a user, a quantity of the items that are returned by the users, or an input from customer service centers regarding problems such as service difficulties, failure rates or poor performance of the items); col. 9, ll. 2-11 (memory card.. assume that the customer recently posted an item on their profile on a social networking web site that stated they were very satisfied with their particular make/model of digital camera…the identity of the camera can them form part of the purchase history); col. 9, ll. 22-24 (in the memory card example, a particular make/model of digital camera is a second item associated with the purchase history…col. 11, ll. 35-44 (memory card example, information in the databases include information detailing the types of memory cards that are compatible with a particular make/model of digital camera as well as identifying information for the selected type of memory card… the identifying information is of sufficient detail as to determine whether the memory card is compatible with the selected consumer electronics device, in this case a particular make/model of digital camera) of Vincent et al.)
perform [[natural language]] processing of the review data to identify that the review relates to compatibility of the memory type; (see at least col. 5, ll. 36-40 (information describing a particular brand of removable memory card of the type commonly used in consumer electronic devices, such as digital cameras, smart phones and media players); col. 5, ll. 44-57 (information is obtained from databases located on one or more server…when an item is the subject of the request, the information can detail features and specifications of the item… included within both types of information are reviews or editorial comments from other users about the particular item… along with prices…); col. 7, ll. 15-22 (databases or other memory areas updated periodically… in addition to positively identifying compatibility between items, contrary indicators for compatibility include, for example a negative review by a user, a quantity of the items that are returned by the users, or an input from customer service centers regarding problems such as service difficulties, failure rates or poor performance of the items); col. 11, ll. 35-44 (memory card example, information i-n the databases include information detailing the types of memory cards that are compatible with a particular make/model of digital camera as well as identifying information for the selected type of memory card… the identifying information is of sufficient detail as to determine whether the memory card is compatible with the selected consumer electronics device, in this case a particular make/model of digital camera) of Vincent et al.)
process the review data to identify one or more keywords indicative of a device type associated with the review data; (see at least col. 7, ll. 15-19 (databases or other memory areas storing compatibility information may be updated periodically. In additional positively identifying compatibility between items, contrary indicators for compatibility include, for example a negative reviews by a user); col. 9, ll. 29-39 of Vincent et al.)
information indicating whether the memory type and the device type are compatible based on the review data. (see at least col. 7, ll. 15-19 (databases or other memory areas storing compatibility information may be updated periodically. In additional positively identifying compatibility between items, contrary indicators for compatibility include, for example a negative reviews by a user); col. 11, ll. 35-43 (information in the databases include information detailing the types of memory cards that are compatible with a particular make/model of digital camera… identifying information is of sufficient detail as to determine whether the memory card is compatible with the selected consumer electronics device…) of Vincent et al.)
Vincent et al. do not explicitly disclose:
[[natural language]] processing
obtain information indicating a hardware configuration and a software configuration associated with the device type; and
provide, for use as training data for a machine learning model to be trained to determine compatibility between a given device type and the memory type, information indicating the at least one of the hardware configuration or the software configuration associated with the device type and
Byron et al. teach [[natural language]] processing (see at least [36] (the product attribute desirability program 110A, 110B identifies the product feature to which the receive user query relates. Using know natural language processing techniques, the product attribute desirability program 110A, 110B may identify the feature or attribute mentioned within the received user query) of Byron et al.). One of ordinary skill in the art would have been motivated to modify Vincent et al. to include the natural language processing of Byron et al. since conjoint analysis while analyzing a user query determines a proper response, conjoint analysis relates to a statistical technique that determines how individuals value various attributes and by leveraging conjoint analysis, more relevant results may be returned during an information/product exploration dialog between the user and conversation system since the desired answer to the conversational query about a specific product may be predicted (see at least [16] of Byron et al.)
Buchheit teaches obtain information indicating a hardware configuration and a software configuration associated with the device type; (see at least Fig. 1(Computing Device 110, Data Store 112[Wingdings font/0xE0] Hardware, Software 114); Fig. 4 (405[Wingdings font/0xE0]Subscriber registers device with compatibility system, 410 [Wingdings font/0xE0] Compatibility system ascertains hardware/software of device, 435 [Wingdings font/0xE0] Perform check with compatibility server to determine if selected item is compatible with identified system, 445 [Wingdings font/0xE0] optimally determine substitute items that are compatible, 465 [Wingdings font/0xE0] Optionally present compatibility report); [0019] (determining a compatibility of hardware/software items with a target computing system… (software, peripheral, expansion card, memory and other device) is compatible with target system…compatibility check can be automatically performed); [0020] (data store 112 of the computing device 110 can specify hardware and software details of the device 110); [0021] (specifics for a computing device 110 are determined, these specifics can be conveyed to the compatibility system 120… the compatibility system 120 can store and manage configuration information for multiple different devices 110 for a single subscriber or for multiple different subscribers… a configuration file 124 stored in data store 122 can be related to a subscriber’s home computer system (device 110)… compatibility system 120 can maintain configuration files 124 for a set of computing devices 110 used by a business entity…); [0023] (…compatibility system 120, which references an appropriate configuration file 124 associated with the target system. An item compatibility engine 130 can determine if each item is compatible with the target system based upon on details specified within the configuration file 124. .. engine 130 can determine if suitable hardware interfaces exist for attaching the item, can determine if minimum system requirements of the item are satisfied and can query historical data stores to determine if know problems exist that would negatively affect the use of a computing item within the target system);[0024] (computing device 110 can be any computing device for which a configuration file 124 can be generated.. computing device 110 can include a stand alone computing system, a thin client, a component of a larger computing system, and/or a peripheral device. Peripheral device (e.g. a copier within peripheral specific memory, interface cards and the like) can have upgradable hardware/firmware/software components. Computing device 110 can include, but is not limited to a computer, a server, a personal data assistant (PDA), a mobile phone, a wearable computer, an embedded computing system, a media player, a network-enabled consumer electronic device, an entertainment system, a network attached storage (NAS) device); [0025] (for-sale item can be any item made to interoperate or to be installed within the computing device 110. For-sale item can include hardware and software. For-sale item is associated with a desktop computer (device 110) for-sale item can include but is not limited to, RAM, an expansion card, a hard drive, a media drive, a CPU, a peripheral (USB, SATA, PATA, FIRMWIRE), a software application, a software extension or upgrade to a preexisting application. For-sale item can have platform compatibility concerns, hardware interface concerns, software driver concerns, minimum hardware/software requirements) of Buchheit. One of ordinary skill in the art would have been motivated to modify Vincent et al. with obtain information indicating a hardware configuration and a software configuration associated with the device type as taught by Buchheit since many times, consumers purchase items only to discover later that these items do not operate with their existing system (see at least [0005] of Buchheit).
