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 .
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.
Claim(s) 1,3-6,9,11-14, 17-24 are rejected under 35 U.S.C. 103 as being unpatentable over Cheetham (2004/0068339) in view of Park et al (20220293216).
As per claim 1, Cheetham (2004/0068339) teaches a computer implemented method comprising:
transmitting, by one or more computing devices, data to cause an interactive user interface to render via a user device (para 0046 – the user tier contains a material search interface, such as JavaServerpages; fig. 6 para 0013);
obtaining, by the one or more computing devices, one or more data packets indicative of user input comprising a
parsing, in response to obtaining the one or more data packets indicative of the
obtaining, by the one or more computing devices, from the index, one or more data packets indicative of one or more candidate chemical materials that match the one or more features (para 0046 in view of para 0025, - The network, tier accepts the user's inputs, and then performs the search/data query over the database layer. The search results may then be returned to the user via the user tier.);
ranking, by the one or more computing device, the one or more candidate chemical materials based on one or more associated characteristics of each respective candidate chemical material (para [0005], [0028]- the highest scores are given to materials having property values that exceed the acceptable property values, or that are well within the specified ranges of acceptable property values; because a material is not eliminated from the search just because it does not match one property, it is possible to have a material that exceeds the desired property values for a few properties rank higher (i.ee, be listed as a better overall match; properties having higher priorities can be given greater weight than properties having lower priorities when the overall match score of the material is being calculated. Finally, embodiments of the systems and methods of this invention can allow materials to be ranked in descending order according to their calculated overall match score. So that the material(s) that best matches the desired properties is readily identifiable by a user);
and transmitting, by the one or more computing devices, one or more data packets which cause the user device to display the one or more candidate chemical materials and the one or more associated characteristics of each respective candidate chemical material ((para [0005], [0028]. [0046], properties having higher priorities can be given greater weight than properties having lower priorities when the overall match score of the material is being calculated, which can allow materials lo be ranked in descending order according to their calculated overall match score, so that the materials that best matches !he desired properties is readily identifiable by a user).
As per claim 1, Cheetham (2004/0068339) teaches the processing of user queries into chemical materials, and the availability of the chemical materials, as applied above; however, Cheetham (2004/0068339) does not explicitly teach an expanded natural language querying of chemical materials, as well as processing a user request toward a further query of the chemical material, towards application of the chemical material/properties; Park et al (20220293216) teaches, not only advanced query processing via a query model module directed to polymeric materials – para 0028, using an AI-machine-learned interface – para 0021, wherein the query processing can handle request for polymeric materials, not only their properties, but possible various rendering of the material with simulations of the combination of materials – para 0052); (these features of Park et al (20220293216) are applicable to the strikethrough claim features above toward advanced natural language processing and advanced properties/relationships of the material). Therefore, it would have been obvious to one of ordinary skill in the art of chemical material/properties modify the access system of Cheetham (2004/0068339) with advanced query processing and material combination suggestions, as taught by Park et al (20220293216), because it would advantageously process queries faster and generate more accurate combination results, at a faster pace, as well – (Park et al (20220293216), para 0029).
As per claim 3, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) teaches the method of claim 1, wherein the plurality of chemical materials comprises at least one polymeric material comprises a compounded polymer material comprising one or more thermoplastic polymers and one or more additives ( Cheetham (2004/0068339) , see para 0047, listing various types of chemical materials, including thermoplastic elastomers, including polyesters, polyolefins, polyamide, etc.; and Park et al (20220293216), para 0051-0053).
As per claim 5, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) teaches the method of claim 1, wherein the natural language query further comprises one or more features comprising at least one of (i) a term, (ii) an application, (iii) a type, (iv) a form, (v) a functionality, or (vi) an industry (as, Cheetham (2004/0068339) , properties of the material – para 0040, such as flexural modulus, RT Izon impact, tensile strength, etc.; and application -- Park et al (20220293216) , para 0051-0053).
As per claim 6, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) teaches the method of claim 1, wherein the property comprises at least one of a chemical property, a physical property, a thermal property, an electrical property or a mechanical property (as, Cheetham (2004/0068339), mechanical properties – para 0040, and scoring for each – para 0037).
As per claim 9, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) teaches the method of claim 1, wherein the user input comprises at least one of a text input or an audio input (as, allowing the user to input properties that they wish to be searched – para 0023).
Claims 11,13,14, 17,19 are computer system claims that perform the steps found in claims 1,3,5,6,9 above and as such, claims 11,13,14, 17,19 are similar in scope and content to claims 1,3,5,6,9; therefore, claims 11,13,14,17,19 are rejected under similar rationale as presented against claims 1, 3,5,6, 9 above.