Bikumala et al. (‘066) teach provide, for use as training data for a machine learning model to be trained to determine compatibility between a given device type and the memory type, information indicating the at least one of the hardware configuration or the software configuration associated with the device type and (see at least Fig. 2 (exploded laptop w parts which includes RAM); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors) of Bikumala et al.). One of ordinary skill in the art would have been motivated to modify Vincent et al. with obtain information indicating at least one of a hardware configuration or a software configuration associated with the device type; and provide, for use as training data for a machine learning model to be trained to determine compatibility between a given device type and the memory type, information indicating the at least one of the hardware configuration or the software configuration associated with the device type as taught by Bikumala et al. (‘066) since it is knows that organizations, such as large enterprises, often employ a wide range of IHSs for various purposes and IHSs often fail and must be repaired, emergency repair of critical IHSs may require immediate replacement of failed parts (see at least [3] of Bikumala et al. (‘066)).
Claim 9: Vincent et al. in view Byron et al. further in view of Bikumala et al. (‘066) further in view of Buchheit teach the system of claim 8 above, Vincent et al. does not explicitly disclose:
wherein the review data indicates a plurality of reviews associated with a plurality of historical interactions relating to the memory type, and
wherein the one or more processors are further configured to: determine that the review is in agreement, as to compatibility, with a majority of the plurality of reviews, wherein the one or more processors are configured to provide the information for use as the training data for the machine learning model based on determining that the review is in agreement with the majority of the plurality of reviews.
Byron et al. teach wherein the one or more processors are further configured to: determine that the review is in agreement, as to compatibility, with a majority of the plurality of reviews, wherein the one or more processors are configured to provide the information for use as the training data for the machine learning model based on determining that the review is in agreement with the majority of the plurality of reviews (see at least Abstract; [38] (machine learning of previous user queries, determine user would be dissatisfied with a particular feature or attribute’s omission from or unfavorable reviews in a product… [42] (desirability score); Table 1-2; [38] (product attribute desirability program 110A, 110B may utilize machine learning ) of Byron et al.). One of ordinary skill in the art would have been motivated to modify Vincent et al. to include wherein the one or more processors are further configured to: determine that the review is in agreement, as to compatibility, with a majority of the plurality of reviews, wherein the one or more processors are configured to provide the information for use as the training data for the machine learning model based on determining that the review is in agreement with the majority of the plurality of reviews of Byron et al. as cohort (cohort product reviews extracted from social media sites, online retailers, online review sites, data analytics repositories) generation may allow a user with specific interests to receive information from a conversational system that more accurately predicts product attributes and features the user may deem favorable, see at least [44] of Byron et al.).
Claim 12: Vincent et al. in view Byron et al. further in view of Bikumala et al. (‘066) further in view of Buchheit teach the system of claim 8 above, Vincent et al. further disclose:
wherein the one or more processors, to perform natural language processing of the review data, are configured to: perform semantic analysis of the review data to further identify a reason for incompatibility between the memory type and the device type; and generate a report that indicates the reason for incompatibility between the memory type and the device type. (see at least col. 5, ll. 36-40 (information describing a particular brand of removable memory card of the type commonly used in consumer electronic devices, such as digital cameras, smart phones and media players); col. 5, ll. 44-57 (information is obtained from databases located on one or more server…when an item is the subject of the request, the information can detail features and specifications of the item… included within both types of information are reviews or editorial comments from other users about the particular item… along with prices…); col. 7, ll. 15-22 (databases or other memory areas updated periodically… in addition to positively identifying compatibility between items, contrary indicators for compatibility include, for example a negative review by a user, a quantity of the items that are returned by the users, or an input from customer service centers regarding problems such as service difficulties, failure rates or poor performance of the items); col. 9, ll. 64-66 (the first item and selected second item are determine to be incompatible, the databases are consulted to determine other items that are compatible with the second item); col. 11, ll. 35-44 (memory card example, information i-n the databases include information detailing the types of memory cards that are compatible with a particular make/model of digital camera as well as identifying information for the selected type of memory card… the identifying information is of sufficient detail as to determine whether the memory card is compatible with the selected consumer electronics device, in this case a particular make/model of digital camera) of Vincent et al.)
Claim 13: Vincent et al. in view Byron et al. further in view of Bikumala et al. (‘066) further in view of Buchheit teach the system of claim 8 above, Vincent et al. does not explicitly disclose:
wherein the hardware configuration identifies at least one of one or more processors of the device type, a motherboard of the device type, one or more expansion cards of the device type, or one or more memory devices of the device type.
Bikumala et al. (‘066) teach wherein the hardware configuration identifies at least one of one or more processors of the device type, a motherboard of the device type, one or more expansion cards of the device type, or one or more memory devices of the device type (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset (#29 (HDD); #30 (optical drive); #23 (CPU); #16 (modem board); #26 (VGA board); #27 (Bluetooth board); #28 (infrared board)); [43]-[46] (HDD); [51]-[54] (GPU cards) of Bikumala et al.). One of ordinary skill in the art would have been motivated to modify Vincent et al. with the hardware configuration identifies at least one of one or more processors of the device type, a motherboard of the device type, one or more expansion cards of the device type, or one or more memory devices of the device type as taught by Bikumala et al. (‘066) since it is knows that organizations, such as large enterprises, often employ a wide range of IHSs for various purposes and IHSs often fail and must be repaired, emergency repair of critical IHSs may require immediate replacement of failed parts (see at least [3] of Bikumala et al. (‘066)).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vincent et al. in view of Byron et al. further in view of Bikumala et al. (‘066) further in view of Buchheit further in view of Teplinsky et al. (US2018/0267506).
Claim 10: Vincent et al. in view Byron et al. further in view of Bikumala et al. (‘066) further in view of Buchheit teach the system of claim 8 above, Vincent et al. does not explicitly disclose:
wherein the one or more processors are further configured to:
obtain information identifying an additional device type of a device that uses a memory device of the memory type and identifying results of a memory speed test for the memory device performed on the device; determine, based on the results of the memory speed test, whether the memory type is compatible with the additional device type; obtain information indicating a configuration associated with the additional device type; and providing, for use as training data for the machine learning model, information indicating the configuration associated with the additional device type and indicating whether the memory type and the additional device type are compatible based on the results of the memory speed test.
Teplinsky et al. teach obtain information identifying an additional device type of a device that uses a memory device of the memory type and identifying results of a memory speed test for the memory device performed on the device; determine, based on the results of the memory speed test, whether the memory type is compatible with the additional device type; obtain information indicating a configuration associated with the additional device type; and providing, for use as training data for the machine learning model, information indicating the configuration associated with the additional device type and indicating whether the memory type and the additional device type are compatible based on the results of the memory speed test (see at least [22] (smart pairing in data may be acquired by receiving manufacturing data for each component, applying a set of compatibility rules to the manufacturing data for each component to determine pairing data, applying a set of paring rules to the pairing data to determine one or more actions to be performed for minimizing a probability of failure of the product under all operating conditions or failure of the product during testing at next stages); [23] (if a memory chip has a low operating frequency, the memory chip may fail a test sequence testing its operating speed…); [29] (product 100 may be formed of components 101-107.. each component may play a role in the operation of product 100… component 102 may be a memory device that interact with one another during operation of product 100… components 101-107 may interact with one another to enable the motherboard to perform various functions …);[31] (assembly of product 100 may be performed by assembling components 101-107 together.. each component sources form a supplier who manufactures the component…);[32]if component 101 is within specification but is relatively fast and component 102 is within specification but relatively slow, the limitations of component 102 may limit capability of component 101, preventing component 101 from being used to its full potential and resulting in a product 100 having below-average performance characteristics…);[33] (smart pairing); [34] (test tool configured to run test sequences to ensure the product is operational and not defective); [59] (memory chip has a max clock speed); [69] (Manufacturing data of component A 502 and component B 504 indicate that they are both approximately within an expected range (e.g., a range centered around a peak of a distribution representing the general population of corresponding components… For instance, if component A 502 is a memory chip having an operational speed that is below a threshold number of standard of deviations away from the center of its distribution and component B 504 is a processor having a processing speed that is also below a threshold number of standard of deviations away from the center of its distribution, pairing the processor with the memory chip may result in a product that performs within expectations and according to what is desired by a business model.);[71] ( a set of compatibility rules applied by a compatibility model may indicate that this pairing would result in a product that performs better than components in a neutral pairing. For instance, if component A 512 is a memory chip having an operational speed that is a threshold number of standard of deviations higher than its center of distribution and component B 514 is a processor having a processor speed that is a threshold number of standard of deviations higher than its center of distribution, pairing a fast memory chip with a fast processor may result in a situation where the product operates at a fast speed altogether, resulting in a relatively superior product when compared to a product having components that have a neutral pairing metric. In such embodiments, it would be beneficial to have this pairing in a product that will constantly require maximum performance from components A and B, 512 and 514. Accordingly, the compatibility model may output a positive metric 516.); [82] (manufacturing data… determining compatibility between two products); [90] (memory speed) of Teplinsky et al.). One of ordinary skill would have been motivated to modify Vincent with testing components in a product of Teplinsky et al. since the measure of compatibility expresses the effect of the pairing of the components on expected performance of the product containing the two or more component, the effect can be neutral (e.g. the pairing performs on par with the expected performance of the product), positive (e.g. the pairing performs better than the expected performance of the product) (see at least [60] of Teplinsky et al.).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vincent et al. in view of Byron et al. further in view of Bikumala et al. (‘066) further in view of Buchheit further in view of Zomaya.