Claims 20 is a non-transitory computer readable medium executing instructions that perform steps found in 1,3-6,9 above and as such, claim 20 is similar in scope and content to claims 1,3-6,9; therefore, claim 20 is rejected under similar rationale as presented against claims 1, 3-6, 9 above.
As per claim 21, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) teaches evaluating at each node, a minimal distance between the polymer parameters (training sets) that minimize the distance between the graph data structure and the training sets – Park et al (20220293216), para 0037, last 2 sentences. At each repetition of checking the edges/nodes, a distance measure is used/minimized, with the result being evaluated (first grade), and then continues to the next level, repeating the growth of the combination at the node/edges, for a second similarity/distance measurement; see also para 0036 of Park et al (20220293216).
As per claims 22,24, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) teaches using a threshold so that, the results focus only on materials that meet a certain threshold – see Cheetham (2004/0068339), para 0006, showing minimal acceptable value, maximum acceptable value, or a range – including, selecting priorities for the material. One of ordinary skill in the art of masking desired materials would easily recognize that, an equivalent to a minimum priority would be to ignore/remove that material from the list. Further to claim 24, a type of high priority, would be a filter to certify the results.
As per claim 23, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) teaches using prior user sessions by storing the answer back to a graph data structure, into the database, to be used for future sessions – see Park et al (20220293216), para 0031, 0032. The minimal distance feature only focus’ on results that are closest in matching, and ignoring the other possibilities – see para 0037, last 2 sentences – minimal distance calculation.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Cheetham (2004/0068339) in view of Park et al (20220293216) in view of Gruber et al (20200279556).
As per claim 2, Cheetham (2004/0068339) in view of Park et al (20220293216) teaches the method of claim 1, comprising: generating, by the one or more computing devices, in response to obtaining the one or more data packets indicative of the user input comprising the query, a second interactive user interface comprising, Cheetham (2004/0068339) in view of Park et al (20220293216) teaches in response to obtaining the one or more data packets indicative of the user input comprising the query, a second question about the one or more features associated with the chemical material or chemical material application and obtaining, by the one or more computing devices, a second user input comprising a response to the message indicating the question about the one or more features associated with the chemical material {para [0023]- Once the user decides which properties they wish to have displayed, they then decide whether or not each property they selected is to be searched 20. If the user wants a particular property to be displayed, but not searched and scored, then they do not need to input a value for that property 30. If however, the user wants the property values to be searched, scored and displayed, then the user inputs the desired or acceptable values for that property 40, and also selects which units are desired for each property (i.e., Sl units or British units)" For example, the user may input a minimum acceptable value, a maximum acceptable value, a range of acceptable values, or an acceptable point value for each property being searched and scored" Next, the user may input the priority assigned to each property 50" The priorities may comprise high, medium and low. Next, the user may input the number of matching materials they wish to have displayed 60. For example, they may wish to see only the ten materials that most closely match the desired properties}. Cheetham (2004/0068339) in view of Park et al (20220293216) does not teach generating in response io obtaining the one or more data packets indicative of the user input comprising the query, a second interactive user interface comprising a message indicating a follow-up question. However, Gruber et al (20200279556) teaches generating in response to obtaining the one or more data packets indicative of the user input comprising the query, a second interactive user interface comprising a message indicating a follow-up question (para [0269}- when assistant 1002 explicitly prompts the user for input, as when it requests a response to a question or offers a menu of next steps from which to choose). It would have been obvious to one of ordinary skill in the art to combine the material recommendation platform taught by Cheetham (2004/0068339) in view of Park et al (20220293216) with the follow up question generation taught by Gruber et al (20200279556) since doing so would improve the user experience and interactivity (para 0296, 0318).
Claim(s) 7,8 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) in further view of Dupond (SpecialChem vs Prospector, 12/14/2023).