Claim 11: Vincent et al. in view Byron et al. further in view of Bikumala et al. (‘066) further in view of Buchheit teach the system of claim 8 above, Vincent et al. does not explicitly disclose:
further comprising: obtain, based on execution of software in a memory device, of the memory type, upon installation of the memory device in a device, information identifying an additional device type of the device; determine that the memory type is compatible with the additional device type based on obtaining the information identifying the additional device type; obtain information indicating a configuration associated with the additional device type; and provide, for use as training data for the machine learning model, information indicating the configuration associated with the additional device type and indicating that the memory type and the additional device type are compatible.
Zomaya teach further comprising: obtain, based on execution of software in a memory device, of the memory type, upon installation of the memory device in a device, information identifying an additional device type of the device; determine that the memory type is compatible with the additional device type based on obtaining the information identifying the additional device type; obtain information indicating a configuration associated with the additional device type; and provide, for use as training data for the machine learning model, information indicating the configuration associated with the additional device type and indicating that the memory type and the additional device type are compatible (see at least [25] (the current hardware configuration is detected by reading serial present detect data, motherboard parameters…detecting of the current hardware configuration bay be facilitate by reading other OS data and/or performing tests to determine certain computer characteristics… information gather is cross references with database of product specs); [31] (detect module is programmed to detect current hardware configuration of client computer…task ‘d’ refers to accessing OS data such as system name and/or operating system version… task ‘e’ refers to performing a test or tests to determine certain computer characterizes such as memory utilization or CPU speed.); [33] memory module contains multiple memory chips (e.g. SDRAM and/or RAMBUS), and SPD chip which is typically EPROM and an SMBIL area of RAM for storing SMBIOS data …[54] (the OS version on client computer is Windows XP and it is also determined that the RAM on client computer is 64MB, the recommended upgrade pkg could include additional RAM because industry or manufacturer standers require more that 64MB RAM to run Windows XP…);[56] (tests to determining memory utilization/CPU speeds); [57] (configurations may be established for all computers on the network substantially simultaneously); [62] (data gathered by detect module will be cross-referenced with a product spec database to more accurately determine current hardware configuration of client computer; product spec database stores specs for many oor all know hardware components, including compatibility restrictions with other hardware components…); [63] (cross-referencing the data gathers related to motherboard, the memory expansion slot capability of motherboard may be determined from database 90a) of Zomaya). One of ordinary skill in the art would have been motivated to modify Vincent et al. with the detect module, upgrade suggestions as taught by Zomaya since optimizing the accuracy of an upgrade recommendation results in enhanced product reliability and component compatibility, an accurate upgrade recommendation should be based on the most reliable data in determining the current system configuration (see at least [8] of Zomaya).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vincent et al. in view of Byron et al. further in view of Bikumala et al. (‘066) further in view of Buchheit further in view of Zomaya.
Claim 14: Vincent et al. in view Byron et al. further in view of Bikumala et al. (‘066) teach the system of claim 8 above, Vincent et al. does not explicitly disclose:
wherein the software configuration identifies at least one of a basic input/output system (BIOS) of the device type, an operating system of the device type, firmware of the device type, or application software of the device type.
Zomaya teaches wherein the software configuration identifies at least one of a basic input/output system (BIOS) of the device type, an operating system of the device type, firmware of the device type, or application software of the device type (see at least [29] (BIOS determines whether the computer's components are operational, and then loads OS files from the computer's hard drive or disk drive into the computer's RAM. BIOS takes an inventory of equipment and resources, and loads SMBIOS data such as configuration information and drivers into SMBIOS area 65. For example, the SMBIOS data contains data from chipset 20 regarding the memory module capacity of chipset 20. Additional SMBIOS data includes motherboard parameters 75 such as the manufacturer ID and product ID of motherboard 10) of Zomaya). One of ordinary skill in the art would have been motivated to modify Vincent et al. to include identifying BIOS and/or OS a device of Zomaya since optimizing the accuracy of an upgrade recommendation results in enhanced product reliability and component compatibility, an accurate upgrade recommendation should be based on the most reliable data in determining the currently system configuration (see at least [8] of Zomaya).
Claim(s) 15-16, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Byron et al.
Claim 15:
Bikumala et al. disclose:
A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: (see at least Fig. 1 of Bikumala et al. (‘066))
one or more instructions that, when executed by one or more processors of a device, cause the device to: (see at least Fig. 1 of Bikumala et al. (‘066))
obtain information indicating a configuration associated with a device type; (see at least Fig. 2 (exploded laptop w parts which includes RAM); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors) of Bikumala et al. (‘066)) and
determine, using a machine learning model associated with a memory type, a compatibility between the memory type and the device type based on the configuration associated with the device type, (see at least Fig. 2 (exploded laptop w parts which includes RAM); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors) of Bikumala et al.(‘066))
Bikumala et al. (‘066) does not explicitly disclose:
wherein the machine learning model is trained to determine a compatibility of the memory type with a given configuration based on review data indicating reviews associated with historical interactions relating to the memory type.
As noted above, Bikumala et al. (‘066) teaches identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models and identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models [23].
Byron et al. teach wherein the machine learning model is trained to determine a compatibility of the memory type with a given configuration based on review data indicating reviews associated with historical interactions relating to the memory type (see at least Abstract; [38] (machine learning of previous user queries, determine user would be dissatisfied with a particular feature or attribute’s omission from or unfavorable reviews in a product…). One of ordinary skill in the art would have been motivated to include wherein the machine learning model is trained to determine a compatibility of the memory type with a given configuration based on review data indicating reviews associated with historical interactions relating to the memory type of Byron et al. since cohort (cohort product reviews extracted from social media sites, online retailers, online review sites, data analytics repositories) generation may allow a user with specific interests to receive information from a conversational system that more accurately predicts product attributes and features the user may deem favorable, see at least [44] of Byron et al.).
Claim 16: Bikumala et al. (‘066) in view of Byron et al. teach the CRM of claim 15 above, Bikumala et al. (‘066) further disclose:
wherein the one or more instructions, when executed by the one or more processors, further cause the device to: determine whether to recommend the memory type as being compatible with the device type based on the compatibility between the memory type and the device type that is determined using the machine learning model. (see at least Fig. 2 (exploded laptop w parts which includes RAM); [23] (identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models); [41] (a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…); [40] (the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning); [42] (service records may assist in training one or more AI/ML models to identify compatible parts); [51] (GPU cards subject to upgrade and/or repair… the total memory on the GPU card…); [65] (recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors) of Bikumala et al.)
Claim 20: Bikumala et al. (‘066) in view of Byron et al. teach the CRM of claim 15 above, Bikumala et al. (‘066) further disclose:
The non-transitory computer-readable medium of claim 15,
wherein the configuration is at least one of a hardware configuration associated with the device type or a software configuration associated with the device type. (see at least Fig. 2 (exploded view of parts used in one example of an electronic asset) of Bikumala et al. (‘066)).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Byron et al., further in view of Angelo et al. (US 20230099700).
Claim 17: Bikumala et al. (‘066)in view of Byron et al. teach the CRM of claim 15 above, Bikumala et al. (‘066) does not explicitly disclose:
wherein the machine learning model is further trained based on at least one of memory speed test data or software execution data.
Angelo et al. teach wherein the machine learning model is further trained based on at least one of memory speed test data or software execution data (see at least [52] (before allowing the operating system patch to be installed and/or warn the user 106 about the potential problem. The image block 204 may be created based on hardware 103/software 105 incompatibilities, such as, changing hardware 103 and using an existing or new device driver. For example, if a large number of anomaly blocks 205 are added to the HFS blockchain 122 after a new device driver is installed, this may indicate a potential incompatibility. The process could use thresholds/amount of changes, etc. to determine when to add a new image block 204 to the HFS blockchain 122. Other factors/information may be stored in the HFS blockchain 122 for accessing risk, such as, who/when/what/an amount of change, etc.); [53] (machine learning module 124 may use unsupervised machine learning based on feedback from the device management module 102 where problems/anomalies occur between different versions of hardware 103, firmware 104, and/or software 105… The machine learning may identify potential anomalies/failures based on past combinations.) of Angelo et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) with the identifying of one or more anomalies associated with a plurality of changes of hardware, firmware and/or software and identifying the one or more anomalies associated with a plurality of changes of the hardware, software, and/or firmware in a communication device since it has been known that when hardware/firmware/software in a device has changed, at times, the change can cause problems to occur where the changes cause the device to not work properly and since the changes to the device are not consistently tracked, it is sometimes difficult to truly know the cause of failure (see at least [2] of Angelo et al.).
Claim(s) 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala et al. (‘066) in view of Byron et al., further in view of Burton et al.
Claim 18: Bikumala et al. (‘066) in view of Byron et al. teach the CRM of claim 15 above, Bikumala et al. (‘066) does not explicitly disclose:
wherein the one or more instructions, that cause the device to obtain the information indicating the configuration associated with the device type, cause the device to: parse a document relating to the device type to identify the configuration associated with the device type.
Bikumala et al. (‘066) in Fig. 2 teaches an exploded view of parts used in one example of an electronic asset. Bikumala et al. (‘066) further teaches a database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset [41].
Burton et al. teaches wherein the one or more instructions, that cause the device to obtain the information indicating the configuration associated with the device type, cause the device to: parse a document relating to the device type to identify the configuration associated with the device type (see at least Fig. 1 (38 (Spec sheet)); col. 3, ll. 41-42 (structured data 38 may for example include spec sheets associated with located products); col. 4, ll. 36-37 (the attribute data 58 may be derived from any source, e.g. a spec sheet…) of Burton et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) to include product spec sheets of Burton et al. to allows for collection of structured data associated with electronic assets of Bikumala et al. (‘066). As stated above, Bikumala et al. (‘066) teaches the electronic assets 515 data source may include granular information such as, for example, the principal part used in the electronic assets, while Burton teaches this information can be extracted from product specification sheets and such information helps users with evaluating competing products that may have overlapping or non-overlapping features (see at least col. 1, l. 18, 21, 27-28 Burton et al.).
Claim 19: Bikumala et al. (‘066) in view of Byron et al. teach the CRM of claim 15 above, Bikumala et al. (‘066) does not explicitly disclose:
wherein the one or more instructions, that cause the device to obtain the information indicating the configuration associated with the device type, cause the device to: retrieve the information indicating the configuration associated with the device type from a data structure.
Bikumala et al. (‘066) in Fig. 2 teaches an exploded view of parts used in one example of an electronic asset. Bikumala et al. (‘066) further teaches a database of electronic assets…the electronic assets 515 data source include data relating to the electronic assets of the organization including, for example, an asset identifier, an asset classification…the electronic assets 515 data source may also include granular information such as, for example, the principal parts used in the electronic asset [41].