Regarding claim 7, Cheetham (2004/0068339) in view of Park et al (20220293216) does not teach the application comprises a specific use. However, Dupond teaches an application property that comprises a specific use (page 2 -- adhesives, sealants, paints, cosmetics, etc .. ). It would have been obvious to one of ordinary skill in the art to combine the material recommendation platform taught by Cheetham (2004/0068339) in view of Park et al (20220293216) with the application property taught by Dupond since doing so would allow efficient and accurate recommendation of the material. Regarding claim 8, Cheetham does not teach the industry comprises at least one of (i) aerospace, (ii) agriculture, (iii) automotive and transportation, (iv) building and constructions, (v) consumer goods, (vi) electrical and electronics, (vii) food and beverage, (viii) home care and industrial and institutional cleaning, (ix) industrial and manufacturing, (x) medical and pharmaceutical, (xi) oil, gas, and mining, (xii) personal care and cosmetics, or (xiii} telecom. However, Dupond teaches industries that comprises at least one of (i) aerospace, (ii) agriculture, (iii) automotive and transportation, (iv) building and constructions, (V) consumer goods, (vi) electrical and electronics, (vii) food and beverage, (viii) home care and industrial and institutional cleaning, {ix) industrial and manufacturing, (x) medical and pharmaceutical, {xi) oil, gas, and mining, (xii) personal care and cosmetics." or (xiii) telecom (Dupond page 2 - food and beverage). It would have been obvious to one of ordinary skill in the art to combine the material recommendation platform, taught by Cheetham in view of Park et al (20220293216) with the industries taught by Dupond since doing so would allow efficient and accurate recommendation of the material.
As per claim 8, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) in further view of Dupond (SpecialChem vs Prospector, 12/14/2023) teaches the method of claim 5, wherein the industry comprises at least one of (i) aerospace, (ii) agriculture, (iii) automotive and transportation, (iv) building and constructions, (v) consumer goods, (vi) electrical and electronics, (vii) food and beverage, (viii) home care and industrial and institutional cleaning, (ix) industrial and manufacturing, (x) medical and pharmaceutical, (xi) oil, gas, and mining, (xii) personal care and cosmetics, or (xiii) telecom (See Dupond (SpecialChem vs Prospector, 12/14/2023), pp4).
Claims 10,15 are rejected under 35 U.S.C. 103 as being unpatentable over Cheetham (2004/0068339) in view of Park et al (20220293216) in further view of King et al (20110153653).
As per claim 10, Cheetham (2004/0068339) in view of Park et al (20220293216) does not teach the user input comprises an image input, wherein the image input comprises a scanned copy of a technical data sheet. However, King et al (20110153653) teaches a search query user input, wherein the user input comprises an image input, wherein the image input comprises a scanned copy of a document (para 0018-0020 – capture information from a rendered document and wirelessly communicate the information to a search engine; scanned from a paper document). Therefore, it would have been obvious to one of ordinary skill in the art to combine the material recommendation platform taught by Cheetham (2004/0068339) in view of Park et al (20220293216) with input taught by King to generate the query from a scanned technical data sheet since doing so would improve the efficiency and speed of the query generation (see King et al (20110153653), para 0474).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Cheetham (2004/0068339) in view of Park et al (20220293216) in view of King et al (20110153653), in further view of Gruber et al (20200279556).
As per claim 16, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) in view of King et al (20110153653) teaches the claim limitations of claim 15, from which claim 16 depends, as noted above. However, the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) in view of King et al (20110153653) does not explicitly teach generating in response to obtaining the one or more data packets indicative of the user input comprising the query, a second interactive user interface comprising a message indicating a follow-up question. However, Gruber et al (20200279556) teaches generating in response to obtaining the one or more data packets indicative of the user input comprising the query, a second interactive user interface comprising a message indicating a follow-up question (para [0269}- when assistant 1002 explicitly prompts the user for input, as when it requests a response to a question or offers a menu of next steps from which to choose). It would have been obvious to one of ordinary skill in the art to combine the material recommendation platform taught by the combination of Cheetham (2004/0068339) in view of Park et al (20220293216) in view of King et al (20110153653) with the follow up question generation taught by Gruber et al (20200279556) since doing so would improve the user experience and interactivity (see Gruber et al (20200279556) para 0296, 0318).
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Examiner notes the introduction of the Park et al (20220293216) reference to teach the improved input query processing via AI/machine learning models, as well as using neural networks in predicting possible chemical material combinations and the results.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see related art listed on the PTO-892 form.
Furthermore, the following references were found, to be pertinent to applicants disclosure/claim features:
Salerno Jr (20200317373) teaches speech recognition to process input queries (para 0039), when querying materials/chemical compounds against a stored database – para 0026.
Katsuki (11901045) teaches the use of machine learning to find desired materials from a chemical database (abstract), fig. 1, fig. 4
Sun (20090261987) teaches sensors for detecting material in fluids (para 0032, 0036)
Agarwal et al (20140201181) teaches selecting and presenting content based on user input – abstract, Figure 2, Fig. 5,6
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 03/02/2026