Burton et al. teaches wherein the one or more instructions, that cause the device to obtain the information indicating the configuration associated with the device type, cause the device to: retrieve the information indicating the configuration associated with the device type from a data structure (see at least Fig. 1 (38 (Spec sheet)); col. 3, ll. 41-42 (structured data 38 may for example include spec sheets associated with located products); col. 4, ll. 36-37 (the attribute data 58 may be derived from any source, e.g. a spec sheet…) of Burton et al.). One of ordinary skill in the art would be motivated to modify Bikumala et al. (‘066) to include product spec sheets of Burton et al. to allows for collection of structured data associated with electronic assets of Bikumala et al. (‘066). As stated above, Bikumala et al. (‘066) teaches the electronic assets 515 data source may include granular information such as, for example, the principal part used in the electronic assets, while Burton teaches this information can be extracted from product specification sheets and such information helps users with evaluating competing products that may have overlapping or non-overlapping features (see at least col. 1, l. 18, 21, 27-28 Burton et al.).
Response to Arguments
Applicant's arguments filed 23 October 2025 have been fully considered but they are not persuasive.
101:
Applicant argues:
claim 1 recites “using a plurality of machine learning models respectively associated with a plurality of memory types, compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type,” claim 8 recites “provide, for use as training data for a machine learning model to be trained to determine compatibility between a given device type and memory type, information indicating the at least one of the hardware configuration or the software configuration associated with the device type and indicating whether the memory type and the device type are compatible based on the review,” and claim 15 recites “determine, using a machine learning model associated with a memory type , a compatibility between the memory type and the device type based on the configuration associated with the device type.” However each of these features clearly show that none of claims 1, 8 and 15 are directed to “commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors and/or business relations” as alleged by the Office Action. As such, claims 1, 8 and 15 are not directed to abstract ideas.
…
Applicant respectfully submits that the above mental process alleged by the Examiner cannot practically be preformed in the human mind. For example, Applicant respectfully submits that a human mind cannot practically “us[e] a plurality of machine learning models respectively associated with a plurality of memory types, compatibility’s between the plurality of memory types and the device type based on the configuration associated with the device types,” as recited by claim 1; “provide, for use as training data for a machine learning model to be trained to determine compatibility between a given device type and the memory type, information indicating the at least one of the hardware configuration or software configuration associated with the device type and indicating whether the memory type and the device type are compatible based on the review,” as recited by claim 8; nor “determine, using machine learning model associated with a memory type, a compatibility between the memory type and the device type based on the configuration associated with the device type,” as recited by claim 15. Each of these features simply cannot be done in the human mind and inherently requires technology.
Examiner response:
The Examiner respectfully disagrees,
Under Step 2A, Prong 1, the claims recite an abstract idea, specifically mental processes, because they broadly recite collecting information, analyzing compatibility information, classifying or determining compatibility, generating recommendations, and preparing training data. Representative offending clauses include:
The claims as drafted still recite an abstract idea because the claim language is directed, at a broad level, to:
gathering information,
evaluating/classifying compatibility,
making a recommendation, and
preparing training data,
without reciting a specific technical mechanism for improving computer technology or another technology.
The claims are directed to:
collecting device-related/configuration/review information,
analyzing that information to assess memory/device compatibility,
generating or using training data for a machine learning model,
recommending memory types, and
outputting/transmitting the result.
The claims are drafted at a functional, results-oriented level. They do not recite:
a specific ML architecture,
a specific training algorithm,
a specific feature representation,
a particular parameter update technique,
a technical mechanism that improves memory hardware operation,
or a technical mechanism that improves how the ML model itself operates.
Claim 1 (mental process) [Wingdings font/0xE0] Step2A, Prong 1:
claim 1: “determining … compatibilities … based on the configuration” and “determining a recommendation”
Under BRI, claim 1 describes receiving information about a device, evaluating which memory types are compatible, deciding which should be recommended, and communicating the result. That is fundamentally an observation/evaluation/judgment/recommendation workflow.
A human technician or product expert, using compatibility knowledge, charts, prior experience, or historical data, could conceptually:
identify the device type,
look up the configuration,
compare that configuration to known memory compatibility information,
determine which memory types are compatible, and
recommend one or more memory types.
The claim’s recitation of “using a plurality of machine learning models” does not, by itself, remove the claim from the mental-process grouping, because the claim does not recite any specific technical implementation of those models. The models are used as black-box tools to produce a classification or recommendation.
Claim 8 (mental process) [Wingdings font/0xE0] Step2A, Prong 1:
claim 8: “perform natural language processing … to identify that the review relates to compatibility”, “process the review data to identify one or more keywords”, and “provide … training data”
Under BRI, claim 8 is directed to:
collecting reviews,
reading them to see if they discuss compatibility,
identifying what device type is being discussed,
obtaining configuration information for that device,
and assembling a labeled training example indicating compatibility.
That is still an information analysis and categorization process. A human reviewer could review text, decide whether it discusses compatibility, extract the device type mentioned, consult configuration information, and then create a training spreadsheet or labeled record.
The fact that the claim says “perform natural language processing” does not rescue it at Prong 1 because the claim does not require any specific NLP architecture or technical mechanism; it merely states the desired result at a functional level.
Claim 15 (mental process) [Wingdings font/0xE0] Step2A, Prong 1:
claim 15: “determine … a compatibility between the memory type and the device type based on the configuration”
Claim 15 broadly covers obtaining configuration information and determining compatibility using a model trained from historical review data. Under BRI, this reads on classification/evaluation of whether a memory type is compatible with a device type.
Again, the claim does not recite a specific technical algorithm, model structure, feature transformation, or hardware interaction. It claims the result of compatibility determination, not a technical improvement in how the computer performs that task.
Step 2A, Prong 2:
Under Step 2A, Prong 2, the claims do not integrate the abstract idea into a practical application. The additional elements recite only generic computer implementation—e.g., processors, memories, machine learning models, NLP, data retrieval/parsing, and transmitting output—without reciting a specific technological improvement to computer functionality, memory technology, or ML/NLP operation itself. The claims do not recite a meaningful tie to a particular machine, a transformation, or a specific technical mechanism beyond using generic computing tools to perform the abstract analysis.
Claims 1, 8 and 15:
the claims do not recite a specific improvement to computer functionality,
do not recite a specific improvement to memory technology,
do not tie the abstract analysis to a particular machine in a meaningful way,
do not transform an article,
and instead recite generic computer/ML/NLP implementation, data gathering, analysis, labeling, recommendation, and output.
The claims do not integrate the recited abstract idea into a practical application.
Under Step 2B, the additional elements, individually and as an ordered combination, do not amount to significantly more than the abstract idea. The specification describes the computing environment, processors, memory, networking, ML algorithms, and NLP operations at a high level of generality, evidencing that these elements are well-understood, routine, and conventional. The claims therefore amount to no more than implementing the abstract idea on generic computer components.
Accordingly, claims 1, 8, and 15 are ineligible, and claims 2-7, 9-14, and 16-20 fall with their respective base claims, as the dependent limitations merely add further data sources, data content, filtering, reporting, or routine retrieval/parsing steps, without curing the eligibility defect.
102 Bikumala:
Applicant response:
Applicant argues that the cited sections of the applied reference do not disclose at least “determining, using a plurality of machine learning models associated with a plurality of memory types, compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type, wherein the each machine learning model of the plurality of machine learning models is trained to determine a compatibility of a respective memory type, of the plurality of memory types with a given configuration,” as recited in claim 1 (as amended).
Examiner response:
Regarding: “determining, using a plurality of machine learning models respectively associated with a plurality of memory types, compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type”, Bikumala et al. (‘066) teaches the following:
Fig. 2 (exploded laptop w parts which includes RAM);
[23] [Wingdings font/0xE0] identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models;
[41] [Wingdings font/0xE0] a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…;
[40] the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning;
[42] service records may assist in training one or more AI/ML models to identify compatible parts;
[51] GPU cards subject to upgrade and/or repair… the total memory on the GPU card…;
[65] recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors
[22] Bikumala et al. (‘066) teach systems, methods, and computer-readable mediums are disclosed to maintain and repair electronic assets (e.g., servers, displays, mobile devices, laptop computers, desktop computers, routers, wireless access points, cables, etc.) of an organization. Certain embodiments of the disclosed systems are implemented with an appreciation that medium and large size datacenters may maintain a low-level inventory of essential parts, such as hard disk drives (HDDs), displays, graphics processing units (GPUs), cooling fans, cooling components, network cards, keyboard components, etc., that are needed to repair such electronic assets. … Please note that the current components installed on within the electronic asset are considered compatible with the organizations electronic assets since those electronic assets are working properly, prior to a component failing.
[41] of Bikumala et al. (‘066) shows a system 500 in which information from a plurality of data sources 502 may be derived to classify parts pursuant to determining the compatibility between parts…. The data sources 502 include one or more of (1) telemetry data 504, (2) purchase orders and invoices 506 (3) an inventory of parts at the organization 508 (4) global parts catalog 510 (5) service records 512 (6) parts images 514 (7) databases of electronic assets… telemetry data 504 includes data that may be used to assess the current health of electronic assets and/or parts deployed within an organization… purchase orders and invoices 506 include data relating to parts and/or electronic assets that have been purchased by the organization… a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. …The global parts catalog 510 may include part numbers and part specifications available from multiple vendors. In certain instances, different vendors may identify parts having similar part specifications using part numbers that are unique to the different vendors. Such information is useful in identifying compatible parts and the availability of such compatible parts….In certain embodiments, the electronic assets 515 data source may also include more granular information such as, for example, the principal parts used in the electronic asset.
[42] of Bikumala et al. (‘066) teach further service records 512 include information identifying electronic assets and/or corresponding parts that have been subject to service. Such service, for example, may include an identification of parts that have failed and been replaced, an identification of the parts used to replace failed parts, the dates of service, the reasons that the part failed, etc. In certain embodiments, the data in the service records 512 may assist in training one or more AI/ML models to identify compatible parts.
Bikumala et al. (‘066) teach “determining, using a plurality of machine learning models respectively associated with a plurality of memory types, compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type”. As pointed out above Bikumala et al. (‘066) a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. …The global parts catalog 510 may include part numbers and part specifications available from multiple vendors. Such information is useful in identifying compatible parts and the availability of such compatible parts….In certain embodiments, the electronic assets 515 data source may also include more granular information such as, for example, the principal parts used in the electronic asset. Bikumala et al. (‘066) further teach service records 512 include information identifying electronic assets and/or corresponding parts that have been subject to service. Such service, for example, may include an identification of parts that have failed and been replaced, an identification of the parts used to replace failed parts, the dates of service, the reasons that the part failed, etc. In certain embodiments, the data in the service records 512 may assist in training one or more AI/ML models to identify compatible parts.
The Examiner maintains that Bikumala ‘066 teaches “determining, using a plurality of machine learning models respectively associated with a plurality of memory types, compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type”.
The examiner notes that claim 1 was amended to recite “wherein each machine learning model of the plurality of machine learning models is trained…” Applicant arguments are moot in view of new grounds of rejection and this amended limitation has been addressed above under the rejection above with newly added reference Bikumala ‘129.
103 Vincent, Byron, Bikumala:
Applicant response:
The references doe not disclose at least “obtain information indicating a hardware configuration and a software configuration associated with the device type,” as recited in claim 8, as amended.
Examiner response:
Applicants argument is moot in view of new grounds of rejection, the amended claim has been addressed above adding reference Buchheit (US 2008/0228503).
103 Bikumala and Byron:
Applicant response:
The references do not disclose at least “determine, using a machine learning model associated with a memory type, a compatibility between the memory type and the device type based on the configuration associated with the device type,” as recited by claim 15.
Examiner response:
The Examiner notes claim 15 was not amended.
Regarding: “determine, using a machine learning model associated with a memory type, a compatibility between the memory type and the device type based on the configuration associated with the device type,”, Bikumala et al. (‘066) teaches the following:
Fig. 2 (exploded laptop w parts which includes RAM);
[23] [Wingdings font/0xE0] identifying parts that are compatible with a part needed for replacement in the repair of an electronic asset using AI/ML models;
[41] [Wingdings font/0xE0] a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. The global parts catalog 510 may include part numbers and part specifications available from multiple vendors… different vendors may identify parts having similar part specifications … such information is useful in identifying compatible parts and the availability of such compatible parts… global parts catalog 510 used in the categorization and/or identification of compatible parts…;
[40] the generation of the trained AI/ML parts similarity model may include both unsupervised and supervised learning;
[42] service records may assist in training one or more AI/ML models to identify compatible parts;
[51] GPU cards subject to upgrade and/or repair… the total memory on the GPU card…;
[65] recommended parts include alternative compatible parts, where the alternative parts include parts not currently used in any of the plurality of electronic assets but available from one or more vendors
[22] Bikumala et al. (‘066) teach systems, methods, and computer-readable mediums are disclosed to maintain and repair electronic assets (e.g., servers, displays, mobile devices, laptop computers, desktop computers, routers, wireless access points, cables, etc.) of an organization. Certain embodiments of the disclosed systems are implemented with an appreciation that medium and large size datacenters may maintain a low-level inventory of essential parts, such as hard disk drives (HDDs), displays, graphics processing units (GPUs), cooling fans, cooling components, network cards, keyboard components, etc., that are needed to repair such electronic assets. … Please note that the current components installed on within the electronic asset are considered compatible with the organizations electronic assets since those electronic assets are working properly, prior to a component failing.
[41] of Bikumala et al. (‘066) shows a system 500 in which information from a plurality of data sources 502 may be derived to classify parts pursuant to determining the compatibility between parts…. The data sources 502 include one or more of (1) telemetry data 504, (2) purchase orders and invoices 506 (3) an inventory of parts at the organization 508 (4) global parts catalog 510 (5) service records 512 (6) parts images 514 (7) databases of electronic assets… telemetry data 504 includes data that may be used to assess the current health of electronic assets and/or parts deployed within an organization… purchase orders and invoices 506 include data relating to parts and/or electronic assets that have been purchased by the organization… a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. …The global parts catalog 510 may include part numbers and part specifications available from multiple vendors. In certain instances, different vendors may identify parts having similar part specifications using part numbers that are unique to the different vendors. Such information is useful in identifying compatible parts and the availability of such compatible parts….In certain embodiments, the electronic assets 515 data source may also include more granular information such as, for example, the principal parts used in the electronic asset.
[42] of Bikumala et al. (‘066) teach further service records 512 include information identifying electronic assets and/or corresponding parts that have been subject to service. Such service, for example, may include an identification of parts that have failed and been replaced, an identification of the parts used to replace failed parts, the dates of service, the reasons that the part failed, etc. In certain embodiments, the data in the service records 512 may assist in training one or more AI/ML models to identify compatible parts.
Bikumala et al. (‘066) teach “determining, using a plurality of machine learning models respectively associated with a plurality of memory types, compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type”. As pointed out above Bikumala et al. (‘066) a global parts catalog 510 includes part numbers and part specifications for all part types used in the electronic asset of the organization. …The global parts catalog 510 may include part numbers and part specifications available from multiple vendors. Such information is useful in identifying compatible parts and the availability of such compatible parts….In certain embodiments, the electronic assets 515 data source may also include more granular information such as, for example, the principal parts used in the electronic asset. Bikumala et al. (‘066) further teach service records 512 include information identifying electronic assets and/or corresponding parts that have been subject to service. Such service, for example, may include an identification of parts that have failed and been replaced, an identification of the parts used to replace failed parts, the dates of service, the reasons that the part failed, etc. In certain embodiments, the data in the service records 512 may assist in training one or more AI/ML models to identify compatible parts.
The Examiner maintains that Bikumala ‘066 teaches “determine, using a machine learning model associated with a memory type, a compatibility between the memory type and the device type based on the configuration associated with the device type,”.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sarah M Monfeldt whose telephone number is (571)270-1833. The examiner can normally be reached M-F.
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/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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